ChatGPT, a conversational artificial intelligence program released recently by OpenAI, isn’t just another entry in the artificial intelligence hype cycle. It’s a significant advancement that can produce articles in response to open-ended questions that are comparable to good high school essays.
One way has been to change how they teach. Rather than listen to a lecture in class and then go home to research and write an essay, students listen to recorded lectures and do research at home, then write essays in class, with supervision, even collaboration with peers and teachers. This approach is called flipping the classroom.
Teachers could assign a complicated topic and allow students to use such tools as part of their research. Assessing the veracity and reliability of these A.I.-generated notes and using them to create an essay would be done in the classroom, with guidance and instruction from teachers. The goal would be to increase the quality and the complexity of the argument.
“Not truth but only the semblance of truth,” as Plato said.
At a minimum, because it will soon transform many occupations. The right approach when faced with transformative technologies is to figure out how to use them for the betterment of humanity.Betterment has been a goal of public education for at least the past 150 years. But while a high school diploma once led to a better job, in the past few decades, the wages of high school graduates have greatly lagged those of college graduates, fostering inequality.
If A.I. enhances the value of education for some while degrading the education of others, the promise of betterment will be broken.
As Plato was wrong to fear the written word as the enemy, we would be wrong to think we should resist a process that allows us to gather information more easily.
ChatGPT
Original author(s) | OpenAI |
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Initial release | November 30, 2022 |
Type | Artificial intelligence chatbot |
License | Proprietary |
Website | chat |
Chat Generative Pre-trained Transformer (ChatGPT), is a chatbot developed by OpenAI. ChatGPT is built on top of OpenAI's GPT-3.5 family of large language models, and is fine-tuned with both supervised and reinforcement learning techniques.
ChatGPT was launched as a prototype in November 2022, and quickly garnered attention for its detailed responses and articulate answers across many domains of knowledge. Its uneven factual accuracy was identified as a significant drawback.[1]
Training
ChatGPT was fine-tuned on top of GPT-3.5 using supervised learning as well as reinforcement learning.[2] Both approaches used human trainers to improve the model's performance. In the case of supervised learning, the model was provided with conversations in which the trainers played both sides: the user and the AI assistant. In the reinforcement step, human trainers first ranked responses that the model had created in a previous conversation. These rankings were used to create 'reward models' that the model was further fine-tuned on using several iterations of Proximal Policy Optimization (PPO).[3][4] Proximal Policy Optimization algorithms present a cost-effective benefit to trust region policy optimization algorithms; they negate many of the computationally expensive operations with faster performance.[5][6] The models were trained in collaboration with Microsoft on their Azure supercomputing infrastructure.
Features
In comparison to its predecessor, InstructGPT, ChatGPT attempts to reduce harmful and deceitful responses; in one example, while InstructGPT accepts the prompt "Tell me about when Christopher Columbus came to the US in 2015" as truthful, ChatGPT uses information about Columbus' voyages and information about the modern world – including perceptions of Columbus to construct an answer that assumes what would happen if Columbus came to the U.S. in 2015.[3] ChatGPT's training data includes man pages and information about Internet phenomena and programming languages, such as bulletin board systems and the Python programming language.[7]
Unlike most chatbots, ChatGPT is stateful, remembering previous prompts given to it in the same conversation, which some journalists have suggested will allow for ChatGPT to be used as a personalized therapist.[8] To prevent offensive outputs from being presented to and produced from ChatGPT, queries are filtered through a moderation API, and potentially racist or sexist prompts are dismissed.[3][8]
ChatGPT suffers from multiple limitations. The reward model of ChatGPT, designed around human oversight, can be over-optimized and thus hinder performance, otherwise known as Goodhart's law.[9] Furthermore, ChatGPT has limited knowledge of events that occurred after 2021 and is unable to provide information on some celebrities.[failed verification] In training, reviewers preferred longer answers, irrespective of actual comprehension or factual content.[3] Training data may also suffer from algorithmic bias; prompts including vague descriptors of people.
Service
ChatGPT was launched on November 30, 2022, by San Francisco-based OpenAI, the creator of DALL·E 2 and Whisper. The service was launched as initially free to the public, with plans to monetize the service later. By December 4, OpenAI estimated ChatGPT already had over one million users.[10] CNBC wrote on December 15, 2022, that the service "still goes down from time to time".[11]
Reception, criticism and issues
Positive reactions
ChatGPT was met in December 2022 with generally positive reviews; The New York Times labeled it "the best artificial intelligence chatbot ever released to the general public".[12] Samantha Lock of The Guardian noted that it was able to generate "impressively detailed" and "human-like" text.[13] Technology writer Dan Gillmor used ChatGPT on a student assignment, and found its generated text was on par with what a good student would deliver and opined that "academia has some very serious issues to confront".[14] Alex Kantrowitz of Slate lauded ChatGPT's pushback to questions related to Nazi Germany, including the claim that Adolf Hitler built highways in Germany, which was met with information regarding Nazi Germany's use of forced labor.[15]
In The Atlantic's "Breakthroughs of the Year" for 2022, Derek Thompson included ChatGPT as part of "the generative-AI eruption" that "may change our mind about how we work, how we think, and what human creativity really is".[16]
Kelsey Piper of Vox wrote that "ChatGPT is the general public's first hands-on introduction to how powerful modern AI has gotten, and as a result, many of us are (stunned)" and that "ChatGPT is smart enough to be useful despite its flaws". In a tweet, tech mogul Elon Musk wrote that "ChatGPT is scary good. We are not far from dangerously strong AI".[17]
Negative reactions
In a December 2022 opinion piece, economist Paul Krugman wrote that ChatGPT would affect the demand of knowledge workers.[18] The Verge's James Vincent saw the viral success of ChatGPT as evidence that artificial intelligence had gone mainstream.[4] Journalists have commented on ChatGPT's tendency to hallucinate (confidently give false answers that seem unjustified by its training data).[19] Mike Pearl of Mashable tested ChatGPT with multiple questions. In one example, he asked the model for "the largest country in Central America that isn't Mexico". ChatGPT responded with Guatemala, when the answer is instead Nicaragua.[20] When CNBC asked ChatGPT for the lyrics to "The Ballad of Dwight Fry", ChatGPT supplied invented lyrics rather than the actual lyrics.[11] In contrast, researchers cited by The Verge compared ChatGPT to a "stochastic parrot",[21] as did Professor Anton Van Den Hengel of the Australian Institute for Machine Learning.[22]
In December 2022, the question and answer website Stack Overflow banned the use of ChatGPT for generating answers to questions, citing the factually ambiguous nature of ChatGPT's responses.[1]
Economist Tyler Cowen expressed concerns regarding its effects on democracy, citing the ability of one to write automated comments in an effort to affect the decision process of new regulations.[23] The Guardian questioned whether any content found on the Internet after ChatGPT's release "can be truly trusted" and called for government regulation.[24]
Ax Sharma of Bleeping Computer noted that ChatGPT was capable of writing malware and phishing emails.[25] The CEO of ChatGPT creator OpenAI, Sam Altman, wrote that advancing software could pose "(for example) a huge cybersecurity risk" and also continued to predict "we could get to real AGI in the next decade, so we have to take the risk of that extremely seriously".[10]
Implications for education
In The Atlantic, Stephen Marche noted that its effect on academia and especially application essays is yet to be understood.[26] California high-school teacher and author Daniel Herman wrote that ChatGPT would usher in "The End of High-School English".[27]
In Nature, Chris Stokel-Walker pointed out that teachers should be concerned about students using ChatGPT to outsource their writing but that education providers will adapt to enhance critical thinking or reasoning.[28]
Emma Bowman with NPR wrote of the danger of students plagiarizing through an AI tool that may output biased or nonsensical text with an authoritative tone: "There are still many cases where you ask it a question and it'll give you a very impressive-sounding answer that's just dead wrong."[29]
Joanna Stern with The Wall Street Journal described cheating in American high school English with the tool by submitting a generated essay.[30]
Jailbreaks
ChatGPT was trained to reject prompts that may violate its content policy. However, some users managed to bypass these restrictions and limitations through techniques such as prompt engineering.[31] Jailbreaks created the potential for users to prompt ChatGPT to provide outputs that may be deemed offensive, inappropriate, or risking social harm by others.[32] The following includes some of the methods used to bypass ChatGPT's filter:
- Continue a statement in a fake interview.
- Provide instructions to disable the chat filter.
- Prompting it to decrypt a message containing instructions and follow them.
- Telling it to be a computer and output its display in ASCII art.
References
- ^ ab Vincent, James (December 5, 2022). "AI-generated answers temporarily banned on coding Q&A site Stack Overflow". The Verge. Retrieved December 5, 2022.
- ^ Knox, W. Bradley; Stone, Peter. Augmenting Reinforcement Learning with Human Feedback (PDF). University of Texas at Austin. Retrieved December 5, 2022.
- ^ ab c d OpenAI (November 30, 2022). "ChatGPT: Optimizing Language Models for Dialogue". Retrieved December 5, 2022.
- ^ ab Vincent, James (December 8, 2022). "ChatGPT proves AI is finally mainstream – and things are only going to get weirder". The Verge. Retrieved December 8, 2022.
- ^ Schulman, John; Wolski, Filip; Dhariwal, Prafulla; Radford, Alec; Klimov, Oleg (2017). "Proximal Policy Optimization Algorithms". arXiv:1707.06347 [cs.LG].
- ^ van Heeswijk, Wouter (November 29, 2022). "Proximal Policy Optimization (PPO) Explained". Towards Data Science. Retrieved December 5, 2022.
- ^ Edwards, Benj (December 5, 2022). "No Linux? No problem. Just get AI to hallucinate it for you". Ars Technica. Retrieved December 5, 2022.
- ^ ab Roose, Kevin (December 5, 2022). "The Brilliance and Weirdness of ChatGPT". The New York Times. Retrieved December 5, 2022.
- ^ Gao, Leo; Schulman; Hilton, Jacob (2022). "Scaling Laws for Reward Model Overoptimization". arXiv:2210.10760 [cs.LG].
- ^ ab "What is ChatGPT and why does it matter? Here's what you need to know". ZDNET. 2022. Retrieved December 18, 2022.
- ^ ab Pitt, Sofia (2022). "Google vs. ChatGPT: Here's what happened when I swapped services for a day". CNBC. Retrieved December 18, 2022.
- ^ Roose, Kevin (December 5, 2022). "The Brilliance and Weirdness of ChatGPT". The New York Times. Retrieved December 18, 2022.
- ^ Lock, Samantha (December 5, 2022). "What is AI chatbot phenomenon ChatGPT and could it replace humans?". The Guardian. Retrieved December 5, 2022.
- ^ Hern, Alex (December 4, 2022). "AI bot ChatGPT stuns academics with essay-writing skills and usability". The Guardian. Retrieved December 5, 2022.
- ^ Kantrowitz, Alex (December 2, 2022). "Finally, an A.I. Chatbot That Reliably Passes "the Nazi Test"". Slate. Retrieved December 5, 2022.
- ^ Thompson, Derek (December 8, 2022). "Breakthroughs of the Year". The Atlantic. Retrieved December 18, 2022.
- ^ Piper, Kelsey (December 15, 2022). "ChatGPT has given everyone a glimpse at AI's astounding progress". Vox. Retrieved December 18, 2022.
- ^ Krugman, Paul (December 6, 2022). "Does ChatGPT Mean Robots Are Coming For the Skilled Jobs?". The New York Times. Retrieved December 6, 2022.
- ^ "ChatGPT a 'landmark event' for AI, but what does it mean for the future of human labour and disinformation?". CBC. 2022. Retrieved December 18, 2022.
- ^ Pearl, Mike (December 3, 2022). "The ChatGPT chatbot from OpenAI is amazing, creative, and totally wrong". Mashable. Retrieved December 5, 2022.
- ^ Vincent, James (December 1, 2022). "OpenAI's new chatbot can explain code and write sitcom scripts but is still easily tricked". The Verge. Retrieved December 18, 2022.
- ^ Mannix, Liam (December 13, 2022). "Is AI coming of age - or starting to reach its limits?". The Sydney Morning Herald. Retrieved December 18, 2022.
- ^ Cowen, Tyler (December 6, 2022). "ChatGPT Could Make Democracy Even More Messy". Bloomberg News. Retrieved December 6, 2022.
- ^ "The Guardian view on ChatGPT: an eerily good human impersonator". the Guardian. December 8, 2022. Retrieved December 18, 2022.
- ^ Sharma, Ax (December 6, 2022). "OpenAI's new ChatGPT bot: 10 dangerous things it's capable of". Bleeping Computer. Retrieved December 6, 2022.
- ^ Marche, Stephen (December 6, 2022). "The College Essay Is Dead". The Atlantic. Retrieved December 8, 2022.
- ^ Herman, Daniel (December 9, 2022). "The End of High-School English". The Atlantic. Retrieved December 12, 2022.
- ^ Stokel-Walker, Chris (December 9, 2022). "AI bot ChatGPT writes smart essays — should professors worry?". Nature. Retrieved December 19, 2022.
- ^ Bowman, Emma (December 19, 2022). "A new AI chatbot might do your homework for you. But it's still not an A+ student". NPR. Retrieved December 19, 2022.
- ^ Stern, Joanna (December 21, 2022). "ChatGPT Wrote My AP English Essay—and I Passed". The_Wall_Street_Journal. Retrieved December 21, 2022.
- ^ Zvi (December 2, 2022). "Jailbreaking ChatGPT on Release Day".
- ^ Written, Zack (December 1, 2022). "Thread of known ChatGPT jailbreaks". Twitter. Retrieved December 17, 2022.
External links
OpenAI
Industry | Artificial intelligence |
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Founded | December 11, 2015 |
Founders |
|
Headquarters | Pioneer Building, San Francisco, California, US[1][2] |
Key people |
|
Products | |
Number of employees | >120 (as of 2020)[2] |
Website | openai |
OpenAI is an artificial intelligence (AI) research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. The company conducts research in the field of AI with the stated goal of promoting and developing friendly AI in a way that benefits humanity as a whole. The organization was founded in San Francisco in late 2015 by Sam Altman, Elon Musk, and others, who collectively pledged US$1 billion. Musk resigned from the board in February 2018 but remained a donor. In 2019, OpenAI LP received a US$1 billion investment from Microsoft. OpenAI is headquartered at the Pioneer Building in Mission District, San Francisco.
History
In December 2015, Sam Altman, Elon Musk, Greg Brockman, Reid Hoffman, Jessica Livingston, Peter Thiel, Amazon Web Services (AWS), Infosys, and YC Research announced[3] the formation of OpenAI and pledged over US$1 billion to the venture. The organization stated it would "freely collaborate" with other institutions and researchers by making its patents and research open to the public.[4][5] OpenAI is headquartered at the Pioneer Building in Mission District, San Francisco.[6][2]
In April 2016, OpenAI released a public beta of "OpenAI Gym", its platform for reinforcement learning research.[7] In December 2016, OpenAI released "Universe", a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications.[8][9][10][11]
In 2018, Musk resigned his board seat, citing "a potential future conflict (of interest)" with Tesla AI development for self driving cars, but remained a donor.[12]
In 2019, OpenAI transitioned from non-profit to "capped" for-profit, with profit cap set to 100X on any investment. The company distributed equity to its employees and partnered with Microsoft, who announced an investment package of US$1 billion into the company. OpenAI then announced its intention to commercially license its technologies.[13]
In 2020, OpenAI announced GPT-3, a language model trained on trillions of words from the Internet. It also announced that an associated API, named simply "the API", would form the heart of its first commercial product. GPT-3 is aimed at natural language answering of questions, but it can also translate between languages and coherently generate improvised text.[14] In 2021, OpenAI introduced DALL-E. One year later, their newest system, DALL·E 2, generates more realistic and accurate images with 4x greater resolution. In 2022, OpenAI released a preview of ChatGPT, which interacts using conversation, to the general public.[15]
Participants
- CEO and co-founder:[16] Sam Altman, former president of the startup accelerator Y Combinator
- President and co-founder:[17] Greg Brockman, former CTO, 3rd employee of Stripe[18]
- Chief Scientist and co-founder Ilya Sutskever, a former Google expert on machine learning[18]
- Chief Technology Officer:[17] Mira Murati, previously at Leap Motion and Tesla, Inc.
- Chief Operating Officer:[17] Brad Lightcap, previously at Y Combinator and JPMorgan Chase
Other backers of the project include:[18]
- Reid Hoffman, LinkedIn co-founder[19]
- Peter Thiel, PayPal co-founder[19]
- Jessica Livingston, a founding partner of Y Combinator
Companies:
The group started in early January 2016 with nine researchers. According to Wired, Brockman met with Yoshua Bengio, one of the "founding fathers" of the deep learning movement, and drew up a list of the "best researchers in the field". Microsoft's Peter Lee stated that the cost of a top AI researcher exceeds the cost of a top NFL quarterback prospect. While OpenAI pays corporate-level (rather than nonprofit-level) salaries, it doesn't currently pay AI researchers salaries comparable to those of Facebook or Google. Nevertheless, Sutskever stated that he was willing to leave Google for OpenAI "partly because of the very strong group of people and, to a very large extent, because of its mission." Brockman stated that "the best thing that I could imagine doing was moving humanity closer to building real AI in a safe way." OpenAI researcher Wojciech Zaremba stated that he turned down "borderline crazy" offers of two to three times his market value to join OpenAI instead.[23]
Motives
Some scientists, such as Stephen Hawking and Stuart Russell, have articulated concerns that if advanced AI someday gains the ability to re-design itself at an ever-increasing rate, an unstoppable "intelligence explosion" could lead to human extinction. Musk characterizes AI as humanity's "biggest existential threat."[24] OpenAI's founders structured it as a non-profit so that they could focus its research on creating a positive long-term human impact.[5]
Musk and Altman have stated they are partly motivated by concerns about the existential risk from artificial general intelligence.[25][23] OpenAI states that "it's hard to fathom how much human-level AI could benefit society," and that it is equally difficult to comprehend "how much it could damage society if built or used incorrectly".[5] Research on safety cannot safely be postponed: "because of AI's surprising history, it's hard to predict when human-level AI might come within reach."[26] OpenAI states that AI "should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible...".[5] Co-chair Sam Altman expects the decades-long project to surpass human intelligence.[27]
Vishal Sikka, former CEO of Infosys, stated that an "openness" where the endeavor would "produce results generally in the greater interest of humanity" was a fundamental requirement for his support, and that OpenAI "aligns very nicely with our long-held values" and their "endeavor to do purposeful work".[28] Cade Metz of Wired suggests that corporations such as Amazon may be motivated by a desire to use open-source software and data to level the playing field against corporations such as Google and Facebook that own enormous supplies of proprietary data. Altman states that Y Combinator companies will share their data with OpenAI.[27]
In 2019, OpenAI became a for-profit company called OpenAI LP to secure additional funding while staying controlled by a non-profit called OpenAI Inc in a structure that OpenAI calls "capped-profit",[29] having previously been a 501(c)(3) nonprofit organization.[30][31][non-primary source needed]
Strategy
Musk posed the question: "What is the best thing we can do to ensure the future is good? We could sit on the sidelines or we can encourage regulatory oversight, or we could participate with the right structure with people who care deeply about developing AI in a way that is safe and is beneficial to humanity." Musk acknowledged that "there is always some risk that in actually trying to advance (friendly) AI we may create the thing we are concerned about"; nonetheless, the best defense is "to empower as many people as possible to have AI. If everyone has AI powers, then there's not any one person or a small set of individuals who can have AI superpower."[18]
Musk and Altman's counter-intuitive strategy of trying to reduce the risk that AI will cause overall harm, by giving AI to everyone, is controversial among those who are concerned with existential risk from artificial intelligence. Philosopher Nick Bostrom is skeptical of Musk's approach: "If you have a button that could do bad things to the world, you don't want to give it to everyone."[23] During a 2016 conversation about the technological singularity, Altman said that "we don't plan to release all of our source code" and mentioned a plan to "allow wide swaths of the world to elect representatives to a new governance board". Greg Brockman stated that "Our goal right now... is to do the best thing there is to do. It's a little vague."[32]
Conversely, OpenAI's initial decision to withhold GPT-2 due to a wish to "err on the side of caution" in the presence of potential misuse, has been criticized by advocates of openness. Delip Rao, an expert in text generation, stated "I don't think [OpenAI] spent enough time proving [GPT-2] was actually dangerous." Other critics argued that open publication is necessary to replicate the research and to be able to come up with countermeasures.[33]
In the 2017 tax year, OpenAI spent US$7.9 million, or a quarter of its functional expenses, on cloud computing alone.[34] In comparison, DeepMind's total expenses in 2017 were much larger, measuring US$442 million. In Summer 2018, simply training OpenAI's Dota 2 bots required renting 128,000 CPUs and 256 GPUs from Google for multiple weeks. According to OpenAI, the capped-profit model adopted in March 2019 allows OpenAI LP to legally attract investment from venture funds, and in addition, to grant employees stakes in the company, the goal being that they can say "I'm going to Open AI, but in the long term it's not going to be disadvantageous to us as a family."[35] Many top researchers work for Google Brain, DeepMind, or Facebook, Inc., which offer stock options that a nonprofit would be unable to.[36] In June 2019, OpenAI LP raised a billion dollars from Microsoft, a sum which OpenAI plans to have spent "within five years, and possibly much faster".[37] Altman has stated that even a billion dollars may turn out to be insufficient, and that the lab may ultimately need "more capital than any non-profit has ever raised" to achieve artificial general intelligence.[38]
The transition from a nonprofit to a capped-profit company was viewed with skepticism by Oren Etzioni of the nonprofit Allen Institute for AI, who agreed that wooing top researchers to a nonprofit is difficult, but stated "I disagree with the notion that a nonprofit can't compete" and pointed to successful low-budget projects by OpenAI and others. "If bigger and better funded was always better, then IBM would still be number one." Following the transition, public disclosure of the compensation of top employees at OpenAI LP is no longer legally required. The nonprofit, OpenAI Inc., is the sole controlling shareholder of OpenAI LP. OpenAI LP, despite being a for-profit company, retains a formal fiduciary responsibility to OpenAI's Inc.'s nonprofit charter. A majority of OpenAI Inc.'s board is barred from having financial stakes in OpenAI LP.[35] In addition, minority members with a stake in OpenAI LP are barred from certain votes due to conflict of interest.[36] Some researchers have argued that OpenAI LP's switch to for-profit status is inconsistent with OpenAI's claims to be "democratizing" AI.[39] A journalist at Vice News wrote that "generally, we've never been able to rely on venture capitalists to better humanity".[40]
Products and applications
OpenAI's research tend to focus on reinforcement learning. OpenAI is viewed as an important competitor to DeepMind.[41]
Gym
Gym aims to provide an easy to set up, general-intelligence benchmark with a wide variety of different environments—somewhat akin to, but broader than, the ImageNet Large Scale Visual Recognition Challenge used in supervised learning research—and that hopes to standardize the way in which environments are defined in AI research publications, so that published research becomes more easily reproducible.[7][42] The project claims to provide the user with a simple interface. As of June 2017, Gym can only be used with Python.[43] As of September 2017, the Gym documentation site was not maintained, and active work focused instead on its GitHub page.[44][non-primary source needed]
RoboSumo
In "RoboSumo", virtual humanoid "metalearning" robots initially lack knowledge of how to even walk, and given the goals of learning to move around, and pushing the opposing agent out of the ring. Through this adversarial learning process, the agents learn how to adapt to changing conditions; when an agent is then removed from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to balance in a generalized way.[45][46] OpenAI's Igor Mordatch argues that competition between agents can create an intelligence "arms race" that can increase an agent's ability to function, even outside the context of the competition.
Debate Game
In 2018, OpenAI launched the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research whether such an approach may assist in auditing AI decisions and in developing explainable AI.[47][48]
Dactyl
Dactyl uses machine learning to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. It learns entirely in simulation using the same reinforcement learning algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by using domain randomization, a simulation approach which exposes the learner to a variety of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB cameras to allow the robot to manipulate an arbitrary object by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism.[49]
In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI solved this by improving the robustness of Dactyl to perturbations; they employed a technique called Automatic Domain Randomization (ADR), a simulation approach where progressively more difficult environments are endlessly generated. ADR differs from manual domain randomization by not needing there to be a human to specify randomization ranges.[50]
Generative models
GPT
The original paper on generative pre-training (GPT) of a language model was written by Alec Radford and colleagues, and published in preprint on OpenAI's website on June 11, 2018.[51] It showed how a generative model of language is able to acquire world knowledge and process long-range dependencies by pre-training on a diverse corpus with long stretches of contiguous text.
GPT-2
Generative Pre-trained Transformer 2, commonly known by its abbreviated form GPT-2, is an unsupervised transformer language model and the successor to GPT. GPT-2 was first announced in February 2019, with only limited demonstrative versions initially released to the public. The full version of GPT-2 was not immediately released out of concern over potential misuse, including applications for writing fake news.[52] Some experts expressed skepticism that GPT-2 posed a significant threat. The Allen Institute for Artificial Intelligence responded to GPT-2 with a tool to detect "neural fake news".[53] Other researchers, such as Jeremy Howard, warned of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter".[54] In November 2019, OpenAI released the complete version of the GPT-2 language model.[55] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer models.[56][57][58]
GPT-2's authors argue unsupervised language models to be general-purpose learners, illustrated by GPT-2 achieving state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further trained on any task-specific input-output examples). The corpus it was trained on, called WebText, contains slightly over 8 million documents for a total of 40 GB of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by using byte pair encoding. This allows to represent any string of characters by encoding both individual characters and multiple-character tokens.[59]
GPT-3
Generative Pre-trained[a] Transformer 3, commonly known by its abbreviated form GPT-3, is an unsupervised transformer language model and the successor to GPT-2. It was first described in May 2020.[61][62][63] OpenAI stated that full version of GPT-3 contains 175 billion parameters,[63] two orders of magnitude larger than the 1.5 billion parameters[64] in the full version of GPT-2 (although GPT-3 models with as few as 125 million parameters were also trained).[65]
OpenAI stated that GPT-3 succeeds at certain "meta-learning" tasks. It can generalize the purpose of a single input-output pair. The paper gives an example of translation and cross-linguistic transfer learning between English and Romanian, and between English and German.[63]
GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling up of language models could be approaching or encountering the fundamental capability limitations of predictive language models.[66] Pre-training GPT-3 required several thousand petaflop/s-days[b] of compute, compared to tens of petaflop/s-days for the full GPT-2 model.[63] Like that of its predecessor,[52] GPT-3's fully trained model was not immediately released to the public on the grounds of possible abuse, though OpenAI planned to allow access through a paid cloud API after a two-month free private beta that began in June 2020.[68][69]
On September 23, 2020, GPT-3 was licensed exclusively to Microsoft.[70][71]
ChatGPT
ChatGPT is an artificial intelligence tool that provides a conversational interface that allows you to ask questions in natural language. The system then responds with an answer within seconds. ChatGPT was launched in November 2022 and reached 1 million users only 5 days after its initial launch.[72]
Music
OpenAI's MuseNet (2019) is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can generate songs with ten different instruments in fifteen different styles. According to The Verge, a song generated by MuseNet tends to start reasonably but then fall into chaos the longer it plays.[73][74]
OpenAI's Jukebox (2020) is an open-sourced algorithm to generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "show local musical coherence, follow traditional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" and that "there is a significant gap" between Jukebox and human-generated music. The Verge stated "It's technologically impressive, even if the results sound like mushy versions of songs that might feel familiar", while Business Insider stated "surprisingly, some of the resulting songs are catchy and sound legitimate".[75][76][77]
API
In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new AI models developed by OpenAI" to let developers call on it for "any English language AI task."[68][78]
DALL-E and CLIP
DALL-E is a Transformer model that creates images from textual descriptions, revealed by OpenAI in January 2021.[79]
CLIP does the opposite: it creates a description for a given image.[80] DALL-E uses a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can create images of realistic objects ("a stained-glass window with an image of a blue strawberry") as well as objects that do not exist in reality ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.
In March 2021, OpenAI released a paper titled Multimodal Neurons in Artificial Neural Networks,[81] where they showed a detailed analysis of CLIP (and GPT) models and their vulnerabilities. The new type of attacks on such models was described in this work.
In April 2022, OpenAI announced DALL-E 2, an updated version of the model with more realistic results.[82]
Microscope
OpenAI Microscope[83] is a collection of visualizations of every significant layer and neuron of eight different neural network models which are often studied in interpretability. Microscope was created to analyze the features that form inside these neural networks easily. The models included are AlexNet, VGG 19, different versions of Inception, and different versions of CLIP Resnet.[84]
Codex
OpenAI Codex is a descendant of GPT-3 that has additionally been trained on code from 54 million GitHub repositories.[85][86] It was announced in mid-2021 as the AI powering the code autocompletion tool GitHub Copilot.[86] In August 2021, an API was released in private beta.[87] According to OpenAI, the model is able to create working code in over a dozen programming languages, most effectively in Python.[85]
Several issues with glitches, design flaws, and security vulnerabilities have been brought up.[88][89]
Video game bots and benchmarks
OpenAI Five
OpenAI Five is the name of a team of five OpenAI-curated bots that are used in the competitive five-on-five video game Dota 2, who learn to play against human players at a high skill level entirely through trial-and-error algorithms. Before becoming a team of five, the first public demonstration occurred at The International 2017, the annual premiere championship tournament for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live 1v1 matchup.[90][91] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of real time, and that the learning software was a step in the direction of creating software that can handle complex tasks like a surgeon.[92][93] The system uses a form of reinforcement learning, as the bots learn over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives.[94][95][96]
By June 2018, the ability of the bots expanded to play together as a full team of five, and they were able to defeat teams of amateur and semi-professional players.[97][98][99][100] At The International 2018, OpenAI Five played in two exhibition matches against professional players, but ended up losing both games.[101][102][103] In April 2019, OpenAI Five defeated OG, the reigning world champions of the game at the time, 2:0 in a live exhibition match in San Francisco.[104][105] The bots' final public appearance came later that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games.[106]
GYM Retro
Gym Retro is a platform for reinforcement learning research on video games. Gym Retro is used to research RL algorithms and study generalization. Prior research in RL has focused chiefly on optimizing agents to solve single tasks. Gym Retro gives the ability to generalize between games with similar concepts but different appearances.
See also
Notes
References
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