48 skills found · Page 1 of 2
AI-Planning / L2pLibrary for LLM-driven action model acquisition via natural language
jacksonllee / PylangacqLanguage Acquisition Research Tools
sled-group / World To WordsOfficial Code for ACL 2023 Outstanding Paper: World-to-Words: Grounded Open Vocabulary Acquisition through Fast Mapping in Vision-Language Models
AdaptVision / AdaptVision[CVPR 2026] AdaptVision: Efficient Vision-Language Models via Adaptive Visual Acquisition
pigoz / LatTools to automate language acquisition through immersion. Includes sentence analysis (from books, subtitles) and Anki cards creation.
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
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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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.
tylerachang / Word Acquisition Language ModelsWord acquisition in neural language models (TACL 2022).
ananya2001gupta / Bitcoin Price Prediction Using AI ML.Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.
MarvinLvn / BabySLMBehavioral probing of language acquisition models at the lexical and syntactic level
kaistAI / Knowledge Entropy[ICLR 2025 Oral] Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition
StephanAkkerman / MnemoraiAutomating language learning with the power of Artificial Intelligence. This repository presents mnemorai, a tool that combines Fluent Forever techniques with AI-driven automation. It streamlines the process of creating Anki flashcards, making language acquisition faster and more efficient.
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neulab / ToM Language AcquisitionCode used to run experiments for the ICLR 2023 paper "Computational Language Acquisition with Theory of Mind".
jeanchristophe13v / CaptiPrepCaptiPrep: Caption + Preparation. Learn English on YouTube, the smart way. With CaptiPrep, you preview key vocabulary and phrases from subtitles before you watch. Then, see them in context to lock in your learning. This context-based approach makes language acquisition highly effective, engaging, and effortless.
leezisheng / SEMG AssistantThe sEMG acquisition device is equipped with the upper computer software and written in Python language to complete the data receiving of USB virtual serial port, the calculation of the time-frequency domain characteristics of the signal, the real-time display of the signal and the action judgment of the mechanical arm
langcog / Metalab Archivemeta-analyses of language acquisition phenomena
nihalnayak / Slam18Code for Context based Approach for Second Language Acquisition
edulim / Linux ImmersionResources for Linux users who follow the any immersion-based method for language acquisition.
NYUCCL / DuolingoSLAM2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM) (http://sharedtask.duolingo.com/)
