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abusufyanvu / 6S191 MIT DeepLearning

244

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

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Updated 3d ago

danderfer / Comp Sci Sem 2

178

According to all known laws of aviation, there is no way that a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway. Because bees don’t care what humans think is impossible.” SEQ. 75 - “INTRO TO BARRY” INT. BENSON HOUSE - DAY ANGLE ON: Sneakers on the ground. Camera PANS UP to reveal BARRY BENSON’S BEDROOM ANGLE ON: Barry’s hand flipping through different sweaters in his closet. BARRY Yellow black, yellow black, yellow black, yellow black, yellow black, yellow black...oohh, black and yellow... ANGLE ON: Barry wearing the sweater he picked, looking in the mirror. BARRY (CONT’D) Yeah, let’s shake it up a little. He picks the black and yellow one. He then goes to the sink, takes the top off a CONTAINER OF HONEY, and puts some honey into his hair. He squirts some in his mouth and gargles. Then he takes the lid off the bottle, and rolls some on like deodorant. CUT TO: INT. BENSON HOUSE KITCHEN - CONTINUOUS Barry’s mother, JANET BENSON, yells up at Barry. JANET BENSON Barry, breakfast is ready! CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 1. INT. BARRY’S ROOM - CONTINUOUS BARRY Coming! SFX: Phone RINGING. Barry’s antennae vibrate as they RING like a phone. Barry’s hands are wet. He looks around for a towel. BARRY (CONT’D) Hang on a second! He wipes his hands on his sweater, and pulls his antennae down to his ear and mouth. BARRY (CONT'D) Hello? His best friend, ADAM FLAYMAN, is on the other end. ADAM Barry? BARRY Adam? ADAM Can you believe this is happening? BARRY Can’t believe it. I’ll pick you up. Barry sticks his stinger in a sharpener. SFX: BUZZING AS HIS STINGER IS SHARPENED. He tests the sharpness with his finger. SFX: Bing. BARRY (CONT’D) Looking sharp. ANGLE ON: Barry hovering down the hall, sliding down the staircase bannister. Barry’s mother, JANET BENSON, is in the kitchen. JANET BENSON Barry, why don’t you use the stairs? Your father paid good money for those. "Bee Movie" - JS REVISIONS 8/13/07 2. BARRY Sorry, I’m excited. Barry’s father, MARTIN BENSON, ENTERS. He’s reading a NEWSPAPER with the HEADLINE, “Queen gives birth to thousandtuplets: Resting Comfortably.” MARTIN BENSON Here’s the graduate. We’re very proud of you, Son. And a perfect report card, all B’s. JANET BENSON (mushing Barry’s hair) Very proud. BARRY Ma! I’ve got a thing going here. Barry re-adjusts his hair, starts to leave. JANET BENSON You’ve got some lint on your fuzz. She picks it off. BARRY Ow, that’s me! MARTIN BENSON Wave to us. We’ll be in row 118,000. Barry zips off. BARRY Bye! JANET BENSON Barry, I told you, stop flying in the house! CUT TO: SEQ. 750 - DRIVING TO GRADUATION EXT. BEE SUBURB - MORNING A GARAGE DOOR OPENS. Barry drives out in his CAR. "Bee Movie" - JS REVISIONS 8/13/07 3. ANGLE ON: Barry’s friend, ADAM FLAYMAN, standing by the curb. He’s reading a NEWSPAPER with the HEADLINE: “Frisbee Hits Hive: Internet Down. Bee-stander: “I heard a sound, and next thing I knew...wham-o!.” Barry drives up, stops in front of Adam. Adam jumps in. BARRY Hey, Adam. ADAM Hey, Barry. (pointing at Barry’s hair) Is that fuzz gel? BARRY A little. It’s a special day. Finally graduating. ADAM I never thought I’d make it. BARRY Yeah, three days of grade school, three days of high school. ADAM Those were so awkward. BARRY Three days of college. I’m glad I took off one day in the middle and just hitchhiked around the hive. ADAM You did come back different. They drive by a bee who’s jogging. ARTIE Hi Barry! BARRY (to a bee pedestrian) Hey Artie, growing a mustache? Looks good. Barry and Adam drive from the suburbs into the city. ADAM Hey, did you hear about Frankie? "Bee Movie" - JS REVISIONS 8/13/07 4. BARRY Yeah. ADAM You going to his funeral? BARRY No, I’m not going to his funeral. Everybody knows you sting someone you die, you don’t waste it on a squirrel. He was such a hot head. ADAM Yeah, I guess he could’ve just gotten out of the way. The DRIVE through a loop de loop. BARRY AND ADAM Whoa...Whooo...wheee!! ADAM I love this incorporating the amusement park right into our regular day. BARRY I guess that’s why they say we don’t need vacations. CUT TO: SEQ. 95 - GRADUATION EXT. GRADUATION CEREMONY - CONTINUOUS Barry and Adam come to a stop. They exit the car, and fly over the crowd to their seats. * BARRY * (re: graduation ceremony) * Boy, quite a bit of pomp...under * the circumstances. * They land in their seats. BARRY (CONT’D) Well Adam, today we are men. "Bee Movie" - JS REVISIONS 8/13/07 5. ADAM We are. BARRY Bee-men. ADAM Amen! BARRY Hallelujah. Barry hits Adam’s forehead. Adam goes into the rapture. An announcement comes over the PA. ANNOUNCER (V.O) Students, faculty, distinguished bees...please welcome, Dean Buzzwell. ANGLE ON: DEAN BUZZWELL steps up to the podium. The podium has a sign that reads: “Welcome Graduating Class of:”, with train-station style flipping numbers after it. BUZZWELL Welcome New Hive City graduating class of... The numbers on the podium change to 9:15. BUZZWELL (CONT’D) ...9:15. (he clears his throat) And that concludes our graduation ceremonies. And begins your career at Honex Industries. BARRY Are we going to pick our job today? ADAM I heard it’s just orientation. The rows of chairs change in transformer-like mechanical motion to Universal Studios type tour trams. Buzzwell walks off stage. BARRY (re: trams) Whoa, heads up! Here we go. "Bee Movie" - JS REVISIONS 8/13/07 6. SEQ. 125 - “FACTORY” FEMALE VOICE (V.O) Keep your hands and antennas inside the tram at all times. (in Spanish) Dejen las manos y antennas adentro del tram a todos tiempos. BARRY I wonder what it’s going to be like? ADAM A little scary. Barry shakes Adam. BARRY AND ADAM AAHHHH! The tram passes under SIGNS READING: “Honex: A Division of Honesco: A Part of the Hexagon Group.” TRUDY Welcome to Honex, a division of Honesco, and a part of the Hexagon group. BARRY This is it! The Honex doors OPEN, revealing the factory. BARRY (CONT’D) Wow. TRUDY We know that you, as a bee, have worked your whole life to get to the point where you can work for your whole life. Honey begins when our valiant pollen jocks bring the nectar to the hive where our top secret formula is automatically color-corrected, scent adjusted and bubble contoured into this... Trudy GRABS a TEST TUBE OF HONEY from a technician. "Bee Movie" - JS REVISIONS 8/13/07 7. TRUDY (CONT’D) ...soothing, sweet syrup with its distinctive golden glow, you all know as... EVERYONE ON THE TRAM (in unison) H-o-n-e-y. Trudy flips the flask into the crowd, and laughs as they all scramble for it. ANGLE ON: A GIRL BEE catching the honey. ADAM (sotto) That girl was hot. BARRY (sotto) She’s my cousin. ADAM She is? BARRY Yes, we’re all cousins. ADAM Right. You’re right. TRUDY At Honex, we also constantly strive to improve every aspect of bee existence. These bees are stress testing a new helmet technology. ANGLE ON: A STUNT BEE in a HELMET getting hit with a NEWSPAPER, then a SHOE, then a FLYSWATTER. He gets up, and gives a “thumb’s up”. The graduate bees APPLAUD. ADAM (re: stunt bee) What do you think he makes? BARRY Not enough. TRUDY And here we have our latest advancement, the Krelman. "Bee Movie" - JS REVISIONS 8/13/07 8. BARRY Wow, what does that do? TRUDY Catches that little strand of honey that hangs after you pour it. Saves us millions. ANGLE ON: The Krelman machine. Bees with hand-shaped hats on, rotating around a wheel to catch drips of honey. Adam’s hand shoots up. ADAM Can anyone work on the Krelman? TRUDY Of course. Most bee jobs are small ones. But bees know that every small job, if it’s done well, means a lot. There are over 3000 different bee occupations. But choose carefully, because you’ll stay in the job that you pick for the rest of your life. The bees CHEER. ANGLE ON: Barry’s smile dropping slightly. BARRY The same job for the rest of your life? I didn’t know that. ADAM What’s the difference? TRUDY And you’ll be happy to know that bees as a species haven’t had one day off in 27 million years. BARRY So you’ll just work us to death? TRUDY (laughing) We’ll sure try. Everyone LAUGHS except Barry. "Bee Movie" - JS REVISIONS 8/13/07 9. The tram drops down a log-flume type steep drop. Cameras flash, as all the bees throw up their hands. The frame freezes into a snapshot. Barry looks concerned. The tram continues through 2 doors. FORM DISSOLVE TO: SEQ. 175 - “WALKING THE HIVE” INT. HONEX LOBBY ANGLE ON: The log-flume photo, as Barry looks at it. ADAM Wow. That blew my mind. BARRY (annoyed) “What’s the difference?” Adam, how could you say that? One job forever? That’s an insane choice to have to make. ADAM Well, I’m relieved. Now we only have to make one decision in life. BARRY But Adam, how could they never have told us that? ADAM Barry, why would you question anything? We’re bees. We’re the most perfectly functioning society on Earth. They walk by a newspaper stand with A SANDWICH BOARD READING: “Bee Goes Berserk: Stings Seven Then Self.” ANGLE ON: A BEE filling his car’s gas tank from a honey pump. He fills his car some, then takes a swig for himself. NEWSPAPER BEE (to the bee guzzling gas) Hey! Barry and Adam begin to cross the street. "Bee Movie" - JS REVISIONS 8/13/07 10. BARRY Yeah but Adam, did you ever think that maybe things work a little too well around here? They stop in the middle of the street. The traffic moves perfectly around them. ADAM Like what? Give me one example. BARRY (thinks) ...I don’t know. But you know what I’m talking about. They walk off. SEQ. 400 - “MEET THE JOCKS” SFX: The SOUND of Pollen Jocks. PAN DOWN from the Honex statue. J-GATE ANNOUNCER Please clear the gate. Royal Nectar Force on approach. Royal Nectar Force on approach. BARRY Wait a second. Check it out. Hey, hey, those are Pollen jocks. ADAM Wow. FOUR PATROL BEES FLY in through the hive’s giant Gothic entrance. The Patrol Bees are wearing fighter pilot helmets with black visors. ADAM (CONT’D) I’ve never seen them this close. BARRY They know what it’s like to go outside the hive. ADAM Yeah, but some of them don’t come back. "Bee Movie" - JS REVISIONS 8/13/07 11. The nectar from the pollen jocks is removed from their backpacks, and loaded into trucks on their way to Honex. A SMALL CROWD forms around the Patrol Bees. Each one has a PIT CREW that takes their nectar. Lou Loduca hurries a pit crew along: LOU LODUCA You guys did great! You’re monsters. You’re sky freaks! I love it! I love it! SCHOOL GIRLS are jumping up and down and squealing nearby. BARRY I wonder where those guys have just been? ADAM I don’t know. BARRY Their day’s not planned. Outside the hive, flying who-knows-where, doing who-knows-what. ADAM You can’t just decide one day to be a Pollen Jock. You have to be bred for that. BARRY Right. Pollen Jocks cross in close proximity to Barry and Adam. Some pollen falls off, onto Barry and Adam. BARRY (CONT’D) Look at that. That’s more pollen than you and I will ever see in a lifetime. ADAM (playing with the pollen) It’s just a status symbol. I think bees make too big a deal out of it. BARRY Perhaps, unless you’re wearing it, and the ladies see you wearing it. ANGLE ON: Two girl bees. "Bee Movie" - JS REVISIONS 8/13/07 12. ADAM Those ladies? Aren’t they our cousins too? BARRY Distant, distant. ANGLE ON: TWO POLLEN JOCKS. JACKSON Look at these two. SPLITZ Couple of Hive Harrys. JACKSON Let’s have some fun with them. The pollen jocks approach. Barry and Adam continue to talk to the girls. GIRL 1 It must be so dangerous being a pollen jock. BARRY Oh yeah, one time a bear had me pinned up against a mushroom. He had one paw on my throat, and with the other he was slapping me back and forth across the face. GIRL 1 Oh my. BARRY I never thought I’d knock him out. GIRL 2 (to Adam) And what were you doing during all of this? ADAM Obviously I was trying to alert the authorities. The girl swipes some pollen off of Adam with a finger. BARRY (re: pollen) I can autograph that if you want. "Bee Movie" - JS REVISIONS 8/13/07 13. JACKSON Little gusty out there today, wasn’t it, comrades? BARRY Yeah. Gusty. BUZZ You know, we’re going to hit a sunflower patch about six miles from here tomorrow. BARRY Six miles, huh? ADAM (whispering) Barry. BUZZ It’s a puddle-jump for us. But maybe you’re not up for it. BARRY Maybe I am. ADAM (whispering louder) You are not! BUZZ We’re going, oh-nine hundred at JGate. ADAM (re: j-gate) Whoa. BUZZ (leaning in, on top of Barry) What do you think, Buzzy Boy? Are you bee enough? BARRY I might be. It all depends on what oh-nine hundred means. CUT TO: SEQ. 450 - “THE BALCONY” "Bee Movie" - JS REVISIONS 8/13/07 14. INT. BENSON HOUSE BALCONY - LATER Barry is standing on the balcony alone, looking out over the city. Martin Benson ENTERS, sneaks up behind Barry and gooses him in his ribs. MARTIN BENSON Honex! BARRY Oh, Dad. You surprised me. MARTIN BENSON (laughing) Have you decided what you’re interested in, Son? BARRY Well, there’s a lot of choices. MARTIN BENSON But you only get one. Martin LAUGHS. BARRY Dad, do you ever get bored doing the same job every day? MARTIN BENSON Son, let me tell you something about stirring. (making the stirring motion) You grab that stick and you just move it around, and you stir it around. You get yourself into a rhythm, it’s a beautiful thing. BARRY You know dad, the more I think about it, maybe the honey field just isn’t right for me. MARTIN BENSON And you were thinking of what, making balloon animals? That’s a bad job for a guy with a stinger. "Bee Movie" - JS REVISIONS 8/13/07 15. BARRY Well no... MARTIN BENSON Janet, your son’s not sure he wants to go into honey. JANET BENSON Oh Barry, you are so funny sometimes. BARRY I’m not trying to be funny. MARTIN BENSON You’re not funny, you’re going into honey. Our son, the stirrer. JANET BENSON You’re going to be a stirrer?! BARRY No one’s listening to me. MARTIN BENSON Wait until you see the sticks I have for you. BARRY I can say anything I want right now. I’m going to get an ant tattoo. JANET BENSON Let’s open some fresh honey and celebrate. BARRY Maybe I’ll pierce my thorax! MARTIN BENSON (toasting) To honey! BARRY Shave my antennae! JANET BENSON To honey! "Bee Movie" - JS REVISIONS 8/13/07 16. BARRY Shack up with a grasshopper, get a gold tooth, and start calling everybody “Dawg.” CUT TO: SEQ. 760 - “JOB PLACEMENT” EXT. HONEX LOBBY - CONTINUOUS ANGLE ON: A BEE BUS STOP. One group of bees stands on the pavement, as another group hovers above them. A doubledecker bus pulls up. The hovering bees get on the top level, and the standing bees get on the bottom. Barry and Adam pull up outside of Honex. ADAM I can’t believe we’re starting work today. BARRY Today’s the day. Adam jumps out of the car. ADAM (O.C) Come on. All the good jobs will be gone. BARRY Yeah, right... ANGLE ON: A BOARD READING: “JOB PLACEMENT BOARD”. Buzzwell, the Bee Processor, is at the counter. Another BEE APPLICANT, SANDY SHRIMPKIN is EXITING. SANDY SHRIMPKIN Is it still available? BUZZWELL Hang on. (he looks at changing numbers on the board) Two left. And...one of them’s yours. Congratulations Son, step to the side please. "Bee Movie" - JS REVISIONS 8/13/07 17. SANDY SHRIMPKIN Yeah! ADAM (to Sandy, leaving) What did you get? SANDY SHRIMPKIN Picking the crud out. That is stellar! ADAM Wow. BUZZWELL (to Adam and Barry) Couple of newbies? ADAM Yes Sir. Our first day. We are ready. BUZZWELL Well, step up and make your choice. ANGLE ON: A CHART listing the different sectors of Honex. Heating, Cooling, Viscosity, Krelman, Pollen Counting, Stunt Bee, Pouring, Stirrer, Humming, Regurgitating, Front Desk, Hair Removal, Inspector No. 7, Chef, Lint Coordinator, Stripe Supervisor, Antennae-ball polisher, Mite Wrangler, Swatting Counselor, Wax Monkey, Wing Brusher, Hive Keeper, Restroom Attendant. ADAM (to Barry) You want to go first? BARRY No, you go. ADAM Oh my. What’s available? BUZZWELL Restroom attendant is always open, and not for the reason you think. ADAM Any chance of getting on to the Krelman, Sir? BUZZWELL Sure, you’re on. "Bee Movie" - JS REVISIONS 8/13/07 18. He plops the KRELMAN HAT onto Adam’s head. ANGLE ON: The job board. THE COLUMNS READ: “OCCUPATION” “POSITIONS AVAILABLE”, and “STATUS”. The middle column has numbers, and the right column has job openings flipping between “open”, “pending”, and “closed”. BUZZWELL (CONT’D) Oh, I’m sorry. The Krelman just closed out. ADAM Oh! He takes the hat off Adam. BUZZWELL Wax Monkey’s always open. The Krelman goes from “Closed” to “Open”. BUZZWELL (CONT’D) And the Krelman just opened up again. ADAM What happened? BUZZWELL Well, whenever a bee dies, that’s an opening. (pointing at the board) See that? He’s dead, dead, another dead one, deady, deadified, two more dead. Dead from the neck up, dead from the neck down. But, that’s life. ANGLE ON: Barry’s disturbed expression. ADAM (feeling pressure to decide) Oh, this is so hard. Heating, cooling, stunt bee, pourer, stirrer, humming, inspector no. 7, lint coordinator, stripe supervisor, antenna-ball polisher, mite wrangler-- Barry, Barry, what do you think I should-- Barry? Barry? "Bee Movie" - JS REVISIONS 8/13/07 19. Barry is gone. CUT TO: SEQ. 775 - “LOU LODUCA SPEECH” EXT. J-GATE - SAME TIME Splitz, Jackson, Buzz, Lou and two other BEES are going through final pre-flight checks. Barry ENTERS. LOU LODUCA Alright, we’ve got the sunflower patch in quadrant nine. Geranium window box on Sutton Place... Barry’s antennae rings, like a phone. ADAM (V.O) What happened to you? Where are you? Barry whispers throughout. BARRY I’m going out. ADAM (V.O) Out? Out where? BARRY Out there. ADAM (V.O) (putting it together) Oh no. BARRY I have to, before I go to work for the rest of my life. ADAM (V.O) You’re going to die! You’re crazy! Hello? BARRY Oh, another call coming in. "Bee Movie" - JS REVISIONS 8/13/07 20. ADAM (V.O) You’re cra-- Barry HANGS UP. ANGLE ON: Lou Loduca. LOU LODUCA If anyone’s feeling brave, there’s a Korean Deli on 83rd that gets their roses today. BARRY (timidly) Hey guys. BUZZ Well, look at that. SPLITZ Isn’t that the kid we saw yesterday? LOU LODUCA (to Barry) Hold it son, flight deck’s restricted. JACKSON It’s okay Lou, we’re going to take him up. Splitz and Jackson CHUCKLE. LOU LODUCA Really? Feeling lucky, are ya? A YOUNGER SMALLER BEE THAN BARRY, CHET, runs up with a release waiver for Barry to sign. CHET Sign here. Here. Just initial that. Thank you. LOU LODUCA Okay, you got a rain advisory today and as you all know, bees cannot fly in rain. So be careful. As always, (reading off clipboard) watch your brooms, hockey sticks, dogs, birds, bears, and bats. "Bee Movie" - JS REVISIONS 8/13/07 21. Also, I got a couple reports of root beer being poured on us. Murphy’s in a home because of it, just babbling like a cicada. BARRY That’s awful. LOU LODUCA And a reminder for all you rookies, bee law number one, absolutely no talking to humans. Alright, launch positions! The Jocks get into formation, chanting as they move. LOU LODUCA (CONT’D) Black and Yellow! JOCKS Hello! SPLITZ (to Barry) Are you ready for this, hot shot? BARRY Yeah. Yeah, bring it on. Barry NODS, terrified. BUZZ Wind! - CHECK! JOCK #1 Antennae! - CHECK! JOCK #2 Nectar pack! - CHECK! JACKSON Wings! - CHECK! SPLITZ Stinger! - CHECK! BARRY Scared out of my shorts - CHECK. LOU LODUCA Okay ladies, let’s move it out. Everyone FLIPS their goggles down. Pit crew bees CRANK their wings, and remove the starting blocks. We hear loud HUMMING. "Bee Movie" - JS REVISIONS 8/13/07 22. LOU LODUCA (CONT'D) LOU LODUCA (CONT’D) Pound those petunia's, you striped stem-suckers! All of you, drain those flowers! A FLIGHT DECK GUY in deep crouch hand-signals them out the archway as the backwash from the bee wings FLUTTERS his jump suit. Barry follows everyone. SEQ. 800 - “FLYING WITH THE JOCKS” The bees climb above tree tops in formation. Barry is euphoric. BARRY Whoa! I’m out! I can’t believe I’m out! So blue. Ha ha ha! (a beat) I feel so fast...and free. (re: kites in the sky) Box kite! Wow! They fly by several bicyclists, and approach a patch of flowers. BARRY (CONT'D) Flowers! SPLITZ This is blue leader. We have roses visual. Bring it around thirty degrees and hold. BARRY (sotto) Roses. JACKSON Thirty degrees, roger, bringing it around. Many pollen jocks break off from the main group. They use their equipment to collect nectar from flowers. Barry flies down to watch the jocks collect the nectar. JOCK Stand to the side kid, it’s got a bit of a kick. The jock fires the gun, and recoils. Barry watches the gun fill up with nectar. "Bee Movie" - JS REVISIONS 8/13/07 23. BARRY Oh, that is one Nectar Collector. JOCK You ever see pollination up close? BARRY No, Sir. He takes off, and the excess pollen dust falls causing the flowers to come back to life. JOCK (as he pollinates) I pick some pollen up over here, sprinkle it over here, maybe a dash over there, pinch on that one...see that? It’s a little bit of magic, ain’t it? The FLOWERS PERK UP as he pollinates. BARRY Wow. That’s amazing. Why do we do that? JOCK ...that’s pollen power, Kid. More pollen, more flowers, more nectar, more honey for us. BARRY Cool. The Jock WINKS at Barry. Barry rejoins the other jocks in the sky. They swoop in over a pond, kissing the surface. We see their image reflected in the water; they’re really moving. They fly over a fountain. BUZZ I’m picking up a lot of bright yellow, could be daisies. Don’t we need those? SPLITZ Copy that visual. We see what appear to be yellow flowers on a green field. "Bee Movie" - JS REVISIONS 8/13/07 24. They go into a deep bank and dive. BUZZ Hold on, one of these flowers seems to be on the move. SPLITZ Say again...Are you reporting a moving flower? BUZZ Affirmative. SEQ. 900 - “TENNIS GAME” The pollen jocks land. It is a tennis court with dozens of tennis balls. A COUPLE, VANESSA and KEN, plays tennis. The bees land right in the midst of a group of balls. KEN (O.C) That was on the line! The other bees start walking around amongst the immense, yellow globes. SPLITZ This is the coolest. What is it? They stop at a BALL on a white line and look up at it. JACKSON I don’t know, but I’m loving this color. SPLITZ (smelling tennis ball) Smells good. Not like a flower. But I like it. JACKSON Yeah, fuzzy. BUZZ Chemical-y. JACKSON Careful, guys, it’s a little grabby. Barry LANDS on a ball and COLLAPSES. "Bee Movie" - JS REVISIONS 8/13/07 25. BARRY Oh my sweet lord of bees. JACKSON Hey, candy brain, get off there! Barry attempts to pulls his legs off, but they stick. BARRY Problem! A tennis shoe and a hand ENTER FRAME. The hand picks up the ball with Barry underneath it. BARRY (CONT'D) Guys! BUZZ This could be bad. JACKSON Affirmative. Vanessa walks back to the service line, BOUNCES the ball. Each time it BOUNCES, the other bees cringe and GASP. ANGLE ON: Barry, terrified. Pure dumb luck, he’s not getting squished. BARRY (with each bounce) Very close...Gonna Hurt...Mamma’s little boy. SPLITZ You are way out of position, rookie. ANGLE ON: Vanessa serving. We see Barry and the ball up against the racket as she brings it back. She tosses the ball into the air; Barry’s eyes widen. The ball is STRUCK, and the rally is on. KEN Coming in at you like a missile! Ken HITS the ball back. Barry feels the g-forces. ANGLE ON: The Pollen Jocks watching Barry pass by them in SLOW MOTION. "Bee Movie" - JS REVISIONS 8/13/07 26. BARRY (in slow motion) Help me! JACKSON You know, I don't think these are flowers. SPLITZ Should we tell him? JACKSON I think he knows. BARRY (O.S) What is this?! Vanessa HITS a high arcing lob. Ken waits, poised for the return. We see Barry having trouble maneuvering the ball from fatigue. KEN (overly confident) Match point! ANGLE ON: Ken running up. He has a killer look in his eyes. He’s going to hit the ultimate overhead smash. KEN (CONT'D) You can just start packing up Honey, because I believe you’re about to eat it! ANGLE ON: Pollen Jocks. JACKSON Ahem! Ken is distracted by the jock. KEN What? No! He misses badly. The ball rockets into oblivion. Barry is still hanging on. ANGLE ON: Ken, berating himself. KEN (CONT’D) Oh, you cannot be serious. We hear the ball WHISTLING, and Barry SCREAMING. "Bee Movie" - JS REVISIONS 8/13/07 27. BARRY Yowser!!! SEQ. 1000 - “SUV” The ball flies through the air, and lands in the middle of the street. It bounces into the street again, and sticks in the grille of an SUV. INT. CAR ENGINE - CONTINUOUS BARRY’S POV: the grille of the SUV sucks him up. He tumbles through a black tunnel, whirling vanes, and pistons. BARRY AHHHHHHHHHHH!! OHHHH!! EECHHH!! AHHHHHH!! Barry gets chilled by the A/C system, and sees a frozen grasshopper. BARRY (CONT’D) (re: grasshopper) Eww, gross. CUT TO: INT. CAR - CONTINUOUS The car is packed with a typical suburban family: MOTHER, FATHER, eight-year old BOY, LITTLE GIRL in a car seat and a GRANDMOTHER. A big slobbery DOG is behind a grate. Barry pops into the passenger compartment, hitting the Mother’s magazine. MOTHER There’s a bee in the car! They all notice the bee and start SCREAMING. BARRY Aaahhhh! Barry tumbles around the car. We see the faces from his POV. MOTHER Do something! "Bee Movie" - JS REVISIONS 8/13/07 28. FATHER I’m driving! Barry flies by the little girl in her CAR SEAT. She waves hello. LITTLE GIRL Hi, bee. SON He’s back here! He’s going to sting me! The car SWERVES around the road. Barry flies into the back, where the slobbery dog SNAPS at him. Barry deftly avoids the jaws and gross, flying SPITTLE. MOTHER Nobody move. If you don’t move, he won’t sting you. Freeze! Everyone in the car freezes. Barry freezes. They stare at each other, eyes going back and forth, waiting to see who will make the first move. Barry blinks. GRANNY He blinked! Granny pulls out a can of HAIR SPRAY. SON Spray him, Granny! Granny sprays the hair spray everywhere. FATHER What are you doing? GRANNY It’s hair spray! Extra hold! MOTHER Kill it! Barry gets sprayed back by the hair spray, then sucked out of the sunroof. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 29. EXT. CITY STREET - CONTINUOUS BARRY Wow. The tension level out here is unbelievable. I’ve got to get home. As Barry flies down the street, it starts to RAIN. He nimbly avoids the rain at first. BARRY (CONT’D) Whoa. Whoa! Can’t fly in rain! Can’t fly in rain! Can’t fly in-- A couple of drops hit him, his wings go limp and he starts falling. BARRY (CONT'D) Mayday! Mayday! Bee going down! Barry sees a window ledge and aims for it and just makes it. Shivering and exhausted, he crawls into an open window as it CLOSES. SEQ. 1100 - “VANESSA SAVES BARRY” INT. VANESSA’S APARTMENT - CONTINUOUS Inside the window, Barry SHAKES off the rain like a dog. Vanessa, Ken, Andy, and Anna ENTER the apartment. VANESSA Ken, can you close the window please? KEN Huh? Oh. (to Andy) Hey, check out my new resume. I made it into a fold-out brochure. You see? It folds out. Ken holds up his brochure, with photos of himself, and a resume in the middle. ANGLE ON: Barry hiding behind the curtains, as Ken CLOSES THE WINDOW. "Bee Movie" - JS REVISIONS 8/13/07 30. BARRY Oh no, more humans. I don’t need this. Barry HOVERS up into the air and THROWS himself into the glass. BARRY (CONT’D) (dazed) Ow! What was that? He does it again, and then multiple more times. BARRY (CONT'D) Maybe this time...this time, this time, this time, this time, this time, this time, this time. Barry JUMPS onto the drapes. BARRY (CONT'D) (out of breath) Drapes! (then, re: glass) That is diabolical. KEN It’s fantastic. It’s got all my special skills, even my top ten favorite movies. ANDY What’s your number one? Star Wars? KEN Ah, I don’t go for that, (makes Star Wars noises), kind of stuff. ANGLE ON: Barry. BARRY No wonder we’re not supposed to talk to them. They’re out of their minds. KEN When I walk out of a job interview they’re flabbergasted. They can’t believe the things I say. Barry looks around and sees the LIGHT BULB FIXTURE in the middle of the ceiling. "Bee Movie" - JS REVISIONS 8/13/07 31. BARRY (re: light bulb) Oh, there’s the sun. Maybe that’s a way out. Barry takes off and heads straight for the light bulb. His POV: The seventy-five watt label grows as he gets closer. BARRY (CONT’D) I don’t remember the sun having a big seventy five on it. Barry HITS the bulb and is KNOCKED SILLY. He falls into a BOWL OF GUACAMOLE. Andy dips his chip in the guacamole, taking Barry with it. ANGLE ON: Ken and Andy. KEN I’ll tell you what. You know what? I predicted global warming. I could feel it getting hotter. At first I thought it was just me. Barry’s POV: Giant human mouth opening. KEN (CONT’D) Wait! Stop! Beeeeeee! ANNA Kill it! Kill it! They all JUMP up from their chairs. Andy looks around for something to use. Ken comes in for the kill with a big TIMBERLAND BOOT on each hand. KEN Stand back. These are winter boots. Vanessa ENTERS, and stops Ken from squashing Barry. VANESSA (grabs Ken’s arm) Wait. Don’t kill him. CLOSE UP: on Barry’s puzzled face. KEN You know I’m allergic to them. This thing could kill me. "Bee Movie" - JS REVISIONS 8/13/07 32. VANESSA Why does his life have any less value than yours? She takes a GLASS TUMBLER and places it over Barry. KEN Why does his life have any less value than mine? Is that your statement? VANESSA I’m just saying, all life has value. You don’t know what he’s capable of feeling. Barry looks up through the glass and watches this conversation, astounded. Vanessa RIPS Ken’s resume in half and SLIDES it under the glass. KEN (wistful) My brochure. There’s a moment of eye contact as she carries Barry to the window. She opens it and sets him free. VANESSA There you go, little guy. KEN (O.C) I’m not scared of them. But, you know, it’s an allergic thing. ANDY (O.C) * Hey, why don’t you put that on your * resume-brochure? * KEN (O.C) It’s not funny, my whole face could puff up. ANDY (O.C) Make it one of your “Special Skills.” KEN (O.C) You know, knocking someone out is also a special skill. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 33. EXT. WINDOWSILL - CONTINUOUS Barry stares over the window frame. He can’t believe what’s just happened. It is still RAINING. DISSOLVE TO: SEQ. 1200 - “BARRY SPEAKS” EXT. WINDOWSILL - LATER Barry is still staring through the window. Inside, everyone’s saying their good-byes. KEN Vanessa, next week? Yogurt night? VANESSA Uh, yeah sure Ken. You know, whatever. KEN You can put carob chips on there. VANESSA Good night. KEN (as he exits) Supposed to be less calories, or something. VANESSA Bye. She shuts the door. Vanessa starts cleaning up. BARRY I’ve got to say something. She saved my life. I’ve got to say something. Alright, here it goes. Barry flies in. "Bee Movie" - JS REVISIONS 8/13/07 34. INT. VANESSA’S APARTMENT - CONTINUOUS Barry hides himself on different PRODUCTS placed along the kitchen shelves. He hides on a Bumblebee Tuna can, and a “Greetings From Coney Island” MUSCLE-MAN POSTCARD on the fridge. BARRY (on fridge) What would I say? (landing on a bottle) I could really get in trouble. He stands looking at Vanessa. BARRY (CONT'D) It’s a bee law. You’re not supposed to talk to a human. I can’t believe I’m doing this. I’ve got to. Oh, I can’t do it! Come on! No, yes, no, do it! I can’t. How should I start it? You like jazz? No, that’s no good. Here she comes. Speak, you fool. As Vanessa walks by, Barry takes a DEEP BREATH. BARRY (CONT’D) (cheerful) Umm...hi. Vanessa DROPS A STACK OF DISHES, and HOPS BACK. BARRY (CONT’D) I’m sorry. VANESSA You’re talking. BARRY Yes, I know, I know. VANESSA You’re talking. BARRY I know, I’m sorry. I’m so sorry. VANESSA It’s okay. It’s fine. It’s just, I know I’m dreaming, but I don’t recall going to bed. "Bee Movie" - JS REVISIONS 8/13/07 35. BARRY Well, you know I’m sure this is very disconcerting. VANESSA Well yeah. I mean this is a bit of a surprise to me. I mean...you’re a bee. BARRY Yeah, I am a bee, and you know I’m not supposed to be doing this, but they were all trying to kill me and if it wasn’t for you...I mean, I had to thank you. It’s just the way I was raised. Vanessa intentionally JABS her hand with a FORK. VANESSA Ow! BARRY That was a little weird. VANESSA (to herself) I’m talking to a bee. BARRY Yeah. VANESSA I’m talking to a bee. BARRY Anyway... VANESSA And a bee is talking to me... BARRY I just want you to know that I’m grateful, and I’m going to leave now. VANESSA Wait, wait, wait, wait, how did you learn to do that? BARRY What? "Bee Movie" - JS REVISIONS 8/13/07 36. VANESSA The talking thing. BARRY Same way you did, I guess. Mama, Dada, honey, you pick it up. VANESSA That’s very funny. BARRY Yeah. Bees are funny. If we didn’t laugh, we’d cry. With what we have to deal with. Vanessa LAUGHS. BARRY (CONT’D) Anyway. VANESSA Can I, uh, get you something? BARRY Like what? VANESSA I don’t know. I mean, I don’t know. Coffee? BARRY Well, uh, I don’t want to put you out. VANESSA It’s no trouble. BARRY Unless you’re making anyway. VANESSA Oh, it takes two minutes. BARRY Really? VANESSA It’s just coffee. BARRY I hate to impose. "Bee Movie" - JS REVISIONS 8/13/07 37. VANESSA Don’t be ridiculous. BARRY Actually, I would love a cup. VANESSA Hey, you want a little rum cake? BARRY I really shouldn’t. VANESSA Have a little rum cake. BARRY No, no, no, I can’t. VANESSA Oh, come on. BARRY You know, I’m trying to lose a couple micrograms here. VANESSA Where? BARRY Well... These stripes don’t help. VANESSA You look great. BARRY I don’t know if you know anything about fashion. Vanessa starts POURING the coffee through an imaginary cup and directly onto the floor. BARRY (CONT'D) Are you alright? VANESSA No. DISSOLVE TO: SEQ. 1300 - “ROOFTOP COFFEE” "Bee Movie" - JS REVISIONS 8/13/07 38. EXT. VANESSA’S ROOF - LATER Barry and Vanessa are drinking coffee on her roof terrace. He is perched on her keychain. BARRY ...He can’t get a taxi. He’s making the tie in the cab, as they’re flying up Madison. So he finally gets there. VANESSA Uh huh? BARRY He runs up the steps into the church, the wedding is on... VANESSA Yeah? BARRY ...and he says, watermelon? I thought you said Guatemalan. VANESSA Uh huh? BARRY Why would I marry a watermelon? Barry laughs. Vanessa doesn’t. VANESSA Oh! Is that, uh, a bee joke? BARRY Yeah, that’s the kind of stuff that we do. VANESSA Yeah, different. A BEAT. VANESSA (CONT’D) So anyway...what are you going to do, Barry? "Bee Movie" - JS REVISIONS 8/13/07 39. BARRY About work? I don’t know. I want to do my part for the hive, but I can’t do it the way they want. VANESSA I know how you feel. BARRY You do? VANESSA Sure, my parents wanted me to be a lawyer or doctor, but I wanted to be a florist. BARRY Really? VANESSA My only interest is flowers. BARRY Our new queen was just elected with that same campaign slogan. VANESSA Oh. BARRY Anyway, see there’s my hive, right there. You can see it. VANESSA Oh, you’re in Sheep Meadow. BARRY (excited) Yes! You know the turtle pond? VANESSA Yes? BARRY I’m right off of that. VANESSA Oh, no way. I know that area. Do you know I lost a toe-ring there once? BARRY Really? "Bee Movie" - JS REVISIONS 8/13/07 40. VANESSA Yes. BARRY Why do girls put rings on their toes? VANESSA Why not? BARRY I don’t know. It’s like putting a hat on your knee. VANESSA Really? Okay. A JANITOR in the background changes a LIGHTBULB. To him, it appears that Vanessa is talking to an imaginary friend. JANITOR You all right, ma’am? VANESSA Oh, yeah, fine. Just having two cups of coffee. BARRY Anyway, this has been great. (wiping his mouth) Thanks for the coffee. Barry gazes at Vanessa. VANESSA Oh yeah, it’s no trouble. BARRY Sorry I couldn’t finish it. Vanessa giggles. BARRY (CONT'D) (re: coffee) If I did, I’d be up the rest of my life. Ummm. Can I take a piece of this with me? VANESSA Sure. Here, have a crumb. She takes a CRUMB from the plate and hands it to Barry. "Bee Movie" - JS REVISIONS 8/13/07 41. BARRY (a little dreamy) Oh, thanks. VANESSA Yeah. There is an awkward pause. BARRY Alright, well then, I guess I’ll see you around, or not, or... VANESSA Okay Barry. BARRY And thank you so much again, for before. VANESSA Oh that? BARRY Yeah. VANESSA Oh, that was nothing. BARRY Well, not nothing, but, anyway... Vanessa extends her hand, and shakes Barry’s gingerly. The Janitor watches. The lightbulb shorts out. The Janitor FALLS. CUT TO: SEQ. 1400 - “HONEX” INT. HONEX BUILDING - NEXT DAY ANGLE ON: A TEST BEE WEARING A PARACHUTE is in a wind tunnel, hovering through increasingly heavy wind. SIGNS UNDER A FLASHING LIGHT READ: “Test In Progress” & “Hurricane Survival Test”. 2 BEES IN A LAB COATS are observing behind glass. "Bee Movie" - JS REVISIONS 8/13/07 42. LAB COAT BEE 1 This can’t possibly work. LAB COAT BEE 2 Well, he’s all set to go, we may as well try it. (into the mic) Okay Dave, pull the chute. The test bee opens his parachute. He’s instantly blown against the rear wall. Adam and Barry ENTER. ADAM Sounds amazing. BARRY Oh, it was amazing. It was the scariest, happiest moment of my life. ADAM Humans! Humans! I can’t believe you were with humans! Giant scary humans! What were they like? BARRY Huge and crazy. They talk crazy, they eat crazy giant things. They drive around real crazy. ADAM And do they try and kill you like on TV? BARRY Some of them. But some of them don’t. ADAM How’d you get back? BARRY Poodle. ADAM Look, you did it. And I’m glad. You saw whatever you wanted to see out there, you had your “experience”, and now you’re back, you can pick out your job, and everything can be normal. "Bee Movie" - JS REVISIONS 8/13/07 43. ANGLE ON: LAB BEES examining a CANDY CORN through a microscope. BARRY Well... ADAM Well? BARRY Well, I met someone. ADAM You met someone? Was she Bee-ish? BARRY Mmm. ADAM Not a WASP? Your parents will kill you. BARRY No, no, no, not a wasp. ADAM Spider? BARRY You know, I’m not attracted to the spiders. I know to everyone else it’s like the hottest thing with the eight legs and all. I can’t get by that face. Barry makes a spider face. ADAM So, who is she? BARRY She’s a human. ADAM Oh no, no, no, no. That didn’t happen. You didn’t do that. That is a bee law. You wouldn’t break a bee law. BARRY Her name’s Vanessa. "Bee Movie" - JS REVISIONS 8/13/07 44. ADAM Oh, oh boy! BARRY She’s so-o nice. And she’s a florist! ADAM Oh, no. No, no, no! You’re dating a human florist? BARRY We’re not dating. ADAM You’re flying outside the hive. You’re talking to human beings that attack our homes with power washers and M-80’s. That’s 1/8 of a stick of dynamite. BARRY She saved my life. And she understands me. ADAM This is over. Barry pulls out the crumb. BARRY Eat this. Barry stuffs the crumb into Adam’s face. ADAM This is not over. What was that? BARRY They call it a crumb. ADAM That was SO STINGING STRIPEY! BARRY And that’s not even what they eat. That just falls off what they eat. Do you know what a Cinnabon is? ADAM No. "Bee Movie" - JS REVISIONS 8/13/07 45. BARRY It’s bread... ADAM Come in here! BARRY and cinnamon, ADAM Be quiet! BARRY and frosting...they heat it up-- ADAM Sit down! INT. ADAM’S OFFICE - CONTINUOUS BARRY Really hot! ADAM Listen to me! We are not them. We’re us. There’s us and there’s them. BARRY Yes, but who can deny the heart that is yearning... Barry rolls his chair down the corridor. ADAM There’s no yearning. Stop yearning. Listen to me. You have got to start thinking bee, my friend. ANOTHER BEE JOINS IN. ANOTHER BEE Thinking bee. WIDER SHOT AS A 3RD BEE ENTERS, popping up over the cubicle wall. 3RD BEE Thinking bee. EVEN WIDER SHOT AS ALL THE BEES JOIN IN. "Bee Movie" - JS REVISIONS 8/13/07 46. OTHER BEES Thinking bee. Thinking bee. Thinking bee. CUT TO: SEQ. 1500 - “POOLSIDE NAGGING” EXT. BACKYARD PARENT’S HOUSE - DAY Barry sits on a RAFT in a hexagon honey pool, legs dangling into the water. Janet Benson and Martin Benson stand over him wearing big, sixties sunglasses and cabana-type outfits. The sun shines brightly behind their heads. JANET BENSON (O.C) There he is. He’s in the pool. MARTIN BENSON You know what your problem is, Barry? BARRY I’ve got to start thinking bee? MARTIN BENSON Barry, how much longer is this going to go on? It’s been three days. I don’t understand why you’re not working. BARRY Well, I’ve got a lot of big life decisions I’m thinking about. MARTIN BENSON What life? You have no life! You have no job! You’re barely a bee! Barry throws his hands in the air. BARRY Augh. JANET BENSON Would it kill you to just make a little honey? Barry ROLLS off the raft and SINKS to the bottom of the pool. We hear his parents’ MUFFLED VOICES from above the surface. "Bee Movie" - JS REVISIONS 8/13/07 47. JANET BENSON (CONT'D) (muffled) Barry, come out from under there. Your father’s talking to you. Martin, would you talk to him? MARTIN BENSON Barry, I’m talking to you. DISSOLVE TO: EXT. PICNIC AREA - DAY MUSIC: “Sugar Sugar” by the Archies. Barry and Vanessa are having a picnic. A MOSQUITO lands on Vanessa’s leg. She SWATS it violently. Barry’s head whips around, aghast. They stare at each other awkwardly in a frozen moment, then BURST INTO HYSTERICAL LAUGHTER. Vanessa GETS UP. VANESSA You coming? BARRY Got everything? VANESSA All set. Vanessa gets into a one-man Ultra Light plane with a black and yellow paint scheme. She puts on her helmet. BARRY You go ahead, I’ll catch up. VANESSA (come hither wink) Don’t be too long. The Ultra Light takes off. Barry catches up. They fly sideby-side. VANESSA (CONT’D) Watch this! Vanessa does a loop, and FLIES right into the side of a mountain, BURSTING into a huge ball of flames. "Bee Movie" - JS REVISIONS 8/13/07 48. BARRY (yelling, anguished) Vanessa! EXT. BARRY’S PARENT’S HOUSE - CONTINUOUS ANGLE ON: Barry’s face bursting through the surface of the pool, GASPING for air, eyes opening in horror. MARTIN BENSON We’re still here, Barry. JANET BENSON I told you not to yell at him. He doesn’t respond when you yell at him. MARTIN BENSON Then why are you yelling at me? JANET BENSON Because you don’t listen. MARTIN BENSON I’m not listening to this. Barry is toweling off, putting on his sweater. BARRY Sorry Mom, I’ve got to go. JANET BENSON Where are you going? BARRY Nowhere. I’m meeting a friend. Barry JUMPS off the balcony and EXITS. JANET BENSON (calling after him) A girl? Is this why you can’t decide? BARRY Bye! JANET BENSON I just hope she’s Bee-ish. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 49. SEQ. 1700 - “STREETWALK/SUPERMARKET” EXT. VANESSA’S FLORIST SHOP - DAY Vanessa FLIPS the sign to say “Sorry We Missed You”, and locks the door. ANGLE ON: A POSTER on Vanessa’s door for the Tournament of Roses Parade in Pasadena. BARRY So they have a huge parade of just flowers every year in Pasadena? VANESSA Oh, to be in the Tournament of Roses, that’s every florist’s dream. Up on a float, surrounded by flowers, crowds cheering. BARRY Wow, a tournament. Do the roses actually compete in athletic events? VANESSA No. Alright, I’ve got one. How come you don’t fly everywhere? BARRY It’s exhausting. Why don’t you run everywhere? VANESSA Hmmm. BARRY Isn’t that faster? VANESSA Yeah, okay. I see, I see. Alright, your turn. Barry and Vanessa walk/fly down a New York side street, no other pedestrians near them. BARRY Ah! Tivo. You can just freeze live TV? That’s insane. "Bee Movie" - JS REVISIONS 8/13/07 50. VANESSA What, you don’t have anything like that? BARRY We have Hivo, but it’s a disease. It’s a horrible, horrible disease. VANESSA Oh my. They turn the corner onto a busier avenue and people start to swat at Barry. MAN Dumb bees! VANESSA You must just want to sting all those jerks. BARRY We really try not to sting. It’s usually fatal for us. VANESSA So you really have to watch your temper? They ENTER a SUPERMARKET. CUT TO: INT. SUPERMARKET BARRY Oh yeah, very carefully. You kick a wall, take a walk, write an angry letter and throw it out. You work through it like any emotion-- anger, jealousy, (under his breath) lust. Barry hops on top of some cardboard boxes in the middle of an aisle. A stock boy, HECTOR, whacks him with a rolled up magazine. VANESSA (to Barry) Oh my goodness. Are you okay? "Bee Movie" - JS REVISIONS 8/13/07 51. BARRY Yeah. Whew! Vanessa WHACKS Hector over the head with the magazine. VANESSA (to Hector) What is wrong with you?! HECTOR It’s a bug. VANESSA Well he’s not bothering anybody. Get out of here, you creep. Vanessa pushes him, and Hector EXITS, muttering. BARRY (shaking it off) What was that, a Pick and Save circular? VANESSA Yeah, it was. How did you know? BARRY It felt like about ten pages. Seventy-five’s pretty much our limit. VANESSA Boy, you’ve really got that down to a science. BARRY Oh, we have to. I lost a cousin to Italian Vogue. VANESSA I’ll bet. Barry stops, sees the wall of honey jars. BARRY What, in the name of Mighty Hercules, is this? How did this get here? Cute Bee? Golden Blossom? Ray Liotta Private Select? VANESSA Is he that actor? "Bee Movie" - JS REVISIONS 8/13/07 52. BARRY I never heard of him. Why is this here? VANESSA For people. We eat it. BARRY Why? (gesturing around the market) You don’t have enough food of your own? VANESSA Well yes, we-- BARRY How do you even get it? VANESSA Well, bees make it... BARRY I know who makes it! And it’s hard to make it! There’s Heating and Cooling, and Stirring...you need a whole Krelman thing. VANESSA It’s organic. BARRY It’s our-ganic! VANESSA It’s just honey, Barry. BARRY Just...what?! Bees don’t know about this. This is stealing. A lot of stealing! You’ve taken our homes, our schools, our hospitals. This is all we have. And it’s on sale? I’m going to get to the bottom of this. I’m going to get to the bottom of all of this! He RIPS the label off the Ray Liotta Private Select. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 53. SEQ. 1800 - “WINDSHIELD” EXT. BACK OF SUPERMARKET LOADING DOCK - LATER THAT DAY Barry disguises himself by blacking out his yellow lines with a MAGIC MARKER and putting on some war paint. He sees Hector, the stock boy, with a knife CUTTING open cardboard boxes filled with honey jars. MAN You almost done? HECTOR Almost. Barry steps in some honey, making a SNAPPING noise. Hector stops and turns. HECTOR (CONT’D) He is here. I sense it. Hector grabs his BOX CUTTER. Barry REACTS, hides himself behind the box again. HECTOR (CONT’D) (talking too loud, to no one in particular) Well, I guess I’ll go home now, and just leave this nice honey out, with no one around. A BEAT. Hector pretends to exit. He takes a couple of steps in place. ANGLE ON: The honey jar. Barry steps out into a moody spotlight. BARRY You’re busted, box boy! HECTOR Ah ha! I knew I heard something. So, you can talk. Barry flies up, stinger out, pushing Hector up against the wall. As Hector backs up, he drops his knife. BARRY Oh, I can talk. And now you’re going to start talking. "Bee Movie" - JS REVISIONS 8/13/07 54. Where are you getting all the sweet stuff? Who’s your supplier?! HECTOR I don’t know what you’re talking about. I thought we were all friends. The last thing we want to do is upset any of you...bees! Hector grabs a PUSHPIN. Barry fences with his stinger. HECTOR (CONT’D) You’re too late. It’s ours now! BARRY You, sir, have crossed the wrong sword. HECTOR You, sir, are about to be lunch for my iguana, Ignacio! Barry and Hector get into a cross-swords, nose-to-nose confrontation. BARRY Where is the honey coming from? Barry knocks the pushpin out of his hand. Barry puts his stinger up to Hector’s nose. BARRY (CONT'D) Tell me where?! HECTOR (pointing to a truck) Honey Farms. It comes from Honey Farms. ANGLE ON: A Honey Farms truck leaving the parking lot. Barry turns, takes off after the truck through an alley. He follows the truck out onto a busy street, dodging a bus, and several cabs. CABBIE Crazy person! He flies through a metal pipe on the top of a truck. BARRY OOOHHH! "Bee Movie" - JS REVISIONS 8/13/07 55. BARRY (CONT'D) Barry grabs onto a bicycle messenger’s backpack. The honey farms truck starts to pull away. Barry uses the bungee cord to slingshot himself towards the truck. He lands on the windshield, where the wind plasters him to the glass. He looks up to find himself surrounded by what appear to be DEAD BUGS. He climbs across, working his way around the bodies. BARRY (CONT’D) Oh my. What horrible thing has happened here? Look at these faces. They never knew what hit them. And now they’re on the road to nowhere. A MOSQUITO opens his eyes. MOOSEBLOOD Pssst! Just keep still. BARRY What? You’re not dead? MOOSEBLOOD Do I look dead? Hey man, they will wipe anything that moves. Now, where are you headed? BARRY To Honey Farms. I am onto something huge here. MOOSEBLOOD I’m going to Alaska. Moose blood. Crazy stuff. Blows your head off. LADYBUG I’m going to Tacoma. BARRY (to fly) What about you? MOOSEBLOOD He really is dead. BARRY Alright. The WIPER comes towards them. "Bee Movie" - JS REVISIONS 8/13/07 56. MOOSEBLOOD Uh oh. BARRY What is that? MOOSEBLOOD Oh no! It’s a wiper, triple blade! BARRY Triple blade? MOOSEBLOOD Jump on. It’s your only chance, bee. They hang on as the wiper goes back and forth. MOOSEBLOOD (CONT'D) (yelling to the truck driver through the glass) Why does everything have to be so dog-gone clean?! How much do you people need to see? Open your eyes! Stick your head out the window! CUT TO: INT. TRUCK CAB SFX: Radio. RADIO VOICE For NPR News in Washington, I’m Carl Kasell. EXT. TRUCK WINDSHIELD MOOSEBLOOD But don’t kill no more bugs! The Mosquito is FLUNG off of the wiper. MOOSEBLOOD (CONT'D) Beeeeeeeeeeeeee! BARRY Moose blood guy! "Bee Movie" - JS REVISIONS 8/13/07 57. Barry slides toward the end of the wiper, is thrown off, but he grabs the AERIAL and hangs on for dear life. Barry looks across and sees a CRICKET on another vehicle in the exact same predicament. They look at each other and SCREAM in unison. BARRY AND CRICKET Aaaaaaaaaah! ANOTHER BUG grabs onto the aerial, and screams as well. INT. TRUCK CAB - SAME TIME DRIVER You hear something? TRUCKER PASSENGER Like what? DRIVER Like tiny screaming. TRUCKER PASSENGER Turn off the radio. The driver reaches down and PRESSES a button, lowering the aerial. EXT. TRUCK WINDSHIELD - SAME TIME Barry and the other bug do a “choose up” to the bottom, Barry wins. BARRY Aha! Then he finally has to let go and gets thrown into the truck horn atop cab. Mooseblood is inside. MOOSEBLOOD Hey, what’s up bee boy? BARRY Hey, Blood! DISSOLVE TO: "Bee Movie" - JS REVISIONS 8/13/07 58. INT. TRUCK HORN - LATER BARRY ...and it was just an endless row of honey jars as far as the eye could see. MOOSEBLOOD Wow. BARRY So I’m just assuming wherever this honey truck goes, that’s where they’re getting it. I mean, that honey’s ours! MOOSEBLOOD Bees hang tight. BARRY Well, we’re all jammed in there. It’s a close community. MOOSEBLOOD Not us, man. We’re on our own. Every mosquito is on his own. BARRY But what if you get in trouble? MOOSEBLOOD Trouble? You're a mosquito. You're in trouble! Nobody likes us. They’re just all smacking. People see a mosquito, smack, smack! BARRY At least you’re out in the world. You must meet a lot of girls. MOOSEBLOOD Mosquito girls try to trade up; get with a moth, dragonfly...mosquito girl don’t want no mosquito. A BLOOD MOBILE pulls up alongside. MOOSEBLOOD (CONT'D) Whoa, you have got to be kidding me. Mooseblood’s about to leave the building. So long bee. "Bee Movie" - JS REVISIONS 8/13/07 59. Mooseblood EXITS the horn, and jumps onto the blood mobile. MOOSEBLOOD (CONT'D) Hey guys. I knew I’d catch you all down here. Did you bring your crazy straws? CUT TO: SEQ. 1900 - “THE APIARY” EXT. APIARY - LATER Barry sees a SIGN, “Honey Farms” The truck comes to a stop. SFX: The Honey farms truck blares its horn. Barry flies out, lands on the hood. ANGLE ON: Two BEEKEEPERS, FREDDY and ELMO, walking around to the back of the gift shop. Barry follows them, and lands in a nearby tree FREDDY ...then we throw it in some jars, slap a label on it, and it’s pretty much pure profit. BARRY What is this place? ELMO Bees got a brain the size of a pinhead. FREDDY They are pinheads. The both LAUGH. ANGLE ON: Barry REACTING. They arrive at the back of the shop where one of them opens a SMOKER BOX. FREDDY (CONT’D) Hey, check out the new smoker. "Bee Movie" - JS REVISIONS 8/13/07 60. ELMO Oh, Sweet. That’s the one you want. FREDDY The Thomas 3000. BARRY Smoker? FREDDY 90 puffs a minute, semi-automatic. Twice the nicotine, all the tar. They LAUGH again, nefariously. FREDDY (CONT’D) Couple of breaths of this, and it knocks them right out. They make the honey, and we make the money. BARRY “They make the honey, and we make the money?” Barry climbs onto the netting of Freddy’s hat. He climbs up to the brim and looks over the edge. He sees the apiary boxes as Freddy SMOKES them. BARRY (CONT'D) Oh my. As Freddy turns around, Barry jumps into an open apiary box, and into an apartment. HOWARD and FRAN are just coming to from the smoking. BARRY (CONT’D) What’s going on? Are you okay? HOWARD Yeah, it doesn’t last too long. HE COUGHS a few times. BARRY How did you two get here? Do you know you’re in a fake hive with fake walls? HOWARD (pointing to a picture on the wall) "Bee Movie" - JS REVISIONS 8/13/07 61. Our queen was moved here, we had no choice. BARRY (looking at a picture on the wall) This is your queen? That’s a man in women’s clothes. That’s a dragqueen! The other wall opens. Barry sees the hundreds of apiary boxes. BARRY (CONT'D) What is this? Barry pulls out his camera, and starts snapping. BARRY (CONT’D) Oh no. There’s hundreds of them. (V.O, as Barry takes pictures) Bee honey, our honey, is being brazenly stolen on a massive scale. CUT TO: SEQ. 2100 - “BARRY TELLS FAMILY” INT. BARRY’S PARENT’S HOUSE - LIVING ROOM - LATER Barry has assembled his parents, Adam, and Uncle Carl. BARRY This is worse than anything the bears have done to us. And I intend to do something about it. JANET BENSON Oh Barry, stop. MARTIN BENSON Who told you that humans are taking our honey? That’s just a rumor. BARRY Do these look like rumors? Barry throws the PICTURES on the table. Uncle Carl, cleaning his glasses with his shirt tail, digs through a bowl of nuts with his finger. "Bee Movie" - JS REVISIONS 8/13/07 62. HOWARD (CONT'D) UNCLE CARL That’s a conspiracy theory. These are obviously doctored photos. JANET BENSON Barry, how did you get mixed up in all this? ADAM (jumping up) Because he’s been talking to humans! JANET BENSON Whaaat? MARTIN BENSON Talking to humans?! Oh Barry. ADAM He has a human girlfriend and they make out! JANET BENSON Make out? Barry? BARRY We do not. ADAM You wish you could. BARRY Who’s side are you on? ADAM The bees! Uncle Carl stands up and pulls his pants up to his chest. UNCLE CARL I dated a cricket once in San Antonio. Man, those crazy legs kept me up all night. Hotcheewah! JANET BENSON Barry, this is what you want to do with your life? BARRY This is what I want to do for all our lives. Nobody works harder than bees. "Bee Movie" - JS REVISIONS 8/13/07 63. Dad, I remember you coming home some nights so overworked, your hands were still stirring. You couldn’t stop them. MARTIN BENSON Ehhh... JANET BENSON (to Martin) I remember that. BARRY What right do they have to our hardearned honey? We’re living on two cups a year. They’re putting it in lip balm for no reason what-soever. MARTIN BENSON Even if it’s true, Barry, what could one bee do? BARRY I’m going to sting them where it really hurts. MARTIN BENSON In the face? BARRY No. MARTIN BENSON In the eye? That would really hurt. BARRY No. MARTIN BENSON Up the nose? That’s a killer. BARRY No. There’s only one place you can sting the humans. One place where it really matters. CUT TO: SEQ. 2300 - “HIVE AT 5 NEWS/BEE LARRY KING” "Bee Movie" - JS REVISIONS 8/13/07 64. BARRY (CONT'D) INT. NEWS STUDIO - DAY DRAMATIC NEWS MUSIC plays as the opening news sequence rolls. We see the “Hive at Five” logo, followed by shots of past news events: A BEE freeway chase, a BEE BEARD protest rally, and a BEAR pawing at the hive as the BEES flee in panic. BOB BUMBLE (V.O.) Hive at Five, the hive’s only full hour action news source... SHOTS of NEWSCASTERS flash up on screen. BOB BUMBLE (V.O.) (CONT'D) With Bob Bumble at the anchor desk... BOB has a big shock of anchorman hair, gray temples and overly white teeth. BOB BUMBLE (V.O.) (CONT'D) ...weather with Storm Stinger, sports with Buzz Larvi, and Jeanette Chung. JEANETTE is an Asian bee. BOB BUMBLE (CONT'D) Good evening, I’m Bob Bumble. JEANETTE CHUNG And I’m Jeanette Chung. BOB BUMBLE Our top story, a tri-county bee, Barry Benson... INSERT: Barry’s graduation picture. BOB BUMBLE (CONT'D) ...is saying he intends to sue the human race for stealing our honey, packaging it, and profiting from it illegally. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 65. INT. BEENN STUDIO - BEE LARRY KING LIVE BEE LARRY KING, wearing suspenders and glasses, is interviewing Barry. A LOWER-THIRD CHYRON reads: “Bee Larry King Live.” BEE LARRY KING Don’t forget, tomorrow night on Bee Larry King, we are going to have three former Queens all right here in our studio discussing their new book, “Classy Ladies,” out this week on Hexagon. (to Barry) Tonight, we’re talking to Barry Benson. Did you ever think, I’m just a kid from the hive, I can’t do this? BARRY Larry, bees have never been afraid to change the world. I mean, what about Bee-Columbus? Bee-Ghandi? Be-geesus? BEE LARRY KING Well, where I’m from you wouldn’t think of suing humans. We were thinking more like stick ball, candy stores. BARRY How old are you? BEE LARRY KING I want you to know that the entire bee community is supporting you in this case, which is certain to be the trial of the bee century. BARRY Thank you, Larry. You know, they have a Larry King in the human world, too. BEE LARRY KING It’s a common name. Next week on Bee Larry King... "Bee Movie" - JS REVISIONS 8/13/07 66. BARRY No, I mean he looks like you. And he has a show with suspenders and different colored dots behind him. BEE LARRY KING Next week on Bee Larry King... BARRY Old guy glasses, and there’s quotes along the bottom from the guest you’re watching even though you just heard them... BEE LARRY KING Bear week next week! They’re scary, they’re hairy, and they’re here live. Bee Larry King EXITS. BARRY Always leans forward, pointy shoulders, squinty eyes... (lights go out) Very Jewish. CUT TO: SEQ. 2400 - “FLOWER SHOP” INT. VANESSA’S FLOWER SHOP - NIGHT Stacks of law books are piled up, legal forms, etc. Vanessa is talking with Ken in the other room. KEN Look, in tennis, you attack at the point of weakness. VANESSA But it was my grandmother, Ken. She’s 81. KEN Honey, her backhand’s a joke. I’m not going to take advantage of that? "Bee Movie" - JS REVISIONS 8/13/07 67. BARRY (O.C) Quiet please. Actual work going on here. KEN Is that that same bee? BARRY (O.C) Yes it is. VANESSA I’m helping him sue the human race. KEN What? Barry ENTERS. BARRY Oh, hello. KEN Hello Bee. Barry flies over to Vanessa. VANESSA This is Ken. BARRY Yeah, I remember you. Timberland, size 10 1/2, Vibram sole I believe. KEN Why does he talk again, Hun? VANESSA (to Ken, sensing the tension) Listen, you’d better go because we’re really busy working. KEN But it’s our yogurt night. VANESSA (pushing him out the door) Oh...bye bye. She CLOSES the door. KEN Why is yogurt night so difficult?! "Bee Movie" - JS REVISIONS 8/13/07 68. Vanessa ENTERS the back room carrying coffee. VANESSA Oh you poor thing, you two have been at this for hours. BARRY Yes, and Adam here has been a huge help. ANGLE ON: A EMPTY CINNABON BOX with Adam asleep inside, covered in frosting. VANESSA How many sugars? BARRY Just one. I try not to use the competition. So, why are you helping me, anyway? VANESSA Bees have good qualities. BARRY (rowing on the sugar cube like a gondola) Si, Certo. VANESSA And it feels good to take my mind off the shop. I don’t know why, instead of flowers, people are giving balloon bouquets now. BARRY Yeah, those are great...if you’re 3. VANESSA And artificial flowers. BARRY (re: plastic flowers) Oh, they just get me psychotic! VANESSA Yeah, me too. BARRY The bent stingers, the pointless pollination. "Bee Movie" - JS REVISIONS 8/13/07 69. VANESSA Bees must hate those fake plastic things. BARRY There’s nothing worse than a daffodil that’s had work done. VANESSA (holding up the lawsuit documents) Well, maybe this can make up for it a little bit. CUT TO: EXT. VANESSA’S FLORIST SHOP They EXIT the store, and cross to the mailbox. VANESSA You know Barry, this lawsuit is a pretty big deal. BARRY I guess. VANESSA Are you sure that you want to go through with it? BARRY Am I sure? (kicking the envelope into the mailbox) When I’m done with the humans, they won’t be able to say, “Honey, I’m home,” without paying a royalty. CUT TO: SEQ. 2700 - “MEET MONTGOMERY” EXT. MANHATTAN COURTHOUSE - DAY P.O.V SHOT - A camera feed turns on, revealing a newsperson. "Bee Movie" - JS REVISIONS 8/13/07 70. PRESS PERSON #2 (talking to camera) Sarah, it’s an incredible scene here in downtown Manhattan where all eyes and ears of the world are anxiously waiting, because for the first time in history, we’re going to hear for ourselves if a honey bee can actually speak. ANGLE ON: Barry, Vanessa, and Adam getting out of the cab. The press spots Barry and Vanessa and pushes in. Adam sits on Vanessa’s shoulder. INT. COURTHOUSE - CONTINUOUS Barry, Vanessa, and Adam sit at the Plaintiff’s Table. VANESSA (turns to Barry) What have we gotten into here, Barry? BARRY I don’t know, but it’s pretty big, isn’t it? ADAM I can’t believe how many humans don’t have to be at work during the day. BARRY Hey, you think these billion dollar multinational food companies have good lawyers? CUT TO: EXT. COURTHOUSE STEPS - CONTINUOUS A BIG BLACK CAR pulls up. ANGLE ON: the grill filling the frame. We see the “L.T.M” monogram on the hood ornament. The defense lawyer, LAYTON T. MONTGOMERY comes out, squashing a bug on the pavement. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 71. INT. COURTHOUSE - CONTINUOUS Barry SHUDDERS. VANESSA What’s the matter? BARRY I don’t know. I just got a chill. Montgomery ENTERS. He walks by Barry’s table shaking a honey packet. MONTGOMERY Well, if it isn’t the B-Team. (re: the honey packet) Any of you boys work on this? He CHUCKLES. The JUDGE ENTERS. SEQ. 3000 - “WITNESSES” BAILIFF All rise! The Honorable Judge Bumbleton presiding. JUDGE (shuffling papers) Alright...Case number 4475, Superior Court of New York. Barry Bee Benson vs. the honey industry, is now in session. Mr. Montgomery, you are representing the five major food companies, collectively. ANGLE ON: Montgomery’s BRIEFCASE. It has an embossed emblem of an EAGLE, holding a gavel in one talon and a briefcase in the other. MONTGOMERY A privilege. JUDGE Mr. Benson. Barry STANDS. JUDGE (CONT’D) You are representing all bees of the world? "Bee Movie" - JS REVISIONS 8/13/07 72. Montgomery, the stenographer, and the jury lean in. CUT TO: EXT. COURTHOUSE - CONTINUOUS The spectators outside freeze. The helicopters angle forward to listen closely. CUT TO: INT. COURTHOUSE BARRY Bzzz bzzz bzzz...Ahh, I’m kidding, I’m kidding. Yes, your honor. We are ready to proceed. ANGLE ON: Courtroom hub-bub. JUDGE And Mr. Montgomery, your opening statement, please. Montgomery rises. MONTGOMERY (grumbles, clears his throat) Ladies and gentlemen of the jury. My grandmother was a simple woman. Born on a farm, she believed it was man's divine right to benefit from the bounty of nature God put before us. If we were to live in the topsy-turvy world Mr. Benson imagines, just think of what it would mean. Maybe I would have to negotiate with the silk worm for the elastic in my britches. Talking bee. How do we know this isn’t some sort of holographic motion picture capture Hollywood wizardry? They could be using laser beams, robotics, ventriloquism, cloning...for all we know he could be on steroids! Montgomery leers at Barry, who moves to the stand. "Bee Movie" - JS REVISIONS 8/13/07 73. JUDGE Mr. Benson? Barry makes his opening statement. BARRY Ladies and Gentlemen of the jury, there’s no trickery here. I’m just an ordinary bee. And as a bee, honey’s pretty important to me. It’s important to all bees. We invented it, we make it, and we protect it with our lives. Unfortunately, there are some people in this room who think they can take whatever they want from us cause we’re the little guys. And what I’m hoping is that after this is all over, you’ll see how by taking our honey, you’re not only taking away everything we have, but everything we are. ANGLE ON: Vanessa smiling. ANGLE ON: The BEE GALLERY wiping tears away. CUT TO: INT. BENSON HOUSE Barry’s family is watching the case on TV. JANET BENSON Oh, I wish he would dress like that all the time. So nice... CUT TO: INT. COURTROOM - LATER JUDGE Call your first witness. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 74. INT. COURTHOUSE - LATER BARRY So, Mr. Klauss Vanderhayden of Honey Farms. Pretty big company you have there? MR. VANDERHAYDEN I suppose so. BARRY And I see you also own HoneyBurton, and Hon-Ron. MR. VANDERHAYDEN Yes. They provide beekeepers for our farms. BARRY Beekeeper. I find that to be a very disturbing term, I have to say. I don’t imagine you employ any bee free-ers, do you? MR. VANDERHAYDEN No. BARRY I’m sorry. I couldn’t hear you. MR. VANDERHAYDEN (louder) No. BARRY No. Because you don’t free bees. You keep bees. And not only that, it seems you thought a bear would be an appropriate image for a jar of honey? MR. VANDERHAYDEN Well, they’re very lovable creatures. Yogi-bear, Fozzy-bear, Build-a-bear. BARRY Yeah, you mean like this?! Vanessa and the SUPERINTENDANT from her building ENTER with a GIANT FEROCIOUS GRIZZLY BEAR. He has a neck collar and chains extending from either side. "Bee Movie" - JS REVISIONS 8/13/07 75. By pulling the chains, they bring him directly in front of Vanderhayden. The bear LUNGES and ROARS. BARRY (CONT'D) Bears kill bees! How would you like his big hairy head crashing into your living room? Biting into your couch, spitting out your throwpillows...rowr, rowr! The bear REACTS. BEAR Rowr!! BARRY Okay, that’s enough. Take him away. Vanessa and the Superintendant pull the bear out of the courtroom. Vanderhayden TREMBLES. The judge GLARES at him. CUT TO: INT. COURTROOM- A LITTLE LATER Barry questions STING. BARRY So, Mr. Sting. Thank you for being here. Your name intrigues me, I have to say. Where have I heard it before? STING I was with a band called "The Police". BARRY But you've never been a police officer of any kind, have you? STING No, I haven't. "Bee Movie" - JS REVISIONS 8/13/07 76. BARRY No, you haven’t. And so, here we have yet another example of bee culture being casually stolen by a human for nothing more than a prance-about stage name. STING Oh please. BARRY Have you ever been stung, Mr. Sting? Because I'm feeling a little stung, Sting. Or should I say, (looking in folder) Mr. Gordon M. Sumner? The jury GASPS. MONTGOMERY (to his aides) That’s not his real name? You idiots! CUT TO: INT. COURTHOUSE- LATER BARRY Mr. Liotta, first may I offer my belated congratulations on your Emmy win for a guest spot on E.R. in 2005. LIOTTA Thank you. Thank you. Liotta LAUGHS MANIACALLY. BARRY I also see from your resume that you’re devilishly handsome, but with a churning inner turmoil that’s always ready to blow. LIOTTA I enjoy what I do. Is that a crime? "Bee Movie" - JS REVISIONS 8/13/07 77. BARRY Not yet it isn’t. But is this what it’s come to for you, Mr. Liotta? Exploiting tiny helpless bees so you don’t have to rehearse your part, and learn your lines, Sir? LIOTTA Watch it Benson, I could blow right now. BARRY This isn’t a goodfella. This is a badfella! LIOTTA (exploding, trying to smash Barry with the Emmy) Why doesn’t someone just step on this little creep and we can all go home? You’re all thinking it. Say it! JUDGE Order! Order in this courtroom! A MONTAGE OF NEWSPAPER HEADLINES FOLLOWS: NEW YORK POST: “Bees to Humans: Buzz Off”. NEW YORK TELEGRAM: “Sue Bee”. DAILY VARIETY: “Studio Dumps Liotta Project. Slams Door on Unlawful Entry 2.” CUT TO: SEQ. 3175 - “CANDLELIGHT DINNER” INT. VANESSA’S APARTMENT Barry and Vanessa are having a candle light dinner. Visible behind Barry is a “LITTLE MISSY” SET BOX, with the flaps open. BARRY Well, I just think that was awfully nice of that bear to pitch in like that. "Bee Movie" - JS REVISIONS 8/13/07 78. VANESSA I’m telling you, I think the jury’s on our side. BARRY Are we doing everything right...you know, legally? VANESSA I’m a florist. BARRY Right, right. Barry raises his glass. BARRY (CONT’D) Well, here’s to a great team. VANESSA To a great team. They toast. Ken ENTERS KEN Well hello. VANESSA Oh...Ken. BARRY Hello. VANESSA I didn’t think you were coming. KEN No, I was just late. I tried to call. But, (holding his cell phone) the battery... VANESSA I didn’t want all this to go to waste, so I called Barry. Luckily he was free. BARRY Yeah. KEN (gritting his teeth) Oh, that was lucky. "Bee Movie" - JS REVISIONS 8/13/07 79. VANESSA Well, there’s still a little left. I could heat it up. KEN Yeah, heat it up. Sure, whatever. Vanessa EXITS. Ken and Barry look at each other as Barry eats. BARRY So, I hear you’re quite a tennis player. I’m not much for the game myself. I find the ball a little grabby. KEN That’s where I usually sit. Right there. VANESSA (O.C) Ken, Barry was looking at your resume, and he agreed with me that “eating with chopsticks” isn’t really a special skill. KEN (to Barry) You think I don’t see what you’re doing? BARRY Hey look, I know how hard it is trying to find the right job. We certainly have that in common. KEN Do we? BARRY Well, bees have 100% employment, of course. But we do jobs like taking the crud out. KEN That’s just what I was thinking about doing. Ken holds his table knife up. It slips out of his hand. He goes under the table to pick it up. "Bee Movie" - JS REVISIONS 8/13/07 80. VANESSA Ken, I let Barry borrow your razor for his fuzz. I hope that was alright. Ken hits his head on the table. BARRY I’m going to go drain the old stinger. KEN Yeah, you do that. Barry EXITS to the bathroom, grabbing a small piece of a VARIETY MAGAZINE on the way. BARRY Oh, look at that. Ken slams the champagne down on the table. Ken closes his eyes and buries his face in his hands. He grabs a magazine on the way into the bathroom. SEQ. 2800 - “BARRY FIGHTS KEN” INT. BATHROOM - CONTINUOUS Ken ENTERS, closes the door behind him. He’s not happy. Barry is washing his hands. He glances back at Ken. KEN You know, I’ve just about had it with your little mind games. BARRY What’s that? KEN Italian Vogue. BARRY Mamma Mia, that’s a lot of pages. KEN It’s a lot of ads. BARRY Remember what Van said. Why is your life any more valuable than mine? "Bee Movie" - JS REVISIONS 8/13/07 81. KEN It’s funny, I just can’t seem to recall that! Ken WHACKS at Barry with the magazine. He misses and KNOCKS EVERYTHING OFF THE VANITY. Ken grabs a can of AIR FRESHENER. KEN (CONT'D) I think something stinks in here. He sprays at Barry. BARRY I love the smell of flowers. KEN Yeah? How do you like the smell of flames? Ken lights the stream. BARRY Not as much. Barry flies in a circle. Ken, trying to stay with him, spins in place. ANGLE ON: Flames outside the bathroom door. Ken slips on the Italian Vogue, falls backward into the shower, pulling down the shower curtain. The can hits him in the head, followed by the shower curtain rod, and the rubber duck. Ken reaches back, grabs the handheld shower head. He whips around, looking for Barry. ANGLE ON: A WATERBUG near the drain. WATERBUG Waterbug. Not taking sides. Barry is on the toilet tank. He comes out from behind a shampoo bottle, wearing a chapstick cap as a helmet. BARRY Ken, look at me! I’m wearing a chapstick hat. This is pathetic. ANGLE ON: Ken turning the hand shower nozzle from “GENTLE”, to “TURBO”, to “LETHAL”. "Bee Movie" - JS REVISIONS 8/13/07 82. KEN I’ve got issues! Ken fires the water at Barry, knocking him into the toilet. The items from the vanity (emory board, lipstick, eye curler, etc.) are on the toilet seat. Ken looks down at Barry. KEN (CONT'D) Well well well, a royal flush. BARRY You’re bluffing. KEN Am I? Ken flushes the toilet. Barry grabs the Emory board and uses it to surf. He puts his hand in the water while he’s surfing. Some water splashes on Ken. BARRY Surf’s up, dude! KEN Awww, poo water! He does some skate board-style half-pipe riding. Barry surfs out of the toilet. BARRY That bowl is gnarly. Ken tries to get a shot at him with the toilet brush. KEN Except for those dirty yellow rings. Vanessa ENTERS. VANESSA Kenneth! What are you doing? KEN You know what? I don’t even like honey! I don’t eat it! VANESSA We need to talk! "Bee Movie" - JS REVISIONS 8/13/07 83. She pulls Ken out by his ear. Ken glares at Barry. CUT TO: INT. HALLWAY - CONTINUOUS VANESSA He’s just a little bee. And he happens to be the nicest bee I’ve met in a long time. KEN Long time? What are you talking about? Are there other bugs in your life? VANESSA No, but there are other things bugging me in life. And you’re one of them! KEN Fine! Talking bees, no yogurt night...my nerves are fried from riding on this emotional rollercoaster. VANESSA Goodbye, Ken. KEN Augh! VANESSA Whew! Ken EXITS, then re-enters frame. KEN And for your information, I prefer sugar-free, artificial sweeteners, made by man! He EXITS again. The DOOR SLAMS behind him. VANESSA (to Barry) I’m sorry about all that. Ken RE-ENTERS. "Bee Movie" - JS REVISIONS 8/13/07 84. KEN I know it’s got an aftertaste! I like it! BARRY (re: Ken) I always felt there was some kind of barrier between Ken and me. (puts his hands in his pockets) I couldn’t overcome it. Oh well. VANESSA Are you going to be okay for the trial tomorrow? BARRY Oh, I believe Mr. Montgomery is about out of ideas. CUT TO: SEQ. 3300 - “ADAM STINGS MONTY” INT. COURTROOM - NEXT DAY ANGLE ON: Medium shot of Montgomery standing at his table. MONTGOMERY We would like to call Mr. Barry Benson Bee to the stand. ADAM (whispering to Vanessa) Now that’s a good idea. (to Barry) You can really see why he’s considered one of the very best lawyers-- Oh. Barry rolls his eyes. He gets up, takes the stand. A juror in a striped shirt APPLAUDS. MR. GAMMIL (whispering) Layton, you’ve got to weave some magic with this jury, or it’s going to be all over. Montgomery is holding a BOOK, “The Secret Life of Bees”. "Bee Movie" - JS REVISIONS 8/13/07 85. MONTGOMERY (confidently whispering) Oh, don’t worry Mr. Gammil. The only thing I have to do to turn this jury around is to remind them of what they don’t like about bees. (to Gammil) You got the tweezers? Mr. Gammil NODS, and pats his breast pocket. MR. GAMMIL Are you allergic? MONTGOMERY Only to losing, son. Only to losing. Montgomery approaches the stand. MONTGOMERY (CONT’D) Mr. Benson Bee. I’ll ask you what I think we’d all like to know. What exactly is your relationship to that woman? Montgomery points to Vanessa. BARRY We’re friends. MONTGOMERY Good friends? BARRY Yes. MONTGOMERY (softly in Barry’s face) How good? BARRY What? MONTGOMERY Do you live together? BARRY Wait a minute, this isn’t about-- "Bee Movie" - JS REVISIONS 8/13/07 86. MONTGOMERY Are you her little... (clearing throat) ... bed bug? BARRY (flustered) Hey, that’s not the kind of-- MONTGOMERY I’ve seen a bee documentary or two. Now, from what I understand, doesn’t your Queen give birth to all the bee children in the hive? BARRY Yeah, but-- MONTGOMERY So those aren’t even your real parents! ANGLE ON: Barry’s parents. MARTIN BENSON Oh, Barry. BARRY Yes they are! ADAM Hold me back! Vanessa holds him back with a COFFEE STIRRER. Montgomery points to Barry’s parents. MONTGOMERY You’re an illegitimate bee, aren’t you Benson? ADAM He’s denouncing bees! All the bees in the courtroom start to HUM. They’re agitated. MONTGOMERY And don’t y’all date your cousins? "Bee Movie" - JS REVISIONS 8/13/07 87. VANESSA (standing, letting go of Adam) Objection! Adam explodes from the table and flies towards Montgomery. ADAM I’m going to pin cushion this guy! Montgomery turns around and positions himself by the judge’s bench. He sticks his butt out. Montgomery winks at his team. BARRY Adam, don’t! It’s what he wants! Adam shoves Barry out of the way. Adam STINGS Montgomery in the butt. The jury REACTS, aghast. MONTGOMERY Ow! I’m hit! Oh, lordy, I am hit! The judge BANGS her gavel. JUDGE Order! Order! Please, Mr. Montgomery. MONTGOMERY The venom! The venom is coursing through my veins! I have been felled by a wing-ed beast of destruction. You see? You can’t treat them like equals. They’re strip-ed savages! Stinging’s the only thing they know! It’s their way! ANGLE ON: Adam, collapsed on the floor. Barry rushes to his side. BARRY Adam, stay with me. ADAM I can’t feel my legs. Montgomery falls on the Bailiff. BAILIFF Take it easy. "Bee Movie" - JS REVISIONS 8/13/07 88. MONTGOMERY Oh, what angel of mercy will come forward to suck the poison from my heaving buttocks? The JURY recoils. JUDGE Please, I will have order in this court. Order! Order, please! FADE TO: SEQ. 3400 - “ADAM AT HOSPITAL” INT. HOSPITAL - STREET LEVEL ROOM - DAY PRESS PERSON #1 (V.O) The case of the honey bees versus the human race took a pointed turn against the bees yesterday, when one of their legal team stung Layton T. Montgomery. Now here’s Don with the 5-day. A NURSE lets Barry into the room. Barry CARRIES a FLOWER. BARRY Thank you. Barry stands over Adam, in a bed. Barry lays the flower down next to him. The TV is on. BARRY (CONT'D) Hey buddy. ADAM Hey. BARRY Is there much pain? Adam has a BEE-SIZED PAINKILLER HONEY BUTTON near his head that he presses. ADAM (pressing the button) Yeah...I blew the whole case, didn’t I? "Bee Movie" - JS REVISIONS 8/13/07 89. BARRY Oh, it doesn’t matter. The important thing is you’re alive. You could have died. ADAM I’d be better off dead. Look at me. Adam THROWS the blanket off his lap, revealing a GREEN SANDWICH SWORD STINGER. ADAM (CONT’D) (voice cracking) They got it from the cafeteria, they got it from downstairs. In a tuna sandwich. Look, there’s a little celery still on it. BARRY What was it like to sting someone? ADAM I can’t explain it. It was all adrenaline...and then...ecstasy. Barry looks at Adam. BARRY Alright. ADAM You think that was all a trap? BARRY Of course. I’m sorry. I flew us right into this. What were we thinking? Look at us, we’re just a couple of bugs in this world. ADAM What do you think the humans will do to us if they win? BARRY I don’t know. ADAM I hear they put the roaches in motels. That doesn’t sound so bad. "Bee Movie" - JS REVISIONS 8/13/07 90. BARRY Adam, they check in, but they don’t check out. Adam GULPS. ADAM Oh my. ANGLE ON: the hospital window. We see THREE PEOPLE smoking outside on the sidewalk. The smoke drifts in. Adam COUGHS. ADAM (CONT’D) Say, could you get a nurse to close that window? BARRY Why? ADAM The smoke. Bees don’t smoke. BARRY Right. Bees don’t smoke. Bees don’t smoke! But some bees are smoking. Adam, that’s it! That’s our case. Adam starts putting his clothes on. ADAM It is? It’s not over? BARRY No. Get up. Get dressed. I’ve got to go somewhere. You get back the court and stall. Stall anyway you can. CUT TO: SEQ. 3500 - “SMOKING GUN” INT. COURTROOM - THE NEXT DAY Adam is folding a piece of paper into a boat. ADAM ...and assuming you’ve done step 29 correctly, you’re ready for the tub. "Bee Movie" - JS REVISIONS 8/13/07 91. ANGLE ON: The jury, all with paper boats of their own. JURORS Ooh. ANGLE ON: Montgomery frustrated with Gammil, who’s making a boat also. Monty crumples Gammil’s boat, and throws it at him. JUDGE Mr. Flayman? ADAM Yes? Yes, Your Honor? JUDGE Where is the rest of your team? ADAM (fumbling with his swordstinger) Well, your honor, it’s interesting. You know Bees are trained to fly kind of haphazardly and as a result quite often we don’t make very good time. I actually once heard a pretty funny story about a bee-- MONTGOMERY Your Honor, haven’t these ridiculous bugs taken up enough of this court’s valuable time? Montgomery rolls out from behind his table. He’s suspended in a LARGE BABY CHAIR with wheels. MONTGOMERY (CONT'D) How much longer are we going to allow these absurd shenanigans to go on? They have presented no compelling evidence to support their charges against my clients who have all run perfectly legitimate businesses. I move for a complete dismissal of this entire case. JUDGE Mr. Flayman, I am afraid I am going to have to consider Mr. Montgomery’s motion. "Bee Movie" - JS REVISIONS 8/13/07 92. ADAM But you can’t. We have a terrific case. MONTGOMERY Where is your proof? Where is the evidence? Show me the smoking gun. Barry bursts through the door. BARRY Hold it, your honor. You want a smoking gun? Here is your smoking gun. Vanessa ENTERS, holding a bee smoker Vanessa slams the beekeeper's SMOKER onto the judge’s bench. JUDGE What is that? BARRY It’s a Bee smoker. Montgomery GRABS the smoker. MONTGOMERY What, this? This harmless little contraption? This couldn’t hurt a fly, let alone a bee. He unintentionally points it towards the bee gallery, KNOCKING THEM ALL OUT. The jury GASPS. The press SNAPS pictures of them. BARRY Members of the jury, look at what has happened to bees who have never been asked, "Smoking or Non?" Is this what nature intended for us? To be forcibly addicted to these smoke machines in man-made wooden slat work camps? Living out our lives as honey slaves to the white man? Barry gestures dramatically towards Montgomery's racially mixed table. The BLACK LAWYER slowly moves his chair away. GAMMIL What are we going to do? "Bee Movie" - JS REVISIONS 8/13/07 93. MONTGOMERY (to Pross) He's playing the species card. Barry lands on the scale of justice, by the judge’s bench. It balances as he lands. BARRY Ladies and gentlemen, please, FreeThese-Bees! ANGLE ON: Jury, chanting "Free the bees". JUDGE The court finds in favor of the bees. The chaos continues. Barry flies over to Vanessa, with his hand up for a “high 5”. BARRY Vanessa, we won! VANESSA Yay! I knew you could do it. Highfive! She high 5’s Barry, sending him crashing to the table. He bounces right back up. VANESSA (CONT'D) Oh, sorry. BARRY Ow!! I’m okay. Vanessa, do you know what this means? All the honey is finally going to belong to the bees. Now we won’t have to work so hard all the time. Montgomery approaches Barry, surrounded by the press. The cameras and microphones go to Montgomery. MONTGOMERY (waving a finger) This is an unholy perversion of the balance of nature, Benson! You’ll regret this. ANGLE ON: Barry’s ‘deer in headlights’ expression, as the press pushes microphones in his face. "Bee Movie" - JS REVISIONS 8/13/07 94. PRESS PERSON 1 Barry, how much honey do you think is out there? BARRY Alright, alright, one at a time... SARAH Barry, who are you wearing? BARRY Uhhh, my sweater is Ralph Lauren, and I have no pants. The Press follows Barry as he EXITS. ANGLE ON: Adam and Vanessa. ADAM (putting papers away) What if Montgomery’s right? VANESSA What do you mean? ADAM We’ve been living the bee way a long time. 27 million years. DISSOLVE TO: SEQ. 3600 - “HONEY ROUNDUP” EXT. HONEY FARMS APIARY - MONTAGE SARAH (V.O) Congratulations on your victory. What are you going to demand as a settlement? BARRY (V.O) (over montage) First, we’re going to demand a complete shutdown of all bee work camps. Then, we want to get back all the honey that was ours to begin with. Every last drop. We demand an end to the glorification of the bear as anything more than a filthy, smelly, big-headed, bad breath, stink-machine. "Bee Movie" - JS REVISIONS 8/13/07 95. I believe we’re all aware of what they do in the woods. We will no longer tolerate derogatory beenegative nick-names, unnecessary inclusion of honey in bogus health products, and la-dee-da tea-time human snack garnishments. MONTAGE IMAGES: Close-up on an ATF JACKET, with the YELLOW LETTERS. Camera pulls back. We see an ARMY OF BEE AND HUMAN AGENTS wearing hastily made “Alcohol, Tobacco, Firearms, and Honey” jackets. Barry supervises. The gate to Honey Farms is locked permanently. All the smokers are collected and locked up. All the bees leave the Apiary. CUT TO: EXT. ATF OUTSIDE OF SUPERMARKET - MONTAGE Agents begin YANKING honey off the supermarket shelves, and out of shopping baskets. CUT TO: EXT. NEW HIVE CITY - MONTAGE The bees tear down a honey-bear statue. CUT TO: EXT. YELLOWSTONE FOREST - MONTAGE POV of a sniper’s crosshairs. An animated BEAR character looka-like, turns his head towards camera. BARRY Wait for my signal. ANGLE ON: Barry lowering his binoculars. BARRY (CONT'D) Take him out. The sniper SHOOTS the bear. It hits him in the shoulder. The bear looks at it. He gets woozy and the honey jar falls out of his lap, an ATF&H agent catches it. "Bee Movie" - JS REVISIONS 8/13/07 96. BARRY (V.O) (CONT'D) ATF&H AGENT (to the bear’s pig friend) He’ll have a little nausea for a few hours, then he’ll be fine. CUT TO: EXT. STING’S HOUSE - MONTAGE ATF&H agents SLAP CUFFS on Sting, who is meditating. STING But it’s just a prance-about stage name! CUT TO: INT. A WOMAN’S SHOWER - MONTAGE A WOMAN is taking a shower, and using honey shampoo. An ATF&H agent pulls the shower curtain aside, and grabs her bottle of shampoo. The woman SCREAMS. The agent turns to the 3 other agents, and Barry. ANGLE ON: Barry looking at the label on the shampoo bottle, shaking his head and writing in his clipboard. CUT TO: EXT. SUPERMARKET CAFE - MONTAGE Another customer, an old lady having her tea with a little jar of honey, gets her face pushed down onto the table and turned to the side by two agents. One of the agents has a gun on her. OLD LADY Can’t breathe. CUT TO: EXT. CENTRAL PARK - MONTAGE An OIL DRUM of honey is connected to Barry’s hive. "Bee Movie" - JS REVISIONS 8/13/07 97. BARRY Bring it in, boys. CUT TO: SEQ. 3650 - “NO MORE WORK” INT. HONEX - MONTAGE ANGLE ON: The honey goes past the 3-cup hash-mark, and begins to overflow. A WORKER BEE runs up to Buzzwell. WORKER BEE 1 Mr. Buzzwell, we just passed 3 cups, and there’s gallons mores coming. I think we need to shutdown. KEYCHAIN BEE (to Buzzwell) Shutdown? We’ve never shutdown. ANGLE ON: Buzzwell overlooking the factory floor. BUZZWELL Shutdown honey production! Stop making honey! ANGLE ON: TWO BEES, each with a KEY. BUZZWELL (CONT’D) Turn your key, Sir! They turn the keys simultaneously, War Games-style, shutting down the honey machines. ANGLE ON: the Taffy-Pull machine, Centrifuge, and Krelman all slowly come to a stop. The bees look around, bewildered. WORKER BEE 5 What do we do now? A BEAT. WORKER BEE 6 Cannon ball!! He jumps into a HONEY VAT, doesn’t penetrate the surface. He looks around, and slowly sinks down to his waist. "Bee Movie" - JS REVISIONS 8/13/07 98. EXT. HONEX FACTORY THE WHISTLE BLOWS, and the bees all stream out the exit. CUT TO: INT. J-GATE - CONTINUOUS Lou Loduca gives orders to the pollen jocks. LOU LODUCA We’re shutting down honey production. Mission abort. CUT TO: EXT. CENTRAL PARK Jackson receives the orders, mid-pollination. JACKSON Aborting pollination and nectar detail. Returning to base. CUT TO: EXT. NEW HIVE CITY ANGLE ON: Bees, putting sun-tan lotion on their noses and antennae, and sunning themselves on the balconies of the gyms. CUT TO: EXT. CENTRAL PARK ANGLE ON: THE FLOWERS starting to DROOP. CUT TO: INT. J-GATE J-Gate is deserted. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 99. EXT. NEW HIVE CITY ANGLE ON: Bees sunning themselves. A TIMER DINGS, and they all turn over. CUT TO: EXT. CENTRAL PARK TIME LAPSE of Central Park turning brown. CUT TO: EXT. VANESSA’S FLORIST SHOP CLOSE-UP SHOT: Vanessa writes “Sorry. No more flowers.” on a “Closed” sign, an turns it facing out. CUT TO: SEQ. 3700 - “IDLE HIVE” EXT. NEW HIVE CITY - DAY Barry flies at high speed. TRACKING SHOT into the hive, through the lobby of Honex, and into Adam’s office. CUT TO: INT. ADAM’S OFFICE - CONTINUOUS Barry meets Adam in his office. Adam’s office is in disarray. There are papers everywhere. He’s filling up his cardboard hexagon box. BARRY (out of breath) Adam, you wouldn’t believe how much honey was out there. ADAM Oh yeah? BARRY What’s going on around here? Where is everybody? Are they out celebrating? "Bee Movie" - JS REVISIONS 8/13/07 100. ADAM (exiting with a cardboard box of belongings) No, they’re just home. They don’t know what to do. BARRY Hmmm. ADAM They’re laying out, they’re sleeping in. I heard your Uncle Carl was on his way to San Antonio with a cricket. BARRY At least we got our honey back. They walk through the empty factory. ADAM Yeah, but sometimes I think, so what if the humans liked our honey? Who wouldn’t? It’s the greatest thing in the world. I was excited to be a part of making it. ANGLE ON: Adam’s desk on it’s side in the hall. ADAM (CONT’D) This was my new desk. This was my new job. I wanted to do it really well. And now...and now I can’t. Adam EXITS. CUT TO: SEQ. 3900 - “WORLD WITHOUT BEES” INT. STAIRWELL Vanessa and Barry are walking up the stairs to the roof. BARRY I don’t understand why they’re not happy. We have so much now. I thought their lives would be better. "Bee Movie" - JS REVISIONS 8/13/07 101. VANESSA Hmmm. BARRY They’re doing nothing. It’s amazing, honey really changes people. VANESSA You don’t have any idea what’s going on, do you? BARRY What did you want to show me? VANESSA This. They reach the top of the stairs. Vanessa opens the door. CUT TO: EXT. VANESSA’S ROOFTOP - CONTINUOUS Barry sees Vanessa’s flower pots and small garden have all turned brown. BARRY What happened here? VANESSA That is not the half of it... Vanessa turns Barry around with her two fingers, revealing the view of Central Park, which is also all brown. BARRY Oh no. Oh my. They’re all wilting. VANESSA Doesn’t look very good, does it? BARRY No. VANESSA And who’s fault do you think that is? "Bee Movie" - JS REVISIONS 8/13/07 102. BARRY Mmmm...you know, I’m going to guess, bees. VANESSA Bees? BARRY Specifically me. I guess I didn’t think that bees not needing to make honey would affect all these other things. VANESSA And it’s not just flowers. Fruits, vegetables...they all need bees. BARRY Well, that’s our whole SAT test right there. VANESSA So, you take away the produce, that affects the entire animal kingdom. And then, of course... BARRY The human species? VANESSA (clearing throat) Ahem! BARRY Oh. So, if there’s no more pollination, it could all just go south here, couldn’t it? VANESSA And I know this is also partly my fault. Barry takes a long SIGH. BARRY How about a suicide pact? VANESSA (not sure if he’s joking) How would we do it? BARRY I’ll sting you, you step on me. "Bee Movie" - JS REVISIONS 8/13/07 103. VANESSA That just kills you twice. BARRY Right, right. VANESSA Listen Barry. Sorry but I’ve got to get going. She EXITS. BARRY (looking out over the park) Had to open my mouth and talk... (looking back) Vanessa..? Vanessa is gone. CUT TO: SEQ. 3935 - “GOING TO PASADENA” EXT. NY STREET - CONTINUOUS Vanessa gets into a cab. Barry ENTERS. BARRY Vanessa. Why are you leaving? Where are you going? VANESSA To the final Tournament of Roses parade in Pasadena. They moved it up to this weekend because all the flowers are dying. It’s the last chance I’ll ever have to see it. BARRY Vanessa, I just want to say I’m sorry. I never meant it to turn out like this. VANESSA I know. Me neither. Vanessa cab drives away. "Bee Movie" - JS REVISIONS 8/13/07 104. BARRY (chuckling to himself) Tournament of Roses. Roses can’t do sports. Wait a minute...roses. Roses? Roses!? Vanessa! Barry follows shortly after. He catches up to it, and he pounds on the window. Barry follows shortly after Vanessa’s cab. He catches up to it, and he pounds on the window. INT. TAXI - CONTINUOUS Barry motions for her to roll the window down. She does so. BARRY Roses?! VANESSA Barry? BARRY (as he flies next to the cab) Roses are flowers. VANESSA Yes, they are. BARRY Flowers, bees, pollen! VANESSA I know. That’s why this is the last parade. BARRY Maybe not. The cab starts pulling ahead of Barry. BARRY (CONT'D) (re: driver) Could you ask him to slow down? VANESSA Could you slow down? The cabs slows. Barry flies in the window, and lands in the change box, which closes on him. "Bee Movie" - JS REVISIONS 8/13/07 105. VANESSA (CONT'D) Barry! Vanessa lets him out. Barry stands on the change box, in front of the driver’s license. BARRY Okay, I made a huge mistake! This is a total disaster, and it’s all my fault! VANESSA Yes, it kind of is. BARRY I’ve ruined the planet. And, I wanted to help with your flower shop. Instead, I’ve made it worse. VANESSA Actually, it’s completely closed down. BARRY Oh, I thought maybe you were remodeling. Nonetheless, I have another idea. And it’s greater than all my previous great ideas combined. VANESSA I don’t want to hear it. Vanessa closes the change box on Barry. BARRY (opening it again) Alright, here’s what I’m thinking. They have the roses, the roses have the pollen. I know every bee, plant, and flower bud in this park. All we’ve got to do is get what they’ve got back here with what we’ve got. VANESSA Bees... BARRY Park... VANESSA Pollen... "Bee Movie" - JS REVISIONS 8/13/07 106. BARRY Flowers... VANESSA Repollination! BARRY (on luggage handle, going up) Across the nation! CUT TO: SEQ. 3950 - “ROSE PARADE” EXT. PASADENA PARADE BARRY (V.O) Alright. Tournament of Roses. Pasadena, California. They’ve got nothing but flowers, floats, and cotton candy. Security will be tight. VANESSA I have an idea. CUT TO: EXT. FLOAT STAGING AREA ANGLE ON: Barry and Vanessa approaching a HEAVILY ARMED GUARD in front of the staging area. VANESSA Vanessa Bloome, FTD. Official floral business. He leans in to look at her badge. She SNAPS IT SHUT, VANESSA (CONT’D) Oh, it’s real. HEAVILY ARMED GUARD Sorry ma’am. That’s a nice brooch, by the way. VANESSA Thank you. It was a gift. "Bee Movie" - JS REVISIONS 8/13/07 107. They ENTER the staging area. BARRY (V.O) Then, once we’re inside, we just pick the right float. VANESSA How about the Princess and the Pea? BARRY Yeah. VANESSA I can be the princess, and-- BARRY ...yes, I think-- VANESSA You could be-- BARRY I’ve-- VANESSA The pea. BARRY Got it. CUT TO: EXT. FLOAT STAGING AREA - A FEW MOMENTS LATER Barry, dressed as a PEA, flies up and hovers in front of the princess on the “Princess and the Pea” float. The float is sponsored by Inflat-a-bed and a SIGN READS: “Inflat-a-bed: If it blows, it’s ours.” BARRY Sorry I’m late. Where should I sit? PRINCESS What are you? BARRY I believe I’m the pea. PRINCESS The pea? It’s supposed to be under the mattresses. "Bee Movie" - JS REVISIONS 8/13/07 108. BARRY Not in this fairy tale, sweetheart. PRINCESS I’m going to go talk to the marshall. BARRY You do that. This whole parade is a fiasco! She EXITS. Vanessa removes the step-ladder. The princess FALLS. Barry and Vanessa take off in the float. BARRY (CONT’D) Let’s see what this baby will do. ANGLE ON: Guy with headset talking to drivers. HEADSET GUY Hey! The float ZOOMS by. A young CHILD in the stands, TIMMY, cries. CUT TO: EXT. FLOAT STAGING AREA - A FEW MOMENTS LATER ANGLE ON: Vanessa putting the princess hat on. BARRY (V.O) Then all we do is blend in with traffic, without arousing suspicion. CUT TO: EXT. THE PARADE ROUTE - CONTINUOUS The floats go flying by the crowds. Barry and Vanessa’s float CRASHES through the fence. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 109. EXT. LA FREEWAY Vanessa and Barry speed, dodging and weaving, down the freeway. BARRY (V.O) And once we’re at the airport, there’s no stopping us. CUT TO: EXT. LAX AIRPORT Barry and Vanessa pull up to the curb, in front of an TSA AGENT WITH CLIPBOARD. TSA AGENT Stop. Security. Did you and your insect pack your own float? VANESSA (O.C) Yes. TSA AGENT Has this float been in your possession the entire time? VANESSA (O.C) Since the parade...yes. ANGLE ON: Barry holding his shoes. TSA AGENT Would you remove your shoes and everything in your pockets? Can you remove your stinger, Sir? BARRY That’s part of me. TSA AGENT I know. Just having some fun. Enjoy your flight. CUT TO: EXT. RUNWAY Barry and Vanessa’s airplane TAKES OFF. "Bee Movie" - JS REVISIONS 8/13/07 110. BARRY (O.C) Then, if we’re lucky, we’ll have just enough pollen to do the job. DISSOLVE TO: SEQ. 4025 - “COCKPIT FIGHT” INT. AIRPLANE Vanessa is on the aisle. Barry is on a laptop calculating flowers, pollen, number of bees, airspeed, etc. He does a “Stomp” dance on the keyboard. BARRY Can you believe how lucky we are? We have just enough pollen to do the job. I think this is going to work, Vanessa. VANESSA It’s got to work. PILOT (V.O) Attention passengers. This is Captain Scott. I’m afraid we have a bit of bad weather in the New York area. And looks like we’re going to be experiencing a couple of hours delay. VANESSA Barry, these are cut flowers with no water. They’ll never make it. BARRY I’ve got to get up there and talk to these guys. VANESSA Be careful. Barry flies up to the cockpit door. CUT TO: INT. COCKPIT - CONTINUOUS A female flight attendant, ANGELA, is in the cockpit with the pilots. "Bee Movie" - JS REVISIONS 8/13/07 111. There’s a KNOCK at the door. BARRY (C.O) Hey, can I get some help with this Sky Mall Magazine? I’d like to order the talking inflatable travel pool filter. ANGELA (to the pilots, irritated) Excuse me. CUT TO: EXT. CABIN - CONTINUOUS Angela opens the cockpit door and looks around. She doesn’t see anybody. ANGLE ON: Barry hidden on the yellow and black “caution” stripe. As Angela looks around, Barry zips into the cockpit. CUT TO: INT. COCKPIT BARRY Excuse me, Captain. I am in a real situation here... PILOT (pulling an earphone back, to the co-pilot) What did you say, Hal? CO-PILOT I didn’t say anything. PILOT (he sees Barry) Ahhh! Bee! BARRY No, no! Don’t freak out! There’s a chance my entire species-- CO-PILOT (taking off his earphones) Ahhh! "Bee Movie" - JS REVISIONS 8/13/07 112. The pilot grabs a “DUSTBUSTER” vacuum cleaner. He aims it around trying to vacuum up Barry. The co-pilot faces camera, as the pilot tries to suck Barry up. Barry is on the other side of the co-pilot. As they dosey-do, the toupee of the co-pilot begins to come up, still attached to the front. CO-PILOT (CONT'D) What are you doing? Stop! The toupee comes off the co-pilot’s head, and sticks in the Dustbuster. Barry runs across the bald head. BARRY Wait a minute! I’m an attorney! CO-PILOT Who’s an attorney? PILOT Don’t move. The pilot uses the Dustbuster to try and mash Barry, who is hovering in front of the co-pilot’s nose, and knocks out the co-pilot who falls out of his chair, hitting the life raft release button. The life raft inflates, hitting the pilot, knocking him into a wall and out cold. Barry surveys the situation. BARRY Oh, Barry. CUT TO: INT. AIRPLANE CABIN Vanessa studies her laptop, looking serious. SFX: PA CRACKLE. BARRY (V.O) (in captain voice) Good afternoon passengers, this is your captain speaking. Would a Miss Vanessa Bloome in 24F please report to the cockpit. And please hurry! "Bee Movie" - JS REVISIONS 8/13/07 113. ANGLE ON: The aisle, and Vanessa head popping up. CUT TO: INT. COCKPIT Vanessa ENTERS. VANESSA What happened here? BARRY I tried to talk to them, but then there was a Dustbuster, a toupee, a life raft exploded...Now one’s bald, one’s in a boat, and they’re both unconscious. VANESSA Is that another bee joke? BARRY No. No one’s flying the plane. The AIR TRAFFIC CONTROLLER, BUD, speaks over the radio. BUD This is JFK control tower. Flight 356, what’s your status? Vanessa presses a button, and the intercom comes on. VANESSA This is Vanessa Bloome. I’m a florist from New York. BUD Where’s the pilot? VANESSA He’s unconscious and so is the copilot. BUD Not good. Is there anyone onboard who has flight experience? A BEAT. BARRY As a matter of fact, there is. "Bee Movie" - JS REVISIONS 8/13/07 114. BUD Who’s that? VANESSA Barry Benson. BUD From the honey trial? Oh great. BARRY Vanessa, this is nothing more than a big metal bee. It’s got giant wings, huge engines. VANESSA I can’t fly a plane. BARRY Why not? Isn’t John Travolta a pilot? VANESSA Yes? BARRY How hard could it be? VANESSA Wait a minute. Barry, we’re headed into some lightning. CUT TO: Vanessa shrugs, and takes the controls. SEQ. 4150 - “BARRY FLIES PLANE” INT. BENSON HOUSE The family is all huddled around the TV at the Benson house. ANGLE ON: TV. Bob Bumble is broadcasting. BOB BUMBLE This is Bob Bumble. We have some late-breaking news from JFK airport, where a very suspenseful scene is developing. Barry Benson, fresh off his stunning legal victory... "Bee Movie" - JS REVISIONS 8/13/07 115. Adam SPRAYS a can of HONEY-WHIP into his mouth. ADAM That’s Barry. BOB BUMBLE ...is now attempting to land a plane, loaded with people, flowers, and an incapacitated flight crew. EVERYONE Flowers?! CUT TO: INT. AIR TRAFFIC CONTROL TOWER BUD Well, we have an electrical storm in the area, and two individuals at the controls of a jumbo jet with absolutely no flight experience. JEANETTE CHUNG Just a minute, Mr. Ditchwater, there’s a honey bee on that plane. BUD Oh, I’m quite familiar with Mr. Benson’s work, and his no-account compadres. Haven’t they done enough damage already? JEANETTE CHUNG But isn’t he your only hope right now? BUD Come on, technically a bee shouldn’t be able to fly at all. CUT TO: INT. COCKPIT. Barry REACTS BUD The wings are too small, their bodies are too big-- "Bee Movie" - JS REVISIONS 8/13/07 116. BARRY (over PA) Hey, hold on a second. Haven’t we heard this million times? The surface area of the wings, and the body mass doesn’t make sense? JEANETTE CHUNG Get this on the air. CAMERAMAN You got it! CUT TO: INT. BEE TV CONTROL ROOM An engineer throws a switch. BEE ENGINEER Stand by. We’re going live. The “ON AIR” sign illuminates. CUT TO: INT. VARIOUS SHOTS OF NEW HIVE CITY The news report plays on TV. The pollen jocks are sitting around, playing paddle-ball, Wheel-o, and one of them is spinning his helmet on his finger. Buzzwell is in an office cubicle, playing computer solitaire. Barry’s family and Adam watch from their living room. Bees sitting on the street curb turn around to watch the TV. BARRY Mr. Ditchwater, the way we work may be a mystery to you, because making honey takes a lot of bees doing a lot of small jobs. But let me tell you something about a small job. If you do it really well, it makes a big difference. More than we realized. To us, to everyone. That’s why I want to get bees back to doing what we do best. "Bee Movie" - JS REVISIONS 8/13/07 117. Working together. That’s the bee way. We’re not made of Jello. We get behind a fellow. Black and yellow. CROWD OF BEES Hello! CUT TO: INT. COCKPIT Barry is giving orders to Vanessa. BARRY Left, right, down, hover. VANESSA Hover? BARRY Forget hover. VANESSA You know what? This isn’t so hard. Vanessa pretends to HONK THE HORN. VANESSA (CONT’D) Beep, beep! Beep, beep! A BOLT OF LIGHTNING HITS the plane. The plane takes a sharp dip. VANESSA (CONT’D) Barry, what happened? BARRY (noticing the control panel) Wait a minute. I think we were on autopilot that whole time. VANESSA That may have been helping me. BARRY And now we’re not! VANESSA (V.O.) (folding her arms) Well, then it turns out I cannot fly a plane. "Bee Movie" - JS REVISIONS 8/13/07 118. BARRY (CONT'D) Vanessa struggles with the yoke. CUT TO: EXT. AIRPLANE The airplane goes into a steep dive. CUT TO: SEQ. 4175 - “CRASH LANDING” INT. J-GATE An ALERT SIGN READING: “Hive Alert. We Need:” Then the SIGNAL goes from “Two Bees” “Some Bees” “Every Bee There Is” Lou Loduca gathers the pollen jocks at J-Gate. LOU LODUCA All of you, let’s get behind this fellow. Move it out! The bees follow Lou Loduca, and EXIT J-Gate. CUT TO: INT. AIRPLANE COCKPIT BARRY Our only chance is if I do what I would do, and you copy me with the wings of the plane! VANESSA You don’t have to yell. BARRY I’m not yelling. We happen to be in a lot of trouble here. VANESSA It’s very hard to concentrate with that panicky tone in your voice. BARRY It’s not a tone. I’m panicking! CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 119. EXT. JFK AIRPORT ANGLE ON: The bees arriving and massing at the airport. CUT TO: INT. COCKPIT Barry and Vanessa alternately SLAP EACH OTHER IN THE FACE. VANESSA I don’t think I can do this. BARRY Vanessa, pull yourself together. Listen to me, you have got to snap out of it! VANESSA You snap out of it! BARRY You snap out of it! VANESSA You snap out of it! BARRY You snap out of it! VANESSA You snap out of it! CUT TO: EXT. AIRPLANE A GIGANTIC SWARM OF BEES flies in to hold the plane up. CUT TO: INT. COCKPIT - CONTINUOUS BARRY You snap out of it! VANESSA You snap out of it! "Bee Movie" - JS REVISIONS 8/13/07 120. BARRY You snap-- VANESSA Hold it! BARRY (about to slap her again) Why? Come on, it’s my turn. VANESSA How is the plane flying? Barry’s antennae ring. BARRY I don’t know. (answering) Hello? CUT TO: EXT. AIRPLANE ANGLE ON: The underside of the plane. The pollen jocks have massed all around the underbelly of the plane, and are holding it up. LOU LODUCA Hey Benson, have you got any flowers for a happy occasion in there? CUT TO: INT. COCKPIT Lou, Buzz, Splitz, and Jackson come up alongside the cockpit. BARRY The pollen jocks! VANESSA They do get behind a fellow. BARRY Black and yellow. LOU LODUCA (over headset) Hello. "Bee Movie" - JS REVISIONS 8/13/07 121. Alright you two, what do you say we drop this tin can on the blacktop? VANESSA What blacktop? Where? I can’t see anything. Can you? BARRY No, nothing. It’s all cloudy. CUT TO: EXT. RUNWAY Adam SHOUTS. ADAM Come on, you’ve got to think bee, Barry. Thinking bee, thinking bee. ANGLE ON: Overhead shot of runway. The bees are in the formation of a flower. In unison they move, causing the flower to FLASH YELLOW AND BLACK. BEES (chanting) Thinking bee, thinking bee. CUT TO: INT. COCKPIT We see through the swirling mist and clouds. A GIANT SHAPE OF A FLOWER is forming in the middle of the runway. BARRY Wait a minute. I think I’m feeling something. VANESSA What? BARRY I don’t know, but it’s strong. And it’s pulling me, like a 27 million year old instinct. Bring the nose of the plane down. "Bee Movie" - JS REVISIONS 8/13/07 122. LOU LODUCA (CONT'D) EXT. RUNWAY All the bees are on the runway chanting “Thinking Bee”. CUT TO: INT. CONTROL TOWER RICK What in the world is on the tarmac? ANGLE ON: Dave OTS onto runway seeing a flower being formed by millions of bees. BUD Get some lights on that! CUT TO: EXT. RUNWAY ANGLE ON: AIRCRAFT LANDING LIGHT SCAFFOLD by the side of the runway, illuminating the bees in their flower formation. INT. COCKPIT BARRY Vanessa, aim for the flower! VANESSA Oh, okay? BARRY Cut the engines! VANESSA Cut the engines? BARRY We’re going in on bee power. Ready boys? LOU LODUCA Affirmative. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 123. INT. AIRPLANE COCKPIT BARRY Good, good, easy now. Land on that flower! Ready boys? Give me full reverse. LOU LODUCA Spin it around! The plane attempts to land on top of an “Aloha Airlines” plane with flowers painted on it. BARRY (V.O) I mean the giant black and yellow pulsating flower made of millions of bees! VANESSA Which flower? BARRY That flower! VANESSA I’m aiming at the flower! The plane goes after a FAT GUY IN A HAWAIIAN SHIRT. BARRY (V.O) That’s a fat guy in a flowered shirt! The other other flower! The big one. He snaps a photo and runs away. BARRY (CONT'D) Full forward. Ready boys? Nose down. Bring your tail up. Rotate around it. VANESSA Oh, this is insane, Barry. BARRY This is the only way I know how to fly. CUT TO: "Bee Movie" - JS REVISIONS 8/13/07 124. AIR TRAFFIC CONTROL TOWER BUD Am I koo-koo kachoo, or is this plane flying in an insect-like pattern? CUT TO: EXT. RUNWAY BARRY (V.O) Get your nose in there. Don’t be afraid of it. Smell it. Full reverse! Easy, just drop it. Be a part of it. Aim for the center! Now drop it in. Drop it in, woman! The plane HOVERS and MANEUVERS, landing in the center of the giant flower, like a bee. The FLOWERS from the cargo hold spill out onto the runway. INT. AIPLANE CABIN The passengers are motionless for a beat. PASSENGER Come on already! They hear the “ding ding”, and all jump up to grab their luggage out of the overheads. SEQ. 4225 - “RUNWAY SPEECH” EXT. RUNWAY - CONTINUOUS The INFLATABLE SLIDES pop out the side of the plane. The passengers escape. Barry and Vanessa slide down out of the cockpit. Barry and Vanessa exhale a huge breath. VANESSA Barry, we did it. You taught me how to fly. Vanessa raises her hand up for a high five. "Bee Movie" - JS REVISIONS 8/13/07 125. BARRY Yes. No high five. VANESSA Right. ADAM Barry, it worked. Did you see the giant flower? BARRY What giant flower? Where? Of course I saw the flower! That was genius, man. Genius! ADAM Thank you. BARRY But we’re not done yet. Barry flies up to the wing of the plane, and addresses the bee crowd. BARRY (CONT’D) Listen everyone. This runway is covered with the last pollen from the last flowers available anywhere on Earth. That means this is our last chance. We’re the only ones who make honey, pollinate flowers, and dress like this. If we’re going to survive as a species, this is our moment. So what do you all say? Are we going to be bees, or just Museum of Natural History key chains? BEES We’re bees! KEYCHAIN BEE Keychain! BARRY Then follow me... Except Keychain. BUZZ Hold on Barry. You’ve earned this. Buzz puts a pollen jock jacket and helmet with Barry’s name on it on Barry. "Bee Movie" - JS REVISIONS 8/13/07 126. BARRY I’m a pollen jock! (looking at the jacket. The sleeves are a little long) And it’s a perfect fit. All I’ve got to do are the sleeves. The Pollen Jocks toss Barry a gun. BARRY (CONT’D) Oh yeah! ANGLE ON: Martin and Janet Benson. JANET BENSON That’s our Barry. All the bees descend upon the flowers on the tarmac, and start collecting pollen. CUT TO: SEQ. 4250 - “RE-POLLINATION” EXT. SKIES - CONTINUOUS The squadron FLIES over the city, REPOLLINATING trees and flowers as they go. Barry breaks off from the group, towards Vanessa’s flower shop. CUT TO: EXT. VANESSA’S FLOWER SHOP - CONTINUOUS Barry REPOLLINATES Vanessa’s flowers. CUT TO: EXT. CENTRAL PARK - CONTINUOUS ANGLE ON: Timmy with a frisbee, as the bees fly by. TIMMY Mom, the bees are back! "Bee Movie" - JS REVISIONS 8/13/07 127. Central Park is completely repollinated by the bees. DISSOLVE TO: INT. HONEX - CONTINUOUS Honex is back to normal and everyone is busily working. ANGLE ON: Adam, putting his Krelman hat on. ADAM If anyone needs to make a call, now’s the time. I’ve got a feeling we’ll be working late tonight! The bees CHEER. CUT TO: SEQ. 4355 EXT: VANESSA’S FLOWER SHOP With a new sign out front. “Vanessa & Barry: Flowers, Honey, Legal Advice” DISSOLVE TO: INT: FLOWER COUNTER Vanessa doing a brisk trade with many customers. CUT TO: INT: FLOWER SHOP - CONTINUOUS Vanessa is selling flowers. In the background, there are SHELVES STOCKED WITH HONEY. VANESSA (O.C.) Don’t forget these. Have a great afternoon. Yes, can I help who’s next? Who’s next? Would you like some honey with that? It is beeapproved. SIGN ON THE BACK ROOM DOOR READS: “Barry Benson: Insects at Law”. "Bee Movie" - JS REVISIONS 8/13/07 128. Camera moves into the back room. ANGLE ON: Barry. ANGLE ON: Barry’s COW CLIENT. COW Milk, cream, cheese...it’s all me. And I don’t see a nickel. BARRY Uh huh? Uh huh? COW (breaking down) Sometimes I just feel like a piece of meat. BARRY I had no idea. VANESSA Barry? I’m sorry, have you got a moment? BARRY Would you excuse me? My mosquito associate here will be able to help you. Mooseblood ENTERS. MOOSEBLOOD Sorry I’m late. COW He’s a lawyer too? MOOSEBLOOD Ma’am, I was already a bloodsucking parasite. All I needed was * a briefcase. * ANGLE ON: Flower Counter. VANESSA (to customer) Have a great afternoon! (to Barry) Barry, I just got this huge tulip order for a wedding, and I can’t get them anywhere. "Bee Movie" - JS REVISIONS 8/13/07 129. BARRY Not a problem, Vannie. Just leave it to me. Vanessa turns back to deal with a customer. VANESSA You’re a life-saver, Barry. (to the next customer) Can I help who’s next? Who’s next? ANGLE ON: Vanessa smiling back at Barry. Barry smiles too, then snaps himself out of it. BARRY (speaks into his antennae) Alright. Scramble jocks, it’s time to fly! VANESSA Thank you, Barry! EXT. FLOWER SHOP - CONTINUOUS ANGLE ON: Ken and Andy walking down the street. KEN (noticing the new sign) Augh! What in the world? It’s that bee again! ANDY (guiding Ken protectively) Let it go, Kenny. KEN That bee is living my life! When will this nightmare end? ANDY Let it all go. They don’t break stride. ANGLE ON: Camera in front of Barry as he flies out the door and up into the sky. Pollen jocks fold in formation behind him as they zoom into the park. BARRY (to Splitz) Beautiful day to fly. "Bee Movie" - JS REVISIONS 8/13/07 130. JACKSON Sure is. BARRY Between you and me, I was dying to get out of that office. FADE OUT: "Bee Movie" - JS REVISIONS 8/13/07 131.

sanusanth / Python Basic Programs

What is Python? Executive Summary Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed. Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python's introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective. What is Python? Python is a popular programming language. It was created by Guido van Rossum, and released in 1991. It is used for: web development (server-side), software development, mathematics, system scripting. What can Python do? Python can be used on a server to create web applications. Python can be used alongside software to create workflows. Python can connect to database systems. It can also read and modify files. Python can be used to handle big data and perform complex mathematics. Python can be used for rapid prototyping, or for production-ready software development. Why Python? Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc). Python has a simple syntax similar to the English language. Python has syntax that allows developers to write programs with fewer lines than some other programming languages. Python runs on an interpreter system, meaning that code can be executed as soon as it is written. This means that prototyping can be very quick. Python can be treated in a procedural way, an object-oriented way or a functional way. Good to know The most recent major version of Python is Python 3, which we shall be using in this tutorial. However, Python 2, although not being updated with anything other than security updates, is still quite popular. In this tutorial Python will be written in a text editor. It is possible to write Python in an Integrated Development Environment, such as Thonny, Pycharm, Netbeans or Eclipse which are particularly useful when managing larger collections of Python files. Python Syntax compared to other programming languages Python was designed for readability, and has some similarities to the English language with influence from mathematics. Python uses new lines to complete a command, as opposed to other programming languages which often use semicolons or parentheses. Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly-brackets for this purpose. Applications for Python Python is used in many application domains. Here's a sampling. The Python Package Index lists thousands of third party modules for Python. Web and Internet Development Python offers many choices for web development: Frameworks such as Django and Pyramid. Micro-frameworks such as Flask and Bottle. Advanced content management systems such as Plone and django CMS. Python's standard library supports many Internet protocols: HTML and XML JSON E-mail processing. Support for FTP, IMAP, and other Internet protocols. Easy-to-use socket interface. And the Package Index has yet more libraries: Requests, a powerful HTTP client library. Beautiful Soup, an HTML parser that can handle all sorts of oddball HTML. Feedparser for parsing RSS/Atom feeds. Paramiko, implementing the SSH2 protocol. Twisted Python, a framework for asynchronous network programming. Scientific and Numeric Python is widely used in scientific and numeric computing: SciPy is a collection of packages for mathematics, science, and engineering. Pandas is a data analysis and modeling library. IPython is a powerful interactive shell that features easy editing and recording of a work session, and supports visualizations and parallel computing. The Software Carpentry Course teaches basic skills for scientific computing, running bootcamps and providing open-access teaching materials. Education Python is a superb language for teaching programming, both at the introductory level and in more advanced courses. Books such as How to Think Like a Computer Scientist, Python Programming: An Introduction to Computer Science, and Practical Programming. The Education Special Interest Group is a good place to discuss teaching issues. Desktop GUIs The Tk GUI library is included with most binary distributions of Python. Some toolkits that are usable on several platforms are available separately: wxWidgets Kivy, for writing multitouch applications. Qt via pyqt or pyside Platform-specific toolkits are also available: GTK+ Microsoft Foundation Classes through the win32 extensions Software Development Python is often used as a support language for software developers, for build control and management, testing, and in many other ways. SCons for build control. Buildbot and Apache Gump for automated continuous compilation and testing. Roundup or Trac for bug tracking and project management. Business Applications Python is also used to build ERP and e-commerce systems: Odoo is an all-in-one management software that offers a range of business applications that form a complete suite of enterprise management applications. Try ton is a three-tier high-level general purpose application platform.

Aryia-Behroziuan / References

Poole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. 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Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12 Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3 Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence".[120] In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".[121] This is a form of Tom Mitchell's widely quoted definition of machine learning: "A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E." Learning: * ACM 1998, I.2.6, * Russell & Norvig 2003, pp. 649–788, * Poole, Mackworth & Goebel 1998, pp. 397–438, * Luger & Stubblefield 2004, pp. 385–542, * Nilsson 1998, chpt. 3.3, 10.3, 17.5, 20 Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260. Bibcode:2015Sci...349..255J. doi:10.1126/science.aaa8415. PMID 26185243. S2CID 677218. Reinforcement learning: * Russell & Norvig 2003, pp. 763–788 * Luger & Stubblefield 2004, pp. 442–449 Natural language processing: * ACM 1998, I.2.7 * Russell & Norvig 2003, pp. 790–831 * Poole, Mackworth & Goebel 1998, pp. 91–104 * Luger & Stubblefield 2004, pp. 591–632 "Versatile question answering systems: seeing in synthesis" Archived 1 February 2016 at the Wayback Machine, Mittal et al., IJIIDS, 5(2), 119–142, 2011 Applications of natural language processing, including information retrieval (i.e. text mining) and machine translation: * Russell & Norvig 2003, pp. 840–857, * Luger & Stubblefield 2004, pp. 623–630 Cambria, Erik; White, Bebo (May 2014). "Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]". IEEE Computational Intelligence Magazine. 9 (2): 48–57. doi:10.1109/MCI.2014.2307227. S2CID 206451986. Vincent, James (7 November 2019). "OpenAI has published the text-generating AI it said was too dangerous to share". The Verge. Archived from the original on 11 June 2020. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. Bibcode:2016arXiv160605830C. doi:10.1109/TRO.2016.2624754. S2CID 2596787. Moravec, Hans (1988). Mind Children. Harvard University Press. p. 15. Chan, Szu Ping (15 November 2015). "This is what will happen when robots take over the world". Archived from the original on 24 April 2018. Retrieved 23 April 2018. "IKEA furniture and the limits of AI". The Economist. 2018. Archived from the original on 24 April 2018. Retrieved 24 April 2018. Kismet. Thompson, Derek (2018). "What Jobs Will the Robots Take?". The Atlantic. Archived from the original on 24 April 2018. Retrieved 24 April 2018. Scassellati, Brian (2002). "Theory of mind for a humanoid robot". Autonomous Robots. 12 (1): 13–24. doi:10.1023/A:1013298507114. S2CID 1979315. Cao, Yongcan; Yu, Wenwu; Ren, Wei; Chen, Guanrong (February 2013). "An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination". IEEE Transactions on Industrial Informatics. 9 (1): 427–438. arXiv:1207.3231. doi:10.1109/TII.2012.2219061. S2CID 9588126. Thro 1993. Edelson 1991. Tao & Tan 2005. Poria, Soujanya; Cambria, Erik; Bajpai, Rajiv; Hussain, Amir (September 2017). "A review of affective computing: From unimodal analysis to multimodal fusion". Information Fusion. 37: 98–125. doi:10.1016/j.inffus.2017.02.003. hdl:1893/25490. Emotion and affective computing: * Minsky 2006 Waddell, Kaveh (2018). "Chatbots Have Entered the Uncanny Valley". The Atlantic. Archived from the original on 24 April 2018. Retrieved 24 April 2018. Pennachin, C.; Goertzel, B. (2007). Contemporary Approaches to Artificial General Intelligence. Artificial General Intelligence. Cognitive Technologies. Cognitive Technologies. Berlin, Heidelberg: Springer. doi:10.1007/978-3-540-68677-4_1. ISBN 978-3-540-23733-4. Roberts, Jacob (2016). "Thinking Machines: The Search for Artificial Intelligence". Distillations. Vol. 2 no. 2. pp. 14–23. Archived from the original on 19 August 2018. Retrieved 20 March 2018. "The superhero of artificial intelligence: can this genius keep it in check?". the Guardian. 16 February 2016. Archived from the original on 23 April 2018. Retrieved 26 April 2018. Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis (26 February 2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. S2CID 205242740. Sample, Ian (14 March 2017). "Google's DeepMind makes AI program that can learn like a human". the Guardian. Archived from the original on 26 April 2018. Retrieved 26 April 2018. "From not working to neural networking". The Economist. 2016. Archived from the original on 31 December 2016. Retrieved 26 April 2018. Domingos 2015. Artificial brain arguments: AI requires a simulation of the operation of the human brain * Russell & Norvig 2003, p. 957 * Crevier 1993, pp. 271 and 279 A few of the people who make some form of the argument: * Moravec 1988 * Kurzweil 2005, p. 262 * Hawkins & Blakeslee 2005 The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. Goertzel, Ben; Lian, Ruiting; Arel, Itamar; de Garis, Hugo; Chen, Shuo (December 2010). "A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures". Neurocomputing. 74 (1–3): 30–49. doi:10.1016/j.neucom.2010.08.012. Nilsson 1983, p. 10. Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. Retrieved 20 February 2020. CNN 2006. Using AI to predict flight delays Archived 20 November 2018 at the Wayback Machine, Ishti.org. N. Aletras; D. Tsarapatsanis; D. Preotiuc-Pietro; V. Lampos (2016). "Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective". PeerJ Computer Science. 2: e93. doi:10.7717/peerj-cs.93. "The Economist Explains: Why firms are piling into artificial intelligence". The Economist. 31 March 2016. Archived from the original on 8 May 2016. Retrieved 19 May 2016. Lohr, Steve (28 February 2016). "The Promise of Artificial Intelligence Unfolds in Small Steps". The New York Times. Archived from the original on 29 February 2016. Retrieved 29 February 2016. Frangoul, Anmar (14 June 2019). "A Californian business is using A.I. to change the way we think about energy storage". CNBC. Archived from the original on 25 July 2020. Retrieved 5 November 2019. Wakefield, Jane (15 June 2016). "Social media 'outstrips TV' as news source for young people". BBC News. Archived from the original on 24 June 2016. Smith, Mark (22 July 2016). "So you think you chose to read this article?". BBC News. Archived from the original on 25 July 2016. Brown, Eileen. "Half of Americans do not believe deepfake news could target them online". ZDNet. Archived from the original on 6 November 2019. Retrieved 3 December 2019. The Turing test: Turing's original publication: * Turing 1950 Historical influence and philosophical implications: * Haugeland 1985, pp. 6–9 * Crevier 1993, p. 24 * McCorduck 2004, pp. 70–71 * Russell & Norvig 2003, pp. 2–3 and 948 Dartmouth proposal: * McCarthy et al. 1955 (the original proposal) * Crevier 1993, p. 49 (historical significance) The physical symbol systems hypothesis: * Newell & Simon 1976, p. 116 * McCorduck 2004, p. 153 * Russell & Norvig 2003, p. 18 Dreyfus 1992, p. 156. Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". The Mathematical Objection: * Russell & Norvig 2003, p. 949 * McCorduck 2004, pp. 448–449 Making the Mathematical Objection: * Lucas 1961 * Penrose 1989 Refuting Mathematical Objection: * Turing 1950 under "(2) The Mathematical Objection" * Hofstadter 1979 Background: * Gödel 1931, Church 1936, Kleene 1935, Turing 1937 Graham Oppy (20 January 2015). "Gödel's Incompleteness Theorems". Stanford Encyclopedia of Philosophy. Archived from the original on 22 April 2016. Retrieved 27 April 2016. These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail. Stuart J. Russell; Peter Norvig (2010). "26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection". Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4. even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations. Mark Colyvan. An introduction to the philosophy of mathematics. Cambridge University Press, 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail." Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society". Contemporary Social Science: 1–15. doi:10.1080/21582041.2018.1563803. ISSN 2158-2041. "Ethical AI Learns Human Rights Framework". Voice of America. Archived from the original on 11 November 2019. Retrieved 10 November 2019. Crevier 1993, pp. 132–144. In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool.[216] Joseph Weizenbaum's critique of AI: * Weizenbaum 1976 * Crevier 1993, pp. 132–144 * McCorduck 2004, pp. 356–373 * Russell & Norvig 2003, p. 961 Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Wendell Wallach (2010). Moral Machines, Oxford University Press. Wallach, pp 37–54. Wallach, pp 55–73. Wallach, Introduction chapter. Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. "Machine Ethics". aaai.org. Archived from the original on 29 November 2014. Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis. 1: 88–100. Archived from the original on 11 June 2012. Brooks, Rodney (10 November 2014). "artificial intelligence is a tool, not a threat". Archived from the original on 12 November 2014. "Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence". Observer. 19 August 2015. Archived from the original on 30 October 2015. Retrieved 30 October 2015. Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. Archived from the original on 8 March 2005. Retrieved 11 October 2018. See also this link Archived 8 April 2011 at the Wayback Machine Horst, Steven, (2005) "The Computational Theory of Mind" Archived 11 September 2018 at the Wayback Machine in The Stanford Encyclopedia of Philosophy Searle 1980, p. 1. This version is from Searle (1999), and is also quoted in Dennett 1991, p. 435. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." [230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently

simonaertssen / MIT 6.172 Performance Engineering Of Software Systems

6.172 is an 18-unit class that provides a hands-on, project-based introduction to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, caching optimizations, parallel programming, and building scalable systems. The course programming language is C. See https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-172-performance-engineering-of-software-systems-fall-2018/

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SingularityT3 / PseudoEngine2

An interpreter for IGCSE/A level pseudocode

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RootKit-Org / Computer Science Certification Roadmap

LEVEL 2 CERTIFICATE: The Computer Science Certification requirements lays out everything a basic computer programmer must know in order to obtain this certification.

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KartikKarakoti / CS50x2025 Introduction To Computer Science

CS50x, Harvard’s intro to computer science, is for all skill levels, teaching algorithmic thinking, problem-solving, and coding. Topics include algorithms, data structures, security, and web development, with languages like C, Python, and JavaScript. Complete 9 problem sets and a final project to earn a certificate in this self-paced course.

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uvhw / Bitcoin Foundation

Bitcoin: A Peer-to-Peer Electronic Cash System Satoshi Nakamoto satoshin@gmx.com www.bitcoin.org Abstract. A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone. 1. Introduction Commerce on the Internet has come to rely almost exclusively on financial institutions serving as trusted third parties to process electronic payments. While the system works well enough for most transactions, it still suffers from the inherent weaknesses of the trust based model. Completely non-reversible transactions are not really possible, since financial institutions cannot avoid mediating disputes. The cost of mediation increases transaction costs, limiting the minimum practical transaction size and cutting off the possibility for small casual transactions, and there is a broader cost in the loss of ability to make non-reversible payments for non- reversible services. With the possibility of reversal, the need for trust spreads. Merchants must be wary of their customers, hassling them for more information than they would otherwise need. A certain percentage of fraud is accepted as unavoidable. These costs and payment uncertainties can be avoided in person by using physical currency, but no mechanism exists to make payments over a communications channel without a trusted party. What is needed is an electronic payment system based on cryptographic proof instead of trust, allowing any two willing parties to transact directly with each other without the need for a trusted third party. Transactions that are computationally impractical to reverse would protect sellers from fraud, and routine escrow mechanisms could easily be implemented to protect buyers. In this paper, we propose a solution to the double-spending problem using a peer-to-peer distributed timestamp server to generate computational proof of the chronological order of transactions. The system is secure as long as honest nodes collectively control more CPU power than any cooperating group of attacker nodes. 1 2. Transactions We define an electronic coin as a chain of digital signatures. Each owner transfers the coin to the next by digitally signing a hash of the previous transaction and the public key of the next owner and adding these to the end of the coin. A payee can verify the signatures to verify the chain of ownership. Transaction Hash Transaction Hash Transaction Hash Owner 1's Public Key Owner 2's Public Key Owner 3's Public Key Owner 0's Signature Owner 1's Signature The problem of course is the payee can't verify that one of the owners did not double-spend the coin. A common solution is to introduce a trusted central authority, or mint, that checks every transaction for double spending. After each transaction, the coin must be returned to the mint to issue a new coin, and only coins issued directly from the mint are trusted not to be double-spent. The problem with this solution is that the fate of the entire money system depends on the company running the mint, with every transaction having to go through them, just like a bank. We need a way for the payee to know that the previous owners did not sign any earlier transactions. For our purposes, the earliest transaction is the one that counts, so we don't care about later attempts to double-spend. The only way to confirm the absence of a transaction is to be aware of all transactions. In the mint based model, the mint was aware of all transactions and decided which arrived first. To accomplish this without a trusted party, transactions must be publicly announced [1], and we need a system for participants to agree on a single history of the order in which they were received. The payee needs proof that at the time of each transaction, the majority of nodes agreed it was the first received. 3. Timestamp Server The solution we propose begins with a timestamp server. A timestamp server works by taking a hash of a block of items to be timestamped and widely publishing the hash, such as in a newspaper or Usenet post [2-5]. The timestamp proves that the data must have existed at the time, obviously, in order to get into the hash. Each timestamp includes the previous timestamp in its hash, forming a chain, with each additional timestamp reinforcing the ones before it. Hash Hash Owner 2's Signature Owner 1's Private Key Owner 2's Private Key Owner 3's Private Key Block Item Item ... 2 Block Item Item ... Verify Verify Sign Sign 4. Proof-of-Work To implement a distributed timestamp server on a peer-to-peer basis, we will need to use a proof- of-work system similar to Adam Back's Hashcash [6], rather than newspaper or Usenet posts. The proof-of-work involves scanning for a value that when hashed, such as with SHA-256, the hash begins with a number of zero bits. The average work required is exponential in the number of zero bits required and can be verified by executing a single hash. For our timestamp network, we implement the proof-of-work by incrementing a nonce in the block until a value is found that gives the block's hash the required zero bits. Once the CPU effort has been expended to make it satisfy the proof-of-work, the block cannot be changed without redoing the work. As later blocks are chained after it, the work to change the block would include redoing all the blocks after it. The proof-of-work also solves the problem of determining representation in majority decision making. If the majority were based on one-IP-address-one-vote, it could be subverted by anyone able to allocate many IPs. Proof-of-work is essentially one-CPU-one-vote. The majority decision is represented by the longest chain, which has the greatest proof-of-work effort invested in it. If a majority of CPU power is controlled by honest nodes, the honest chain will grow the fastest and outpace any competing chains. To modify a past block, an attacker would have to redo the proof-of-work of the block and all blocks after it and then catch up with and surpass the work of the honest nodes. We will show later that the probability of a slower attacker catching up diminishes exponentially as subsequent blocks are added. To compensate for increasing hardware speed and varying interest in running nodes over time, the proof-of-work difficulty is determined by a moving average targeting an average number of blocks per hour. If they're generated too fast, the difficulty increases. 5. Network The steps to run the network are as follows: 1) New transactions are broadcast to all nodes. 2) Each node collects new transactions into a block. 3) Each node works on finding a difficult proof-of-work for its block. 4) When a node finds a proof-of-work, it broadcasts the block to all nodes. 5) Nodes accept the block only if all transactions in it are valid and not already spent. 6) Nodes express their acceptance of the block by working on creating the next block in the chain, using the hash of the accepted block as the previous hash. Nodes always consider the longest chain to be the correct one and will keep working on extending it. If two nodes broadcast different versions of the next block simultaneously, some nodes may receive one or the other first. In that case, they work on the first one they received, but save the other branch in case it becomes longer. The tie will be broken when the next proof- of-work is found and one branch becomes longer; the nodes that were working on the other branch will then switch to the longer one. 3 Block Nonce Tx Tx ... Block Nonce Tx Tx ... Prev Hash Prev Hash New transaction broadcasts do not necessarily need to reach all nodes. As long as they reach many nodes, they will get into a block before long. Block broadcasts are also tolerant of dropped messages. If a node does not receive a block, it will request it when it receives the next block and realizes it missed one. 6. Incentive By convention, the first transaction in a block is a special transaction that starts a new coin owned by the creator of the block. This adds an incentive for nodes to support the network, and provides a way to initially distribute coins into circulation, since there is no central authority to issue them. The steady addition of a constant of amount of new coins is analogous to gold miners expending resources to add gold to circulation. In our case, it is CPU time and electricity that is expended. The incentive can also be funded with transaction fees. If the output value of a transaction is less than its input value, the difference is a transaction fee that is added to the incentive value of the block containing the transaction. Once a predetermined number of coins have entered circulation, the incentive can transition entirely to transaction fees and be completely inflation free. The incentive may help encourage nodes to stay honest. If a greedy attacker is able to assemble more CPU power than all the honest nodes, he would have to choose between using it to defraud people by stealing back his payments, or using it to generate new coins. He ought to find it more profitable to play by the rules, such rules that favour him with more new coins than everyone else combined, than to undermine the system and the validity of his own wealth. 7. Reclaiming Disk Space Once the latest transaction in a coin is buried under enough blocks, the spent transactions before it can be discarded to save disk space. To facilitate this without breaking the block's hash, transactions are hashed in a Merkle Tree [7][2][5], with only the root included in the block's hash. Old blocks can then be compacted by stubbing off branches of the tree. The interior hashes do not need to be stored. Block Hash0 Hash1 Hash2 Hash3 Tx0 Tx1 Tx2 Tx3 Block Header (Block Hash) Prev Hash Nonce Root Hash Hash01 Hash23 Block Block Header (Block Hash) Prev Hash Nonce Root Hash Hash01 Hash23 Hash2 Hash3 Tx3 Transactions Hashed in a Merkle Tree After Pruning Tx0-2 from the Block A block header with no transactions would be about 80 bytes. If we suppose blocks are generated every 10 minutes, 80 bytes * 6 * 24 * 365 = 4.2MB per year. With computer systems typically selling with 2GB of RAM as of 2008, and Moore's Law predicting current growth of 1.2GB per year, storage should not be a problem even if the block headers must be kept in memory. 4 8. Simplified Payment Verification It is possible to verify payments without running a full network node. A user only needs to keep a copy of the block headers of the longest proof-of-work chain, which he can get by querying network nodes until he's convinced he has the longest chain, and obtain the Merkle branch linking the transaction to the block it's timestamped in. He can't check the transaction for himself, but by linking it to a place in the chain, he can see that a network node has accepted it, and blocks added after it further confirm the network has accepted it. Longest Proof-of-Work Chain Block Header Block Header Block Header Prev Hash Nonce Prev Hash Nonce Prev Hash Nonce Merkle Root Merkle Root Merkle Root Hash01 Hash23 Merkle Branch for Tx3 Hash2 Hash3 Tx3 As such, the verification is reliable as long as honest nodes control the network, but is more vulnerable if the network is overpowered by an attacker. While network nodes can verify transactions for themselves, the simplified method can be fooled by an attacker's fabricated transactions for as long as the attacker can continue to overpower the network. One strategy to protect against this would be to accept alerts from network nodes when they detect an invalid block, prompting the user's software to download the full block and alerted transactions to confirm the inconsistency. Businesses that receive frequent payments will probably still want to run their own nodes for more independent security and quicker verification. 9. Combining and Splitting Value Although it would be possible to handle coins individually, it would be unwieldy to make a separate transaction for every cent in a transfer. To allow value to be split and combined, transactions contain multiple inputs and outputs. Normally there will be either a single input from a larger previous transaction or multiple inputs combining smaller amounts, and at most two outputs: one for the payment, and one returning the change, if any, back to the sender. It should be noted that fan-out, where a transaction depends on several transactions, and those transactions depend on many more, is not a problem here. There is never the need to extract a complete standalone copy of a transaction's history. 5 Transaction In Out In ... ... 10. Privacy The traditional banking model achieves a level of privacy by limiting access to information to the parties involved and the trusted third party. The necessity to announce all transactions publicly precludes this method, but privacy can still be maintained by breaking the flow of information in another place: by keeping public keys anonymous. The public can see that someone is sending an amount to someone else, but without information linking the transaction to anyone. This is similar to the level of information released by stock exchanges, where the time and size of individual trades, the "tape", is made public, but without telling who the parties were. Traditional Privacy Model Identities Transactions New Privacy Model Identities Transactions As an additional firewall, a new key pair should be used for each transaction to keep them from being linked to a common owner. Some linking is still unavoidable with multi-input transactions, which necessarily reveal that their inputs were owned by the same owner. The risk is that if the owner of a key is revealed, linking could reveal other transactions that belonged to the same owner. 11. Calculations We consider the scenario of an attacker trying to generate an alternate chain faster than the honest chain. Even if this is accomplished, it does not throw the system open to arbitrary changes, such as creating value out of thin air or taking money that never belonged to the attacker. Nodes are not going to accept an invalid transaction as payment, and honest nodes will never accept a block containing them. An attacker can only try to change one of his own transactions to take back money he recently spent. The race between the honest chain and an attacker chain can be characterized as a Binomial Random Walk. The success event is the honest chain being extended by one block, increasing its lead by +1, and the failure event is the attacker's chain being extended by one block, reducing the gap by -1. The probability of an attacker catching up from a given deficit is analogous to a Gambler's Ruin problem. Suppose a gambler with unlimited credit starts at a deficit and plays potentially an infinite number of trials to try to reach breakeven. We can calculate the probability he ever reaches breakeven, or that an attacker ever catches up with the honest chain, as follows [8]: p = probability an honest node finds the next block q = probability the attacker finds the next block qz = probability the attacker will ever catch up from z blocks behind Trusted Third Party q ={ 1 if p≤q} z q/pz if pq 6 Counterparty Public Public Given our assumption that p > q, the probability drops exponentially as the number of blocks the attacker has to catch up with increases. With the odds against him, if he doesn't make a lucky lunge forward early on, his chances become vanishingly small as he falls further behind. We now consider how long the recipient of a new transaction needs to wait before being sufficiently certain the sender can't change the transaction. We assume the sender is an attacker who wants to make the recipient believe he paid him for a while, then switch it to pay back to himself after some time has passed. The receiver will be alerted when that happens, but the sender hopes it will be too late. The receiver generates a new key pair and gives the public key to the sender shortly before signing. This prevents the sender from preparing a chain of blocks ahead of time by working on it continuously until he is lucky enough to get far enough ahead, then executing the transaction at that moment. Once the transaction is sent, the dishonest sender starts working in secret on a parallel chain containing an alternate version of his transaction. The recipient waits until the transaction has been added to a block and z blocks have been linked after it. He doesn't know the exact amount of progress the attacker has made, but assuming the honest blocks took the average expected time per block, the attacker's potential progress will be a Poisson distribution with expected value:  = z qp To get the probability the attacker could still catch up now, we multiply the Poisson density for each amount of progress he could have made by the probability he could catch up from that point: ∞ ke−{q/pz−k ifk≤z} ∑k=0 k!⋅ 1 ifkz Rearranging to avoid summing the infinite tail of the distribution... z ke− z−k 1−∑k=0 k! 1−q/p  Converting to C code... #include <math.h> double AttackerSuccessProbability(double q, int z) { double p = 1.0 - q; double lambda = z * (q / p); double sum = 1.0; int i, k; for (k = 0; k <= z; k++) { double poisson = exp(-lambda); for (i = 1; i <= k; i++) poisson *= lambda / i; sum -= poisson * (1 - pow(q / p, z - k)); } return sum; } 7 Running some results, we can see the probability drop off exponentially with z. q=0.1 z=0 P=1.0000000 z=1 P=0.2045873 z=2 P=0.0509779 z=3 P=0.0131722 z=4 P=0.0034552 z=5 P=0.0009137 z=6 P=0.0002428 z=7 P=0.0000647 z=8 P=0.0000173 z=9 P=0.0000046 z=10 P=0.0000012 q=0.3 z=0 P=1.0000000 z=5 P=0.1773523 z=10 P=0.0416605 z=15 P=0.0101008 z=20 P=0.0024804 z=25 P=0.0006132 z=30 P=0.0001522 z=35 P=0.0000379 z=40 P=0.0000095 z=45 P=0.0000024 z=50 P=0.0000006 Solving for P less than 0.1%... P < 0.001 q=0.10 z=5 q=0.15 z=8 q=0.20 z=11 q=0.25 z=15 q=0.30 z=24 q=0.35 z=41 q=0.40 z=89 q=0.45 z=340 12. Conclusion We have proposed a system for electronic transactions without relying on trust. We started with the usual framework of coins made from digital signatures, which provides strong control of ownership, but is incomplete without a way to prevent double-spending. To solve this, we proposed a peer-to-peer network using proof-of-work to record a public history of transactions that quickly becomes computationally impractical for an attacker to change if honest nodes control a majority of CPU power. The network is robust in its unstructured simplicity. Nodes work all at once with little coordination. They do not need to be identified, since messages are not routed to any particular place and only need to be delivered on a best effort basis. Nodes can leave and rejoin the network at will, accepting the proof-of-work chain as proof of what happened while they were gone. They vote with their CPU power, expressing their acceptance of valid blocks by working on extending them and rejecting invalid blocks by refusing to work on them. Any needed rules and incentives can be enforced with this consensus mechanism. 8 References [1] W. Dai, "b-money," http://www.weidai.com/bmoney.txt, 1998. [2] H. Massias, X.S. Avila, and J.-J. Quisquater, "Design of a secure timestamping service with minimal trust requirements," In 20th Symposium on Information Theory in the Benelux, May 1999. [3] S. Haber, W.S. Stornetta, "How to time-stamp a digital document," In Journal of Cryptology, vol 3, no 2, pages 99-111, 1991. [4] D. Bayer, S. Haber, W.S. Stornetta, "Improving the efficiency and reliability of digital time-stamping," In Sequences II: Methods in Communication, Security and Computer Science, pages 329-334, 1993. [5] S. Haber, W.S. Stornetta, "Secure names for bit-strings," In Proceedings of the 4th ACM Conference on Computer and Communications Security, pages 28-35, April 1997. [6] A. Back, "Hashcash - a denial of service counter-measure," http://www.hashcash.org/papers/hashcash.pdf, 2002. [7] R.C. Merkle, "Protocols for public key cryptosystems," In Proc. 1980 Symposium on Security and Privacy, IEEE Computer Society, pages 122-133, April 1980. [8] W. Feller, "An introduction to probability theory and its applications," 1957. 9

Lew29 / 75 Mark AQA A Level Computer Science NEA

My computer science coursework on maze generation and pathfinding that got 75/75 marks.

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Updated 1d ago

SaadiSave / Cambridge Asm

A compiler and stack-based VM for pseudo-assembly as defined in the Computer Science Coursebook for Cambridge International AS & A Level, second edition, by Langfield & Duddell.

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a-levela-level-computer-sciencea-levels+6

Updated 4mo ago

dia2018 / What Is The Difference Between AI And Machine Learning

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

abdulrysrr / CAIE 9618 CS P4 Algorithms Compilation 2024

A complete compilation of essential algorithms and programming concepts for Cambridge International AS & A Level Computer Science (9618) Paper 4. Includes detailed examples of binary trees, linked lists, recursion, OOP, searching, sorting, stacks, queues, and more—curated to aid students preparing for CAIE 9618 CS P4. Compiled in 2024.

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Updated 7d ago

weasteam / Coursera Machine Learning

About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

mhoare / Compsci Notes

Notes covering the AQA Computer Science A Level specification :notebook_with_decorative_cover:

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Updated 1y ago

khamedtaha / L2 Computer Science Students

This repository is intended to help students of Computer Science at Level II (Level Two) learn and understand key concepts and their practical applications. The project aims to provide a variety of educational resources and tools to facilitate learning, encourage interaction and enhance cooperation among students.

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Updated 10mo ago

noahjonesx / MarkovModel

Markov Text Generation Problem Description The Infinite Monkey Theorem1 (IFT) says that if a monkey hits keys at random on a typewriter it will almost surely, given an infinite amount of time, produce a chosen text (like the Declaration of Independence, Hamlet, or a script for ... Planet of the Apes). The probability of this actually happening is, of course, very small but the IFT claims that it is still possible. Some people have tested this hypotheis in software and, after billions and billions of simulated years, one virtual monkey was able to type out a sequence of 19 letters that can be found in Shakespeare’s The Two Gentlemen of Verona. (See the April 9, 2007 edition of The New Yorker if you’re interested; but, hypothesis testing with real monkeys2 is far more entertaining.) The IFT might lead to some interesting conversations with Rust Cohle, but the practical applications are few. It does, however, bring up the idea of automated text generation, and there the ideas and applications are not only interesting but also important. Claude Shannon essentially founded the field of information theory with the publication of his landmark paper A Mathematical Theory of Computation3 in 1948. Shannon described a method for using Markov chains to produce a reasonable imitation of a known text with sometimes startling results. For example, here is a sample of text generated from a Markov model of the script for the 1967 movie Planet of the Apes. "PLANET OF THE APES" Screenplay by Michael Wilson Based on Novel By Pierre Boulle DISSOLVE TO: 138 EXT. GROVE OF FRUIT TREES - ESTABLISHING SHOT - DAY Zira run back to the front of Taylor. The President, I believe the prosecutor's charge of this man. ZIRA Well, whoever owned them was in pretty bad shape. He picks up two of the strain. You got what you wanted, kid. How does it taste? Silence. Taylor and cuffs him. Over this we HEAR from a distance is a crude horse-drawn wagon is silhouetted-against the trunks and branches of great trees and bushes on the horse's rump. Taylor lifts his right arm to ward off the blow, and the room and lands at the feet of Cornelius and Lucius are sorting out equipment falls to his knees, buries his head silently at the Ranch). DISSOLVE TO: 197 INT. CAGES - CLOSE SHOT - FEATURING LANDON - FROM TAYLOR'S VOICE (o.s.) I've got a fine veternary surgeons under my direction? ZIRA Taylor! ZIRA There is a small lake, looking like a politician. TAYLOR Dodge takes a pen and notebook from the half-open door of a guard room. Taylor bursts suddenly confronted by his 1https://en.wikipedia.org/wiki/Infinite_monkey_theorem2https://web.archive.org/web/20130120215600/http://www.vivaria.net/experiments/notes/publication/NOTES_ EN.pdf3http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6773024 1 original pursuer (the dismounted cop coming up with a cigar butt and places it in the drawer beside them. TAYLOR What's the best there is a. loud RAP at the doll was found beside the building. Zira waits at the third table. TAYLOR Good question. Is he a man? CORNELIUS (impatiently. DODGE Blessed are the vegetation. These SHOTS are INTERCUT with: 94 WHAT THE ASTRONAUTS They examine the remnants of the cage. ZIRA (plunging on) Their speech organs are adequate. The flaw lies not in anatomy but in the back of his left sleeve. TAYLOR (taking off his shirt. 80 DODGE AND LANDON You don't sound happy in your work. GALEN (defensively) Gorilla hunter stands over a dead man, one fo Besides a few spelling errors and some rather odd things that make you wonder about the author, this passage is surprisingly human-like. This is a simple example of natural language generation, a sub-area of natural language processing—a very active area of research in computer science. The particular approach we’re using in this assignment was famously implemented as the fictitious Mark V. Shaney4 and the Emacs command Disassociated Press5. Approach So, here’s the basic idea: Imagine taking a book (say, Tom Sawyer) and determining the probability with which each character occurs. You would probably find that spaces are the most common, that the character ‘e’ is fairly common, and that the character ‘q’ is rather uncommon. After completing this “level 0” analysis, you would be able to produce random Tom Sawyer text based on character probabilities. It wouldn’t have much in common with the real thing, but at least the characters would tend to occur in the proper propor- tion. In fact, here’s an example of what you might produce: Level 0 rla bsht eS ststofo hhfosdsdewno oe wee h .mr ae irii ela iad o r te u t mnyto onmalysnce, ifu en c fDwn oee iteo Now imagine doing a slightly more sophisticated level 1 analysis by determining the probability with which each character follows every other character. You would probably discover that ‘h’ follows ‘t’ more frequently than ‘x’ does, and you would probably discover that a space follows ‘.’ more frequently than ‘,’ does. You could now produce some randomly generated Tom Sawyer text by picking a character to begin with and then always choosing the next character based on the previous one and the probabilities revealed by the analysis. Here’s an example: Level 1 "Shand tucthiney m?" le ollds mind Theybooure He, he s whit Pereg lenigabo Jodind alllld ashanthe ainofevids tre lin-p asto oun theanthadomoere Now imagine doing a level k analysis by determining the probability with which each character follows every possible sequence of characters of length k (kgrams). A level 5 analysis of Tom Sawyer for example, would reveal that ‘r’ follows “Sawye” more frequently than any other character. After a level k analysis, you would be able to produce random Tom Sawyer by always choosing the next character based on the previous k characters (a kgram) and the probabilities revealed by the analysis. 4https://en.wikipedia.org/wiki/Mark_V._Shaney5https://en.wikipedia.org/wiki/Dissociated_press Page 2 of 5 At only a moderate level of analysis (say, levels 5-7), the randomly generated text begins to take on many of the characteristics of the source text. It probably won’t make complete sense, but you’ll be able to tell that it was derived from Tom Sawyer as opposed to, say, The Sound and the Fury. Here are some more examples of text that is generated from increasing levels of analysis of Tom Sawyer. (These “levels of analysis” are called order K Markov models.) K = 2 "Yess been." for gothin, Tome oso; ing, in to weliss of an’te cle - armit. Papper a comeasione, and smomenty, fropeck hinticer, sid, a was Tom, be suck tied. He sis tred a youck to themen K = 4 en themself, Mr. Welshman, but him awoke, the balmy shore. I’ll give him that he couple overy because in the slated snufflindeed structure’s kind was rath. She said that the wound the door a fever eyes that WITH him. K = 6 people had eaten, leaving. Come - didn’t stand it better judgment; His hands and bury it again, tramped herself! She’d never would be. He found her spite of anything the one was a prime feature sunset, and hit upon that of the forever. K = 8 look-a-here - I told you before, Joe. I’ve heard a pin drop. The stillness was complete, how- ever, this is awful crime, beyond the village was sufficient. He would be a good enough to get that night, Tom and Becky. K = 10 you understanding that they don’t come around in the cave should get the word "beauteous" was over-fondled, and that together" and decided that he might as we used to do - it’s nobby fun. I’ll learn you." To create an order K Markov model of a given source text, you would need to identify all kgrams in the source text and associate with each kgram all the individual characters that follow it. This association or mapping must also capture the frequency with which a given character follows a given kgram. For example, suppose that k = 2 and the sample text is: agggcagcgggcg The Markov model would have to represent all the character strings of length two (2-grams) in the source text, and associate with them the characters that follow them, and in the correct proportion. The following table shows one way of representing this information. kgram Characters that follow ag gc gg gcgc gc agg ca g cg g Once you have created an order K Markov model of a given source text, you can generate new text based on this model as follows. Page 3 of 5 1. Randomly pick k consecutive characters that appear in the sample text and use them as the initial kgram. 2. Append the kgram to the output text being generated. 3. Repeat the following steps until the output text is sufficiently long. (a) Select a character c that appears in the sample text based on the probability of that character following the current kgram. (b) Append this character to the output text. (c) Update the kgram by removing its first character and adding the character just chosen (c) as its last character. If this process encounters a situation in which there are no characters to choose from (which can happen if the only occurrence of the current kgram is at the exact end of the source), simply pick a new kgram at random and continue. As an example, suppose that k = 2 and the sample text is that from above: agggcagcgggcg Here are four different output text strings of length 10 that could have been the result of the process described above, using the first two characters (’ag’) as the initial kgram. agcggcagcg aggcaggcgg agggcaggcg agcggcggca For another example, suppose that k = 2 and the sample text is: the three pirates charted that course the other day Here is how the first three characters of new text might be generated: •A two-character sequence is chosen at random to become the initial kgram. Let’s suppose that “th” is chosen. So, kgram = th and output = th. •The first character must be chosen based on the probability that it follows the kgram (currently “th”) in the source. The source contains five occurrences of “th”. Three times it is followed by ’e’, once it is followed by ’r’, and once it is followed by ’a’. Thus, the next character must be chosen so that there is a 3/5 chance that an ’e’ will be chosen, a 1/5 chance that an ’r’ will be chosen, and a 1/5 chance that an ’a’ will be chosen. Let’s suppose that we choose an ’e’ this time. So, kgram = he and output = the. •The next character must be chosen based on the probability that it follows the kgram (currently “he”) in the source. The source contains three occurrences of “he”. Twice it is followed by a space and once it is followed by ’r’. Thus, the next character must be chosen so that there is a 2/3 chance that a space will be chosen and a 1/3 chance that an ’r’ will be chosen. Let’s suppose that we choose an ’r’ this time. So, kgram = er and output = ther. •The next character must be chosen based on the probability that it follows the kgram (currently “er”) in the source. The source contains only one occurrence of “er”, and it is followed by a space. Thus, the next character must be a space. So, kgram = r_ and output = ther_, where ’_’ represents a blank space. Page 4 of 5 Implementation Details You are provided with two Java files that you must use to develop your solution: MarkovModel.java and TextGenerator.java. The constructors of MarkovModel build the order-k model of the source text. You are required to represent the model with the provided HashMap field. The main method of TextGenerator must process the following three command line arguments (in the args array): •A non-negative integer k •A non-negative integer length. •The name of an input file source that contains more than k characters. Your program must validate the command line arguments by making sure that k and length are non- negative and that source contains at least k characters and can be opened for reading. If any of the command line arguments are invalid, your program must write an informative error message to System.out and terminate. If there are not enough command line arguments, your program must write an informative error message to System.out and terminate. With valid command line arguments, your program must use the methods of the MarkovModel class to create an order k Markov model of the sample text, select the initial kgram, and make each character selection. You must implement the MarkovModel methods according to description of the Markov modeling process in the section above. A few sample texts have been provided, but Project Gutenberg (http://www.gutenberg.org) maintains a large collection of public domain literary works that you can use as source texts for fun and practice. Acknowledgments This assignment is based on the ideas of many people, Jon Bentley and Owen Astrachan in particular.

Utopian88 / Oxford Programmer

Oxford Programmer This repository is a showcase for academic-level programming projects and solutions to advanced computer science problems. It contains a variety of well-documented projects and algorithms, reflecting a focus on clean code, theoretical rigor, and educational quality.

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Updated 3mo ago

Bandashah / 9618 Paper 2 VB

Cambridge A Level Computer Science Paper 2 Programming

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Updated 4mo ago

Billy-Ellis / FlappyBird NeuralNet

My A-level computer science project from 2017-2018, a neural network that learns to play Flappy Bird

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Updated 2mo ago