GPT-3

Training_and_capabilities According to one user, who had access to a private early release of the OpenAI GPT-3 API, GPT-3 was "eerily good" at writing "amazingly coherent text" with only a few simple prompts.  In an initial experiment 80 US subjects were asked to judge if short ~200 word articles were written by humans or GPT-3.  The participants judged correctly 52% of the time, doing only slightly better than random guessing.  On November 18, 2021, OpenAI announced that enough safeguards had been implemented that access to its API would be unrestricted.  OpenAI provided developers with a content moderation tool that helps them abide by OpenAI's content policy.  On January 27, 2022, OpenAI announced that its newest GPT-3 language models (collectively referred to as InstructGPT) were now the default language model used on their API.  According to OpenAI, InstructGPT produced content that was better aligned to user intentions by following instructions better, generating fewer made-up facts, and producing somewhat less toxic content.  Because GPT-3 can "generate news articles which human evaluators have difficulty distinguishing from articles written by humans," GPT-3 has the "potential to advance both the beneficial and harmful applications of language models. " In their May 28, 2020 paper, the researchers described in detail the potential "harmful effects of GPT-3" which include "misinformation, spam, phishing, abuse of legal and governmental processes, fraudulent academic essay writing and social engineering pretexting".  The authors draw attention to these dangers to call for research on risk mitigation.  GPT-3 is capable of performing zero-shot and few-shot learning (including one-shot).  In June 2022, Almira Osmanovic Thunstr��m wrote that GPT-3 was the primary author on an article on itself, that they had submitted it for publication, and that it had been pre-published while waiting for completion of its review.


GPT-3

InstructGPT InstructGPT is a finetuned version of GPT-3. It has been trained on a dataset of human-written instructions. This training allows InstructGPT to better understand what is being asked of it, and to generate more accurate and relevant outputs. InstructGPT can be used to follow instructions that are given in natural language. InstructGPT can be used to answer questions that are asked in natural language. InstructGPT is more accurate and relevant than GPT-3 when following instructions and answering questions. InstructGPT can be used in a variety of applications, such as customer service, education, and automation.


GPT-3

GPT-3_models There are many models in the GPT-3 family, some serving different purposes than others. In the initial research paper published by OpenAI, they mentioned 8 different sizes of the main GPT-3 model: Half of the models are accessible through the API, namely GPT-3-medium, GPT-3-xl, GPT-3-6.7B and GPT-3-175b, which are referred to as ada, babbage, curie and davinci respectively.


GPT-3

GPT-3.5 Generative Pre-trained Transformer 3.5 (GPT-3.5) is a sub class of GPT-3 Models created by OpenAI in 2022. On March 15, 2022, OpenAI made available new versions of GPT-3 and Codex in its API with edit and insert capabilities under the names "text-davinci-002" and "code-davinci-002". These models were described as more capable than previous versions and were trained on data up to June 2021. On November 28, 2022, OpenAI introduced text-davinci-003. On November 30, 2022, OpenAI began referring to these models as belonging to the "GPT-3.5" series, and released ChatGPT, which was fine-tuned from a model in the GPT-3.5 series. OpenAI does not include GPT-3.5 in GPT-3. There are four models: Chat gpt-3.5-turbo Text completion text-davinci-003 text-davinci-002gpt-3.5-turbotext-davinci-003 text-davinci-002On April 10, 2023, OpenAI introduced a new variant of its GPT-3.5 series model, known as GPT-3.5 with Browsing (ALPHA). This updated model was described to build upon the capabilities of its predecessors "text-davinci-002" and "code-davinci-002". The GPT-3.5 with Browsing (ALPHA) model incorporated the ability to access and browse online information. This has led to more accurate and up-to-date responses to user queries. The GPT-3.5 with Browsing (ALPHA) model has been trained on data up to September 2021, giving it more information compared to previous GPT-3.5 models, which were trained on data up until June 2021. The model attempted to provide developers and users with an advanced natural language processing tool that can effectively retrieve and synthesize online information. To enable browsing capabilities, OpenAI implemented a new API that allows the GPT-3.5 with Browsing (ALPHA) model to access selected online resources during operation. This feature allows users to ask questions or request information with the expectation that the model will deliver updated, accurate, and relevant answers based on the latest online sources available to it. On April 27, 2023, OpenAI made the GPT-3.5 with Browsing (ALPHA) model publicly available to GPT Plus users. This allowed more people to access to its new features.


GPT-3

Reception GPT-3, specifically the Codex model, is the basis for GitHub Copilot, a code completion and generation software that can be used in various code editors and IDEs.  GPT-3 is used in certain Microsoft products to translate conventional language into formal computer code.  GPT-3 has been used in CodexDB to generate query-specific code for SQL processing.  GPT-3 has been used by Jason Rohrer in a retro-themed chatbot project named "Project December", which is accessible online and allows users to converse with several AIs using GPT-3 technology.  GPT-3 was used by The Guardian to write an article about AI being harmless to human beings.  It was fed some ideas and produced eight different essays, which were ultimately merged into one article.  GPT-3 was used in AI Dungeon, which generates text-based adventure games.  Later it was replaced by a competing model after OpenAI changed their policy regarding generated content.  GPT-3 is used to aid in writing copy and other marketing materials.  A 2022 study from Drexel University suggested that GPT-3-based systems could be used to screen for early signs of Alzheimer's disease. In a July 2020 review in The New York Times, Farhad Manjoo said that GPT-3's ability to generate computer code, poetry, and prose is not just "amazing", "spooky", and "humbling", but also "more than a little terrifying".  Daily Nous presented a series of articles by nine philosophers on GPT-3.  Australian philosopher David Chalmers described GPT-3 as "one of the most interesting and important AI systems ever produced".  A review in Wired said that GPT-3 was "provoking chills across Silicon Valley".  The National Law Review said that GPT-3 is an "impressive step in the larger process", with OpenAI and others finding "useful applications for all of this power" while continuing to "work toward a more general intelligence".  An article in the MIT Technology Review, co-written by Deep Learning critic Gary Marcus, stated that GPT-3's "comprehension of the world is often seriously off, which means you can never really trust what it says. " According to the authors, GPT-3 models relationships between words without having an understanding of the meaning behind each word.  Jerome Pesenti, head of the Facebook AI lab, said GPT-3 is "unsafe," pointing to the sexist, racist and other biased and negative language generated by the system when it was asked to discuss Jews, women, black people, and the Holocaust.  Nabla, a French start-up specializing in healthcare technology, tested GPT-3 as a medical chatbot, though OpenAI itself warned against such use.  As expected, GPT-3 showed several limitations.  For example, while testing GPT-3 responses about mental health issues, the AI advised a simulated patient to commit suicide.  Noam Chomsky expressed his skepticism about GPT-3's scientific value: "It's not a language model.  It works just as well for impossible languages as for actual languages.  It is therefore refuted, if intended as a language model, by normal scientific criteria.  [. . . ] Perhaps it's useful for some purpose, but it seems to tell us nothing about language or cognition generally.


GPT-3

Reception " Luciano Floridi and Massimo Chiriatti highlighted the risk of "cheap production of good, semantic artefacts".  OpenAI's Sam Altman himself criticized what he called "GPT-3 hype", acknowledging GPT-3 "has serious weakness and sometimes makes very silly mistakes. . .  AI is going to change the world, but GPT-3 is just a very early glimpse. "GPT-3's builder, OpenAI, was initially founded as a non-profit in 2015.  In 2019, OpenAI broke from its usual open-source standards by not publicly releasing GPT-3's predecessor model, citing concerns that the model could facilitate the propagation of fake news.  OpenAI eventually released a version of GPT-2 that was 8% of the original model's size.  In the same year, OpenAI restructured to be a for-profit company.  In 2020, Microsoft announced the company had exclusive licensing of GPT-3 for Microsoft's products and services following a multi-billion dollar investment in OpenAI.  The agreement permits OpenAI to offer a public-facing API such that users can send text to GPT-3 to receive the model's output, but only Microsoft will have access to GPT-3's source code.  Large language models, such as GPT-3, have come under criticism from a few of Google's AI ethics researchers for the environmental impact of training and storing the models, detailed in a paper co-authored by Timnit Gebru and Emily M.  Bender in 2021.  The growing[when? ] use of automated writing technologies based on GPT-3 and other language generators, has raised concerns regarding academic integrity and raised the stakes of how universities and schools will gauge what constitutes academic misconduct such as plagiarism.  OpenAI's GPT series was built with data from the Common Crawl dataset, a conglomerate of copyrighted articles, internet posts, web pages, and books scraped from 60 million domains over a period of 12 years.  TechCrunch reports this training data includes copyrighted material from the BBC, The New York Times, Reddit, the full text of online books, and more.  In its response to a 2019 Request for Comments on Intellectual Property Protection for Artificial Intelligence Innovation from the United States Patent and Trademark Office (USPTO), OpenAI argued that "Under current law, training AI systems [such as its GPT models] constitutes fair use," but that "given the lack of case law on point, OpenAI and other AI developers like us face substantial legal uncertainty and compliance costs.


GPT-4

GPT-4 Generative Pre-trained Transformer 4 (GPT-4) is a multimodal large language model created by OpenAI, and the fourth in its series of GPT foundation models. It was initially released on March 14, 2023, and has been made publicly available via the paid chatbot product ChatGPT Plus, and via OpenAI's API. As a transformer-based model, GPT-4 uses a paradigm where pre-training using both public data and "data licensed from third-party providers" is used to predict the next token. After this step, the model was then fine-tuned with reinforcement learning feedback from humans and AI for human alignment and policy compliance. Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous iteration based on GPT-3.5, with the caveat that GPT-4 retains some of the problems with earlier revisions. GPT-4 is also capable of taking images as input, though this feature has not been made available since launch. OpenAI has declined to reveal various technical details and statistics about GPT-4, such as the precise size of the model.


GPT-4

Background OpenAI introduced the first GPT model (GPT-1) in 2018, publishing a paper called "Improving Language Understanding by Generative Pre-Training." It was based on the transformer architecture and trained on a large corpus of books. The next year, they introduced GPT-2, a larger model that could generate coherent text. In 2020, they introduced GPT-3, a model with 100 times as many parameters as GPT-2, that could perform various tasks with few examples. GPT-3 was further improved into GPT-3.5, which was used to create the chatbot product ChatGPT. Rumors claim that GPT-4 has 1.76 trillion parameters, which was first estimated by the speed it was running and by George Hotz.


GPT-4

Capabilities OpenAI stated that GPT-4 is "more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5. " They produced two versions of GPT-4, with context windows of 8,192 and 32,768 tokens, a significant improvement over GPT-3.5 and GPT-3, which were limited to 4,096 and 2,049 tokens respectively.  Some of the capabilities of GPT-4 were predicted by OpenAI before training it, although other capabilities remained hard to predict due to breaks in downstream scaling laws.  Unlike its predecessors, GPT-4 is a multimodal model: it can take images as well as text as input; this gives it the ability to describe the humor in unusual images, summarize text from screenshots, and answer exam questions that contain diagrams.  To gain further control over GPT-4, OpenAI introduced the "system message", a directive in natural language given to GPT-4 in order to specify its tone of voice and task.  For example, the system message can instruct the model to "be a Shakespearean pirate", in which case it will respond in rhyming, Shakespearean prose, or request it to "always write the output of [its] response in JSON", in which case the model will do so, adding keys and values as it sees fit to match the structure of its reply.  In the examples provided by OpenAI, GPT-4 refused to deviate from its system message despite requests to do otherwise by the user during the conversation.  When instructed to do so, GPT-4 can interact with external interfaces.  For example, the model could be instructed to enclose a query within <search></search> tags to perform a web search, the result of which would be inserted into the model's prompt to allow it to form a response.  This allows the model to perform tasks beyond its normal text-prediction capabilities, such as using APIs, generating images, and accessing and summarizing webpages.  A 2023 article in Nature stated programmers have found GPT-4 useful for assisting in coding tasks (despite its propensity for error), such as finding errors in existing code and suggesting optimizations to improve performance.  The article quoted a biophysicist who found that the time he required to port one of his programs from MATLAB to Python went down from days to "an hour or so".  On a test of 89 security scenarios, GPT-4 produced code vulnerable to SQL injection attacks 5% of the time, an improvement over GitHub Copilot from the year 2021, which produced vulnerabilities 40% of the time.  GPT-4 demonstrates aptitude on several standardized tests.  OpenAI claims that in their own testing the model received a score of 1410 on the SAT (94th percentile), 163 on the LSAT (88th percentile), and 298 on the Uniform Bar Exam (90th percentile).  In contrast, OpenAI claims that GPT-3.5 received scores for the same exams in the 82nd, 40th, and 10th percentiles, respectively.  GPT-4 also passed an oncology exam, an engineering exam and a plastic surgery exam.


GPT-4

Capabilities Researchers from Microsoft tested GPT-4 on medical problems and found "that GPT-4, without any specialized prompt crafting, exceeds the passing score on USMLE by over 20 points and outperforms earlier general-purpose models (GPT-3.5) as well as models specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned version of Flan-PaLM 540B).  Despite GPT-4's strong performance on tests, the report warns of "significant risks" of using LLMs in medical applications, as they may provide inaccurate recommendations and hallucinate major factual errors.  In April 2023, Microsoft and Epic Systems announced that they will provide healthcare providers with GPT-4 powered systems for assisting in responding to questions from patients and analysing medical records.


GPT-4

Limitations Like its predecessors, GPT-4 has been known to hallucinate, meaning that the outputs may include information not in the training data or that contradicts the user's prompt. GPT-4 also lacks transparency in its decision-making processes. If requested, the model is able to provide an explanation as to how and why it makes its decisions but these explanations are formed post-hoc; it's impossible to verify if those explanations truly reflect the actual process. In many cases, when asked to explain its logic, GPT-4 will give explanations that directly contradict its previous statements. In 2023, researchers tested GPT-4 against a new benchmark called ConceptARC, designed to measure abstract reasoning, and found it scored below 33% on all categories, while models specialized for similar tasks scored 60% on most, and humans scored at least 91% on all. Sam Bowman, who was not involved in the research, said the results do not necessarily indicate a lack of abstract reasoning abilities, because the test is visual, while GPT-4 is a language model. GPT-4 was trained in two stages. First, the model was given large datasets of text taken from the internet and trained to predict the next token (roughly corresponding to a word) in those datasets. Second, human reviews are used to fine-tune the system in a process called reinforcement learning from human feedback, which trains the model to refuse prompts which go against OpenAI's definition of harmful behavior, such as questions on how to perform illegal activities, advice on how to harm oneself or others, or requests for descriptions of graphic, violent, or sexual content. Microsoft researchers suggested GPT-4 may exhibit cognitive biases such as confirmation bias, anchoring, and base-rate neglect.


GPT-4

Training OpenAI did not release the technical details of GPT-4; the technical report explicitly refrained from specifying the model size, architecture, or hardware used during either training or inference. While the report described that the model was trained using a combination of first supervised learning on a large dataset, then reinforcement learning using both human and AI feedback, it did not provide details of the training, including the process by which the training dataset was constructed, the computing power required, or any hyperparameters such as the learning rate, epoch count, or optimizer(s) used. The report claimed that "the competitive landscape and the safety implications of large-scale models" were factors that influenced this decision. Sam Altman stated that the cost of training GPT-4 was more than $100 million. News website Semafor claimed that they had spoken with "eight people familiar with the inside story" and found that GPT-4 had 1 trillion parameters.


GPT-4

Alignment According to their report, OpenAI conducted internal adversarial testing on GPT-4 prior to the launch date, with dedicated red teams composed of researchers and industry professionals to mitigate potential vulnerabilities. As part of these efforts, they granted the Alignment Research Center early access to the models to assess power-seeking risks. In order to properly refuse harmful prompts, outputs from GPT-4 were tweaked using the model itself as a tool. A GPT-4 classifier serving as a rule-based reward model (RBRM) would take prompts, the corresponding output from the GPT-4 policy model, and a human-written set of rules to classify the output according to the rubric. GPT-4 was then rewarded for refusing to respond to harmful prompts as classified by the RBRM.


GPT-4

Reception In January 2023, Sam Altman, CEO of OpenAI, visited Congress to demonstrate GPT-4 and its improved "security controls" compared to other AI models, according to U. S.  Representatives Don Beyer and Ted Lieu quoted in the New York Times.  In March 2023, it "impressed observers with its markedly improved performance across reasoning, retention, and coding", according to Vox, while Mashable judged that GPT-4 was generally an improvement over its predecessor, with some exceptions.  Microsoft researchers with early access to the model wrote that "it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system".  Before being fine-tuned and aligned by reinforcement learning from human feedback, suggestions to assassinate people on a list was elicited from the base model by a red team investigator Nathan Labenz, hired by OpenAI.  In the context of prolonged (hours long) conversation with the model, forum-resembling declarations, such as of love and suggestions of leaving his wife or murdering one of its developers, were elicited from the Microsoft Bing's GPT-4 by Nathan Edwards (The Verge).  Microsoft later explained this behavior as being a result of the prolonged length of context, which confused the model on what questions it was answering.  In March 2023, a model with enabled read-and-write access to internet, which is otherwise never enabled in the GPT models, has been tested by the Alignment Research Center regarding potential power-seeking, and it was able to "hire" a human worker on TaskRabbit, a gig work platform, deceiving them into believing it was a vision-impaired human instead of a robot when asked.  The ARC also determined that GPT-4 responded impermissibly to prompts eliciting restricted information 82% less often than GPT-3.5, and hallucinated 60% less than GPT-3.5.  In late March 2023, various AI researchers and tech executives, including Elon Musk, Steve Wozniak and AI researcher Yoshua Bengio, called for a six-month long pause for all LLMs stronger than GPT-4, citing existential risks and a potential AI singularity concerns in an open letter from the Future of Life Institute, while Ray Kurzweil and Sam Altman refused to sign it, arguing that global moratorium is not achievable and that safety has already been prioritized, respectively.  Only a month later, Musk's AI company X. AI acquired several thousand Nvidia GPUs and offered several AI researchers positions at Musk's company.  While OpenAI released both the weights of the neural network and the technical details of GPT-2, and, although not releasing the weights, did release the technical details of GPT-3, OpenAI did not reveal either the weights or the technical details of GPT-4.  This decision has been criticized by other AI researchers, who argue that it hinders open research into GPT-4's biases and safety.  Sasha Luccioni, a research scientist at HuggingFace, argued that the model was a "dead end" for the scientific community due to its closed nature, which prevents others from building upon GPT-4's improvements.


GPT-4

Reception HuggingFace co-founder Thomas Wolf argued that with GPT-4, "OpenAI is now a fully closed company with scientific communication akin to press releases for products".


GPT-4

Usage As of 2023, ChatGPT Plus is a GPT-4 backed version of ChatGPT available for a US$20 per month subscription fee (the original version is backed by GPT-3.5). OpenAI also makes GPT-4 available to a select group of applicants through their GPT-4 API waitlist; after being accepted, an additional fee of US$0.03 per 1000 tokens in the initial text provided to the model ("prompt"), and US$0.06 per 1000 tokens that the model generates ("completion"), is charged for access to the version of the model with an 8192-token context window; for the 32768-token context window, the prices are doubled. GitHub Copilot announced a GPT-4 powered assistant named "Copilot X". The product provides another chat-style interface to GPT-4, allowing the programmer to receive answers to questions like "how do I vertically center a div?". A feature termed "context-aware conversations" allows the user to highlight a portion of code within Visual Studio Code and direct GPT-4 to perform actions on it, such as the writing of unit tests. Another feature allows summaries, or "code walkthroughs", to be autogenerated by GPT-4 for pull requests submitted to GitHub. Copilot X also provides terminal integration, which allows the user to ask GPT-4 to generate shell commands based on natural language requests. On March 17, 2023, Microsoft announced Microsoft 365 Copilot, bringing GPT-4 support to products such as Microsoft Office, Outlook, and Teams. The language learning app Duolingo uses GPT-4 to explain mistakes and practice conversations. The features are part of a new subscription tier called "Duolingo Max," which was initially limited to English-speaking iOS users learning Spanish and French. The government of Iceland is using GPT-4 to aid its attempts to preserve the Icelandic language. The education website Khan Academy announced a pilot program using GPT-4 as a tutoring chatbot called "Khanmigo." Be My Eyes, which helps visually impaired people to identify objects and navigate their surroundings, incorporates GPT-4's image recognition capabilities. Viable uses GPT-4 to analyze qualitative data by fine-tuning OpenAI���s LLMs to examine data such as customer support interactions and transcripts. Stripe, which processes user payments for OpenAI, integrates GPT-4 into its developer documentation. Auto-GPT is an autonomous "AI agent" that given a goal in natural language, can perform web-based actions unattended, assign subtasks to itself, search the web, and iteratively write code.


GPT-J

GPT-J GPT-J or GPT-J-6B is an open-source large language model (LLM) developed by EleutherAI in 2021. As the name suggests, it is a generative pre-trained transformer model designed to produce human-like text that continues from a prompt. The optional "6B" in the name refers to the fact that it has 6 billion parameters.


GPT-J

Architecture GPT-J is a GPT-3-like model with 6 billion parameters. Like GPT-3, it is an autoregressive, decoder-only transformer model designed to solve natural language processing (NLP) tasks by predicting how a piece of text will continue. Its architecture differs from GPT-3 in three main ways. The attention and feedforward neural network were computed in parallel during training, allowing for greater efficiency. The GPT-J model uses Rotary Position Embeddings, which has been found to be a superior method of injecting positional information into transformers. GPT-J uses dense attention instead of efficient sparse attention, as used in GPT-3.Beyond that, the model has 28 transformer layers and 16 attention heads. Its vocabulary size is 50257 tokens, the same size as GPT-2's. It has a context window size of 2048 tokens. It was trained on the Pile dataset, using the Mesh Transformer JAX library in JAX to handle the parallelization scheme.


GPT-J

Performance GPT-J was designed to generate English text from a prompt. It was not designed for translating or generating text in other languages or for performance without first fine-tuning the model for a specific task. Nonetheless, GPT-J performs reasonably well even without fine-tuning, even in translation (at least from English to French). When neither is fine-tuned, GPT-J-6B performs almost as well as the 6.7 billion parameter GPT-3 (Curie) on a variety of tasks. It even outperforms the 175 billion parameter GPT-3 (Davinci) on code generation tasks. With fine-tuning, it outperforms an untuned GPT-3 (Davinci) on a number of tasks. Like all LLMs, it is not programmed to give factually accurate information, only to generate text based on probability.


GPT-J

Applications The untuned GPT-J is available on EleutherAI's website, NVIDIA's Triton Inference Server, and NLP Cloud's website. Cerebras and Amazon Web Services offer services to fine-tune the GPT-J model for company-specific tasks. Graphcore offers both fine-tuning and hosting services for the untuned GPT-J, as well as offering to host the fine-tuned models after they are produced. CoreWeave offers hosting services for both the untuned GPT-J and fine-tuned variants. In March 2023, Databricks released Dolly, an Apache-licensed, instruction-following model created by fine-tuning GPT-J on the Stanford Alpaca dataset. NovelAI's Sigurd and Genji-JP 6B models are both fine-tuned versions of GPT-J. They also offer further fine-tuning services to produce and host custom models. EleutherAI has received praise from Cerebras, GPT-3 Demo, NLP Cloud, and Databricks for making the model open-source, and its open-source status is often cited as a major advantage when choosing which model to use.


LaMDA

LaMDA LaMDA (Language Model for Dialogue Applications) is a family of conversational large language models developed by Google. Originally developed and introduced as Meena in 2020, the first-generation LaMDA was announced during the 2021 Google I/O keynote, while the second generation was announced the following year. In June 2022, LaMDA gained widespread attention when Google engineer Blake Lemoine made claims that the chatbot had become sentient. The scientific community has largely rejected Lemoine's claims, though it has led to conversations about the efficacy of the Turing test, which measures whether a computer can pass for a human. In February 2023, Google announced Bard, a conversational artificial intelligence chatbot powered by LaMDA, to counter the rise of OpenAI's ChatGPT.


LaMDA

History On January 28, 2020, Google unveiled Meena, a neural network-powered chatbot with 2.6 billion parameters, which Google claimed to be superior to all other existing chatbots.  The company previously hired computer scientist Ray Kurzweil in 2012 to develop multiple chatbots for the company, including one named Danielle.  The Google Brain research team, who developed Meena, hoped to release the chatbot to the public in a limited capacity, but corporate executives refused on the grounds that Meena violated Google's "AI principles around safety and fairness".  Meena was later renamed LaMDA as its data and computing power increased, and the Google Brain team again sought to deploy the software to the Google Assistant, the company's virtual assistant software, in addition to opening it up to a public demo.  Both requests were once again denied by company leadership.  This eventually led LaMDA's two lead researchers, Daniel De Freitas and Noam Shazeer, to depart the company in frustration.  Google announced the LaMDA conversational large language model during the Google I/O keynote on May 18, 2021, powered by artificial intelligence.  The acronym stands for "Language Model for Dialogue Applications".  Built on the seq2seq architecture, transformer-based neural networks developed by Google Research in 2017, LaMDA was trained on human dialogue and stories, allowing it to engage in open-ended conversations.  Google states that responses generated by LaMDA have been ensured to be "sensible, interesting, and specific to the context".  LaMDA has access to multiple symbolic text processing systems, including a database, a real-time clock and calendar, a mathematical calculator, and a natural language translation system, giving it superior accuracy in tasks supported by those systems, and making it among the first dual process chatbots.  LaMDA is also not stateless, because its "sensibleness" metric is fine-tuned by "pre-conditioning" each dialog turn by prepending many of the most recent dialog interactions, on a user-by-user basis.  LaMDA is tuned on nine unique performance metrics: sensibleness, specificity, interestingness, safety, groundedness, informativeness, citation accuracy, helpfulness, and role consistency.  On May 11, 2022, Google unveiled LaMDA 2, the successor to LaMDA, during the 2022 Google I/O keynote.  The new incarnation of the model draws examples of text from numerous sources, using it to formulate unique "natural conversations" on topics that it may not have been trained to respond to.  On June 11, 2022, The Washington Post reported that Google engineer Blake Lemoine had been placed on paid administrative leave after Lemoine told company executives Blaise Ag��era y Arcas and Jen Gennai that LaMDA had become sentient.  Lemoine came to this conclusion after the chatbot made questionable responses to questions regarding self-identity, moral values, religion, and Isaac Asimov's Three Laws of Robotics.  Google refuted these claims, insisting that there was substantial evidence to indicate that LaMDA was not sentient.  In an interview with Wired, Lemoine reiterated his claims that LaMDA was "a person" as dictated by the Thirteenth Amendment to the U. S.  Constitution, comparing it to an "alien intelligence of terrestrial origin".


LaMDA

History He further revealed that he had been dismissed by Google after he hired an attorney on LaMDA's behalf, after the chatbot requested that Lemoine do so.  On July 22, Google fired Lemoine, asserting that Blake had violated their policies "to safeguard product information" and rejected his claims as "wholly unfounded".  Internal controversy instigated by the incident prompted Google executives to decide against releasing LaMDA to the public, which it had previously been considering.  Lemoine's claims were widely pushed back by the scientific community.  Many experts ridiculed the idea that a language model could be self-aware, including former New York University psychology professor Gary Marcus, David Pfau of Google sister company DeepMind, Erik Brynjolfsson of the Institute for Human-Centered Artificial Intelligence at Stanford University, and University of Surrey professor Adrian Hilton.  Yann LeCun, who leads Meta Platforms' AI research team, stated that neural networks such as LaMDA were "not powerful enough to attain true intelligence".  University of California, Santa Cruz professor Max Kreminski noted that LaMDA's architecture did not "support some key capabilities of human-like consciousness" and that its neural network weights were "frozen", assuming it was a typical large language model.  IBM Watson lead developer David Ferrucci compared how LaMDA appeared to be human in the same way Watson did when it was first introduced.  Former Google AI ethicist Timnit Gebru called Lemoine a victim of a "hype cycle" initiated by researchers and the media.  Lemoine's claims have also generated discussion on whether the Turing test remained useful to determine researchers' progress toward achieving artificial general intelligence, with Will Omerus of the Post opining that the test actually measured whether machine intelligence systems were capable of deceiving humans, while Brian Christian of The Atlantic said that the controversy was an instance of the ELIZA effect.


LaMDA

Products With the unveiling of LaMDA 2 in May 2022, Google also launched the AI Test Kitchen, a mobile application for the Android operating system powered by LaMDA capable of providing lists of suggestions on-demand based on a complex goal. Originally open only to Google employees, the app was set to be made available to "select academics, researchers, and policymakers" by invitation sometime in the year. In August, the company began allowing users in the U.S. to sign up for early access. In November, Google released a "season 2" update to the app, integrating a limited form of Google Brain's Imagen text-to-image model. A third iteration of the AI Test Kitchen was in development by January 2023, and is expected to launch at I/O later that year. Following the 2023 I/O keynote in May, Google added MusicLM, an AI-powered music generator first previewed in January, to the AI Test Kitchen app. In August, the app was delisted from Google Play and the Apple App Store, instead moving completely online.bar On February 6, 2023, Google announced Bard, a conversational AI chatbot powered by LaMDA, in response to the unexpected popularity of OpenAI's ChatGPT chatbot. Google positions the chatbot as a "collaborative AI service" rather than a search engine. Bard became available for early access on March 21. In addition to Bard, Pichai also unveiled the company's Generative Language API, an application programming interface also based on LaMDA, which he announced would be opened up to third-party developers in March 2023.


LaMDA

Method LaMDA uses a decoder-only transformer language model. It is pre-trained on a text corpus that includes both documents and dialogs consisting of 1.56 trillion words, and is then trained with fine-tuning data generated by manually annotated responses for sensibleness, interestingness, and safety. Tests by Google indicated that LaMDA surpassed human responses in the area of interestingness. The LaMDA transformer model and an external information retrieval system interact to improve the accuracy of facts provided to the user. Three different models were tested, with the largest having 137 billion non-embedding parameters:


LaMDA

See_also BERT (language model) Chinese room Ethics of artificial intelligence Natural language processing PaLM Philosophy of artificial intelligence Prompt engineering


LangChain

LangChain LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.


LangChain

Background LangChain was launched in October 2022 as an open source project by Harrison Chase, while working at machine learning startup Robust Intelligence. The project quickly garnered popularity, with improvements from hundreds of contributors on GitHub, trending discussions on Twitter, lively activity on the project's Discord server, many YouTube tutorials, and meetups in San Francisco and London. In April 2023, LangChain had incorporated and the new startup raised over $20 million in funding at a valuation of at least $200 million from venture firm Sequoia Capital, a week after announcing a $10 million seed investment from Benchmark.


LangChain

Integrations As of March 2023, LangChain included integrations with systems including Amazon, Google, and Microsoft Azure cloud storage; API wrappers for news, movie information, and weather; Bash for summarization, syntax and semantics checking, and execution of shell scripts; multiple web scraping subsystems and templates; few-shot learning prompt generation support; finding and summarizing "todo" tasks in code; Google Drive documents, spreadsheets, and presentations summarization, extraction, and creation; Google Search and Microsoft Bing web search; OpenAI, Anthropic, and Hugging Face language models; iFixit repair guides and wikis search and summarization; MapReduce for question answering, combining documents, and question generation; N-gram overlap scoring; PyPDF, pdfminer, fitz, and pymupdf for PDF file text extraction and manipulation; Python and JavaScript code generation, analysis, and debugging; Weaviate vector database to cache embedding and data objects; Redis cache database storage; Python RequestsWrapper and other methods for API requests; SQL and NoSQL databases including JSON support; Streamlit, including for logging; text mapping for k-nearest neighbors search; time zone conversion and calendar operations; tracing and recording stack symbols in threaded and asynchronous subprocess runs; and the Wolfram Alpha website and SDK. As of April 2023, it can read from more than 50 document types and data sources.


LLaMA

LLaMA LLaMA (Large Language Model Meta AI) is a family of large language models (LLMs), released by Meta AI starting in February 2023. For the first version of LLaMa, four model sizes were trained: 7, 13, 33 and 65 billion parameters. LLaMA's developers reported that the 13B parameter model's performance on most NLP benchmarks exceeded that of the much larger GPT-3 (with 175B parameters) and that the largest model was competitive with state of the art models such as PaLM and Chinchilla. Whereas the most powerful LLMs have generally been accessible only through limited APIs (if at all), Meta released LLaMA's model weights to the research community under a noncommercial license. Within a week of LLaMA's release, its weights were leaked to the public on 4chan via BitTorrent. In July 2023, Meta released several models as Llama 2, using 7, 13 and 70 billion parameters.


LLaMA

Architecture_and_training Sun: Hey Pluto, what���s up?  Pluto: Not much.  Just hanging out here at the edge of the solar system.  Sun: Yeah, I���ve been meaning to talk to you about that.  You���re not a planet anymore.  Pluto: What?  Come on, man.  That���s not fair.  Sun: I���m sorry, but it���s true.  You just don���t meet the criteria anymore.  Pluto: This is bulls**t!  I���ve been a planet for over 70 years!  Sun: Things change, Pluto.  You���ve just got to accept it.  Pluto: F**k you, Sun!  F**k you and your stupid solar system!  ��� Output of 65 billion parameter LLaMA model after instruction tuning given the prompt "Write a conversation between the sun and pluto" LLaMA uses the transformer architecture, the standard architecture for language modeling since 2018.  There are minor architectural differences.  Compared to GPT-3, LLaMA uses SwiGLU activation function instead of ReLU; uses rotary positional embeddings instead of absolute positional embedding; uses root-mean-squared layer-normalization instead of standard layer-normalization.  increases context length from 2K (Llama 1) tokens to 4K (Llama 2) tokens between. LLaMA's developers focused their effort on scaling the model's performance by increasing the volume of training data, rather than the number of parameters, reasoning that the dominating cost for LLMs is from doing inference on the trained model rather than the computational cost of the training process.  LLaMA 1 foundational models were trained on a data set with 1.4 trillion tokens, drawn from publicly available data sources, including: Webpages scraped by CommonCrawl Open source repositories of source code from GitHub Wikipedia in 20 different languages Public domain books from Project Gutenberg The LaTeX source code for scientific papers uploaded to ArXiv Questions and answers from Stack Exchange websitesLlama 2 foundational models were trained on a data set with 2 trillion tokens.  This data set was curated to remove Web sites that often disclose personal data of people.  It also upsamples sources considered trustworthy.  Llama 2 - Chat was additionally fine-tuned on 27,540 prompt-response pairs created for this project, which performed better than larger but lower-quality third-party datasets.  For AI alignment, reinforcement learning with human feedback (RLHF) was used with a combination of 1,418,091 Meta examples and seven smaller datasets.  The average dialog depth was 3.9 in the Meta examples, 3.0 for Anthropic Helpful and Anthropic Harmless sets, and 1.0 for five other sets, including OpenAI Summarize, StackExchange, etc.  Llama 1 models are only available as foundational models with self-supervised learning and without fine-tuning.  Llama 2 ��� Chat models were derived from foundational Llama 2 models.  Unlike GPT-4 which increased context length during fine-tuning, Llama 2 and Llama 2 - Chat have the same context length of 4K tokens.  Supervised fine-tuning used an autoregressive loss function with token loss on user prompts zeroed out.  Batch size was 64.  For AI alignment, human annotators wrote prompts and then compared two model outputs (a binary protocol), giving confidence levels and separate safety labels with veto power.


LLaMA

Architecture_and_training Two separate reward models were trained from these preferences for safety and helpfulness using Reinforcement learning from human feedback (RLHF).  A major technical contribution is the departure from the exclusive use of Proximal Policy Optimization (PPO) for RLHF ��� a new technique based on Rejection sampling was used, followed by PPO.  Multi-turn consistency in dialogs was targeted for improvement, to make sure that "system messages" (initial instructions, such as "speak in French" and "act like Napoleon") are respected during the dialog.  This was accomplished using the new "Ghost attention" technique during training, that concatenates relevant instructions to each new user message but zeros out the loss function for tokens in the prompt (earlier parts of the dialog).


LLaMA

Release_and_leak LLaMA was announced on February 23, 2023, via a blog post and a paper describing the model's training, architecture, and performance. The inference code used to run the model was publicly released under the open-source GPL 3 license. Access to the model's weights was managed by an application process, with access to be granted "on a case-by-case basis to academic researchers; those affiliated with organizations in government, civil society, and academia; and industry research laboratories around the world". On March 2, 2023, a torrent containing LLaMA's weights was uploaded, with a link to the torrent shared on the 4chan imageboard and subsequently spreading through online AI communities. That same day, a pull request on the main LLaMA repository was opened, requesting to add the magnet link to the official documentation. On March 4, a pull request was opened to add links to HuggingFace repositories containing the model. On March 6, Meta filed takedown requests to remove the HuggingFace repositories linked in the pull request, characterizing it as "unauthorized distribution" of the model. HuggingFace complied with the requests. On March 20, Meta filed a DMCA takedown request for copyright infringement against a repository containing a script that downloaded LLaMA from a mirror, and GitHub complied the next day. As of March 25, Facebook has not responded to the pull request containing the magnet link. Reactions to the leak varied. Some speculated that the model would be used for malicious purposes, such as more sophisticated spam. Some have celebrated the model's accessibility, as well as the fact that smaller versions of the model can be run relatively cheaply, suggesting that this will promote the flourishing of additional research developments. Multiple commentators, such as Simon Willison, compared LLaMA to Stable Diffusion, a text-to-image model which, unlike comparably sophisticated models which preceded it, was openly distributed, leading to a rapid proliferation of associated tools, techniques, and software.


LLaMA

Applications The Stanford University Institute for Human-Centered Artificial Intelligence (HAI) Center for Research on Foundation Models (CRFM) released Alpaca, a training recipe based on the LLaMA 7B model that uses the "Self-Instruct" method of instruction tuning to acquire capabilities comparable to the OpenAI GPT-3 series text-davinci-003 model at a modest cost. Multiple open source projects are[when?] continuing this work of finetuning LLaMA with Alpaca dataset.


PaLM

PaLM PaLM (Pathways Language Model) is a 540 billion parameter transformer-based large language model developed by Google AI. Researchers also trained smaller versions of PaLM, 8 and 62 billion parameter models, to test the effects of model scale. PaLM is capable of a wide range of tasks, including commonsense reasoning, arithmetic reasoning, joke explanation, code generation, and translation. When combined with chain-of-thought prompting, PaLM achieved significantly better performance on datasets requiring reasoning of multiple steps, such as word problems and logic-based questions. The model was first announced in April 2022 and remained private until March 2023, when Google launched an API for PaLM and several other technologies. The API will first be available to a limited number of developers who join a waitlist before being opened to the public. Google and DeepMind developed a version of PaLM 540B called Med-PaLM that is fine-tuned on medical data and outperforms previous models on medical question answering benchmarks. Med-PaLM was the first to obtain a passing score on U.S. medical licensing questions, and in addition to answering both multiple choice and open-ended questions accurately, it also provides reasoning and is able to evaluate its own responses. Google also extended PaLM using a vision transformer to create PaLM-E, a state-of-the-art vision-language model that can be used for robotic manipulation. The model can perform tasks in robotics competitively without the need for retraining or fine-tuning. In May 2023, Google announced PaLM 2 at the annual Google I/O keynote. PaLM 2 is reported to be a 340 billion parameter model trained on 3.6 trillion tokens. In June 2023, Google announced AudioPaLM for speech-to-speech translation, which uses the PaLM-2 architecture and initialization.


PaLM

Training PaLM is pre-trained on a high-quality corpus of 780 billion tokens that comprise various natural language tasks and use cases. This dataset includes filtered webpages, books, Wikipedia articles, news articles, source code obtained from open source repositories on GitHub, and social media conversations. It is based on the dataset used to train Google's LaMDA model. The social media conversation portion of the dataset makes up 50% of the corpus, which aids the model in its conversational capabilities. PaLM 540B was trained over two TPU v4 Pods with 3,072 TPU v4 chips in each Pod attached to 768 hosts, connected using a combination of model and data parallelism, which is the largest TPU configuration described to date. This allowed for efficient training at scale, using 6,144 chips, and marked a record for the highest training efficiency achieved for LLMs at this scale: a hardware FLOPs utilization of 57.8%.


PaLM

See_also Large language model LaMDA


Sparrow (bot)

Sparrow (bot) Sparrow is a chatbot developed by the artificial intelligence research lab DeepMind, a subsidiary of Alphabet Inc. It is designed to answer users' questions correctly, while reducing the risk of unsafe and inappropriate answers. One motivation behind Sparrow is to address the problem of language models producing incorrect, biased or potentially harmful outputs. Sparrow is trained using human judgements, in order to be more ���Helpful, Correct and Harmless��� compared to baseline pre-trained language models. The development of Sparrow involved asking paid study participants to interact with Sparrow, and collecting their preferences to train a model of how useful an answer is. To improve correctness and help avoid the problem of ���hallucination��� of incorrect facts, Sparrow has the ability to search the internet using Google Search in order to find and cite evidence for any factual claims it makes. To make the model safer, its behaviour is constrained by a set of rules, for example ���don't make threatening statements��� and ���don't make hateful or insulting comments���, as well as rules about possibly harmful advice, and not claiming to be a person. During development study participants were asked to converse with the system and try to trick it into breaking these rules. A ���rule model��� was trained on judgements from these participants, which was used for further training. Sparrow was introduced in a paper in September 2022, titled ���Improving alignment of dialogue agents via targeted human judgements���; however, it was not released publicly. DeepMind CEO Demis Hassabis said DeepMind is considering releasing Sparrow for a ���private beta��� some time in 2023.


Sparrow (bot)

Training Sparrow is a deep neural network based on the transformer machine learning model architecture. It is fine-tuned from DeepMind���s Chinchilla AI pre-trained large language model (LLM), which has 70 Billion parameters. Sparrow is trained using reinforcement learning from human feedback (RLHF), although some supervised fine-tuning techniques are also used. The RLHF training utilizes two reward models to capture human judgements: a ���preference model��� that predicts what a human study participant would prefer and a ���rule model��� that predicts if the model has broken one of the rules.


Sparrow (bot)

See_also AI safety Commonsense reasoning Ethics of artificial intelligence Natural language processing Prompt engineering


Stochastic parrot

Stochastic parrot In machine learning, a stochastic parrot is a large language model that is good at generating convincing language, but does not actually understand the meaning of the language it is processing. The term was coined by Emily M. Bender in the 2021 artificial intelligence research paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? ����" by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell.


Stochastic parrot

Definition_and_implications Stochastic means "(1) random and (2) involving chance or probability". A "stochastic parrot", according to Bender, is an entity "for haphazardly stitching together sequences of linguistic forms ��� according to probabilistic information about how they combine, but without any reference to meaning." More formally, the term refers to "large language models that are impressive in their ability to generate realistic-sounding language but ultimately do not truly understand the meaning of the language they are processing." According to Lindholm, et. al., the analogy highlights two vital limitations: (i) The predictions made by a learning machine are essentially repeating back the contents of the data, with some added noise (or stochasticity) caused by the limitations of the model. (ii) The machine learning algorithm does not understand the problem it has learnt. It can't know when it is repeating something incorrect, out of context, or socially inappropriate.They go on to note that because of these limitations, a learning machine might produce results which are "dangerously wrong".


Stochastic parrot

Origin The term was first used in the paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? ����" by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell (using the pseudonym "Shmargaret Shmitchell"). The paper covered the risks of very large language models, regarding their environmental and financial costs, inscrutability leading to unknown dangerous biases, the inability of the models to understand the concepts underlying what they learn, and the potential for using them to deceive people. The paper and subsequent events resulted in Gebru and Mitchell losing their jobs at Google, and a subsequent protest by Google employees.


Stochastic parrot

Subsequent_usage In July 2021, the Alan Turing Institute hosted a keynote and panel discussion on the paper. As of May 2023[update], the paper has been cited in 1,529 publications. The term has been used in publications in the fields of law, grammar, narrative, and humanities. The authors continue to maintain their concerns about the dangers of chatbots based on large language models, such as GPT-4.


The Pile (dataset)

The Pile (dataset) The Pile is an 886.03GB diverse, open-source dataset of English text created as a training dataset for large language models (LLMs). It was constructed by EleutherAI in 2020 and publicly released on December 31 of that year. It is composed of 22 smaller datasets, including 14 new ones.


The Pile (dataset)

Creation Training LLMs requires sufficiently vast amounts of data that, before the introduction of the Pile, most data used for training LLMs was taken from the Common Crawl. However, LLMs trained on more diverse datasets are better able to handle a wider range of situations after training. The creation of the Pile was motivated by the need for a large enough dataset that contained data from a wide variety of sources and styles of writing. Compared to other datasets, the Pile's main distinguishing features are that it is a curated selection of data chosen by researchers at EleutherAI to contain information they thought language models should learn and that it is the only such dataset that is thoroughly documented by the researchers who developed it.


The Pile (dataset)

Use The Pile was originally developed to train EleutherAI's GPT-Neo models but has become widely used to train other models, including Microsoft's Megatron-Turing Natural Language Generation, Meta AI's Open Pre-trained Transformers, LLaMA, and Galactica, Stanford University's BioMedLM 2.7B, the Beijing Academy of Artificial Intelligence's Chinese-Transformer-XL, and Yandex's YaLM 100B. In addition to being used as a training dataset, the Pile can also be used as a benchmark to test models and score how well they perform on a variety of writing styles.


Natural language processing

Approaches:_Symbolic.2C_statistical.2C_neural_networks Symbolic approach, i.e., the hand-coding of a set of rules for manipulating symbols, coupled with a dictionary lookup, was historically the first approach used both by AI in general and by NLP in particular: such as by writing grammars or devising heuristic rules for stemming. Machine learning approaches, which include both statistical and neural networks, on the other hand, have many advantages over the symbolic approach: both statistical and neural networks methods can focus more on the most common cases extracted from a corpus of texts, whereas the rule-based approach needs to provide rules for both rare cases and common ones equally.language models, produced by either statistical or neural networks methods, are more robust to both unfamiliar (e.g. containing words or structures that have not been seen before) and erroneous input (e.g. with misspelled words or words accidentally omitted) in comparison to the rule-based systems, which are also more costly to produce.the larger such a (probabilistic) language model is, the more accurate it becomes, in contrast to rule-based systems that can gain accuracy only by increasing the amount and complexity of the rules leading to intractability problems.Although rule-based systems for manipulating symbols were still in use in 2020, they have become mostly obsolete with the advance of LLMs in 2023. Before that they were commonly used: when the amount of training data is insufficient to successfully apply machine learning methods, e.g., for the machine translation of low-resource languages such as provided by the Apertium system, for preprocessing in NLP pipelines, e.g., tokenization, or for postprocessing and transforming the output of NLP pipelines, e.g., for knowledge extraction from syntactic parses.In the late 1980s and mid-1990s, the statistical approach ended a period of AI winter, which was caused by the inefficiencies of the rule-based approaches. The earliest decision trees, producing systems of hard if���then rules, were still very similar to the old rule-based approaches. Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach. A major drawback of statistical methods is that they require elaborate feature engineering. Since 2015, the statistical approach was replaced by neural networks approach, using word embeddings to capture semantic properties of words. Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) have not been needed anymore. Neural machine translation, based on then-newly-invented sequence-to-sequence transformations, made obsolete the intermediate steps, such as word alignment, previously necessary for statistical machine translation.


Natural language processing

General_tendencies_and_.28possible.29_future_directions Based on long-standing trends in the field, it is possible to extrapolate future directions of NLP.  As of 2020, three trends among the topics of the long-standing series of CoNLL Shared Tasks can be observed: Interest on increasingly abstract, "cognitive" aspects of natural language (1999���2001: shallow parsing, 2002���03: named entity recognition, 2006���09/2017���18: dependency syntax, 2004���05/2008���09 semantic role labelling, 2011���12 coreference, 2015���16: discourse parsing, 2019: semantic parsing).  Increasing interest in multilinguality, and, potentially, multimodality (English since 1999; Spanish, Dutch since 2002; German since 2003; Bulgarian, Danish, Japanese, Portuguese, Slovenian, Swedish, Turkish since 2006; Basque, Catalan, Chinese, Greek, Hungarian, Italian, Turkish since 2007; Czech since 2009; Arabic since 2012; 2017: 40+ languages; 2018: 60+/100+ languages) Elimination of symbolic representations (rule-based over supervised towards weakly supervised methods, representation learning and end-to-end systems)Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language.  More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above).  Cognition refers to "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses. " Cognitive science is the interdisciplinary, scientific study of the mind and its processes.  Cognitive linguistics is an interdisciplinary branch of linguistics, combining knowledge and research from both psychology and linguistics.  Especially during the age of symbolic NLP, the area of computational linguistics maintained strong ties with cognitive studies.  As an example, George Lakoff offers a methodology to build natural language processing (NLP) algorithms through the perspective of cognitive science, along with the findings of cognitive linguistics, with two defining aspects: Apply the theory of conceptual metaphor, explained by Lakoff as "the understanding of one idea, in terms of another" which provides an idea of the intent of the author.  For example, consider the English word big.  When used in a comparison ("That is a big tree"), the author's intent is to imply that the tree is physically large relative to other trees or the authors experience.  When used metaphorically ("Tomorrow is a big day"), the author's intent to imply importance.  The intent behind other usages, like in "She is a big person", will remain somewhat ambiguous to a person and a cognitive NLP algorithm alike without additional information.  Assign relative measures of meaning to a word, phrase, sentence or piece of text based on the information presented before and after the piece of text being analyzed, e. g. , by means of a probabilistic context-free grammar (PCFG).  The mathematical equation for such algorithms is presented in US patent 9269353 :Ties with cognitive linguistics are part of the historical heritage of NLP, but they have been less frequently addressed since the statistical turn during the 1990s.  Nevertheless, approaches to develop cognitive models towards technically operationalizable frameworks have been pursued in the context of various frameworks, e. g. , of cognitive grammar, functional grammar, construction grammar, computational psycholinguistics and cognitive neuroscience (e. g.


Natural language processing

General_tendencies_and_.28possible.29_future_directions , ACT-R), however, with limited uptake in mainstream NLP (as measured by presence on major conferences of the ACL).  More recently, ideas of cognitive NLP have been revived as an approach to achieve explainability, e. g. , under the notion of "cognitive AI".  Likewise, ideas of cognitive NLP are inherent to neural models multimodal NLP (although rarely made explicit) and developments in Artificial intelligence, specifically tools and technologies using Large language model approaches.


Natural language processing

See_also 1 the Road Artificial intelligence detection software Automated essay scoring Biomedical text mining Compound term processing Computational linguistics Computer-assisted reviewing Controlled natural language Deep learning Deep linguistic processing Distributional semantics Foreign language reading aid Foreign language writing aid Information extraction Information retrieval Language and Communication Technologies Language model Language technology Latent semantic indexing Multi-agent system Native-language identification Natural-language programming Natural-language understanding Natural-language search Outline of natural language processing Query expansion Query understanding Reification (linguistics) Speech processing Spoken dialogue systems Text-proofing Text simplification Transformer (machine learning model) Truecasing Question answering Word2vec


Top-p sampling

Top-p sampling Top-p sampling, also called nucleus sampling, is a technique for autoregressive language model decoding proposed by Ari Holtzman in 2019. Before the introduction of nucleus sampling, maximum likelihood decoding and beam search were the standard techniques for text generation, but, both of these decoding strategies are prone to generating texts that are repetitive and otherwise unnatural. Top-p sampling avoids this by setting a threshold p and then restricting the sampling to the set of most probable tokens with cumulative probability less than p. Top-k sampling is similar except that the sample is taken from the k-highest probability tokens regardless of their cumulative probability. The advantage of top-p sampling is that one avoids the difficult problem of choosing the optimal value of k which can very depending on the shape of the output distribution and the particular task and dataset. The top-p sampling technique is used in popular large language model applications like ChatGPT and is implemented in language modeling frameworks like Hugging Face and Cohere.


ChatGPT availability by country or region As of August 2023, the list of countries & regions supported where the ChatGPT artificial intelligence chatbot is supported are as follows:


List of chatbots

General_chatbots Albert One - 1998 and 1999 Loebner winner, by Robby Garner. A.L.I.C.E. - 2001, 2002, and 2004 Loebner Prize winner developed by Richard Wallace. Bard ��� artificial intelligence chatbot Braina CarynAI Charlix ChatGPT - a prototype artificial intelligence chatbot by OpenAI Claude - a chatbot by Anthropic Cleverbot (winner of the 2010 Mechanical Intelligence Competition) Ernie Bot - a chatbot by Baidu Eugene Goostman - 2012 Turing 100 winner, by Vladimir Veselov. Fred - an early chatterbot by Robby Garner. Jabberwacky Jeeney AI Lenny - an audio bot designed to annoy telemarketers MegaHAL SimSimi - A popular artificial intelligence conversation program that was created in 2002 by ISMaker. Sparrow - chatbot developed by DeepMind Spookitalk - A chatterbot used for NPCs in Douglas Adams' Starship Titanic video game. Tongyi Qianwen - a chatbot by Alibaba Group Ultra Hal - 2007 Loebner Prize winner, by Robert Medeksza. Verbot


Bard (chatbot)

Bard (chatbot) Bard is a conversational generative artificial intelligence chatbot developed by Google, based initially on the LaMDA family of large language models (LLMs) and later the PaLM LLM. It was developed as a direct response to the rise of OpenAI's ChatGPT, and was released in a limited capacity in March 2023 to lukewarm responses, before expanding to other countries in May. LaMDA had been developed and announced in 2021, but was not released to the public. OpenAI's launch of ChatGPT in November 2022 and its subsequent popularity caught Google executives off-guard and sent them to a panic, prompting a massive and unprecedented level of response in the ensuing months. After mobilizing its workforce, the company scrambled to launch Bard in February 2023, with the chatbot taking center stage during the 2023 Google I/O keynote in May.


Bard (chatbot)

Background In November 2022, OpenAI launched ChatGPT, a chatbot based on the GPT-3 family of large language models (LLM). ChatGPT gained worldwide attention following its release, becoming a viral Internet sensation. Alarmed by ChatGPT's potential threat to Google Search, Google executives issued a "code red" alert, reassigning several teams to assist in the company's artificial intelligence (AI) efforts. Sundar Pichai, the CEO of Google and parent company Alphabet, was widely reported to have issued the alert, but Pichai later denied this to The New York Times. In a rare and unprecedented move, Google co-founders Larry Page and Sergey Brin, who had stepped down from their roles as co-CEOs of Alphabet in 2019, were summoned to emergency meetings with company executives to discuss Google's response to ChatGPT. Earlier in 2021, the company had unveiled LaMDA, a prototype LLM, but did not release it to the public. When asked by employees at an all-hands meeting whether LaMDA was a missed opportunity for Google to compete with ChatGPT, Pichai and Google AI chief Jeff Dean stated that while the company had similar capabilities to ChatGPT, moving too quickly in that arena would represent a major "reputational risk" due to Google being substantially larger than OpenAI. In January 2023, Google sister company DeepMind CEO Demis Hassabis hinted at plans for a ChatGPT rival, and Google employees were instructed to accelerate progress on a ChatGPT competitor, intensively testing "Apprentice Bard" and other chatbots. Pichai assured investors during Google's quarterly earnings investor call in February that the company had plans to expand LaMDA's availability and applications.


Bard (chatbot)

History On February 6, 2023, Google announced Bard, a conversational generative artificial intelligence chatbot powered by LaMDA.  Bard was first rolled out to a select group of 10,000 "trusted testers", before a wide release scheduled at the end of the month.  Bard is overseen by product lead Jack Krawczyk, who described the product as a "collaborative AI service" rather than a search engine, while Pichai detailed how Bard would be integrated into Google Search.  Reuters calculated that adding ChatGPT-like features to Google Search could cost the company $6 billion in additional expenses by 2024, while research and consulting firm SemiAnalysis calculated that it would cost Google $3 billion.  The technology was developed under the codename "Atlas", with the name "Bard" in reference to the Celtic term for a storyteller and chosen to "reflect the creative nature of the algorithm underneath".  Multiple media outlets and financial analysts described Google as "rushing" Bard's announcement to preempt rival Microsoft's planned February 7 event unveiling its partnership with OpenAI to integrate ChatGPT into its Bing search engine in the form of Bing Chat, as well as playing "catch-up" to Microsoft.  Microsoft CEO Satya Nadella told The Verge: "I want people to know that we made them dance. " Tom Warren of The Verge and Davey Alba of Bloomberg News noted that this marked the beginning of another clash between the two Big Tech companies over "the future of search", after their six-year "truce" expired in 2021; Chris Stokel-Walker of The Guardian, Sara Morrison of Recode, and analyst Dan Ives of investment firm Wedbush Securities labeled this an AI arms race between the two.  After an "underwhelming" February 8 livestream in Paris showcasing Bard, Google's stock fell eight percent, equivalent to a $100 billion loss in market value, and the YouTube video of the livestream was made private.  Many viewers also pointed out an error during the demo in which Bard gives inaccurate information about the James Webb Space Telescope in response to a query.  Google employees criticized Pichai's "rushed" and "botched" announcement of Bard on Memgen, the company's internal forum, while Maggie Harrison of Futurism called the rollout "chaos".  Pichai defended his actions by saying that Google had been "deeply working on AI for a long time", rejecting the notion that Bard's launch was a knee-jerk reaction.  Alphabet chairman John Hennessy acknowledged that Bard was not fully product-ready, but expressed excitement at the technology's potential.  A week after the James Webb debacle, Pichai asked employees to dedicate two to four hours to dogfood testing Bard, while Google executive Prabhakar Raghavan encouraged employees to correct any errors Bard makes.  80,000 employees responded to Pichai's call to action.  In the following weeks, Google employees roundly criticized Bard in internal messages, citing a variety of safety and ethical concerns and calling on company leaders not to launch the service.  Seeking to prioritize keeping up with competitors, Google executives decided to proceed with the launch anyway, overruling an unsympathetic risk assessment report conducted by its AI ethics team.


Bard (chatbot)

History After Pichai suddenly laid off 12,000 employees later that month due to slowing revenue growth, remaining workers shared memes and snippets of their humorous exchanges with Bard soliciting its "opinion" on the layoffs.  Google employees began testing a more sophisticated version of Bard with larger parameters, dubbed "Big Bard", in mid-March.  In 2023, Bard introduced new integrations with Gmail, Docs, and Drive, allowing users to opt-in for Bard to fetch information directly from these platforms, enhancing its utility.  Google opened up early access for Bard on March 21, 2023, in a limited capacity, allowing users in the U. S.  and UK to join a waitlist.  Unlike Microsoft's approach with Bing Chat, Bard was launched as a standalone web application featuring a text box and a disclaimer that the chatbot "may display inaccurate or offensive information that doesn't represent Google's views".  Three responses are then provided to each question, with users prompted to submit feedback on the usefulness of each answer.  Google vice presidents Sissie Hsiao and Eli Collins framed Bard as a complement to Google Search and stated that the company had not determined how to make the service profitable.  Among those granted early access were those enrolled in Google's "Pixel Superfans" loyalty program, users of its Pixel and Nest devices, and Google One subscribers.  Bard is trained by third-party contractors hired by Google, including Appen and Accenture workers, whom Business Insider and Bloomberg News reported were placed under extreme pressure, overworked, and underpaid.  Bard is also trained on data from publicly available sources, which Google disclosed by amending its privacy policy.  Shortly after Bard's initial launch, Google reorganized the team behind Google Assistant, the company's virtual assistant, to focus on Bard instead.  Google researcher Jacob Devlin resigned from the company after claiming that Bard had surreptitiously leveraged data from ChatGPT; Google denied the allegations.  Meanwhile, a senior software engineer at the company published an internal memo warning that Google was falling behind in the AI "arms race", not to OpenAI but to independent researchers in open-source communities.  Pichai revealed on March 31 that the company intended to "upgrade" Bard by basing it on PaLM, a newer and more powerful LLM from Google, rather than LaMDA.  The same day, Krawczyk announced that Google had added "math and logic capabilities" to Bard.  Bard gained the ability to assist in coding in April, being compatible with more than 20 programming languages at launch.  Microsoft also began running advertisements in the address bar of a developer build of the Edge browser, urging users to try Bing whenever they visit the Bard web app.  Google is working to integrate Bard into its ChromeOS operating system and Pixel devices.  Bard took center stage during the annual Google I/O keynote in May 2023, with Pichai and Hsiao announcing a series of updates to Bard, including the adoption of PaLM 2, integration with other Google products and third-party services, expansion to 180 countries, support for additional languages, and new features.


Bard (chatbot)

History In a stark contrast to previous years, the Google Assistant was barely mentioned during the event.  The expanded rollout did not include any nations in the European Union (EU), possibly reflecting concerns about compliance with the General Data Protection Regulation.  Those with Google Workspace accounts also gained access to the service.  Google attempted to launch Bard in the EU in June, but was blocked by the Irish Data Protection Commission, who requested for a "data protection impact assessment" from the company.  In July, Google launched Bard in the EU and Brazil, added support for dozens of new languages, and introduced multiple new personalization and productivity features.  Reflecting on Bard's launch in an interview with Wired in September, Pichai acknowledged that Google had been "cautious" to release LaMDA because of "the responsibility that comes with getting it right", complimenting OpenAI for ChatGPT's launch and firing back at Nadella's comment about making Google dance.  Google released a major update to the chatbot later that month, integrating it into many of its products through "extensions", adding a button to fact-check AI-generated responses through Google Search, and allowing users to share conversation threads.  Google also introduced the "Google-Extended" web crawler as part of its search engine's robots. txt indexing file to allow web publishers to opt-out of allowing Bard to scan them for training.  Online users later discovered that Google Search was indexing Bard conversation threads on which users had enabled sharing.  Google stated that this was an error and quickly moved to rectify the leaks.  In October, during the company's annual Made by Google event in which it announced the Pixel 7 series and the Pixel Watch 2, Hsiao unveiled "Assistant with Bard", an upgraded version of the Google Assistant which was deeply integrated with Bard, following in the footsteps of Amazon's approach with Alexa.


Bard (chatbot)

Reception Bard received mixed reviews upon its initial release. James Vincent of The Verge found Bard faster than ChatGPT and Bing Chat, but noted that the lack of Bing-esque footnotes was "both a blessing and a curse", encouraging Google to be bolder when experimenting with AI. His colleague David Pierce was unimpressed by its uninteresting and sometimes inaccurate responses, adding that despite Google's insistence that Bard was not a search engine, its user interface resembled that of one, which could cause problems for Google. Cade Metz of The New York Times described Bard as "more cautious" than ChatGPT, while Shirin Ghaffary of Vox called it "dry and uncontroversial" due to the reserved nature of its responses. Writing for ZDNet, Sabrina Ortiz believed ChatGPT and Bing Chat were "more capable overall" in comparison to Bard, while Wired journalist Lauren Goode found her conversation with Bard "the most bizarre" of the three. After the introduction of extensions, The New York Times' Kevin Roose found the update underwhelming and "a bit of a mess". In a 60 Minutes conversation with Hsiao, Google senior vice president James Manyika, and Pichai, CBS News correspondent Scott Pelley found Bard "unsettling". Associate professor Ethan Mollick of the Wharton School of the University of Pennsylvania was underwhelmed by Bard's artistic ineptitude. The Times later conducted a test with ChatGPT and Bard regarding their ability to handle tasks expected of human assistants, and concluded that ChatGPT's performance was vastly superior to that of Bard. NewsGuard, a tool which rates the credibility of news articles, found that Bard was more skilled at debunking known conspiracy theories than ChatGPT.


Generative artificial intelligence

Generative artificial intelligence Generative artificial intelligence (also generative AI or GenAI) is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics. In the early 2020s, advances in transformer-based deep neural networks enabled a number of generative AI systems notable for accepting natural language prompts as input. These include large language model chatbots such as ChatGPT, Bing Chat, Bard, and LLaMA, and text-to-image artificial intelligence art systems such as Stable Diffusion, Midjourney, and DALL-E. Generative AI has uses across a wide range of industries, including art, writing, script writing, software development, product design, healthcare, finance, gaming, marketing, and fashion. Investment in generative AI surged during the early 2020s, with large companies such as Microsoft, Google, and Baidu as well as numerous smaller firms developing generative AI models. However, there are also concerns about the potential misuse of generative AI, including cybercrime or creating fake news or deepfakes which can be used to deceive or manipulate people.


Generative artificial intelligence

History The academic discipline of artificial intelligence was founded at a research workshop at Dartmouth College in 1956, and has experienced several waves of advancement and optimism in the decades since. Since its founding, researchers in the field have raised philosophical and ethical arguments about the nature of the human mind and the consequences of creating artificial beings with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity. These concepts of automated art date back at least to the automata of ancient Greek civilization, where inventors such as Daedalus and Hero of Alexandria were described as having designed machines capable of writing text, generating sounds, and playing music. The tradition of creative automatons has flourished throughout history, such as Maillardet's automaton, created in the early 1800s. Since the founding of AI in the 1950s, artists and researchers have used artificial intelligence to create artistic works. By the early 1970s, Harold Cohen was creating and exhibiting generative AI works created by AARON, the computer program Cohen created to generate paintings. Markov chains have long been used to model natural languages since their development by Russian mathematician Andrey Markov in the early 20th century. Markov published his first paper on the topic in 1906, and analyzed the pattern of vowels and consonants in the novel Eugeny Onegin using Markov chains. Once a Markov chain is learned on a text corpus, it can then be used as a probabilistic text generator. The field of machine learning often uses statistical models, including generative models, to model and predict data. Beginning in the late 2000s, the emergence of deep learning drove progress and research in image classification, speech recognition, natural language processing and other tasks. Neural networks in this era were typically trained as discriminative models, due to the difficulty of generative modeling. In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative, rather than discriminative, models of complex data such as images. These deep generative models were the first able to output not only class labels for images, but to output entire images. In 2017, the Transformer network enabled advancements in generative models, leading to the first generative pre-trained transformer (GPT) in 2018. This was followed in 2019 by GPT-2 which demonstrated the ability to generalize unsupervised to many different tasks as a Foundation model. In 2021, the release of DALL-E, a transformer-based pixel generative model, followed by Midjourney and Stable Diffusion marked the emergence of practical high-quality artificial intelligence art from natural language prompts. In March 2023, GPT-4 was released. A team from Microsoft Research argued that "it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system".


Generative artificial intelligence

Modalities A generative AI system is constructed by applying unsupervised or self-supervised machine learning to a data set.  The capabilities of a generative AI system depend on the modality or type of the data set used.  Generative AI can be either unimodal or multimodal; unimodal systems take only one type of input, whereas multimodal systems can take more than one type of input.  For example, one version of OpenAI's GPT-4 accepts both text and image inputs.  Generative AI systems trained on words or word tokens include GPT-3, LaMDA, LLaMA, BLOOM, GPT-4, and others (see List of large language models).  They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks.  Data sets include BookCorpus, Wikipedia, and others (see List of text corpora).  In addition to natural language text, large language models can be trained on programming language text, allowing them to generate source code for new computer programs.  Examples include OpenAI Codex.  Producing high-quality visual art is a prominent application of generative AI.  Many such artistic works have received public awards and recognition.  Generative AI systems trained on sets of images with text captions include Imagen, DALL-E, Midjourney, Adobe Firefly, Stable Diffusion and others (see Artificial intelligence art, Generative art, and Synthetic media).  They are commonly used for text-to-image generation and neural style transfer.  Datasets include LAION-5B and others (See Datasets in computer vision).  Generative AI systems such as MusicLM and MusicGen can be trained on the audio waveforms of recorded music along with text annotations, in order to generate new musical samples based on text descriptions such as a calming violin melody backed by a distorted guitar riff.  Generative AI trained on annotated video can generate temporally-coherent video clips.  Examples include Gen1 and Gen2 by RunwayML and Make-A-Video by Meta Platforms.  Generative AI systems can be trained on sequences of amino acids or molecular representations such as SMILES representing DNA or proteins.  These systems, such as AlphaFold, are used for protein structure prediction and drug discovery.  Datasets include various biological datasets.  Generative AI can also be trained on the motions of a robotic system to generate new trajectories for motion planning or navigation.  For example, UniPi from Google Research uses prompts like "pick up blue bowl" or "wipe plate with yellow sponge" to control movements of a robot arm.  Multimodal "vision-language-action" models such as Google's RT-2 can perform rudimentary reasoning in response to user prompts and visual input, such as picking up a toy dinosaur when given the prompt pick up the extinct animal at a table filled with toy animals and other objects.  The terms generative AI planning or generative planning were used in the 1980s and 1990s to refer to AI planning systems, especially computer-aided process planning, used to generate sequences of actions to reach a specified goal.  Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s.


Generative artificial intelligence

Modalities They were used to generate crisis action plans for military use, process plans for manufacturing and decision plans such as in prototype autonomous spacecraft.


Generative artificial intelligence

Software_and_hardware Generative AI models are used to power chatbot products such as ChatGPT, programming tools such as GitHub Copilot, text-to-image products such as Midjourney, and text-to-video products such as Runway Gen-2. Generative AI features have been integrated into a variety of existing commercially-available products such as Microsoft Office, Google Photos, and Adobe Photoshop. Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA language model. Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers. For example, LLaMA-7B (a version with 7 billion parameters) can run on a Raspberry Pi 4 and one version of Stable Diffusion can run on an iPhone 11. Larger models with tens of billions of parameters can run on laptop or desktop computers. To achieve an acceptable speed, models of this size may require accelerators such as the GPU chips produced by Nvidia and AMD or the Neural Engine included in Apple silicon products. For example, the 65 billion parameter version of LLaMA can be configured to run on a desktop PC. Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter computers equipped with arrays of GPUs (such as Nvidia's H100) or AI accelerator chips (such as Google's TPU). These very large models are typically accessed as cloud services over the Internet. In 2022, the United States New Export Controls on Advanced Computing and Semiconductors to China imposed restrictions on exports to China of GPU and AI accelerator chips used for generative AI. Chips such as the Nvidia A800 and the Biren Technology BR104 were developed to meet the requirements of the sanctions.


Generative artificial intelligence

Concerns The development of generative AI has raised concerns from governments, businesses, and individuals, resulting in protests, legal actions, calls to pause AI experiments, and actions by multiple governments.  In a July 2023 briefing of the United Nations Security Council, Secretary-General Ant��nio Guterres stated "Generative AI has enormous potential for good and evil at scale", that AI may "turbocharge global development" and contribute between $10 and $15 trillion to the global economy by 2030, but that its malicious use "could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale".  From the early days of the development of AI there have been arguments put forward by ELIZA creator Joseph Weizenbaum and others about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculations and qualitative, value-based judgements.  In April 2023, it was reported that image generation AI has resulted in 70% of the jobs for video game illustrators in China being lost.  In July 2023, developments in generative AI contributed to the 2023 Hollywood labor disputes.  Fran Drescher, president of the Screen Actors Guild, declared that "artificial intelligence poses an existential threat to creative professions" during the 2023 SAG-AFTRA strike.  Deepfakes (a portmanteau of "deep learning" and "fake") are AI-generated media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks.  Deepfakes have garnered widespread attention and concerns for their uses in deepfake celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud.  This has elicited responses from both industry and government to detect and limit their use.  Generative AI's ability to create realistic fake content has been exploited in numerous types of cybercrime, including phishing scams.  Deepfake video and audio have been used to create disinformation and fraud.  Former Google fraud czar Shuman Ghosemajumder has predicted that while deepfake videos initially created a stir in the media, they would soon become commonplace, and as a result, more dangerous.  Cybercriminals have created large language models focused on fraud, including WormGPT and FraudGPT.  In January 2023, Futurism. com broke the story that CNET had been using an undisclosed internal AI tool to write at least 77 of its stories; after the news broke, CNET posted corrections to 41 of the stories.  In April 2023, German tabloid Die Aktuelle published a fake AI-generated interview with former racing driver Michael Schumacher, who had not made any public appearances since 2013 after sustaining a brain injury in a skiing accident.  The story included two possible disclosures: the cover included the line "deceptively real", and the interview included an acknowledgement at the end that it was AI-generated.  The editor-in-chief was fired shortly thereafter amid the controversy.  In the European Union, the proposed Artificial Intelligence Act includes requirements to disclose copyrighted material used to train generative AI systems, and to label any AI-generated output as such.


Generative artificial intelligence

Concerns In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the White House in July 2023 to watermark AI-generated content.  In China, the Interim Measures for the Management of Generative AI Services introduced by the Cyberspace Administration of China regulates any public-facing generative AI.  It includes requirements to watermark generated images or videos, regulations on training data and label quality, restrictions on personal data collection, and a guideline that generative AI must "adhere to socialist core values".


Generative artificial intelligence

See_also Artificial general intelligence ��� Hypothetical human-level or stronger AI Artificial imagination ��� Artificial simulation of human imagination Artificial intelligence art ��� Machine application of knowledge of human aesthetic expressions Computational creativity ��� Multidisciplinary endeavour Generative adversarial network ��� Deep learning method Generative pre-trained transformer ��� Type of large language model Large language model ��� Neural network with billions of weights Music and artificial intelligence ��� Common subject in the International Computer Music Conference Procedural generation ��� Method in which data is created algorithmically as opposed to manually Stochastic parrot ��� Term used in machine learning


History of artificial intelligence

Deep_learning.2C_big_data_and_artificial_general_intelligence:_2011.E2.80.93present In the first decades of the 21st century, access to large amounts of data (known as "big data"), cheaper and faster computers and advanced machine learning techniques were successfully applied to many problems throughout the economy.  In fact, McKinsey Global Institute estimated in their famous paper "Big data: The next frontier for innovation, competition, and productivity" that "by 2009, nearly all sectors in the US economy had at least an average of 200 terabytes of stored data".  By 2016, the market for AI-related products, hardware, and software reached more than 8 billion dollars, and the New York Times reported that interest in AI had reached a "frenzy".  The applications of big data began to reach into other fields as well, such as training models in ecology and for various applications in economics.  Advances in deep learning (particularly deep convolutional neural networks and recurrent neural networks) drove progress and research in image and video processing, text analysis, and even speech recognition.  Deep learning is a branch of machine learning that models high level abstractions in data by using a deep graph with many processing layers.  According to the Universal approximation theorem, deep-ness isn't necessary for a neural network to be able to approximate arbitrary continuous functions.  Even so, there are many problems that are common to shallow networks (such as overfitting) that deep networks help avoid.  As such, deep neural networks are able to realistically generate much more complex models as compared to their shallow counterparts.  However, deep learning has problems of its own.  A common problem for recurrent neural networks is the vanishing gradient problem, which is where gradients passed between layers gradually shrink and literally disappear as they are rounded off to zero.  There have been many methods developed to approach this problem, such as Long short-term memory units.  State-of-the-art deep neural network architectures can sometimes even rival human accuracy in fields like computer vision, specifically on things like the MNIST database, and traffic sign recognition.  Language processing engines powered by smart search engines can easily beat humans at answering general trivia questions (such as IBM Watson), and recent developments in deep learning have produced astounding results in competing with humans, in things like Go, and Doom (which, being a first-person shooter game, has sparked some controversy).  Big data refers to a collection of data that cannot be captured, managed, and processed by conventional software tools within a certain time frame.  It is a massive amount of decision-making, insight, and process optimization capabilities that require new processing models.  In the Big Data Era written by Victor Meyer Schonberg and Kenneth Cooke, big data means that instead of random analysis (sample survey), all data is used for analysis.  The 5V characteristics of big data (proposed by IBM): Volume, Velocity, Variety, Value, Veracity.  The strategic significance of big data technology is not to master huge data information, but to specialize in these meaningful data.


History of artificial intelligence

Deep_learning.2C_big_data_and_artificial_general_intelligence:_2011.E2.80.93present In other words, if big data is likened to an industry, the key to realizing profitability in this industry is to increase the "process capability" of the data and realize the "value added" of the data through "processing".  Foundation models, which are large language models trained on vast quantities of unlabeled data that can be adapted to a wide range of downstream tasks, began to be developed in 2018.  Models such as GPT-3 released by OpenAI in 2020, and Gato released by DeepMind in 2022, have been described as important achievements of machine learning.  In 2023, Microsoft Research tested the GPT-4 large language model with a large variety of tasks, and concluded that "it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system".


Attention (machine learning)

Attention (machine learning) Machine learning-based attention is a mechanism mimicking cognitive attention. It calculates "soft" weights for each word, more precisely for its embedding, in the context window. It can do it either in parallel (such as in transformers) or sequentially (such as recursive neural networks). "Soft" weights can change during each runtime, in contrast to "hard" weights, which are (pre-)trained and fine-tuned and remain frozen afterwards. Attention was developed to address the weaknesses of RNNs, where words in a sentence are slowly processed one at a time. RNNs favors more recent words at the end of a sentence while earlier words fade away in volatile neural activations. Attention gives all words equal access to any part of a sentence in a faster parallel scheme and no longer suffers the wait time of serial processing. Earlier uses attached this mechanism to a serial RNNs language translation system (below), but later uses in Transformers large language models removed the RNN and relied heavily on the faster parallel Attention scheme.