Timeline of OpenAI
This is a timeline of OpenAI. OpenAI is a non-profit safety-conscious artificial intelligence capabilities company.
- 1 Sample questions
- 2 Big picture
- 3 Numerical and visual data
- 4 Full timeline
- 5 Meta information on the timeline
- 6 See also
- 7 External links
- 8 References
The following are some interesting questions that can be answered by reading this timeline:
- What are some significant events previous to the creation of OpenAI?
- Sort the full timeline by "Event type" and look for the group of rows with value "Prelude".
- You will see some events involving key people like Elon Musk and Sam Altman, that would eventually lead to the creation of OpenAI.
- What are the various papers and posts published by OpenAI?
- Sort the full timeline by "Event type" and look for the group of rows with value "Publication".
- You will see mostly papers submitted to the ArXiv by OpenAI-affiliated researchers. Also blog posts.
- What are the several toolkits, implementations, algorithms, systems and software in general released by OpenAI?
- Sort the full timeline by "Event type" and look for the group of rows with value "Software release".
- You will see a variety of releases, some of them open-sourced.
- What are some other significant events describing advances in research?
- Sort the full timeline by "Event type" and look for the group of rows with value "Research progress".
- You will see some discoveries and other significant results obtained by OpenAI.
- What is the staff composition and what are the different roles in the organization?
- Sort the full timeline by "Event type" and look for the group of rows with value "Staff".
- You will see the names of incorporated people and their roles.
- What are the several partnerships between OpenAI and other organizations?
- What are some significant fundings granted to OpenAI by donors?
- Sort the full timeline by "Event type" and look for the group of rows with value "Donation".
- You will see names like the Open Philanthropy Project, and Nvidia, among others.
- What are some notable events hosted by OpenAI?
- Sort the full timeline by "Event type" and look for the group of rows with value "Event hosting".
- What are some notable publications by third parties about OpenAI?
- Sort the full timeline by "Event type" and look for the group of rows with value "Coverage".
|Time period||Development summary||More details|
|2014–2015||Background||Nick Bostrom's book Superintelligence: Paths, Dangers, Strategies, about the dangers of superhuman machine intelligence, is published. Soon after the book's publication, Elon Musk and Sam Altman, the two people who would become co-chairs and initial donors of OpenAI, publicly state their concern of superhuman machine intelligence.|
|2015||Establishment||OpenAI is founded as a nonprofit and begins producing research.|
|2019||Reorganization||OpenAI shifts from nonprofit to ‘capped-profit’ with the purpose to attract capital.|
Numerical and visual data
The following table summarizes per-year mentions on Google Scholar as of August 15, 2021.
The chart below shows Google Trends data for OpenAI (Artificial intelligence company), from November 2015 to April 2021, when the screenshot was taken. Interest is also ranked by country and displayed on world map, with China standing out. This correlates with that country emerging as one of the two AI superpowers.
The comparative chart below shows Google Trends data for OpenAI (Artificial intelligence company) and DeepMind (Subsidiary), from November 2015 to April 2021, when the screenshot was taken. Interest is also ranked by country and displayed on world map.
The comparative chart below shows Google Trends data for OpenAI (Artificial intelligence company), The Boring Company (Company) and Neuralink (Company), from November 2015 to April 2021, when the screenshot was taken. Interest is also ranked by country and displayed on world map.
Google Ngram Viewer
The chart below shows Google Ngram Viewer data for OpenAI, from 2000 to 2019.
The chart below shows pageviews of the English Wikipedia article OpenAI, from July 2015 to March 2021.
The comparative chart below shows pageviews on desktop of the English Wikipedia articles OpenAI and DeepMind, from July 2015 to March 2021.
|Year||Month and date||Domain||Event type||Details|
|2014||October 22–24||Prelude||During an interview at the AeroAstro Centennial Symposium, Elon Musk, who would later become co-chair of OpenAI, calls artificial intelligence humanity's "biggest existential threat".|
|2015||February 25||Prelude||Sam Altman, president of Y Combinator who would later become a co-chair of OpenAI, publishes a blog post in which he writes that the development of superhuman AI is "probably the greatest threat to the continued existence of humanity".|
|2015||May 6||Prelude||Greg Brockman, who would become CTO of OpenAI, announces in a blog post that he is leaving his role as CTO of Stripe. In the post, in the section "What comes next" he writes "I haven't decided exactly what I'll be building (feel free to ping if you want to chat)".|
|2015||June||Prelude||Sam Altman and Greg Brockman have a conversation about next steps for Brockman.|
|2015||June 4||Prelude||At Airbnb's Open Air 2015 conference, Sam Altman, president of Y Combinator who would later become a co-chair of OpenAI, states his concern for advanced artificial intelligence and shares that he recently invested in a company doing AI safety research.|
|2015||July (approximate)||Prelude||Sam Altman sets up a dinner in Menlo Park, California to talk about starting an organization to do AI research. Attendees include Greg Brockman, Dario Amodei, Chris Olah, Paul Christiano, Ilya Sutskever, and Elon Musk.|
|2015||December 11||Creation||OpenAI is announced to the public. (The news articles from this period make it sound like OpenAI launched sometime after this date.) Co-founders include Wojciech Zaremba,|
|2015||December||Coverage||The article "OpenAI" is created on Wikipedia.|
|2015||December||Team||OpenAI announces Y Combinator founding partner Jessica Livingston as one of its financial backers.|
|2016||January||Team||Ilya Sutskever joins OpenAI as Research Director.|
|2016||January 9||Education||The OpenAI research team does an AMA ("ask me anything") on r/MachineLearning, the subreddit dedicated to machine learning.|
|2016||February 25||Optimization||Publication||"Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks", a paper on optimization, is first submitted to the ArXiv. The paper presents weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction.|
|2016||March 31||Team||A blog post from this day announces that Ian Goodfellow has joined OpenAI. Previously, Goodfellow worked as Senior Research Scientist at Google.|
|2016||April 26||Team||A blog post from this day announces that Pieter Abbeel has joined OpenAI.|
|2016||April 27||Software release||The public beta of OpenAI Gym, an open source toolkit that provides environments to test AI bots, is released.|
|2016||May 25||Safety||Publication||"Adversarial Training Methods for Semi-Supervised Text Classification" is submitted to the ArXiv. The paper proposes a method that achieves better results on multiple benchmark semi-supervised and purely supervised tasks.|
|2016||May 31||Generative models||Publication||"VIME: Variational Information Maximizing Exploration", a paper on generative models, is submitted to the ArXiv. The paper introduces Variational Information Maximizing Exploration (VIME), an exploration strategy based on maximization of information gain about the agent's belief of environment dynamics.|
|2016||June 5||Reinforcement learning||Publication||"OpenAI Gym", a paper on reinforcement learning, is submitted to the ArXiv. It presents OpenAI Gym as a toolkit for reinforcement learning research. OpenAI Gym is considered by some as "a huge opportunity for speeding up the progress in the creation of better reinforcement algorithms, since it provides an easy way of comparing them, on the same conditions, independently of where the algorithm is executed".|
|2016||June 10||Generative models||Publication||"Improved Techniques for Training GANs", a paper on generative models, is submitted to the ArXiv. It presents a variety of new architectural features and training procedures that OpenAI applies to the generative adversarial networks (GANs) framework.|
|2016||June 12||Generative models||Publication||"InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets", a paper on generative models, is submitted to ArXiv. It describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.|
|2016||June 15||Generative models||Publication||"Improving Variational Inference with Inverse Autoregressive Flow", a paper on generative models, is submitted to the ArXiv. We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to earlier published flows, scales well to high-dimensional latent spaces.|
|2016||June 16||Generative models||Publication||OpenAI publishes post describing four projects on generative models, a branch of unsupervised learning techniques in machine learning.|
|2016||June 21||Publication||"Concrete Problems in AI Safety" by Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané is submitted to the arXiv. The paper explores practical problems in machine learning systems. The paper would receive a shoutout from the Open Philanthropy Project. It would become a landmark in AI safety literature, and many of its authors would continue to do AI safety work at OpenAI in the years to come.|
|2016||July||Team||Dario Amodei joins OpenAI, working on the Team Lead for AI Safety.|
|2016||July 8||Publication||"Adversarial Examples in the Physical World" is published. One of the authors is Ian Goodfellow, who is at OpenAI at the time.|
|2016||July 28|| OpenAI publishes post calling for applicants to work in the following problem areas of interest:
|2016||August 15||Donation||The technology company Nvidia announces that it has donated the first Nvidia DGX-1 (a supercomputer) to OpenAI. OpenAI plans to use the supercomputer to train its AI on a corpus of conversations from Reddit.|
|2016||August 29||Infrastructure||Publication||OpenAI publishes an article discussing the infrastructure necessary for deep learning. The research process starts with small ad-hoc experiments that need to be quickly conducted, so deep learning infrastructure must be flexible and allow users to analyze the models effectively. Then, the model is scaled up, and experiment management becomes critical. The article describes an example of improving Generative Adversarial Network training, from a prototype on MNIST and CIFAR-10 datasets to a larger model on the ImageNet dataset. The article also discusses the software and hardware infrastructure necessary for deep learning, such as Python, TensorFlow, and high-end GPUs. Finally, the article emphasizes the importance of simple and usable infrastructure management tools.|
|2016||October 11||Robotics||Publication||"Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model", a paper on robotics, is submitted to the ArXiv. It investigates settings where the sequence of states traversed in simulation remains reasonable for the real world.|
|2016||October 18||Safety||Publication||"Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data", a paper on safety, is submitted to the ArXiv. It shows an approach to providing strong privacy guarantees for training data: Private Aggregation of Teacher Ensembles (PATE).|
|2016||November 14||Generative models||Publication||"On the Quantitative Analysis of Decoder-Based Generative Models", a paper on generative models, is submitted to the ArXiv. It introduces a technique to analyze the performance of decoder-based models.|
|2016||November 15||Partnership||A partnership between OpenAI and Microsoft's artificial intelligence division is announced. As part of the partnership, Microsoft provides a price reduction on computing resources to OpenAI through Microsoft Azure.|
|2016||December 5||Software release||OpenAI's Universe, "a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications", is released.|
|2017||January||Staff||Paul Christiano joins OpenAI to work on AI alignment. He was previously an intern at OpenAI in 2016.|
|2017||March||Donation||The Open Philanthropy Project awards a grant of $30 million to OpenAI for general support. The grant initiates a partnership between Open Philanthropy Project and OpenAI, in which Holden Karnofsky (executive director of Open Philanthropy Project) joins OpenAI's board of directors to oversee OpenAI's safety and governance work. The grant is criticized by Maciej Cegłowski and Benjamin Hoffman (who would write the blog post "OpenAI makes humanity less safe") among others.|
|2017||March 24||Research progress||OpenAI announces having discovered that evolution strategies rival the performance of standard reinforcement learning techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL’s inconveniences.|
|2017||March||Reorganization||Greg Brockman and a few other core members of OpenAI begin drafting an internal document to lay out a path to artificial general intelligence. As the team studies trends within the field, they realize staying a nonprofit is financially untenable.|
|2017||April||Coverage||An article entitled "The People Behind OpenAI" is published on Red Hat's Open Source Stories website, covering work at OpenAI.|
|2017||April 6||Software release||OpenAI unveils an unsupervised system which is able to perform a excellent sentiment analysis, despite being trained only to predict the next character in the text of Amazon reviews.|
|2017||April 6||Publication||"Learning to Generate Reviews and Discovering Sentiment" is published.|
|2017||April 6||Neuroevolution||Research progress||OpenAI unveils reuse of an old field called “neuroevolution”, and a subset of algorithms from it called “evolution strategies,” which are aimed at solving optimization problems. In one hour training on an Atari challenge, an algorithm is found to reach a level of mastery that took a reinforcement-learning system published by DeepMind in 2016 a whole day to learn. On the walking problem the system took 10 minutes, compared to 10 hours for DeepMind's approach.|
|2017||May 15||Robotics||Software release||OpenAI releases Roboschool, an open-source software for robot simulation, integrated with OpenAI Gym.|
|2017||May 16||Robotics||Software release||OpenAI introduces a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it done once.|
|2017||May 24||Reinforcement learning||Software release||OpenAI releases Baselines, a set of implementations of reinforcement learning algorithms.|
|2017||June 12||Safety||Publication||"Deep reinforcement learning from human preferences" is first uploaded to the arXiv. The paper is a collaboration between researchers at OpenAI and Google DeepMind.|
|2017||June 28||Robotics||Open sourcing||OpenAI open sources a high-performance Python library for robotic simulation using the MuJoCo engine, developed over OpenAI research on robotics.|
|2017||June||Reinforcement learning||Partnership||OpenAI partners with DeepMind’s safety team in the development of an algorithm which can infer what humans want by being told which of two proposed behaviors is better. The learning algorithm uses small amounts of human feedback to solve modern reinforcement learning environments.|
|2017||July 27||Reinforcement learning||Research progress||OpenAI announces having found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance.|
|2017||August 12||Achievement||OpenAI's Dota 2 bot beats Danil "Dendi" Ishutin, a professional human player, (and possibly others?) in one-on-one battles.|
|2017||August 13||Coverage||The New York Times publishes a story covering the AI safety work (by Dario Amodei, Geoffrey Irving, and Paul Christiano) at OpenAI.|
|2017||August 18||Reinforcement learning||Software release||OpenAI releases two implementations: ACKTR, a reinforcement learning algorithm, and A2C, a synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C).|
|2017||September 13||Reinforcement learning||Publication||OpenAI publishes a paper introducing a new method for training agents in multi-agent settings called "Learning with Opponent-Learning Awareness" (LOLA). The method takes into account how an agent's policy affects the learning of the other agents in the environment. The paper shows that LOLA leads to the emergence of cooperation in the iterated prisoners' dilemma and outperforms naive learning in this domain. The LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making it suitable for model-free RL. The method is applied to a grid world task with an embedded social dilemma using recurrent policies and opponent modeling.|
|2017||October 11||Software release||RoboSumo, a game that simulates sumo wrestling for AI to learn to play, is released.|
|2017||November 6||Team||The New York Times reports that Pieter Abbeel (a researcher at OpenAI) and three other researchers from Berkeley and OpenAI have left to start their own company called Embodied Intelligence.|
|2017||December 6||Neural network||Software release||OpenAI releases highly-optimized GPU kernels for networks with block-sparse weights, an underexplored class of neural network architectures. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE.|
|2017||December||Publication||The 2017 AI Index is published. OpenAI contributed to the report.|
|2018||February 20||Safety||Publication||OpenAI co-authors a paper forecasting the potential misuse of AI technology by malicious actors and ways to prevent and mitigate these threats. The report makes high-level recommendations for companies, research organizations, individual practitioners, and governments to ensure a safer world, including acknowledging AI's dual-use nature, learning from cybersecurity practices, and involving a broader cross-section of society in discussions. The paper highlights concrete scenarios where AI can be maliciously used, such as cybercriminals using neural networks to create computer viruses with automatic exploit generation capabilities and rogue states using AI-augmented surveillance systems to pre-emptively arrest people who fit a predictive risk profile.|
|2018||February 20||Donation||OpenAI announces changes in donors and advisors. New donors are: Jed McCaleb, Gabe Newell, Michael Seibel, Jaan Tallinn, and Ashton Eaton and Brianne Theisen-Eaton. Reid Hoffman is "significantly increasing his contribution". Pieter Abbeel (previously at OpenAI), Julia Galef, and Maran Nelson become advisors. Elon Musk departs the board but remains as a donor and advisor.|
|2018||February 26||Robotics||Software release||OpenAI releases eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for OpenAI research over the previous year. These environments were to train models which work on physical robots.|
|2018||March 3||Event hosting||OpenAI hosts its first hackathon. Applicants include high schoolers, industry practitioners, engineers, researchers at universities, and others, with interests spanning healthcare to AGI.|
|2018||April 5 – June 5||Event hosting||The OpenAI Retro Contest takes place. As a result of the release of the Gym Retro library, OpenAI's Universe become deprecated.|
|2018||April 9||Commitment||OpenAI releases a charter stating that the organization commits to stop competing with a value-aligned and safety-conscious project that comes close to building artificial general intelligence, and also that OpenAI expects to reduce its traditional publishing in the future due to safety concerns.|
|2018||April 19||Financial||The New York Times publishes a story detailing the salaries of researchers at OpenAI, using information from OpenAI's 2016 Form 990. The salaries include $1.9 million paid to Ilya Sutskever and $800,000 paid to Ian Goodfellow (hired in March of that year).|
|2018||May 2||safety||Publication||The paper "AI safety via debate" by Geoffrey Irving, Paul Christiano, and Dario Amodei is uploaded to the arXiv. The paper proposes training agents via self play on a zero sum debate game, in order to adress tasks that are too complicated for a human to directly judge.|
|2018||May 16||Computation||Publication||OpenAI releases an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time.|
|2018||June 11||Unsupervised learning||Research progress||OpenAI announces having obtained significant results on a suite of diverse language tasks with a scalable, task-agnostic system, which uses a combination of transformers and unsupervised pre-training.|
|2018||June 25||Neural network||Software release||OpenAI announces set of AI algorithms able to hold their own as a team of five and defeat human amateur players at Dota 2, a multiplayer online battle arena video game popular in e-sports for its complexity and necessity for teamwork. In the algorithmic A team, called OpenAI Five, each algorithm uses a neural network to learn both how to play the game, and how to cooperate with its AI teammates.|
|2018||June 26||Notable comment|| Bill Gates comments on Twitter: |
AI bots just beat humans at the video game Dota 2. That’s a big deal, because their victory required teamwork and collaboration – a huge milestone in advancing artificial intelligence.
|2018||July 18||Commitment||Elon Musk, along with other tech leaders, sign a pledge promising to not develop “lethal autonomous weapons.” They also call on governments to institute laws against such technology. The pledge is organized by the Future of Life Institute, an outreach group focused on tackling existential risks.|
|2018||July 30||Robotics||Software release||OpenAI announces a robotics system that can manipulate objects with humanlike dexterity. The system is able to develop these behaviors all on its own. It uses a reinforcement model, where the AI learns through trial and error, to direct robot hands in grasping and manipulating objects with great precision.|
|2018||August 7||Achievement||Algorithmic team OpenAI Five defeats a team of semi-professional Dota 2 players ranked in the 99.95th percentile in the world, in their second public match in the traditional five-versus-five settings, hosted in San Francisco.|
|2018||August 16||Arboricity||Publication||OpenAI publishes paper on constant arboricity spectral sparsifiers. The paper shows that every graph is spectrally similar to the union of a constant number of forests.|
|2018||September||Team||Dario Amodei becomes OpenAI's Research Director.|
|2018||October 31||Reinforcement learning||Software release||OpenAI unveils its Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time exceeds average human performance on videogame Montezuma’s Revenge.|
|2018||November 8||Reinforcement learning||Education||OpenAI launches Spinning Up, an educational resource designed to teach anyone deep reinforcement learning. The program consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials.|
|2018||November 9||Notable comment|| Ilya Sutskever gives speech at the AI Frontiers Conference in San Jose, and declares: |
We (OpenAI) have reviewed progress in the field over the past six years. Our conclusion is near term AGI should be taken as a serious possibility.
|2018||November 19||Reinforcement learning||Partnership||OpenAI partners with DeepMind in a new paper that proposes a new method to train reinforcement learning agents in ways that enables them to surpass human performance. The paper, titled Reward learning from human preferences and demonstrations in Atari, introduces a training model that combines human feedback and reward optimization to maximize the knowledge of RL agents.|
|2018||December 4||Reinforcement learning||Researh progress||OpenAI announces having discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training on a wide range of tasks.|
|2018||December 6||Reinforcement learning||Software release||OpenAI releases CoinRun, a training environment designed to test the adaptability of reinforcement learning agents. A training environment is a type of educational setting that helps individuals acquire new skills or become familiar with a product.|
|2019||February 14||Natural-language generation||Software release||OpenAI unveils its language-generating system called GPT-2, a system able to write news, answer reading comprehension problems, and shows promise at tasks like translation. However, the data or the parameters of the model are not released, under expressed concerns about potential abuse. OpenAI initially tries to communicate the risk posed by this technology.|
|2019||February 19||Safety||Publication||OpenAI affiliated researchers publish an article arguing that aligning advanced AI systems with human values requires resolving uncertainties related to human psychology and biases, which can only be resolved empirically through experimentation. The authors call for social scientists with experience in human cognition, behavior, and ethics to collaborate with AI researchers to improve our understanding of the human side of AI alignment. The paper highlights the limitations of existing machine learning in addressing the complexities of human values and biases and suggests conducting experiments consisting entirely of people to replace machine learning agents with people playing the role of those agents. The authors emphasize the importance of interdisciplinary collaborations between social scientists and ML researchers to achieve long-term AI safety.|
|2019||March 4||Reinforcement learning||Software release||OpenAI releases a Neural MMO (massively multiplayer online), a multiagent game environment for reinforcement learning agents. The platform supports a large, variable number of agents within a persistent and open-ended task.|
|2019||March 6||Software release||OpenAI introduces activation atlases, created in collaboration with Google researchers. Activation atlases comprise a new technique for visualizing what interactions between neurons can represent.|
|2019||March 11||Reorganization||OpenAI announces the creation of OpenAI LP, a new “capped-profit” company owned and controlled by the OpenAI nonprofit organization’s board of directors. The new company is purposed to allow OpenAI to rapidly increase their investments in compute and talent while including checks and balances to actualize their mission.|
|2019||March 21||Software release||OpenAI announces progress towards stable and scalable training of energy-based models (EBMs) resulting in better sample quality and generalization ability than existing models.|
|2019||March||Team||Sam Altman leaves his role as the president of Y Combinator to become the Chief executive officer of OpenAI.|
|2019||April 23||Deep learning||Publication||OpenAI publishes paper announcing Sparse Transformers, a deep neural network for learning sequences of data, including text, sound, and images. It utilizes an improved algorithm based on the attention mechanism, being able to extract patterns from sequences 30 times longer than possible previously.|
|2019||April 25||Neural network||Software release||OpenAI announces MuseNet, a deep neural network able to generate 4-minute musical compositions with 10 different instruments, and can combine multiple styles from country to Mozart to The Beatles. The neural network uses general-purpose unsupervised technology.|
|2019||April 27||Event hosting||OpenAI hosts the OpenAI Robotics Symposium 2019.|
|2019||May||Natural-language generation||Software release||OpenAI releases a limited version of its language-generating system GPT-2. This version is more powerful (though still significantly limited compared to the whole thing) than the extremely restricted initial release of the system, citing concerns that it’d be abused. The potential of the new system is recognized by various experts.|
|2019||June 13||Natural-language generation||Coverage||Connor Leahy publishes article entitled The Hacker Learns to Trust which discusses the work of OpenAI, and particularly the potential danger of its language-generating system GPT-2. Leahy highlights: "Because this isn’t just about GPT2. What matters is that at some point in the future, someone will create something truly dangerous and there need to be commonly accepted safety norms before that happens."|
|2019||June 13||OpenAI appears before the United States Congress to discuss the potential consequences of synthetic media, including a specific focus on synthetic text. The House Permanent Select Committee on Intelligence holds an open hearing to discuss the national security challenges posed by artificial intelligence, manipulated media, and deepfake technology. This is the first House hearing focused on examining deepfakes and other AI-generated synthetic data. The Committee discusses the threats posed by fake content and ways to detect and combat it, as well as the roles of the public and private sectors and society as a whole in countering a potentially bleak future.|
|2019||July 22||Partnership||OpenAI announces an exclusive partnership with Microsoft. As part of the partnership, Microsoft invests $1 billion in OpenAI, and OpenAI switches to exclusively using Microsoft Azure (Microsoft's cloud solution) as the platform on which it will develop its AI tools. Microsoft will also be OpenAI's "preferred partner for commercializing new AI technologies."|
|2019||August 20||Natural-language generation||Software release||OpenAI announces plan to release a version of its language-generating system GPT-2, which stirred controversy after it release in February.|
|2019||September 17||Research progress||OpenAI announces having observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training, the agents were able to build a series of six distinct strategies and counterstrategies, some of which were unknown to be supported by the environment.|
|2019||October 16||Neural networks||Research progress||OpenAI announces having trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The experiment demonstrates that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot.|
|2019||November 5||Natural-language generation||Software release||OpenAI releases the largest version (1.5B parameters) of its language-generating system GPT-2 along with code and model weights to facilitate detection of outputs of GPT-2 models.|
|2019||November 21||Reinforcement learning||Software release||OpenAI releases Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.|
|2019||December 3||Reinforcement learning||Software release||OpenAI releases Procgen Benchmark, a set of 16 simple-to-use procedurally-generated environments (CoinRun, StarPilot, CaveFlyer, Dodgeball, FruitBot, Chaser, Miner, Jumper, Leaper, Maze, BigFish, Heist, Climber, Plunder, Ninja, and BossFight) which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills. Procgen Benchmark prevents AI model overfitting.|
|2019||December 4||Publication||"Deep Double Descent: Where Bigger Models and More Data Hurt" is submitted to the ArXiv. The paper shows that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as the model size increases, performance first gets worse and then gets better. The paper is summarized on the OpenAI blog. MIRI researcher Evan Hubinger writes an explanatory post on the subject on LessWrong and the AI Alignment Forum, and follows up with a post on the AI safety implications.|
|2019||December||Team||Dario Amodei is promoted as OpenAI's Vice President of Research.|
|2020||January 30||Deep learning||Software adoption||OpenAI announces migration to the social network’s PyTorch machine learning framework in future projects, setting it as its new standard deep learning framework.|
|2020||February 5||Safety||Publication|| Beth Barnes and Paul Christiano on |
|2020||February 17||Coverage||AI reporter Karen Hao at MIT Technology Review publishes review on OpenAI titled The messy, secretive reality behind OpenAI’s bid to save the world, which suggests the company is surrendering its declaration to be transparent in order to outpace competitors. As a response, Elon Musk criticizes OpenAI, saying it lacks transparency. On his Twitter account, Musk writes "I have no control & only very limited insight into OpenAI. Confidence in Dario for safety is not high", alluding to OpenAI Vice President of Research Dario Amodei.|
|2020||May 28 (release), June and July (discussion and exploration)||Natural-language generation||Software release||OpenAI releases the natural language model GPT-3 on GitHub and uploads to the ArXiV the paper Language Models are Few-Shot Learners explaining how GPT-3 was trained and how it performs. Games, websites, and chatbots based on GPT-3 are created for exploratory purposes in the next two months (mostly by people unaffiliated with OpenAI), with a general takeaway that GPT-3 performs significantly better than GPT-2 and past natural language models. Commentators also note many weaknesses such as: trouble with arithmetic because of incorrect pattern matching, trouble with multi-step logical reasoning even though it could do the individual steps separately, inability to identify that a question is nonsense, inability to identify that it does not know the answer to a question, and picking up of racist and sexist content when trained on corpuses that contain some such content.|
|2020||June 11||OpenAI announces the release of an API for accessing new AI models that can be used for virtually any English language task. The API provides a general-purpose "text in, text out" interface, which can be integrated into products or used to develop new applications. Users can program the AI by showing it a few examples of what is required, and hone its performance by training it on small or large datasets or learning from human feedback. The API is designed to be both simple to use and flexible, with many speed and throughput improvements. While the API is launched as a private beta, it intends to share what it learns to build more human-positive AI systems.|
|2020||September 22||Partnership||Microsoft announces a partnership with OpenAI to exclusively license their GPT-3 language model, the largest and most advanced language model in the world by this time. This would allow Microsoft to leverage its technical innovations to develop and deliver advanced AI solutions for its customers, as well as create new solutions that harness the power of natural language generation. Microsoft sees this as an opportunity to expand its Azure-powered AI platform in a way that democratizes AI technology and enables new products, services, and experiences. OpenAI would continue to offer GPT-3 and other models via its own Azure-hosted API.|
|2021||January 5||OpenAI introduces DALL-E as a neural network that can generate images from text captions. It has a diverse set of capabilities including creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images. DALL-E is a 12-billion parameter version of GPT-3 that is trained using a dataset of text-image pairs. It can generate images from scratch and regenerate any rectangular region of an existing image that extends to the bottom-right corner, in a way that is consistent with the text prompt. It can also modify several attributes of an object and the number of times it appears. However, controlling multiple objects and their attributes simultaneously presents a challenge at this time.|
|2022||July 14||OpenAI reports on DALL·E 2 having incorporated into the creative workflows of over 3,000 artists in more than 118 countries. By this time DALL·E 2 has been used by a wide range of creative professionals, including illustrators, chefs, sound designers, dancers, and tattoo artists, among others. Examples of how DALL·E has been used include creating personalized cartoons, designing menus and plate dishes, transforming 2D artwork into 3D renders for AR filters, and much more. An exhibition of the works of some of the artists using DALL·E in the Leopold Museum is announced.|
|2022||July 18||OpenAI announces implementation of a new technique to reduce bias in its DALL-E image generator, specifically for generating images of people that more accurately reflect the diversity of the world's population. The technique is applied at the system level when a prompt describing a person does not specify race or gender. The mitigation was informed by early user feedback during a preview phase, and other steps have been taken to improve safety systems, including content filters and monitoring systems. These improvements allowed OpenAI to gain confidence in expanding access to DALL-E.|
|2022||July 20||OpenAI introduces beta version of DALL-E, an AI system that creates realistic images and art from natural language descriptions. DALL-E also includes features such as editing, variations, and a "My Collection" option for saved generations. OpenAI took steps to curb misuse, prevent harmful images, and reduce bias in the tool, and users by this time have full usage rights to commercialize the images they create with DALL-E.|
|2022||August 10||OpenAI introduces a new and improved content moderation tool, the Moderation endpoint, which is free for OpenAI API developers to use. This endpoint uses GPT-based classifiers to detect prohibited content such as self-harm, hate, violence, and sexual content. The tool was designed to be accurate, quick, and robust across various applications. By using the Moderation endpoint, developers can access accurate classifiers through a single API call rather than building and maintaining their classifiers. OpenAI hopes this tool will make the AI ecosystem safer and spur further research in this area.|
|2022||August 24||OpenAI publishes a blog post explaining its approach to alignment research aiming to make artificial general intelligence (AGI) aligned with human values and intentions. They take an iterative, empirical approach by attempting to align highly capable AI systems to learn what works and what doesn't. OpenAI claims being committed to sharing their alignment research when it is safe to do so to ensure that every AGI developer uses the best alignment techniques. They also claim aiming to build and align a system that can make faster and better alignment research progress than humans can. Language models are particularly well-suited for automating alignment research because they come "preloaded" with a lot of knowledge and information about human values. However, their approach is reported to have limitations and needs to be adapted and improved as AI technology develops.|
|2022||August 31||OpenAI introduces Outpainting, a new feature for DALL-E that allows users to extend the original image beyond its borders by adding visual elements or taking the story in new directions using a natural language description. This new feature can create large-scale images in any aspect ratio and takes into account the existing visual elements to maintain the context of the original image. The new feature is available for all DALL·E users on desktop.|
|2022||September 28||OpenAI announces that the waitlist for its DALL·E beta is now removed, and new users can start creating immediately. By this time, over 1.5 million users actively create over 2 million images per day with DALL·E, with more than 100,000 users sharing their creations and feedback in the Discord community. The iterative deployment approach has allowed OpenAI to scale DALL·E responsibly while discovering new uses for the tool. User feedback has inspired the development of new features such as Outpainting and collections.|
|2022||November 3||OpenAI announces the public beta release of its DALL·E API, which allows developers to integrate image generation capabilities of DALL·E into their applications and products. DALL·E's flexibility enables users to create and edit original images ranging from the artistic to the photorealistic, and its built-in moderation ensures responsible deployment. Several companies, including Microsoft and Mixtiles, have already integrated DALL·E into their products by this time. The DALL·E API joins OpenAI's other powerful models, GPT-3, Embeddings, and Codex, on its API platform.|
|2022||December 8||OpenAI publishes an interview to Christian Gibson, an engineer on the Supercomputing team at the company. He explains his journey into engineering and how he got into OpenAI. He also speaks about the problems he is focused on solving, such as the complexity of exploratory AI workflows and bottlenecks in the running of codes on supercomputers. He talks about what makes working on supercomputing at OpenAI different from other places, such as the sheer scale of the operation, and his typical day at OpenAI.|
|2022||December 15||OpenAI announces a new text-embedding-ada-002 model that replaces five separate models for text search, text similarity, and code search. This new model outperforms their previous most capable model, Davinci, at most tasks, while being priced 99.8% lower. The new model has stronger performance, longer context, smaller embedding size, and reduced price. However, it does not outperform text-similarity-davinci-001 on the SentEval linear probing classification benchmark. The model has already been implemented by Kalendar AI and Notion to improve sales outreach and search capabilities.|
|2023||January 23||Partnership||OpenAI and Microsoft extend their partnership with a multi-billion dollar investment to continue their research and development of AI that is safe, useful, and powerful. OpenAI remains a capped-profit company and is governed by the OpenAI non-profit to ensure their mission of benefiting humanity is prioritized. Microsoft would increase its investment in supercomputing systems powered by Azure to accelerate independent research, and Azure would remain the exclusive cloud provider for all OpenAI workloads. They also partner to deploy OpenAI's technology through their API and the Azure OpenAI Service, and to build and deploy safe AI systems. The two teams collaborate regularly to review and synthesize shared lessons and inform future research and best practices for use of powerful AI systems across the industry.|
|2023||January 31||OpenAI launches a new classifier that can distinguish between text written by humans and text written by AI. The classifier is not fully reliable, but it can inform mitigations for false claims that AI-generated text was written by a human. OpenAI makes this classifier publicly available for feedback and recommends using it as a complement to other methods of determining the source of a piece of text. The classifier has limitations and is very unreliable on short texts, but it can be useful for educators and researchers to identify AI-generated text. By this time, OpenAI is engaging with educators to learn about their experiences and welcomes feedback on the preliminary resource they have developed.|
|2023||February 1||OpenAI introduces ChatGPT Plus, a pilot subscription plan that provides faster response times, general access to ChatGPT during peak times, and priority access to new features and improvements. The subscription costs $20 per month and is accessible to customers worldwide. Although OpenAI would continue to offer free access to ChatGPT, they hope to support free access availability to as many people as possible through the subscription plan. The company reports on its intention to refine and expand the offering according to user feedback and needs, and that they are exploring options for lower-cost plans, business plans, and data packs to provide wider accessibility.|
|2023||February 24||OpenAI publishes a blog discussing the potential benefits and risks of Artificial General Intelligence, which are AI systems that are generally smarter than humans. The authors state that AGI could increase abundance, aid scientific discoveries, and elevate humanity, but it also comes with serious risks, such as misuse, accidents, and societal disruption. To ensure that AGI benefits all of humanity, the authors articulate several principles they care about the most, such as maximizing the good, minimizing the bad, and empowering humanity. The authors suggest that a gradual transition to a world with AGI is better than a sudden one to allow people to understand what's happening, personally experience the benefits and downsides, and adapt to the economy and put regulation in place. The authors emphasize the importance of a tight feedback loop of rapid learning and careful iteration to successfully navigate AI deployment challenges, combat bias, and deal with job displacement. They believe that democratized access will lead to more and better research, decentralized power, more benefits, and a broader set of people contributing new ideas.|
Meta information on the timeline
How the timeline was built
The initial version of the timeline was written by Issa Rice. It has been expanded considerably by Sebastian.
Funding information for this timeline is available.
What the timeline is still missing
- New GPT-3 tool
- bunch of people leaving OpenAI in December 2020/January 2021, including people working on AI safety/transparency: "OpenAI departures: Dario Amodei, Sam McCandlish, Tom Brown, Tom Henighan, Chris Olah, Jack Clark, Ben Mann, Paul Christiano et al leave—most for an unspecified new entity (“the elves leave Middle Earth”?)" https://www.gwern.net/newsletter/2021/01#ai] [https://www.lesswrong.com/posts/7r8KjgqeHaYDzJvzF/dario-amodei-leaves-openai
- January 2021: Jan Leike joins OpenAI to lead AI alignment work [https://twitter.com/janleike/status/1352681093007200256
Timeline update strategy
- https://blog.OpenAI.com/ (but check to see if the announcement of a blog post is covered by other sources)
- Timeline of ChatGPT
- Timeline of large language models
- Timeline of transformers
- Timeline of DeepMind
- Timeline of Neuralink
- Timeline of Future of Humanity Institute
- Timeline of Centre for the Study of Existential Risk
- ↑ "OpenAI". Google Trends. Retrieved 20 April 2021.
- ↑ "OpenAI and DeepMind". Google Trends. Retrieved 20 April 2021.
- ↑ "OpenAI, The Boring Company and Neuralink". Google Trends. Retrieved 20 April 2021.
- ↑ "OpenAI". books.google.com. Retrieved 20 April 2021.
- ↑ "OpenAI". wikipediaviews.org. Retrieved 20 April 2021.
- ↑ "OpenAI and DeepMind". wikipediaviews.org. Retrieved 20 April 2021.
- ↑ Samuel Gibbs (October 27, 2014). "Elon Musk: artificial intelligence is our biggest existential threat". The Guardian. Retrieved July 25, 2017.
- ↑ "AeroAstro Centennial Webcast". Retrieved July 25, 2017.
The high point of the MIT Aeronautics and Astronautics Department's 2014 Centennial celebration is the October 22-24 Centennial Symposium
- ↑ "Machine intelligence, part 1". Sam Altman. Retrieved July 27, 2017.
- ↑ Brockman, Greg (May 6, 2015). "Leaving Stripe". Greg Brockman on Svbtle. Retrieved May 6, 2018.
- ↑ Carson, Biz (May 6, 2015). "One of the first employees of $3.5 billion startup Stripe is leaving to form his own company". Business Insider. Retrieved May 6, 2018.
- ↑ 12.0 12.1 "My path to OpenAI". Greg Brockman on Svbtle. May 3, 2016. Retrieved May 8, 2018.
- ↑ Matt Weinberger (June 4, 2015). "Head of Silicon Valley's most important startup farm says we're in a 'mega bubble' that won't last". Business Insider. Retrieved July 27, 2017.
- ↑ John Markoff (December 11, 2015). "Artificial-Intelligence Research Center Is Founded by Silicon Valley Investors". The New York Times. Retrieved July 26, 2017.
The organization, to be named OpenAI, will be established as a nonprofit, and will be based in San Francisco.
- ↑ "Introducing OpenAI". OpenAI Blog. December 11, 2015. Retrieved July 26, 2017.
- ↑ Drew Olanoff (December 11, 2015). "Artificial Intelligence Nonprofit OpenAI Launches With Backing From Elon Musk And Sam Altman". TechCrunch. Retrieved March 2, 2018.
- ↑ "Wojciech Zaremba". linkedin.com. Retrieved 28 February 2020.
- ↑ "OpenAI: Revision history". wikipedia.org. Retrieved 6 April 2020.
- ↑ Priestly, Theo (December 11, 2015). "Elon Musk And Peter Thiel Launch OpenAI, A Non-Profit Artificial Intelligence Research Company". Forbes. Retrieved 8 July 2019.
- ↑ "Ilya Sutskever". AI Watch. April 8, 2018. Retrieved May 6, 2018.
- ↑ 21.0 21.1 21.2 21.3 21.4 "Information for OpenAI". orgwatch.issarice.com. Retrieved 5 May 2020.
- ↑ "AMA: the OpenAI Research Team • r/MachineLearning". reddit. Retrieved May 5, 2018.
- ↑ Salimans, Tim; Kingma, Diederik P. "Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks". arxiv.org. Retrieved 27 March 2020.
- ↑ Brockman, Greg (March 22, 2017). "Team++". OpenAI Blog. Retrieved May 6, 2018.
- ↑ "Ian Goodfellow". linkedin.com. Retrieved 24 April 2020.
- ↑ Sutskever, Ilya (March 20, 2017). "Welcome, Pieter and Shivon!". OpenAI Blog. Retrieved May 6, 2018.
- ↑ "OpenAI Gym Beta". OpenAI Blog. March 20, 2017. Retrieved March 2, 2018.
- ↑ "Inside OpenAI, Elon Musk's Wild Plan to Set Artificial Intelligence Free". WIRED. April 27, 2016. Retrieved March 2, 2018.
This morning, OpenAI will release its first batch of AI software, a toolkit for building artificially intelligent systems by way of a technology called "reinforcement learning"
- ↑ Shead, Sam (April 28, 2016). "Elon Musk's $1 billion AI company launches a 'gym' where developers train their computers". Business Insider. Retrieved March 3, 2018.
- ↑ Miyato, Takeru; Dai, Andrew M.; Goodfellow, Ian. "Adversarial Training Methods for Semi-Supervised Text Classification". arxiv.org. Retrieved 28 March 2020.
- ↑ Houthooft, Rein; Chen, Xi; Duan, Yan; Schulman, John; De Turck, Filip; Abbeel, Pieter. "VIME: Variational Information Maximizing Exploration". arxiv.org. Retrieved 27 March 2020.
- ↑ Brockman, Greg; Cheung, Vicki; Pettersson, Ludwig; Schneider, Jonas; Schulman, John; Tang, Jie; Zaremba, Wojciech. "OpenAI Gym". arxiv.org. Retrieved 27 March 2020.
- ↑ "OPENAI GYM". theconstructsim.com. Retrieved 16 May 2020.
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- ↑ "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets". arxiv.org. Retrieved 27 March 2020.
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- ↑ "Generative Models". openai.com. Retrieved 5 April 2020.
- ↑ "[1606.06565] Concrete Problems in AI Safety". June 21, 2016. Retrieved July 25, 2017.
- ↑ Karnofsky, Holden (June 23, 2016). "Concrete Problems in AI Safety". Retrieved April 18, 2020.
- ↑ "Dario Amodei - Research Scientist @ OpenAI". Crunchbase. Retrieved May 6, 2018.
- ↑ 41.0 41.1 41.2 "Dario Amodei". linkedin.com. Retrieved 29 February 2020.
- ↑ Metz, Cade (July 29, 2016). "How To Fool AI Into Seeing Something That Isn't There". WIRED. Retrieved March 3, 2018.
- ↑ "Special Projects". openai.com. Retrieved 5 April 2020.
- ↑ "NVIDIA Brings DGX-1 AI Supercomputer in a Box to OpenAI". The Official NVIDIA Blog. August 15, 2016. Retrieved May 5, 2018.
- ↑ Vanian, Jonathan (August 15, 2016). "Nvidia Just Gave A Supercomputer to Elon Musk-backed Artificial Intelligence Group". Fortune. Retrieved May 5, 2018.
- ↑ De Jesus, Cecille (August 17, 2016). "Elon Musk's OpenAI is Using Reddit to Teach An Artificial Intelligence How to Speak". Futurism. Retrieved May 5, 2018.
- ↑ "Infrastructure for Deep Learning". openai.com. Retrieved 28 March 2020.
- ↑ Christiano, Paul; Shah, Zain; Mordatch, Igor; Schneider, Jonas; Blackwell, Trevor; Tobin, Joshua; Abbeel, Pieter; Zaremba, Wojciech. "Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model". arxiv.org. Retrieved 28 March 2020.
- ↑ Papernot, Nicolas; Abadi, Martín; Erlingsson, Úlfar; Goodfellow, Ian; Talwar, Kunal. "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data". arxiv.org. Retrieved 28 March 2020.
- ↑ Wu, Yuhuai; Burda, Yuri; Salakhutdinov, Ruslan; Grosse, Roger. "On the Quantitative Analysis of Decoder-Based Generative Models". arxiv.org. Retrieved 28 March 2020.
- ↑ Statt, Nick (November 15, 2016). "Microsoft is partnering with Elon Musk's OpenAI to protect humanity's best interests". The Verge. Retrieved March 2, 2018.
- ↑ Metz, Cade. "The Next Big Front in the Battle of the Clouds Is AI Chips. And Microsoft Just Scored a Win". WIRED. Retrieved March 2, 2018.
According to Altman and Harry Shum, head of Microsoft new AI and research group, OpenAI's use of Azure is part of a larger partnership between the two companies. In the future, Altman and Shum tell WIRED, the two companies may also collaborate on research. "We're exploring a couple of specific projects," Altman says. "I'm assuming something will happen there." That too will require some serious hardware.
- ↑ "universe". GitHub. Retrieved March 1, 2018.
- ↑ John Mannes (December 5, 2016). "OpenAI's Universe is the fun parent every artificial intelligence deserves". TechCrunch. Retrieved March 2, 2018.
- ↑ "Elon Musk's Lab Wants to Teach Computers to Use Apps Just Like Humans Do". WIRED. Retrieved March 2, 2018.
- ↑ "OpenAI Universe". Hacker News. Retrieved May 5, 2018.
- ↑ "AI Alignment". Paul Christiano. May 13, 2017. Retrieved May 6, 2018.
- ↑ "Team Update". OpenAI Blog. March 22, 2017. Retrieved May 6, 2018.
- ↑ "Open Philanthropy Project donations made (filtered to cause areas matching AI safety)". Retrieved July 27, 2017.
- ↑ "OpenAI — General Support". Open Philanthropy Project. December 15, 2017. Retrieved May 6, 2018.
- ↑ "Pinboard on Twitter". Twitter. Retrieved May 8, 2018.
What the actual fuck… “Open Philanthropy” dude gives a $30M grant to his roommate / future brother-in-law. Trumpy!
- ↑ "OpenAI makes humanity less safe". Compass Rose. April 13, 2017. Retrieved May 6, 2018.
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- ↑ Naik, Vipul. "I'm having a hard time understanding the rationale...". Retrieved May 8, 2018.
- ↑ "Evolution Strategies as a Scalable Alternative to Reinforcement Learning". openai.com. Retrieved 5 April 2020.
- ↑ "The messy, secretive reality behind OpenAI's bid to save the world". technologyreview.com. Retrieved 28 February 2020.
- ↑ Simoneaux, Brent; Stegman, Casey. "Open Source Stories: The People Behind OpenAI". Retrieved May 5, 2018. In the HTML source, last-publish-date is shown as Tue, 25 Apr 2017 04:00:00 GMT as of 2018-05-05.
- ↑ "Profile of the people behind OpenAI • r/OpenAI". reddit. April 7, 2017. Retrieved May 5, 2018.
- ↑ "The People Behind OpenAI". Hacker News. July 23, 2017. Retrieved May 5, 2018.
- ↑ "Unsupervised Sentiment Neuron". openai.com. Retrieved 5 April 2020.
- ↑ John Mannes (April 7, 2017). "OpenAI sets benchmark for sentiment analysis using an efficient mLSTM". TechCrunch. Retrieved March 2, 2018.
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- ↑ "OpenAI Just Beat Google DeepMind at Atari With an Algorithm From the 80s". singularityhub.com. Retrieved 29 June 2019.
- ↑ "Roboschool". openai.com. Retrieved 5 April 2020.
- ↑ "Robots that Learn". openai.com. Retrieved 5 April 2020.
- ↑ "OpenAI Baselines: DQN". OpenAI Blog. November 28, 2017. Retrieved May 5, 2018.
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- ↑ "Two Giants of AI Team Up to Head Off the Robot Apocalypse". WIRED. Retrieved March 2, 2018.
A new paper from the two organizations on a machine learning system that uses pointers from humans to learn a new task, rather than figuring out its own—potentially unpredictable—approach, follows through on that. Amodei says the project shows it's possible to do practical work right now on making machine learning systems less able to produce nasty surprises.
- ↑ "Faster Physics in Python". openai.com. Retrieved 5 April 2020.
- ↑ "Learning from Human Preferences". OpenAI.com. Retrieved 29 June 2019.
- ↑ "Better Exploration with Parameter Noise". openai.com. Retrieved 5 April 2020.
- ↑ Jordan Crook (August 12, 2017). "OpenAI bot remains undefeated against world's greatest Dota 2 players". TechCrunch. Retrieved March 2, 2018.
- ↑ "Did Elon Musk's AI champ destroy humans at video games? It's complicated". The Verge. August 14, 2017. Retrieved March 2, 2018.
- ↑ "Elon Musk's $1 billion AI startup made a surprise appearance at a $24 million video game tournament — and crushed a pro gamer". Business Insider. August 11, 2017. Retrieved March 3, 2018.
- ↑ Cade Metz (August 13, 2017). "Teaching A.I. Systems to Behave Themselves". The New York Times. Retrieved May 5, 2018.
- ↑ "OpenAI Baselines: ACKTR & A2C". openai.com. Retrieved 5 April 2020.
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- ↑ "AI Sumo Wrestlers Could Make Future Robots More Nimble". WIRED. Retrieved March 3, 2018.
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