96 skills found · Page 2 of 4
mehmetaltugakgul / GOugeGOuge is a lightweight system monitoring tool that sits in your system tray, providing real-time information about your computer's CPU usage, RAM usage, disk usage, and system uptime.
giordi91 / PerformanceWidgetA series of classes to create Qt Widgets to plot computer performance like ram and cpu usage
guoguo12 / HivemindUsage stats for the Berkeley EECS instructional computers.
Yogapriya2512 / A Simple Chatbot A chatbot (also known as a talkbot, chatterbot, Bot, IM bot, interactive agent, or Artificial Conversational Entity)The classic historic early chatbots are ELIZA (1966) and PARRY (1972).More recent notable programs include A.L.I.C.E., Jabberwacky and D.U.D.E (Agence Nationale de la Recherche and CNRS 2006). While ELIZA and PARRY were used exclusively to simulate typed conversation, many chatbots now include functional features such as games and web searching abilities. In 1984, a book called The Policeman's Beard is Half Constructed was published, allegedly written by the chatbot Racter (though the program as released would not have been capable of doing so). One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities. Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval. Chatbot competitions focus on the Turing test or more specific goals. Two such annual contests are the Loebner Prize and The Chatterbox Challenge (offline since 2015, materials can still be found from web archives). According to Forrester (2015), AI will replace 16 percent of American jobs by the end of the decade.Chatbots have been used in applications such as customer service, sales and product education. However, a study conducted by Narrative Science in 2015 found that 80 percent of their respondents believe AI improves worker performance and creates jobs.[citation needed] is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database. The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs.Today, most chatbots are either accessed via virtual assistants such as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites. Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities. Background
teavuihuang / Jetson EmulatorThe Jetson Emulator emulates the NVIDIA Jetson AI-Computer's Inference and Utilities API for image classification, object detection and image segmentation (i.e. imageNet, detectNet and segNet). The intended users are makers, learners, developers and students who are curious about AI computers and AI edge-computing but are not ready or able to invest in an actual device such as the NVIDIA Jetson Nano Developer Kit (https://developer.nvidia.com/embedded/jetson-nano-developer-kit). E.g. this allows every student in a computer class to have their own personal AI computer to explore and experiment with. This Jetson Emulator presents a pre-configured, ready-to-run kit with 2 virtual HDMI displays and 4 virtual live-cameras. This enables usage familiarisation with the Jetson API and experimentation with AI computer vision inference. It is a great way to quickly and easily get 'hands-on' with Jetson and experience the power of AI.
3urobeat / Arduino Resource Monitor📊 Display your computer's CPU, RAM & GPU usage on a LCD display, powered by an Arduino Nano! [Supports Linux & Windows]
BlockchainLabs / PebblecoinPebblecoin UPDATE 2015/12/31: Version 0.4.4.1 is now out. The major change is optimizing the daemon to use less RAM. It no longer keeps all the blocks, which are rarely needed, in RAM, and so RAM usage has decreased from around 2 gigabytes, to under 200 megabytes. Mac binaries are also now available. The new wallet is compatible with the old wallet - simply turn off the old wallet, and start the new wallet, and the blockchain will update automatically to use less RAM. Code: Release Notes 0.4.4.1 - (All) Fix blockchain RAM usage, from almost 2 GB to less than 200 MB - Seamless blockchain conversion on first run with new binaries - (Qt) Fix high CPU usage - (Qt) Fix sync indicator (# of total blocks) - (Mac) Mac binaries - Technical Notes: - (All) Blockchain disk-backed storage with sqlite3 and stxxl - (Mac) Fix mac compilation - (All) Update build files & instructions for linux, mac, windows - (All) Remove unused protobuf and OpenSSL dependencies for Qt wallet - (Tests) Fix valgrind errors - (Tests) Use local directory for blockchain instead of default directory - (Tests) Run tests on Windows if using new enough MSVC LINKS: Windows 64-bit: https://www.dropbox.com/s/b4kubwwnb4t7o4w/pebblecoin-all-win32-x64-v0.4.4.1.zip?dl=0 Mac 64-bit: https://www.dropbox.com/s/uoy9z1oxu4x53cv/pebblecoin-all-mac-x64-v0.4.4.1.tar.gz?dl=0 Linux 64-bit: https://www.dropbox.com/s/jq3h3bc29jmndks/pebblecoin-all-linux-x64-v0.4.4.1.tar.gz?dl=0 Exchange: https://poloniex.com/exchange#btc_xpb . Source: https://github.com/xpbcreator/pebblecoin/ CONTACT: xpbcreator@torguard.tg IRC: irc.freenode.net, #pebblecoin UPDATE 2015/06/08: Version 0.4.3.1 is now out. This is a minor, mostly bug-fix release. Work continues on the next major release which will bring us user-created currencies and user-graded contracts. Release notes: Code: Release Notes 0.4.3.1 - RPC calls for DPOS: - getdelegateinfos RPC call - get kimageseqs RPC call - block header contains signing_delegate_id - fix checkpoint rollback bug - fix inability to send coins if voting history was lost UPDATE 2015/05/04: Version 0.4.2.2 is now out. This is a bug-fix/cosmetic release. Release notes: Payment ID support Windows installer Logos updated Improved DPOS tab Sync issues fully fixed Fix rare crash bug Fix min out 0 bug Fix debit display Fix GUI not updating Updated hard-coded seed nodes UPDATE 2015/04/24: The switch-over to DPOS has succeeded without a hitch! DPOS blocks are being signed as we speak, at the far faster pace of 15 seconds per block. This marks the start of a new era for Pebblecoin. UPDATE 2015/04/21: Congratulations to the first registered delegate! This indicates the start of the forking change so everybody please update your daemons if you haven't already. To promote the coin and encourage people to become delegates, we've come up with an incentive scheme. First, we'll send a free 100 XPB to anybody who PMs me their public address, for people to play around with and to start using the coin. Second, once DPOS starts, for the first month of DPOS I'll send an extra 0.5 XPB to the signing delegate for every block they process. This is on top of the usual transaction fees they will receive. This is to encourage more people to become delegates at this important phase of the coin. UPDATE 2015/04/19: All went well on the testnet release, so after a few further minor modifications, we are releasing version 0.4.1.2 to the public. This is a forking change, so please update your clients and servers (links below). At block 83120, sometime on April 21st, registration for DPOS delegates will begin. At block 85300, sometime on April 24th, the network will switch over to DPOS. As with the testnet, to become a delegate and receive block fees for securing the network, just turn on your wallet, register to be a delegate (5 XPB fee), and then leave your wallet on. It will sign the blocks when it is your turn. While Roman works on the next phase of the release - introducing subcurrencies - I will be fixing up some loose ends on the wallet, adding payment ID support, etc. This is truly an exciting time for Pebblecoin. RELEASE NOTES: All clients adjust internal clocks using ntp (client list in src/common/ntp_time.cpp) Added testnet support DPOS registration starts Block 83120 (~April 21st) DPOS phase starts Block 85300 (~April 24th) Default fee bumped to 0.10 XPB Low-free transactions no longer get relayed by default Significantly improved wallet sync Checkpoint at Block 79000 TOTAL CURRENT COINS: Available at this link. BLOCK TARGET TIME: 2 minutes EXPECTED EMISSION: At Block 3600 (End of Day 5): ~78 XPBs At Block 6480 (End of Day 9): ~758 XPBs At Block 9360 (End of Day 13): 6,771.0 XPBs At Block 12240 (End of Day 17): ~61,000 XPBs At Block 15120 (End of Day 21): ~550,000 XPBs, start of regular 300/block emission At Block 21900 (End of Month 1): ~2,600,000 XPBs, 300/block At Block 43800 (End of Month 2): ~9,150,000 XPBs, 300/block At Block 85300 (End of POW phase): ~21,500,300 XPBs. UPDATE: The Pebblecoin Pool is now live! Instructions: Download the linux miner and run it: ./minerd -o stratum+tcp://69.60.113.21:3350 -u YOUR_WALLET_ADDRESS -p x UPDATE: The Pebblecoin wallet is now live! There have been thousands of attempts at alternative currencies in the community. Many are 100% copies of existing blockchains with a different name. Some are very slight variations with no significant differences. From recent history it is apparent the only realistic chance for viability of a new currency is one that is innovation and continued support and development. The bitcoin community for good reason has shown interest in currencies that provide privacy of transactions, several currencies such as darkcoin, have become popular based on this desire. The best technology for privacy is cryptonote although for a variety of reasons there hasnt been much development for ease of use, and as a result there has not been significant adoption. Pebblecoin (XPB) is a cryptonote based coin with improvements and changes in some areas, and the promise of development in others. I invite developers to work on this technology with me. There is no premine, any tips or support of any developer including myself will be completely voluntary. These are the following areas which I have determined needs changes/updates: I welcome suggestions, and am interested what else I can try to improve. 1) New Mining algorithm (active) A mining algorithm is either susceptible to ASIC development or to being botnetted, meaning it is either more efficient to have a centralized mining entity (as is the case with bitcoin) or to have an algorithm that requires a real CPU, in which case botnets become very attractive. To my knowledge there does not exist a blockchain that attempts to solve both problems, by having an algorithm that only works on a general purpose computer and is difficult to botnet. Cryptonote coins currently are primarily mined with botnets. Boulderhash is a new mining algorithm requiring 13 GB RAM, nearly eliminating all possible zombie (botnet controlled) computers from mining. Most infected computers in the world do not have 13 GB available, so an algorithm that requires that much RAM severely limits the productivity of a botnet. 13 GB also makes ASICs cost prohibitive, and the current GPUs do not have that much RAM. What's left is general purpose computers as was the original intent of bitcoin's mining process. 2) Distribution of coins (active) It is very common in the launch of a new cryptocurrency the distribution algorithm heavily is weighted towards the very early adopters. Such distribution is designed to give a massive advantage to people who are fully prepared to mine at launch, with a very large difference shortly after sometimes a few days later. If the point of mining is to both secure the network and fairly distribute coins a gradual build up of rewards makes more sense, with no drop off in mining rewards. At a standard block reward of 300, at launch each block will reward 0.3 coins leading up to 3, 30, and finally the standard reward of 300 which will be the standard unchanging reward from that point. It will take approximately 3 weeks for the block reward of 300 to be reached. 3) GUI Software (active) There are no current cryptonote coins that have a downloadable GUI, which makes the user experience much worse than that of bitcoin. It is hard to achieve signficant adoption with a command line interface. The very first update had the exact GUI written for bitcoin fully working with Pebblecoin. The GUI was released on Jan 19, before the full 300 XPB reward was awarded for winning the block. 4) IRC Chat support embedded in Client GUI (active) For user support, and to talk to core developers message boards such as Bitcointalk and reddit are primarily used. I have embedded an IRC client in the GUI and be available at set hours for any kind of support. 5) Address aliasing (to be worked on) Just as a user visiting google does not need to know the ip address, similarly an address should have the ability to have an associated userid. If I ask a friend to send me pebblecoins it would be easier to tell him send it to @myuserid rather than a very long address or scanning a QR code. There should be a way of registering a userid on the blockchain that will permanently translate to a pebblecoin addresss. QT INSTRUCTIONS: Download the package for your respective platform Run the Qt executable. The software will generate a new wallet for you and use a default folder: ~/.pebblecoin on Linux and %appdata%\pebblecoin on Windows. To use an existing wallet, copy the wallet.keys file into the default folder. To use a different data directory and/or wallet file, run the software like so: ./pebblecoin-qt --data-dir <DataDir> --wallet-file <FileName>. To enable mining, run the start_mining_NEEDS_13GB_RAM.bat batch file. Or run the qt wallet with the --enable-boulderhash command line option, or put enable-boulderhash=1 into the config file. It will start mining to the wallet address. To change the number of mining threads (13GB required per thread), do --mining-threads <NumThreads> or edit the batch file. DAEMON + SIMPLEWALLET INSTRUCTIONS: Download the package, run: ./pebblecoind --data-dir pebblecoin_data Once the daemon finished syncing, run the simplewallet: ./simplewallet POOL INSTRUCTIONS: Download the miner binary for your platform. Run the miner using a wallet address gotten from simplewallet or the Qt Wallet: Code: minerd -o stratum+tcp://69.60.113.21:3350 -u YOUR_WALLET_ADDRESS -p x [/li] DEV WALLET (for donations): PByFqCfuDRUPVsNrzrUXnuUdF7LpXsTTZXeq5cdHpJDogbJ8EBXopciN7DmQiGhLEo5ArA7dFqGga2A AhbRaZ2gL8jjp9VmYgk
yqmark / AgreementPrivacy Policy introduction We understand the importance of personal information to you and will do our utmost to protect your personal information. We are committed to maintaining your trust in us and to abide by the following principles to protect your personal information: the principle of consistency of rights and responsibilities, the principle of purpose , choose the principle of consent, at least the principle of sufficient use, ensure the principle of security, the principle of subject participation, the principle of openness and transparency, and so on. At the same time, we promise that we will take appropriate security measures to protect your personal information according to the industry's mature security solutions. In view of this, we have formulated this "Private Privacy Policy" (hereinafter referred to as "this policy" /This Privacy Policy") and remind you: This policy applies to products or services on this platform. 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This privacy policy section will help you understand the following: How we collect and use your personal information How do we use cookies and similar technologies? How do we share, transfer, and publicly disclose your personal information? How we protect your personal information How do you manage your personal information? How do we deal with the personal information of minors? How your personal information is transferred globally How to update this privacy policy How to contact us 一、How we collect and use your personal information Personal information refers to various information recorded electronically or otherwise that can identify a specific natural person or reflect the activities of a particular natural person, either alone or in combination with other information. We collect and use your information for the purposes described in this policy. 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Our partners are not authorized to use shared personal information for other purposes unrelated to the product or service. Currently, our authorized partners include the following types: (2) Suppliers, service providers and other partners. We send information to suppliers, service providers and other partners who support our business, including providing technical infrastructure services, analyzing how our services are used, measuring the effectiveness of advertising and services, providing customer service, and facilitating payments. Or conduct academic research and investigations. (1) Authorized partners in advertising and analytics services. We will not use your personally identifiable information (information that identifies you, such as your name or email address, which can be used to contact you or identify you) and provide advertising and analytics services, unless you have your permission. Shared by partners. We will provide these partners with information about their advertising coverage and effectiveness, without providing your personally identifiable information, or we may aggregate this information so that it does not identify you personally. For example, we’ll only tell advertisers how effective their ads are when they agree to comply with our advertising guidelines, or how many people see their ads or install apps after seeing ads, or work with them. Partners provide statistical information that does not identify individuals (eg “male, 25-29 years old, in Beijing”) to help them understand their audience or customers. For companies, organizations and individuals with whom we share personal information, we will enter into strict data protection agreements with them to process individuals in accordance with our instructions, this Privacy Policy and any other relevant confidentiality and security measures. information. (2) Transfer We do not transfer your personal information to any company, organization or individual, except: Transfer with the express consent: After obtaining your explicit consent, we will transfer your personal information to other parties; 2, in the case of mergers, acquisitions or bankruptcy liquidation, or other circumstances involving mergers, acquisitions or bankruptcy liquidation, if it involves the transfer of personal information, we will require new companies and organizations that hold your personal information to continue to receive This policy is bound, otherwise we will ask the company, organization and individual to re-seek your consent. (3) Public disclosure We will only publicly disclose your personal information in the following circumstances: We may publicly disclose your personal information by obtaining your explicit consent or based on your active choice; 2, if we determine that you have violated laws and regulations or serious violations of the relevant rules of the platform, or to protect the personal safety of the platform and its affiliates users or the public from infringement, we may be based on laws and regulations or The relevant agreement rules of this platform disclose your personal information, including related violations, and the measures that the platform has taken against you, with your consent. (4) Exceptions for prior authorization of consent when sharing, transferring, and publicly disclosing personal information In the following situations, sharing, transferring, and publicly disclosing your personal information does not require prior authorization from you: Related to national security and national defense security; Related to public safety, public health, and major public interests; 3, related to criminal investigation, prosecution, trial and judgment execution; 4, in order to protect your or other individuals' life, property and other important legal rights but it is difficult to get my consent; Personal information that you disclose to the public on your own; Collect personal information from legally publicly disclosed information, such as legal news reports and government information disclosure. According to the law, sharing, transferring and de-identifying personal information, and ensuring that the data recipient cannot recover and re-identify the personal information subject, does not belong to the external sharing, transfer and public disclosure of personal information. The preservation and processing of the class data will not require additional notice and your consent. How do we protect your personal information? (1) We have taken reasonable and feasible security measures in accordance with the industry's general solutions to protect the security of personal information provided by you, and to prevent unauthorized access, public disclosure, use, modification, damage or loss of personal information. For example, SSL (Secure Socket) when exchanging data (such as credit card information) between your browser and the server Layer) protocol encryption protection; we use encryption technology to improve the security of personal information; we use a trusted protection mechanism to prevent personal information from being maliciously attacked; we will deploy access control mechanisms to ensure that only authorized personnel can access individuals Information; and we will conduct security and privacy protection training courses to enhance employees' awareness of the importance of protecting personal information. (2) We have advanced data security management system around the data life cycle, which enhances the security of the whole system from organizational construction, system design, personnel management, product technology and other aspects. (3) We will take reasonable and feasible measures and try our best to avoid collecting irrelevant personal information. We will only retain your personal information for the period of time required to achieve the purposes stated in this policy, unless the retention period is extended or permitted by law. (4) The Internet is not an absolutely secure environment. We strongly recommend that you do not use personal communication methods that are not recommended by this platform. You can connect and share with each other through our services. When you create communications, transactions, or sharing through our services, you can choose who you want to communicate, trade, or share as a third party who can see your trading content, contact information, exchange information, or share content. If you find that your personal information, especially your account or password, has been leaked, please contact our customer service immediately so that we can take appropriate measures according to your application. Please note that the information you voluntarily share or even share publicly when using our services may involve personal information of you or others or even sensitive personal information, such as when you post a news or choose to upload in public in group chats, circles, etc. A picture containing personal information. Please consider more carefully whether you share or even share information publicly when using our services. Please use complex passwords to help us keep your account secure. We will do our best to protect the security of any information you send us. At the same time, we will report the handling of personal information security incidents in accordance with the requirements of the regulatory authorities. V. How your personal information is transferred globally Personal information collected and generated by us during our operations in the People's Republic of China is stored in China, with the following exceptions: Laws and regulations have clear provisions; 2, get your explicit authorization; 3, you through the Internet for cross-border live broadcast / release dynamics and other personal initiatives. In response to the above, we will ensure that your personal information is adequately protected in accordance with this Privacy Policy.
NestieGuilas / Marketing Platform Marketing Platform Google Analytics Terms of Service These Google Analytics Terms of Service (this "Agreement") are entered into by Google LLC ("Google") and the entity executing this Agreement ("You"). This Agreement governs Your use of the standard Google Analytics (the "Service"). BY CLICKING THE "I ACCEPT" BUTTON, COMPLETING THE REGISTRATION PROCESS, OR USING THE SERVICE, YOU ACKNOWLEDGE THAT YOU HAVE REVIEWED AND ACCEPT THIS AGREEMENT AND ARE AUTHORIZED TO ACT ON BEHALF OF, AND BIND TO THIS AGREEMENT, THE OWNER OF THIS ACCOUNT. In consideration of the foregoing, the parties agree as follows: 1. Definitions. "Account" refers to the account for the Service. All Profiles (as applicable) linked to a single Property will have their Hits aggregated before determining the charge for the Service for that Property. 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This Agreement will be binding upon and inure to the benefit of the respective successors and assigns of the parties hereto. The following sections of this Agreement will survive any termination thereof: 1, 4, 5, 6 (except the last two sentences), 7, 8, 9, 10, 11, 12, 14, 16, and 17. 17. Google Analytics for Firebase. If You link a Property to Firebase (“Firebase Linkage”) as part of using the Service, the following terms, in addition to Sections 1-16 above, will also apply to You, and will also govern Your use of the Service, including with respect to Your use of Firebase Linkage. Other than as modified below, all other terms will stay the same and continue to apply. In the event of a conflict between this Section 17 and Sections 1-16 above, the terms in Section 17 will govern and control solely with respect to Your use of the Firebase Linkage. The following definition in Section 1 is modified as follows: "Hit" means a collection of interactions that results in data being sent to the Service and processed. Examples of Hits may include page view hits and ecommerce hits. A Hit can be a call to the Service by various libraries, but does not have to be so (e.g., a Hit can be delivered to the Service by other Google Analytics-supported protocols and mechanisms made available by the Service to You). For the sake of clarity, a Hit does not include certain events whose collection reflects interactions with certain Properties capable of supporting multiple data streams, and which may include screen views and custom events (the collection of events, an “Enhanced Packet”). 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lujian9328 / LungCancerDetectionThis project presents the better Computer Aided Diagnosing (CAD) system for automatic detection of lung cancer. The initial process is lung region detection by applying basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms to the CT scan images. After the lung region is detected, the segmentation is carried out with the help of Mean Shift clustering algorithm. With these, the features are extracted and the diagnosis rules are generated. These rules are then used for learning with the help of Random Forest. The experimentation is performed with 15, 000 images obtained from the kaggle contest. The experimental result shows that the proposed CAD system can able to tell the posterior probability of lung cancer for a patient based on the detection algorithm. Also the usage of Random Forest will increase the accuracy of detecting the cancer nodules.
grover66 / KidtimerScript to manage computer usage by time and schedule.
arsenyinfo / CeeveePython library for various computer vision problems with a focus on easy usage.
kyleconroy / TimesuckPrivately track your computer usage
jordanhoare / Pybot LostarkA computer-vision based python tool for player-automation in the AMMORPG game 'Lost Ark'. Modules target automation of waypoint pathfinding and skills usage, and include a HUD.
hans-ob1 / Deep EyeDeepEye is a surveillance application leveraging the current sate of art deep learning and computer vision techniques. Current back-end framework uses Google Object Detection Api for object detection and tracking. It is open-source and feel free to modify for your own usage.
dia2018 / What Is The Difference Between AI And Machine LearningArtificial 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.
libertyequalitydata / RescueTimeDownload and analyze your computer usage data from RescueTime
tsgrgo / Disable Microsoft Compatibility TelemetryScript to disable Microsoft Compatibility Telemetry that collects data about your computer's performance and usage
f4inu / Anytone PttAnytone BLE PTT button usage with a computer
leafo / Selfwatchrecord your keypress counts into a database to track computer usage
