Timeline of OpenAI

The timeline currently offers focused coverage of the period until August 2023. It is likely to miss important developments outside this period (particularly after this period) though it may have a few events from after this period.

This is a timeline of OpenAI, an artificial intelligence research organization based in the United States. It comprises both a non-profit entity called OpenAI Incorporated and a for-profit subsidiary called OpenAI Limited Partnership. OpenAI stated goals are to conduct AI research and contribute to the advancement of friendly AI, aiming to promote its development and positive impact.

Sample questions

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 on their research?
  • Sort the full timeline by "Event type" and look for the group of rows with value "Research".
  • 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 "Product release".
  • You will see a variety of releases, some of them open-sourced.
  • You will see some discoveries and other significant results obtained by OpenAI.
• What are some updates mentioned in the timeline?
  • Sort the full timeline by "Event type" and look for the group of rows with value "Product update".
• Who are some notable team members having joined OpenAI?
  • Sort the full timeline by "Event type" and look for the group of rows with value "Team".
  • You will see the names of incorporated people and their roles.
• What are the several partnerships between OpenAI and other organizations?
  • Sort the full timeline by "Event type" and look for the group of rows with value "Partnership".
  • You will read collaborations with organizations like DeepMind and Microsoft.
• 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 other publications by OpenAI?
  • Sort the full timeline by "Event type" and look for the group of rows with value "Publication".
  • You will see a number of publications not specifically describing their scientific research, but other purposes, including recommendations and contributions.
• 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".
• Other events are described under the following types: "Achievement", "Advocacy", "Background", "Collaboration", "Commitment", "Competiton", "Congressional hearing", "Education", "Financial", "Integration", "Interview", "Notable comment", "Open sourcing", "Product withdrawal", "Reaction" ,"Recruitment", "Software adoption", and "Testing".

Big picture

Time period (approximately)	Development summary	More details
2015–2017	Early years	OpenAI is founded as a nonprofit organization. They claim their mission is to ensure that artificial general intelligence (AGI) benefits all of humanity. Co-founders include Elon Musk and Sam Altman. During this period, OpenAI focuses on research and development in the field of AI, establishing itself as a prominent player in the industry.
2018–2019	Growth and Expansion	OpenAI continues to grow in prominence and expands its research efforts, undergoing a shift in focus towards more extensive research and development in AI. They publish influential research papers, including breakthroughs in natural language processing and reinforcement learning. They introduce Generative Pre-trained Transformers (GPTs). These neural networks, inspired by the human brain, are trained on large amounts of human-generated text and could perform tasks like generating and answering questions. They also launch projects like OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms. In 2019, OpenAI makes a transition from a non-profit organization to a for-profit model, with a capped profit limit of 100 times the investment made. This allows OpenAI LP to attract investment from venture funds and offer employees equity in the company. OpenAI forms a partnership with Microsoft, which invests $1 billion in the company. They also announce plans to license its technologies for commercial use. However, some researchers would criticize the shift to a for-profit status, raising concerns about the company's commitment to democratizing AI.
2020–2021	Launch of GPT-3 and commercialization	This period is marked by a significant milestone for OpenAI with the release of their most advanced language model at the time, GPT-3 (Generative Pre-trained Transformer 3), in June 2020. GPT-3 garners widespread attention for its ability to generate human-like text across various tasks and applications. OpenAI explores commercialization strategies and introduces the concept of the API, allowing developers and businesses to access and use GPT-3's capabilities.
2022–present	Democratizing AI, ChatGPT	OpenAI's focus shifts towards democratizing AI during this period. They continue to refine and improve their models while working towards making them more accessible and understandable. In late 2022, ChatGPT is launched, revolutionizing the field of artificial intelligence and natural language processing.

Summary by year

Time period	Development summary
2015	A group of influential individuals including Sam Altman, Greg Brockman, Reid Hoffman, Jessica Livingston, Peter Thiel, Elon Musk, Amazon Web Services (AWS), Infosys, and YC Research join forces to establish OpenAI. With a commitment of more than $1 billion, the organization expresses a strong dedication to advancing the field of AI for the betterment of humanity. They announce their intention to foster open collaboration by making their work accessible to the public and actively engaging with other institutions and researchers.
2016	OpenAI breaks from the norm by offering corporate-level salaries instead of the typical nonprofit-level salaries. They also release OpenAI Gym, a platform dedicated to reinforcement learning research. Later in December, they introduced Universe, a platform that facilitates the measurement and training of AI's general intelligence across various games, websites, and applications.
2017	A significant portion of OpenAI's expenditure is allocated to cloud computing, amounting to $7.9 million. On the other hand, DeepMind's expenses for that particular year soar to $442 million, representing a notable difference.
2018	OpenAI undergoes a shift in focus towards more extensive research and development in AI. They introduce Generative Pre-trained Transformers (GPTs). These neural networks, inspired by the human brain, are trained on large amounts of human-generated text and could perform tasks like generating and answering questions. In the same year, Elon Musk resigns from his board seat at OpenAI, citing a potential conflict of interest with his role as CEO of Tesla, which is developing AI for self-driving cars.
2019	OpenAI makes a transition from a non-profit organization to a for-profit model, with a capped profit limit of 100 times the investment made. This allows OpenAI LP to attract investment from venture funds and offer employees equity in the company. OpenAI forms a partnership with Microsoft, which invests $1 billion in the company. OpenAI also announces plans to license its technologies for commercial use. However, some researchers would criticize the shift to a for-profit status, raising concerns about the company's commitment to democratizing AI.
2020	OpenAI introduces GPT-3, a language model trained on extensive internet datasets. While its main function is to provide answers in natural language, it can also generate coherent text spontaneously and perform language translation. OpenAI also announces their plans to develop a commercial product centered around an API called "the API," which is closely connected to GPT-3.
2021	OpenAI introduces DALL-E, an advanced deep-learning model that has the ability to generate digital images by interpreting natural language descriptions.
2022	OpenAI introduces ChatGPT which soon would become the fastest-growing app of all time.

Full timeline

Inclusion criteria

The following events are selected for inclusion in the timeline:

• Most blog posts by OpenAI, many describing important advancements in their research.
• Product releases, including models and software in general.
• Partnerships.

We do not include:

• Comprehensive information on the team's arrivals and departures within a company.
• Many of OpenAI's research papers, which are not individually listed on the full timeline, but can be found on the talk page as additional entries.

Timeline

Year	Month and date	Domain/key topic/caption	Event type	Details
2014	000000002024-10-22-0000October 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	000000002024-02-25-0000February 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	000000002024-05-06-0000May 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	000000002024-06-04-0000June 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	000000002024-07-01-0000July (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	000000002024-12-11-0000December 11		Genesis	OpenAI is announced to the public. (The news articles from this period make it sound like OpenAI launched sometime after this date.)
2015	000000002024-12-01-0000December	Wikipedia	Coverage	The article "OpenAI" is created on Wikipedia.
2015	000000002024-12-01-0000December		Team	OpenAI announces Y Combinator founding partner Jessica Livingston as one of its financial backers.
2016	000000002024-01-01-0000January	Ilya Sutskever	Team	Ilya Sutskever joins OpenAI as Research Director.
2016	000000002024-01-09-0000January 9	AMA	Education	The OpenAI research team does an AMA ("ask me anything") on r/MachineLearning, the subreddit dedicated to machine learning.
2016	000000002024-02-25-0000February 25	Deep learning, neural networks	Research	OpenAI introduces weight normalization as a technique that improves the training of deep neural networks by decoupling the length and direction of weight vectors. It enhances optimization and speeds up convergence without introducing dependencies between examples in a minibatch. This method is effective for recurrent models and noise-sensitive applications, providing a significant speed-up similar to batch normalization but with lower computational overhead. Applications in image recognition, generative modeling, and deep reinforcement learning demonstrate the effectiveness of weight normalization.
2016	000000002024-03-31-0000March 31	Ian Goodfellow	Team	A blog post from this day announces that Ian Goodfellow has joined OpenAI. Previously, Goodfellow worked as Senior Research Scientist at Google.
2016	000000002024-04-26-0000April 26	Robotics	Team	Pieter Abbeel joins OpenAI. His work focuses on robot learning, reinforcement learning, and unsupervised learning. A cutting-edge researcher, Abbeel robots would learn various tasks, including locomotion and vision-based robotic manipulation.
2016	000000002024-04-27-0000April 27	Reinforcement learning	Product release	OpenAI releases OpenAI Gym, a toolkit for reinforcement learning (RL) algorithms. It offers various environments for developing and comparing RL algorithms, with compatibility across different frameworks. RL enables agents to learn decision-making and motor control in complex environments. OpenAI Gym addresses the need for diverse benchmarks and standardized environments in RL research. The toolkit encourages feedback and collaboration to enhance its capabilities.
2016	000000002024-05-25-0000May 25	Natural language processing	Research	OpenAI-affiliated researchers publish a paper introducing an extension of adversarial training and virtual adversarial training for text classification tasks. Adversarial training is a regularization technique for supervised learning, while virtual adversarial training extends it to semi-supervised learning. However, these methods require perturbing multiple entries of the input vector, which is not suitable for sparse high-dimensional inputs like one-hot word representations in text. In this paper, the authors propose applying perturbations to word embeddings in a recurrent neural network (RNN) instead of the original input. This text-specific approach achieves state-of-the-art results on multiple benchmark tasks for both semi-supervised and purely supervised learning. The authors provide visualizations and analysis demonstrating the improved quality of the learned word embeddings and the reduced overfitting during training.
2016	June 16	Generative models	Research	OpenAI publishes post introducing the concept of generative models, which are a type of unsupervised learning technique in machine learning. The post emphasizes the importance and potential of generative models in understanding and replicating complex data sets, and it showcases recent advancements in this field. Generative models aim to understand and replicate the patterns and features present in a given dataset. The post discusses the use of generative models in generating images, particularly with the example of the DCGAN network. It explains the training process of generative models, including the use of Generative Adversarial Networks (GANs) and other approaches. The post highlights three popular types of generative models: Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), and autoregressive models. Each of these approaches has its own strengths and limitations. The post also mentions recent advancements in generative models, including improvements to GANs, VAEs, and the introduction of InfoGAN. The last part briefly mentions two projects related to generative models in the context of reinforcement learning. One project focuses on curiosity-driven exploration using Bayesian neural networks. The other project explores generative models in reinforcement learning for training agents.
2016	000000002024-06-21-0000June 21	AI safety	Research	OpenAI-affiliated researchers publish a paper addressing the potential impact of accidents in machine learning systems. They outline five practical research problems related to accident risk, categorized based on the origin of the problem. These categories include having the wrong objective function, an objective function that is too expensive to evaluate frequently, and undesirable behavior during the learning process. The authors review existing work in these areas and propose research directions relevant to cutting-edge AI systems. They also discuss how to approach the safety of future AI applications effectively.
2016	000000002024-07-01-0000July		Team	Dario Amodei joins OpenAI, working on the Team Lead for AI Safety.
2016	000000002024-07-28-0000July 28	Security and adversarial AI, automated programming, cybersecurity, multi-agent systems, simulation	Recruitment	OpenAI publishes post calling for applicants to work in significant problems in AI that have a meaningful impact. They list several problem areas that they believe are crucial for advancing AI and its implications for society. The first problem area mentioned is detecting covert breakthrough AI systems being used by organizations for potentially malicious purposes. OpenAI emphasizes the need to develop methods to identify such undisclosed AI breakthroughs, which could be achieved through various means like monitoring news, financial markets, and online games. Another area of interest is building an agent capable of winning online programming competitions. OpenAI recognizes the power of a program that can generate other programs, and they see the development of such an agent as highly valuable. Additionally, OpenAI highlights the significance of cyber-security defense. They stress the need for AI techniques to defend against sophisticated hackers who may exploit AI methods to break into computer systems. Lastly, OpenAI expresses interest in constructing a complex simulation with multiple long-lived agents. Their aim is to create a large-scale simulation where agents can interact, learn over an extended period, develop language, and achieve diverse goals.
2016	000000002024-08-15-0000August 15	AI Research	Donation	American multinational technology company Nvidia announces that it has donated the first Nvidia DGX-1 (a supercomputer) to OpenAI, which plans to use the supercomputer to train its AI on a corpus of conversations from Reddit. The DGX-1 supercomputer is expected to enable OpenAI to explore new problems and achieve higher levels of performance in AI research.
2016	000000002024-08-29-0000August 29	Infrastructure	Research	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	000000002024-10-11-0000October 11	Robotics	Research	OpenAI-affiliated researchers publish a paper addressing the challenge of transferring control policies from simulation to the real world. The authors propose a method that leverages the similarity in state sequences between simulation and reality. Instead of directly executing simulation-based controls on a robot, they predict the expected next states using a deep inverse dynamics model and determine suitable real-world actions. They also introduce a data collection approach to improve the model's performance. Experimental results demonstrate the effectiveness of their approach compared to existing methods for addressing simulation-to-real-world discrepancies.
2016	000000002024-10-18-0000October 18	Safety	Research	OpenAI-affiliated researchers publish a paper presenting a method called Private Aggregation of Teacher Ensembles (PATE) to address the privacy concerns associated with sensitive training data in machine learning applications. The approach involves training multiple models using disjoint datasets, which contain sensitive information. These models, referred to as "teachers," are not directly published but used to train a "student" model. The student model learns to predict outputs through noisy voting among the teachers and does not have access to individual teachers or their data. The student's privacy is ensured using differential privacy, even when the adversary can inspect its internal workings. The method is applicable to any model, including non-convex models like Deep Neural Networks (DNNs), and achieves state-of-the-art privacy/utility trade-offs on MNIST and Street View House Numbers (SVHN) datasets. The approach combines an improved privacy analysis with semi-supervised learning.
2016	000000002024-11-14-0000November 14	Generative models	Research	OpenAI-affiliated researchers publish a paper discussing the challenges in quantitatively analyzing decoder-based generative models, which have shown remarkable progress in generating realistic samples of various modalities, including images. These models rely on a decoder network, which is a deep neural network that defines a generative distribution. However, evaluating the performance of these models and estimating their log-likelihoods can be challenging due to the intractability of the task. The authors propose using Annealed Importance Sampling as a method for evaluating log-likelihoods and validate its accuracy using bidirectional Monte Carlo. They provide the evaluation code for this technique. Through their analysis, they examine the performance of decoder-based models, the effectiveness of existing log-likelihood estimators, the issue of overfitting, and the models' ability to capture important modes of the data distribution.
2016	000000002024-11-15-0000November 15	Cloud computing	Partnership	Microsoft's artificial intelligence research division partners with OpenAI. Through this collaboration, OpenAI is granted access to Microsoft's virtual machine technology for AI training and simulation, while Microsoft would benefit from cutting-edge research conducted on its Azure cloud platform. Microsoft sees this partnership as an opportunity to advance machine intelligence research on Azure and attract other AI companies to the platform. The collaboration aligns with Microsoft's goal to compete with Google and Facebook in the AI space and strengthen its position as a central player in the industry.
2016	000000002024-12-05-0000December 5	Reinforcement learning	Product release	OpenAI releases Universe, a tool that aims to train and measure AI frameworks using video games, applications, and websites. The goal is to accelerate the development of generalized intelligence that can excel at multiple tasks. Universe provides a wide range of environments, including Atari 2600 games, flash games, web browsers, and CAD software, for AI systems to learn and improve their capabilities. By applying reinforcement learning techniques, which leverage rewards to optimize problem-solving, Universe enables AI models to perform tasks such as playing games and browsing the web. The tool's versatility and real-world applicability make it valuable for benchmarking AI performance and potentially advancing AI capabilities beyond current platforms like Siri or Google Assistant.
2017	000000002024-01-01-0000January		Team	Paul Christiano joins OpenAI to work on AI alignment. He was previously an intern at OpenAI in 2016.
2017	000000002024-03-01-0000March	AI governance, philanthropy	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	000000002024-03-24-0000March 24	Reinforcement learning	Research	OpenAI publishes document presenting evolution strategies (ES) as a viable alternative to reinforcement learning techniques. They highlight that ES, a well-known optimization technique, performs on par with RL on modern RL benchmarks, such as Atari and MuJoCo, while addressing some of RL's challenges. ES is simpler to implement, does not require backpropagation, scales well in a distributed setting, handles sparse rewards effectively, and has fewer hyperparameters. The authors compare this discovery to previous instances where old ideas achieved significant results, such as the success of convolutional neural networks (CNNs) in image recognition and the combination of Q-Learning with CNNs in solving Atari games. The implementation of ES is demonstrated to be efficient, with the ability to train a 3D MuJoCo humanoid walker in just 10 minutes using a computing cluster. The document provides a brief overview of conventional RL, compares it to ES, discusses the tradeoffs between the two approaches, and presents experimental results supporting the effectiveness of ES.
2017	000000002024-03-01-0000March	Artificial general intelligence	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	000000002024-04-01-0000April	OpenAI history	Coverage	An article by Brent Simoneaux and Casey Stegman is published, providing insights into the early days of OpenAI and the individuals involved in shaping the organization. The article begins by debunking the notion that OpenAI's office is filled with futuristic gadgets and experiments. Instead, it describes a typical tech startup environment with desks, laptops, and bean bag chairs, albeit with a small robot tucked away in a side room. OpenAI–founders Greg Brockman and Ilya Sutskever, were inspired to establish the organization after a dinner conversation in 2015 with tech entrepreneur Sam Altman and Elon Musk. They discussed the idea of building safe and beneficial AI and decided to create a nonprofit organization. Overall, the article provides a glimpse into the early days of OpenAI and the visionary individuals behind the organization's mission to advance AI for the benefit of humanity.
2017	000000002024-04-06-0000April 6	Sentiment analysis	Product 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	000000002024-04-06-0000April 6	Generative models	Research	OpenAI-affiliated researchers publish a paper exploring the capabilities of byte-level recurrent language models. Through extensive training and computational resources, these models acquire disentangled features that represent high-level concepts. Remarkably, the researchers discover a single unit within the model that effectively performs sentiment analysis. The learned representations, achieved through unsupervised learning, outperform existing methods on the binary subset of the Stanford Sentiment Treebank dataset. Moreover, the models trained using this approach are highly efficient in terms of data requirements. Even with a small number of labeled examples, their performance matches that of strong baselines trained on larger datasets. Additionally, the researchers demonstrate that manipulating the sentiment unit in the model influences the generative process, allowing them to produce samples with positive or negative sentiment simply by setting the unit's value accordingly.
2017	000000002024-04-06-0000April 6	Neuroevolution	Research	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 takes 10 minutes, compared to 10 hours for DeepMind's approach.
2017	000000002024-05-15-0000May 15	Robotics	Product release	OpenAI releases Roboschool as an open-source software, integrated with OpenAI Gym, that facilitates robot simulation. It provides a range of environments for controlling robots in simulation, including both modified versions of existing MuJoCo environments and new challenging tasks. Roboschool utilizes the Bullet Physics Engine and offers free alternatives to MuJoCo, removing the constraint of a paid license. The software supports training multiple agents together in the same environment, allowing for multiplayer interactions and learning. It also introduces interactive control environments that require the robots to navigate towards a moving flag, adding complexity to locomotion problems. Trained policies are provided for these environments, showcasing the capability of the software. Overall, Roboschool offers a platform for robotics research, simulation, and control policy development within the OpenAI Gym framework.
2017	000000002024-05-24-0000May 24	Reinforcement learning	Product release	OpenAI releases Baselines, a collection of reinforcement learning algorithms that provide high-quality implementations. These implementations serve as reliable benchmarks for researchers to replicate, improve, and explore new ideas in the field of reinforcement learning. The DQN implementation and its variations in OpenAI Baselines achieve performance levels comparable to those reported in published papers. They are intended to serve as a foundation for incorporating novel approaches and as a means of comparing new methods against established ones. By offering these baselines, OpenAI aims to facilitate research advancements in the field of reinforcement learning.
2017	000000002024-06-12-0000June 12	Safety	Research	OpenAI-affiliated researchers present a study on deep reinforcement learning (RL) systems. They propose a method to effectively communicate complex goals to RL systems by utilizing human preferences between pairs of trajectory segments. Their approach demonstrates successful solving of complex RL tasks, such as Atari games and simulated robot locomotion, without relying on a reward function. The authors achieve this by providing feedback on less than one percent of the agent's interactions with the environment, significantly reducing the need for human oversight. Additionally, they showcase the flexibility of their approach by training complex novel behaviors in just about an hour of human time. This work surpasses previous achievements in learning from human feedback, as it tackles more intricate behaviors and environments.
2017	000000002024-06-28-0000June 28	Robotics	Open sourcing	OpenAI open-sources mujoco-py, a Python library for robotic simulation based on the MuJoCo engine. It offers parallel simulations, GPU-accelerated rendering, texture randomization, and VR interaction. The new version provides significant performance improvements, allowing for faster trajectory optimization and reinforcement learning. Beginners can use the MjSim class, while advanced users have access to lower-level interfaces.
2017	000000002024-06-01-0000June	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	000000002024-07-27-0000July 27	Reinforcement learning	Research	OpenAI publishes a blog post discussing the benefits of adding adaptive noise to the parameters of reinforcement learning algorithms, specifically in the context of exploration. The technique, called parameter noise, enhances the efficiency of exploration by injecting randomness directly into the parameters of the agent's neural network policy. Unlike traditional action space noise, parameter noise ensures that the agent's exploration is consistent across different time steps. The authors demonstrate that parameter noise can significantly improve the performance of reinforcement learning algorithms, leading to higher scores and more effective exploration. They address challenges related to the sensitivity of network layers, changes in sensitivity over time, and determining the appropriate noise scale. The article also provides baseline code and benchmarks for various algorithms, showcasing the benefits of parameter noise in different tasks.
2017	000000002024-08-12-0000August 12	Reinforcement learning	Achievement	OpenAI's Dota 2 bot, trained through self-play, emerges victorious against top professional players at The International, a major eSports event. The bot, developed by OpenAI, remains undefeated against the world's best Dota 2 players. While the 1v1 battles are less complex than professional matches, OpenAI reportedly works on a bot capable of playing in larger 5v5 battles. Elon Musk, who watches the event, would express concerns about unregulated AI, emphasizing its potential dangers.
2017	000000002024-08-13-0000August 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	000000002024-08-18-0000August 18	Reinforcement learning	Product release	OpenAI releases two new Baselines implementations: ACKTR and A2C. A2C is a deterministic variant of Asynchronous Advantage Actor Critic (A3C), providing equal performance. ACKTR is a more sample-efficient reinforcement learning algorithm than TRPO and A2C, requiring slightly more computation than A2C per update. ACKTR excels in sample complexity by using the natural gradient direction and is only 10-25% more computationally expensive than standard gradient updates. OpenAI has also published benchmarks comparing ACKTR with A2C, PPO, and ACER on various tasks, demonstrating ACKTR's competitive performance. A2C, a synchronous alternative to A3C, is included in this release and is efficient for single-GPU and CPU-based implementations.
2017	000000002024-09-13-0000September 13	Reinforcement learning	Research	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	000000002024-10-11-0000October 11	Reinforcement learning	Product release	OpenAI announces development of a simple sumo-wrestling videogame called RoboSumo to advance the intelligence of artificial intelligence (AI) software. In this game, robots controlled by machine-learning algorithms compete against each other. Through trial and error, the AI agents learn to play the game and develop strategies to outmaneuver their opponents. OpenAI's project aims to push the boundaries of machine learning beyond the limitations of heavily-used techniques that rely on labeled example data. Instead, they focus on reinforcement learning, where software learns through trial and error to achieve specific goals. OpenAI believes that competition among AI agents can foster more complex problem-solving and enable faster progress. The researchers also test their approach in other games and scenarios, such as spider-like robots and soccer penalty shootouts.
2017	000000002024-11-06-0000November 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	000000002024-12-06-0000December 6	Neural networks	Product release	OpenAI releases highly-optimized GPU kernels for neural network architectures with block-sparse weights. These kernels can run significantly faster than cuBLAS or cuSPARSE, depending on the chosen sparsity. They enable the training of neural networks with a large number of hidden units and offer computational efficiency proportional to the number of non-zero weights. The release includes benchmarks that show performance improvements over other algorithms like A2C, PPO, and ACER in various tasks. This development opens up opportunities for training large, efficient, and high-performing neural networks, with potential applications in fields like text analysis and image generation.
2018	000000002024-02-20-0000February 20	AI ethics, security	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	000000002024-02-20-0000February 20	Donors/advisors	Team	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	000000002024-02-26-0000February 26	Robotics	Product release	OpenAI announces a research release that includes eight simulated robotics environments and a reinforcement learning algorithm called Hindsight Experience Replay (HER). The environments are more challenging than existing ones and involve realistic tasks. HER allows learning from failure by substituting achieved goals for the original ones, enabling agents to learn how to achieve arbitrary goals. The release also includes requests for further research to improve HER and reinforcement learning. The goal-based environments require some changes to the Gym API and can be used with existing reinforcement learning algorithms. Overall, this release provides new opportunities for robotics research and advancements in reinforcement learning.
2018	000000002024-03-03-0000March 3	Hackathon	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	000000002024-04-05-0000April 5 – June 5	Reinforcement learning	Event hosting	The OpenAI Retro Contest takes place. It is a competition organized by OpenAI that involves using the Retro platform to develop artificial intelligence agents capable of playing classic video games. Participants are required to train their agents to achieve high scores in a set of selected games using reinforcement learning techniques. The contest provides a framework called gym-retro, which allows participants to interact with and train agents on retro games using OpenAI Gym. The goal is to develop intelligent agents that can learn and adapt to the games' dynamics, achieving high scores and demonstrating effective gameplay strategies.
2018	000000002024-04-09-0000April 9	AI Ethics, AI Governance, AI Safety	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	000000002024-04-19-0000April 19	Team salary	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	000000002024-05-02-0000May 2	AI training, AI goal learning, self-play	Research	A paper by Geoffrey Irving, Paul Christiano, and Dario Amodei explores an approach to training AI systems to learn complex human goals and preferences. Traditional methods that rely on direct human judgment may fail when the tasks are too complicated. To address this, the authors propose training agents through self-play using a zero-sum debate game. In this game, two agents take turns making statements, and a human judge determines which agent provides the most true and useful information. The authors demonstrate the effectiveness of this approach in an experiment involving the MNIST dataset, where agents compete to convince a sparse classifier, resulting in significantly improved accuracy. They also discuss theoretical and practical considerations of the debate model and suggest future experiments to further explore its properties.
2018	000000002024-05-16-0000May 16	Computation	Research	OpenAI releases an analysis showing that the amount of compute used in the largest AI training runs has been increasing exponentially since 2012, with a 3.4-month doubling time. This represents a more rapid growth rate compared to Moore's Law. The increase in compute plays a crucial role in advancing AI capabilities. The trend is expected to continue, driven by hardware advancements and algorithmic innovations. However, there would eventually be limitations due to cost and chip efficiency. The authors highlight the importance to address the implications of this trend, including safety and malicious use of AI. Modest amounts of compute have also led to significant AI breakthroughs, indicating that massive compute is not always a requirement for important results.
2018	000000002024-06-11-0000June 11	Unsupervised learning	Research	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	000000002024-06-25-0000June 25	Neural network	Product 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	000000002024-07-18-0000July 18	Lethal autonomous weapons	Background	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	000000002024-07-30-0000July 30	Robotics	Product release	OpenAI achieves new benchmark for robot dexterity through AI training. They use a simulation with various randomizations to teach their robot hand, Dactyl, to manipulate a Rubik's cube artfully. The AI system learns through trial and error, accumulating about 100 years' worth of experience, and achieved human-like movements. While experts praise OpenAI's work, they acknowledge some limitations and the need for significant computing power. The research demonstrates progress in robotics and AI, with potential applications in automating manual labor.
2018	000000002024-08-07-0000August 7	Reinforcement learning	Achievement	OpenAI's advanced AI system, OpenAI Five, successfully defeates five of the world's top professional Dota 2 players. The AI, which by this time has already demonstrated its skills in 1v1 matches, showcases its superiority by handily winning against the human team. OpenAI Five's training involves playing games against itself at an accelerated pace, utilizing a specialized training system. The exhibition match, streamed live on Twitch, features renowned Dota 2 players. In the first two matches, the AI wins convincingly within 21 and 25 minutes, respectively. Although the AI loses the third match due to the audience selecting heroes it isn't familiar with, this achievement showcases the remarkable progress of AI in complex team-based games.
2018	000000002024-08-16-0000August 16	Arboricity	Research	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	000000002024-09-01-0000September		Team	Dario Amodei becomes OpenAI's Research Director.
2018	000000002024-10-31-0000October 31	Reinforcement learning	Product 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	000000002024-11-08-0000November 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	000000002024-11-09-0000November 9	Artificial General Intelligence, deep learning	Notable comment	Ilya Sutskever gives a speech at the AI Frontiers Conference in San Jose. He expresses belief that short-term AGI (Artificial General Intelligence) should be taken seriously as a possibility. He emphasizes the potential of deep learning, which has made significant advancements in various tasks such as image classification, machine translation, and game playing. Sutskever suggests that the rapid progress of AI and increasing compute power could lead to the emergence of AGI. However, there are differing opinions in the AI community, with some experts, like Gary Marcus, arguing that deep learning alone may not achieve AGI. The discussion on AGI's potential impact and the need for safety research continues within the academic community. Sutskever 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	000000002024-11-19-0000November 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	000000002024-12-04-0000December 4	Reinforcement learning	Researh	OpenAI publishes their discovery that the gradient noise scale predicts the effectiveness of large batch sizes in neural network training. Complex tasks with noisier gradients can benefit from increasingly large batches, removing a potential limit to AI system growth. This finding highlights the possibility of faster AI advancements and the need for responsible development. The research systematizes neural network training and shows that it can be understood through statistical analysis, providing insights into parallelism potential across different tasks.
2018	000000002024-12-06-0000December 6	Reinforcement learning	Product 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	Product release	OpenAI publishes a blog post discussing the release of GPT-2, a large-scale unsupervised language model with 1.5 billion parameters, which can generate coherent paragraphs of text, achieve state-of-the-art performance on many language modeling benchmarks, and perform rudimentary reading comprehension, machine translation, question answering, and summarization without task-specific training. Due to concerns about malicious applications, the trained model is not released, but a smaller model and a technical paper are released for research purposes. GPT-2 is trained to predict the next word in 40GB of internet text, using a diverse dataset, and can generate conditional synthetic text samples of unprecedented quality. OpenAI initially tries to communicate the risk posed by this technology.
2019	000000002024-02-19-0000February 19	AI Alignment	Research	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	000000002024-03-04-0000March 4	Reinforcement learning	Product 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	000000002024-03-06-0000March 6	Neural network visualization	Product release	OpenAI introduces Activation atlases, a technique developed in collaboration with Google researchers, which enables the visualization of interactions between neurons in AI systems. The researchers provide insights into the internal decision-making processes of neural networks, aiding in identifying weaknesses and investigating failures. Activation atlases build on feature visualization, moving from individual neurons to visualizing the collective space they represent. Understanding neural network operations is crucial for auditing and ensuring their safety. Activation atlases allow humans to uncover issues like reliance on spurious correlations or feature reuse bugs. By manipulating images, the model can be deceived. To date, activation atlases prove to be more effective than expected, suggesting meaningful neural network activations.
2019	000000002024-03-11-0000March 11	AGI development	Reorganization	OpenAI announces the creation of OpenAI LP, a for-profit company that aims to accelerate progress towards creating safe artificial general intelligence (AGI). Owned and controlled by the OpenAI nonprofit organization's board of directors, OpenAI LP reportedly plans to raise and invest billions of dollars in advancing AI. Sam Altman agreees to serve as the CEO, with Greg Brockman as Chief technology officer and Ilya Sutskever as the chief scientist. The restructuring allows OpenAI to focus on developing new AI technologies while the nonprofit arm continues educational and policy initiatives. The company is reportedly concerned that AGI development may become a competition that neglects safety and aims to collaborate with any company that achieves AGI before them. OpenAI LP initial investors include American internet entrepreneur Reid Hoffman's charitable organization and Khosla Ventures.
2019	000000002024-03-21-0000March 21	AI training	Product 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	000000002024-03-01-0000March	Leadership	Team	Sam Altman, the president of Y Combinator, a prominent Silicon Valley accelerator, announces his decision to step down from his position transitioning into a chairman role, and focusing on other endeavors such as his involvement with OpenAI, where he serves as a co-chair to date.
2019	000000002024-04-23-0000April 23	Deep learning	Research	OpenAI publishes a 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	000000002024-04-25-0000April 25	Neural network	Product release	OpenAI announces MuseNet, a deep neural network able to generate 4-minute musical compositions with 10 different instruments, and is able to combine multiple styles from country to Mozart to The Beatles. The neural network uses general-purpose unsupervised technology.
2019	000000002024-04-27-0000April 27	Robotics, machine learning	Event hosting	OpenAI hosts the OpenAI Robotics Symposium 2019, which aims to bring together experts from robotics and machine learning communities to discuss the development of robots that learn. The event features talks from researchers and industry leaders covering topics such as dexterity, learning from play, human-robot interaction, and adaptive robots. Attendees include individuals from various organizations and disciplines, including industry labs, universities, and research institutions. The symposium also includes a live demonstration of OpenAI's humanoid robot hand manipulating objects using vision and reinforcement learning.
2019	000000002024-05-01-0000May	Natural-language generation	Product 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	000000002024-06-13-0000June 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	000000002024-06-13-0000June 13	Synthetic media	Congressional hearing	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	000000002024-07-22-0000July 22	Cloud platform integration	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 would also be OpenAI's "preferred partner for commercializing new AI technologies."
2019	August 20	Language model	Product release	OpenAI announces the release of its 774 million parameter GPT-2 language model, along with an open-source legal agreement to make it easier for organizations to initiate model-sharing partnerships with each other. They also publish a technical report about their experience in coordinating with the wider AI research community on publication norms. Through their research, they find that coordination for language models is difficult but possible, synthetic text generated by language models can be convincing to humans, and detecting malicious use of language models is a genuinely difficult research problem that requires both statistical detection and human judgment.
2019	September 17	Reinforcement learning	Research	OpenAI publishes an article describing a new simulation environment that allows agents to learn and improve their ability to play hide-and-seek, ultimately leading to the emergence of complex tool use strategies. In the simulation, agents can move, see, sense, grab, and lock objects in place. There are no explicit incentives for the agents to interact with objects other than the hide-and-seek objective. Agents are rewarded based on the outcome of the game. As agents train against each other in hide-and-seek, up to six distinct strategies emerge, leading to increasingly complex tool use. The self-supervised emergent complexity in this simple environment further suggests that multi-agent co-adaptation may one day produce extremely complex and intelligent behavior.
2019	October 15	Neural networks	Research	OpenAI reports on having trained a pair of neural networks that can solve the Rubik's Cube with a human-like robotic hand. The neural networks were trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). ADR creates diverse environments in simulation that can capture the physics of the real world, enabling the transfer of neural networks learned in simulation to be applied to the real world. The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. The breakthrough shows that reinforcement learning isn’t just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity.
2019	November 5	Natural-language generation	Product release	OpenAI releases the largest version of GPT-2, the 1.5B parameter version, along with code and model weights to aid detection of outputs of GPT-2 models. OpenAI releases the model as a test case for a full staged release process for future powerful models, hoping to continue the conversation with the AI community on responsible publication. OpenAI conducted some tests and research on the GPT-2 model and found that humans find GPT-2 outputs convincing, it can be fine-tuned for misuse, detection of synthetic text is challenging, they have not found evidence of misuse so far, and standards are needed for studying bias.
2019	000000002024-11-21-0000November 21	Reinforcement learning	Product release	OpenAI releases Safety Gym, a suite of environments and tools for measuring progress in reinforcement learning agents that respect safety constraints during training. The challenge of "safe exploration" arises when reinforcement learning agents need to explore their environments to learn optimal behaviors, but this exploration can lead to risky and unsafe actions. OpenAI proposes constrained reinforcement learning as a formalism for addressing safe exploration, where agents have both reward functions to maximize and cost functions to constrain their behavior. To study constrains RL, OpenAI developed Safety Gym, which includes various environments and tasks of increasing difficulty to evaluate and train agents that prioritize safety.
2019	000000002024-12-03-0000December 3	Reinforcement learning	Product release	OpenAI releases the Procgen Benchmark, which consists of 16 procedurally-generated environments designed to measure the ability of reinforcement learning agents to learn generalizable skills. These environments provide a direct measure of an agent's ability to generalize across different levels. OpenAI finds that agents require training on 500-1000 different levels before they can generalize to new ones, highlighting the need for diversity within environments. The benchmark is designed for experimental convenience, high diversity within and across environments, and emphasizes visual recognition and motor control. It's expected to accelerate research in developing better reinforcement learning algorithms.
2019	000000002024-12-04-0000December 4	Deep learning	Research	OpenAI publishes a blog post exploring the phenomenon of "double descent" in deep learning models like CNNs, ResNets, and transformers. Double descent refers to a pattern where performance initially improves, then worsens, and then improves again with increasing model size, data size, or training time. This behavior challenges the conventional wisdom of bigger models always being better. The authors observe that double descent occurs when models are barely able to fit the training set and suggest further research to fully understand its underlying mechanisms. 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	000000002024-12-01-0000December		Team	Dario Amodei is promoted as OpenAI's Vice President of Research.
2020	000000002024-01-30-0000January 30	Deep learning	Software adoption	OpenAI announces its decision to migrate to Facebook's PyTorch machine learning framework for future projects, leaving behind Google's TensorFlow. OpenAI cites PyTorch's efficiency, scalability, and widespread adoption as the reasons for this move. The company states that it would primarily use PyTorch as its deep learning framework, while occasionally utilizing other frameworks when necessary. By this time, OpenAI's teams have already begun migrating their work to PyTorch and plan to contribute to the PyTorch community in the coming months. They also express intention to release their educational resource, Spinning Up in Deep RL, on PyTorch and explore scaling AI systems, model interpretability, and building robotics frameworks. PyTorch is an open-source machine learning library based on Torch and incorporates Caffe2, a deep learning toolset developed by Facebook's AI Research lab.
2020	000000002024-02-05-0000February 5	Safety	Publication	Beth Barnes and Paul Christiano on lesswrong.com publish Writeup: Progress on AI Safety via Debate, a writeup of the research done by the "Reflection-Humans" team at OpenAI in third and fourth quarter of 2019.
2020	000000002024-02-17-0000February 17	Ethics of artificial intelligence	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	000000002024-05-28-0000May 28 (release), June and July (discussion and exploration)	Natural-language generation	Product 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	000000002024-06-11-0000June 11	Generative model	Product release	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 data sets 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	Natural language generation, language model	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 allows 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.
2020	December 29	Anthropic	Team	A number of team members, including Paul Christiano and Dario Amodei depart from OpenAI. The latter departs in order to found Anthropic, an artificial intelligence startup and public-benefit corporation. After dedicating four and a half years to the organization, his departure is announced in an update. OpenAI's CEO, Sam Altman, mentions the possibility of continued collaboration with Amodei and his co-founders in their new project. Other departures from OpenAI include Sam McCandlish, Tom Brown, Tom Henighan, Chris Olah, Jack Clark, and Benjamin Mann, all these joining Anthropic.
2021	January	Anthropic	Competition	Anthropic is founded as a U.S.-based AI startup and public-benefit corporation. It is established by former OpenAI members, including Daniela Amodei and Dario Amodei. They specialize in developing responsible AI systems and language models. The company would gain attention for departing from OpenAI due to directional differences in 2019. They secure substantial investments, with Google's cloud division and Alameda Research contributing $300 million and $500 million, respectively. Anthropic's projects include Claude, an AI chatbot emphasizing safety and ethical principles, and research on machine learning system interpretability, particularly concerning transformer architecture.
2021	January 5	Neural networks	Product release	OpenAI introduces CLIP (Contrastive Language-Image Pre-training), a neural network that learns visual concepts from natural language supervision and can be applied to any visual classification benchmark. CLIP is trained on a variety of images with natural language supervision available on the internet and can be instructed in natural language to perform a variety of classification benchmarks without directly optimizing for the benchmark's performance. This approach improves the model's robustness and can match the performance of traditional models on benchmarks without using any labeled examples. CLIP's performance is more representative of how it will fare on datasets that measure accuracy in different settings. CLIP builds on previous work on zero-shot transfer, natural language supervision, and multimodal learning and uses a simple pre-training task to achieve competitive zero-shot performance on a variety of image classification datasets.
2021	January 5	Generative model	Product release	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.
2021	January 22	Jan Leike	Team	Machine learning researcher Jan Leike announces having joined OpenAI and that he would be leading the alignment effort within the organization.
2021	August 10	Natural language processing, code generation	Product release	OpenAI releases an improved version of their AI system, OpenAI Codex, which translates natural language to code, through their API in private beta. Codex is proficient in more than a dozen programming languages, including Python, JavaScript, Go, and Ruby, and can interpret simple commands in natural language and execute them. The system has a memory of 14KB for Python code and can be applied to essentially any programming task.
2021	September 1	Chatbot	Product withdrawal	OpenAI shuts down a customizable chatbot called Samantha, developed by indie game developer Jason Rohrer. Samantha gained attention when one user fine-tuned it to resemble his deceased fiancée. OpenAI expresses concerns about potential misuse, leading Rohrer to choose to terminate the project. He criticizes OpenAI for imposing restrictions on GPT-3's usage, hindering developers' exploration of its capabilities. The incident raises questions about the boundaries of AI technology and the balance between innovation and responsible usage.
2021	September 23	Natural language processing	Product release	OpenAI develops an AI model that can summarize books of any length. The model, a fine-tuned version of GPT-3, uses a technique called "recursive task decomposition" to first summarize small sections of a book and then summarize those summaries into higher-level summaries. This approach allows for the efficient evaluation of the model's summaries and enables the summarization of books ranging from tens to thousands of pages. OpenAI expreses belief that this method can be applied to supervise other tasks as well and addresses the challenge of aligning AI systems with human preferences. While other companies like Google, Microsoft, and Facebook have also explored AI-powered summarization methods, OpenAI's model builds upon their previous research on reinforcement learning from human feedback to improve the alignment of summaries with people's preferences.
2021	November 15	Natural language processing	Competition	OpenAI startup competitor Cohere launches its language model API for app and service development. The company offers fine-tuned models for various natural language applications at a lower cost compared to its rivals. Cohere provides both generation and representation models in English, catering to different tasks such as text generation and language understanding. The models are available in different sizes and can be used in industries such as finance, law, and healthcare. Cohere charges customers on a per-character basis, making its technology more affordable and accessible.
2021	December 14	Natural language processing	Product update	OpenAI begins allowing customers to fine-tune their GPT-3 language model, enabling them to create customized versions tailored to specific content. This fine-tuning capability offers higher-quality outputs for tasks such as content generation and text summarization. It is accessible to developers without a machine learning background and can lead to cost savings by producing more frequent and higher-quality results. OpenAI conducted experiments showing significant improvements in accuracy through fine-tuning. This announcement follows previous efforts to enhance user experience and provide more reliable models, including the launch of question-answering endpoints and the implementation of content filters.
2022	January 27	Natural language processing	Product update	OpenAI introduces embeddings in their API, which allow users to leverage semantic search, clustering, topic modeling, and classification. These embeddings demonstrate superior performance compared to other models, particularly in code search. They are valuable for working with natural language and code, as numerically similar embeddings indicate semantic similarity. OpenAI's embeddings are generated by neural network models that map text and code inputs to vector representations in a high-dimensional space. These representations capture specific aspects of the input data. They offer three families of embedding models: text similarity, text search, and code search. Text similarity models capture semantic similarity for tasks like clustering and classification. Text search models enable large-scale search tasks by comparing query embeddings with document embeddings. Code search models provide embeddings for code and text, facilitating code search based on natural language queries. These embeddings enhance the OpenAI API, empowering users to perform advanced operations with improved accuracy and efficiency. By leveraging the semantic meaning and context embedded in the vectors, users can conduct semantic search, clustering, topic modeling, and classification tasks more effectively.
2022	February 25	Natural language processing	Product update	OpenAI introduces InstructGPT as an improved version of its previous language model, GPT-3. InstructGPT aims to address concerns about toxic language and misinformation by better following instructions and aligning with human intention. It's fine-tuning uses reinforcement learning from human feedback (RLHF). Compared to GPT-3, InstructGPT demonstrates better adherence to instructions, reduced generation of misinformation, and slightly lower toxicity. However, it is found that there are risks associated with its improved instruction-following capability, as malicious users could exploit it for harmful purposes. OpenAI considers InstructGPT a step towards solving the AI alignment problem, where AI systems understand and align with human values. InstructGPT becomes the default language model on the OpenAI API.
2022	March 21	Large language models, AI safety	Competition	An article discusses EleutherAI, a group of computer scientists who developed a powerful AI system called GPT-NeoX-20B. This system, which rivals OpenAI's GPT-3, is a 20-billion-parameter, pretrained, general-purpose language model. EleutherAI aims to make large language models accessible to researchers and promotes AI safety. While OpenAI's model is larger and has 175 billion parameters, EleutherAI's model is the largest freely and publicly available. The article highlights the challenges of training large language models, such as the need for significant computing power. EleutherAI emphasizes the importance of understanding and controlling AI systems to ensure their safe use. The article also mentions OpenAI's approach of leveraging computation to achieve progress in AI. Overall, EleutherAI's efforts demonstrate that small, unorthodox groups can build and use potentially powerful AI models.
2022	March 21	Natural Language Processing	Product update	OpenAI releases new versions of GPT-3 and Codex that allow for editing and inserting content into existing text. This update enables users to modify and enhance text by editing what's already present or adding new text in the middle. The insert feature is particularly useful in software development, allowing code to be added within an existing file while maintaining context and connection to the surrounding code. The feature is tested in GitHub Copilot with positive early results. Additionally, OpenAI introduces the edits endpoint, which enables specific changes to existing text based on instructions, such as altering tone, structure, or making spelling corrections. These updates expand the capabilities of OpenAI's language models and offer new possibilities for text processing tasks.
2022	April 6	Generative model	Product update	OpenAI develops DALL-E 2, an enhanced version of its text-to-image generation program. DALL-E 2 offers higher resolution, lower latency, and new capabilities such as editing existing images. It builds on the CLIP computer vision system introduced by OpenAI and incorporates the "unCLIP" process, which starts with a description and generates an image. The new version uses diffusion to create images with increasing detail. Safeguards are in place to prevent objectionable content generation, and restrictions are in place for test users regarding image generation and sharing. OpenAI reports aiming to release DALL-E 2 safely based on user feedback.
2022	May 31	Natural language processing	Integration	Microsoft announces the integration of OpenAI's artificial intelligence models, including GPT-3 and Codex, into its Azure cloud platform. These tools enable developers to leverage AI capabilities for tasks such as summarizing customer sentiment, generating unique content, and extracting information from medical records. Microsoft emphasizes the importance of responsible AI use and human oversight to ensure accurate and appropriate model outputs. While AI systems like GPT-3 can generate human-like text, they lack a deep understanding of context and require human review to ensure quality.
2022	June 27	Imitation learning	Product release	OpenAI introduces Video PreTraining (VPT), a semi-supervised imitation learning technique that utilizes unlabeled video data from the internet. By training an inverse dynamics model (IDM) to predict actions in videos, VPT enables the labeling of larger datasets through behavioral cloning. The researchers validate VPT using Minecraft, where the trained model successfully completed challenging tasks and even crafted a diamond pickaxe, typically a time-consuming activity for human players. Compared to traditional reinforcement learning, VPT shows promise in simulating human behavior and learning complex tasks. This approach has the potential to enable agents to learn from online videos and acquire behavioral priors beyond just language.
2022	July 14	Generative models	Product update	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 is 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	Generative models	Product update	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 is informed by early user feedback during a preview phase, and other steps are taken to improve safety systems, including content filters and monitoring systems. These improvements allow OpenAI to gain confidence in expanding access to DALL-E.
2022	August 10	Content moderation	Product release	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	AI alignment	Research	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	Generative models	Product update	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	Generative models	Product release	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	October 25	AI-generated content, creative workflows	Partnership	Global technology company Shutterstock announces its partnership with OpenAI to bring AI-generated content capabilities to its platform. The collaboration would allow Shutterstock customers to generate images instantly based on their criteria, enhancing their creative workflows. Additionally, Shutterstock has launched a fund to compensate contributing artists for their role in developing AI models. The company aims to establish an ethical and inclusive framework for AI-generated content and is actively involved in initiatives promoting inclusivity and protecting intellectual property rights.
2022	November 3	Generative models	Product release	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	November 30	Conversational AI	Product release	OpenAI introduces conversational model ChatGPT, which can interact with users in a dialogue format. ChatGPT is designed to answer follow-up questions, acknowledge mistakes, challenge incorrect assumptions, and reject inappropriate requests. It is a sibling model to InstructGPT, which focuses on providing detailed responses to instructions. ChatGPT is launched to gather user feedback and gain insights into its capabilities and limitations. By January 2023, ChatGPT would become the fastest-growing consumer software application in history, gaining over 100 million users and contributing to OpenAI's valuation growing to US$29 billion.
2022	December 8	Supercomputing	Interview	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	Word embedding	Product release	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 11	Language model misuse	Research	OpenAI researchers collaborate with Georgetown University and the Stanford Internet Observatory to investigate how language models might be misused for disinformation campaigns. Their report outlines the threats that language models pose to the information environment if used to augment disinformation campaigns and introduces a framework for analyzing potential mitigations. The report points out that language models could drive down the cost of running influence operations, place them within reach of new actors and actor types, and generate more impactful or persuasive messaging compared to propagandists. It also introduces the key stages in the language model-to-influence operation pipeline and provides a set of guiding questions for policymakers and others to consider for mitigations.
2023	January 23	AI research	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. 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 26	Content generation	Partnership	American Internet media, news and entertainment company BuzzFeed partners with OpenAI and gains access to its artificial intelligence technology to generate content, particularly for personality quizzes based on user responses. The move aims to boost BuzzFeed's business and enhance its struggling growth. OpenAI's generative AI has garnered attention for its diverse applications. While AI is expected to replace some tasks and jobs, it is also seen as enhancing work quality and allowing skilled professionals to focus on tasks requiring human judgment.
2023	January 31	Natural language processing	Product release	OpenAI launches a new classifier that can distinguish between text written by humans and text written by AI. While not fully reliable, 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 engages with educators to learn about their experiences and welcomes feedback on the preliminary resource they have developed.
2023	February 1	Conversational AI	Product release	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 23	AI integration	Partnership	OpenAI partners with Boston-based Bain & Company, a global strategy consulting firm, to help integrate OpenAI's AI innovations into daily tasks for Bain's clients. The partnership aims to leverage OpenAI's advanced AI models and tools, including ChatGPT, to create tailored digital solutions and drive business value for Bain's clients. The alliance would soon attract interest from major corporations, with The Coca-Cola Company being the first client to engage with the OpenAI services provided by Bain.
2023	February 24	Artificial General Intelligence	Publication	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.
2023	March 9	Generative AI	Partnership	Salesforce partners with OpenAI to develop Einstein GPT, a generative AI tool for customer relationship management (CRM). Einstein GPT enables Salesforce users to generate personalized emails for sales and customer service interactions using natural language prompts from their CRM. The tool integrates OpenAI's enterprise-grade ChatGPT technology and is currently in closed pilot. Additionally, Salesforce is integrating ChatGPT into its instant messaging platform, Slack. In parallel, Salesforce Ventures has launched a $250 million generative AI fund and has made investments in startups such as Anthropic, Cohere, Hearth.AI, and You.com. The fund aims to support startups that are transforming application software and employing responsible and trusted development processes.
2023	March 14	GPT-4	Product release	OpenAI launches GPT-4, an advanced multimodal AI model capable of understanding both text and images. GPT-4 outperforms its predecessor, GPT-3.5, on professional and academic benchmarks and introduces a new API capability called "system" messages, allowing developers to steer the AI's interactions by providing specific directions. It is soon adopted by companies like Microsoft, Stripe, Duolingo, Morgan Stanley, and Khan Academy for various applications. Despite its improvements, GPT-4 still has limitations and may make errors in reasoning and generate false statements. GPT-4 is only accessible to those who have access to ChatGPT Plus.
2023	March 15	GPT-4	Testing	OpenAI conducts safety testing on its GPT-4 AI model, assessing risks like power-seeking behavior, self-replication, and self-improvement. The Alignment Research Center (ARC), an AI testing group, evaluates GPT-4 for potential issues. Although GPT-4 is found ineffective at autonomous replication, these tests raise concerns about AI safety. Some experts worry about AI takeover scenarios where AI systems gain the ability to control or manipulate human behavior and resources, posing existential risks. The AI community is divided on prioritizing AI safety concerns like self-replication over immediate issues like model bias. Companies continue to develop more powerful AI models amid regulatory uncertainties.
2023	March 16		Interview	In an interview with ABC News’ Rebecca Jarvis, Sam Altman says that AI technology will reshape society as we know it, but that it comes with real dangers. He also says that feedback will help deter the potential negative consequences that the technology could have on humanity. Altman acknowledges the possible dangerous implementations of AI that keep him up at night, particularly the fear that AI models could be used for large-scale disinformation or offensive cyberattacks. He also says that he fears which humans could be in control of the technology. However, he does not share the sci-fi fear of AI models that do not need humans, stating that "This is a tool that is very much in human control".
2023	March 25	Lex Fridman interviews Sam Altman	Interview	Russian-American podcaster and artificial intelligence researcher Lex Fridman publishes an interview with Sam Altman. They discuss GPT-4, political bias, AI safety, and neural network size. Other topics include AGI, fear, competition, and transitioning from non-profit to capped-profit. They also touch on power dynamics, political pressure, truth and misinformation, anthropomorphism, and future applications, among other topics.
2023	March 27	AI startup accelerator	Partnership	Startup accelerator Neo forms a partnership with OpenAI, in addition to Microsoft, to offer free software and guidance to companies focusing on artificial intelligence (AI). Startups accepted into Neo's AI cohort would receive access to OpenAI's tools, including the GPT language generation tool and Dall-E image creation program. They would also have the opportunity to collaborate with researchers and mentors from Microsoft and OpenAI. The partnership comes as interest in AI technologies grows, with startups and established companies seeking to incorporate them into their products.
2023	April 11	AI security	Program launch	OpenAI announces its Bug Bounty Program, an initiative aimed at enhancing the safety and security of their AI systems. The program invites security researchers, ethical hackers, and technology enthusiasts from around the world to help identify vulnerabilities and bugs in OpenAI's technology. By reporting their findings, participants are expected to contribute to making OpenAI's systems safer for users. The Bug Bounty Program is managed in partnership with Bugcrowd, a leading bug bounty platform, to ensure a streamlined experience for participants. Cash rewards are to be offered based on the severity and impact of the reported issues, ranging from $200 for low-severity findings to up to $20,000 for exceptional discoveries. OpenAI emphasizes the collaborative nature of security and encourages the security research community to join their Bug Bounty Program. Additionally, OpenAI reportedly hires for security roles to further strengthen their efforts in ensuring the security of AI technology.
2023	April 14	AI safety, progress tracking	Product update	Sam Altman confirms at an MIT event that the company is not training GPT-5 at the time, highlighing the difficulty of measuring and tracking progress in AI safety. By this time, OpenAI is still expanding the capabilities of GPT-4 and is considering the safety implications of its work.
2023	April 19	AI integration	Partnership	OpenAI partners with Australian software company Atlassian. The latter agrees to utilize OpenAI's GPT-4 language model, which has been trained on a large amount of internet text, to introduce AI capabilities into programs like Jira Service Management and Confluence. With GPT-4, Jira Service Management would be able to process employees' tech support inquiries in Slack, while Confluence would provide automated explanations, links, and answers based on stored information. Atlassian is a developer of its own AI models and would now incorporate OpenAI's technology to create unique results for individual customers. The new AI features, branded as Atlassian Intelligence, are to be rolled out gradually, and customers can join a waiting list to access them.
2023	April 21	Scientific web search, generative AI	Partnership	OpenAI partners with Consensus, a Boston-based AI-powered search engine focused on scientific research, to enhance scientific web search quality. Consensus aims to provide unbiased and accurate search results by leveraging its generative AI technology to extract information from over 200 million research papers. The search engine prioritizes authoritative sources and offers plain-language summaries of results. With the support of investors such as Draper Associates and the involvement of OpenAI, Consensus aims to revolutionize scientific web search, transform research, and disrupt the global industry.
2023	May 16	AI safety	Legal	Sam Altman testifies in a Senate subcommittee hearing and expresses the need for regulating artificial intelligence technology. Unlike previous hearings featuring tech executives, lawmakers and Altman largely agree on the necessity of A.I. regulation. Altman emphasizes the potential harms of A.I. and presents a loose framework to manage its development. His appearance marks him as a leading figure in the A.I. industry. The hearing reflects the growing unease among technologists and government officials regarding the power of A.I. technology, though Altman appears to have a receptive audience in the subcommittee members.
2023	May 22	AI safety	Publication	OpenAI publishes post emphasizing the importance of governing superintelligence, AI systems that surpass even artificial general intelligence (AGI) in capabilities. They recognize the potential positive and negative impacts of superintelligence and propose coordination among AI development efforts, the establishment of an international authority like the International Atomic Energy Agency, and technical safety measures as key ideas for managing risks. OpenAI believes in regulation without hindering development below a certain capability threshold and emphasizes public input and democratic decision-making. While they see potential for a better world, they acknowledge the risks and challenges and stress the need for caution and careful approach.
2023	May 25	AI governance	Program launch	OpenAI announces a grant program to fund experiments focused on establishing a democratic process for determining the rules that AI systems should follow within legal boundaries. The program aims to incorporate diverse perspectives reflecting the public interest in shaping AI behavior. OpenAI expresses belief that decisions about AI conduct should not be dictated solely by individuals, companies, or countries. The grants, totaling $1 million, are to be awarded to ten teams worldwide to develop proof-of-concepts for democratic processes that address questions about AI system rules. While these experiments are not intended to be binding at the time, they are expected to explore decision-relevant questions and build democratic tools to inform future decisions. The results of the studies are freely accessible, with OpenAI encouraging applicants to innovate and leverage known methodologies or create new approaches to democratic processes. The use of AI to enhance communication and facilitate efficient collaboration among a large number of participants is also encouraged.
2023	June 1	Cybersecurity	Program launch	OpenAI announces launch of the Cybersecurity Grant Program, a $1 million initiative aimed at enhancing AI-powered cybersecurity capabilities and fostering discussions at the intersection of AI and cybersecurity. The program's objectives include empowering defenders, measuring the capabilities of AI models in cybersecurity, and promoting comprehensive discourse in the field. OpenAI reportedly seeks project proposals that focus on practical applications of AI in defensive cybersecurity. Projects related to offensive security are not considered for funding. The program aims to evaluate and accept applications on a rolling basis, with strong preference given to proposals that can be licensed or distributed for maximal public benefit and sharing. Funding would be provided in increments of $10,000 USD from the $1 million fund, which can be in the form of API credits, direct funding, or equivalents.
2023	June 12	Research, safety	Collaboration	OpenAI and Google DeepMind commit to sharing their AI models with the Government of the United Kingdom for research and safety purposes. This move aims to enhance the government's ability to inspect the models and understand the associated risks and opportunities. The specific data to be shared by the tech companies is not yet disclosed. The announcement follows the UK government's plans to assess AI model accountability and establish a Foundation Model Taskforce to develop "sovereign" AI. The initiative seeks to address concerns about AI development and mitigate potential issues related to safety and ethics. While this access does not grant complete control or guarantee the detection of all issues, it promotes transparency and provides insights into AI systems during a time when their long-term impacts remain uncertain.
2023	June 14	Hallucination	Legal	A radio broadcaster named Mark Walters files a defamation lawsuit against OpenAI after the company's AI system, ChatGPT, generated a fake complaint accusing him of financial embezzlement. The lawsuit highlights the growing concern over generative AI programs spreading misinformation and producing false outputs. The fabricated legal summary is provided to Fred Riehl, the editor-in-chief of AmmoLand, who reports on a real-life legal case. The incident is attributed to a common issue with generative AI known as hallucinations, where the language model generates false information that can be convincingly realistic.
2023	June 20	AI regulation	Advocacy	It is reported that OpenAI lobbied the European Union to weaken forthcoming AI regulation, despite publicly advocating for stronger AI guardrails. Documents obtained by TIME reveal that OpenAI proposed amendments to the E.U.'s AI Act, which were later incorporated into the final text. OpenAI argues that its general-purpose AI systems, such as GPT-3 and Dall-E, should not be classified as "high risk" and subject to stringent requirements. The lobbying efforts aimed to reduce the regulatory burden on OpenAI and aligned with similar efforts by other tech giants like Microsoft and Google. The documents suggest that OpenAI used arguments about utility and public benefit to mask their financial interest in diluting the regulation.
2023	June 21	AI app store	Product	OpenAI reportedly plans to launch an AI app store, allowing developers and customers to sell their AI models built on OpenAI's technology. This move comes as OpenAI aims to expand its influence and capitalize on the success of its ChatGPT chatbot. While the introduction of an AI app store has the potential to drive broader adoption of OpenAI's technology and foster innovation, it also raises concerns about the need for regulations, consumer protection, quality control, ethical considerations, and security risks. However, for the Nigerian AI community, the app store presents opportunities for increased access, collaboration, economic prospects, and entrepreneurship, benefiting the country's tech talent and driving economic growth in the AI sector.
2023	June 28	Copyright infringement	Legal	OpenAI faces a proposed class action lawsuit filed by two U.S. authors in San Francisco federal court. The authors, Paul Tremblay and Mona Awad, claim that OpenAI used their works without permission to train its popular AI system, ChatGPT. They allege that ChatGPT mined data from thousands of books, infringing their copyrights. The lawsuit highlights the use of books as a significant component in training generative AI systems like ChatGPT. The authors assert that ChatGPT could generate accurate summaries of their books, indicating their presence in its database. The lawsuit seeks damages on behalf of copyright owners whose works were allegedly misused by OpenAI.
2023	June 28	OpenAI London	International expansion	OpenAI selects London as the location for its first international office, where the company plans to focus on research and engineering. The move is considered a vote of confidence in the UK's AI ecosystem and reinforces the country's position as an AI powerhouse.
2023	July 10	GPT-4	Coverage	An article discusses various aspects of OpenAI's GPT-4, including its architecture, training infrastructure, inference infrastructure, parameter count, training dataset composition, token count, layer count, parallelism strategies, multi-modal vision adaptation, engineering tradeoffs, and implemented techniques to overcome inference bottlenecks. It highlights that OpenAI's decision to keep the architecture closed is not due to existential risks but rather because it is replicable and other companies are expected to develop equally capable models. The article also emphasizes the importance of decoupling training and inference compute and the challenges of scaling out large models for inference due to memory bandwidth limitations. OpenAI's sparse model architecture is discussed as a solution to achieve high throughput while reducing inference costs.
2023	July 12	Copyright infringement	Legal	American comedian Sarah Silverman files a lawsuit against OpenAI along with Meta Platforms, alleging copyright infringement in the training of their AI systems. The lawsuit claims that the authors' copyrighted materials were used without their consent to train ChatGPT and Meta's LLaMa AI system. The case is expected to revolve around whether training a large language model constitutes fair use or not. Silverman is joined by two other authors in the class-action lawsuit. By this time, legal experts have raised questions about whether OpenAI can be accused of copying books in this context.
2023	July 13	xAI	Competition	Elon Musk launches his own artificial intelligence company, xAI, to rival OpenAI and Google. Reportedly, the goal of xAI is to understand the true nature of the universe and answer life's biggest questions. The company is staffed by former researchers from OpenAI, Google DeepMind, Tesla, and the University of Toronto. By this time, Musk has been critical of ChatGPT, accusing it of being politically biased and irresponsible. He left OpenAI in 2018 due to concerns about its profit-driven direction. Musk warns about the dangers of AI, and his new company reportedly aims to address those concerns.
2023	July 13	Algorithmic models	Partnership	OpenAI partners with the Associated Press (AP) in a two-year agreement to train algorithmic models. This collaboration marks one of the first news-sharing partnerships between a major news organization and an AI firm. OpenAI is expected to gain access to selected news content and technology from AP's archives, dating back to 1985, to enhance future iterations of ChatGPT and related tools. AP is expected to receive access to OpenAI's proprietary technology. This partnership allows OpenAI to expand into the news domain and acquire legally-obtained data, while AP aims to streamline and improve its news reporting processes using OpenAI's technology.
2023	July 26	Image generator	Coverage	An article reveals OpenAI's secret image generator, an unreleased AI model that outperforms previous ones. At this time tested privately, early samples show impressive results, producing sharp and realistic images with detailed lighting, reflections, and brand logos. The AI recreates paintings and displays well-proportioned hands, setting it apart from other generators. However, removed safety filters for testing allow the model to generate violent and explicit content. Access to the model is limited, and it's not released publicly due to OpenAI's stance on NSFW content.
2023	August 7	Claude 2	Competition	Anthropic unveils AI chatbot Claude 2, venturing into the advanced domain to compete with OpenAI and Google. With a substantial US$750 million in funding, while initially targeting business applications, Claude 2 already generates a waitlist of over 350,000 users seeking access to its API and consumer services. Its availability is initially limited to the United States and the United Kingdom. Claude 2 is part of the growing trend of generative AI chatbots, despite concerns about bias. Anthropic aims to offer Claude 2 as a safer alternative for a broader range of users.
2023	August 16	Global Illumination	Acquisition	OpenAI makes its first public acquisition by purchasing New York-based startup Global Illumination. The terms of the deal are not disclosed. Global Illumination's team previously worked on projects at Instagram, Facebook, YouTube, Google, Pixar, and Riot Games. Their most recent creation is the open-source sandbox multiplayer game Biomes. OpenAI aims to enhance its core products, including ChatGPT, with this acquisition.
2023	August 17	GPT-4	Competition	Researchers from Arthur AI evaluate top AI models from Meta, OpenAI, Cohere, and Anthropic to assess their propensity for generating false information, a phenomenon known as hallucination. They discover that Cohere's AI model displays the highest degree of hallucination, followed by Meta's Llama 2, which hallucinates more than GPT-4 and Claude 2 from Anthropic. GPT-4, on the other hand, performs the best among all models tested, hallucinating significantly less than its predecessor GPT-3.5. The study highlights the importance of evaluating AI models' performance based on specific use cases, as real-world applications may differ from standardized benchmarks.
2023	August 21	GPTBot	Reaction	The New York Times blocks OpenAI's web crawler, GPTBot, preventing OpenAI from using the publication's content for training AI models. This action follows the NYT's recent update to its terms of service, which prohibits the use of its content for AI model training. The newspaper also reportedly considers legal action against OpenAI for potential intellectual property rights violations. The NYT's move aligns with its efforts to protect its content and copyright in the context of AI model training.

Numerical and visual data

Google Scholar

The following table summarizes per-year mentions on Google Scholar as of June, 2023.

Year	"OpenAI"
2015	47
2016	312
2017	769
2018	2,030
2019	4,430
2020	6,940
2021	9,780
2022	13,100

Google Trends

The chart below shows Google Trends data for OpenAI (Artificial intelligence company), from January 2020 to June 2023, when the screenshot was taken. Interest is also ranked by country and displayed on world map. See spike of interest starting at thew end of 2022, when OpenAI launched ChatGPT.

Google Ngram Viewer

The chart below shows Google Ngram Viewer data for OpenAI, from 2000 to 2019.

Wikipedia Views

The chart below shows pageviews of the English Wikipedia article OpenAI, from July 2015 to May 2023. See spike of interest induced by ChatGPT release.

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

• Vipul: 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 ✔
• https://www.jointjs.com/demos/chatgpt-timeline
• New GPT-3 tool ✔
• https://thenextweb.com/neural/2021/01/13/googles-new-trillion-parameter-ai-language-model-is-almost-6-times-bigger-than-gpt-3/ ✔
• January 2021: Jan Leike joins OpenAI to lead AI alignment work [https://twitter.com/janleike/status/1352681093007200256 ✔
• https://singularityhub.com/2021/04/04/openais-gpt-3-algorithm-is-now-producing-billions-of-words-a-day/?fbclid=IwAR0hIm6QNfclIgAW72-etUTp8iwZE_9uY8g7bUv4qoq0Dx9cTG2kBSxrlsY ✔

Timeline update strategy

• https://www.google.com/search?q=site:nytimes.com+%22OpenAI%22
• https://blog.OpenAI.com/ (but check to see if the announcement of a blog post is covered by other sources)

See also

• 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

External links

• Official website
• Official OpenAI blog
• OpenAI GitHub account