Announced in 2016, Gym is an open-source Python library designed to help with the advancement of support knowing algorithms. It aimed to standardize how environments are defined in AI research, making published research study more quickly reproducible [24] [144] while offering users with an easy user interface for engaging with these environments. In 2022, brand-new advancements of Gym have actually been moved to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to fix single tasks. Gym Retro offers the capability to generalize in between games with similar principles however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first do not have understanding of how to even stroll, however are provided the goals of to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the agents discover how to adjust to altering conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives could create an intelligence "arms race" that could increase an agent's capability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that discover to play against human players at a high ability level entirely through trial-and-error algorithms. Before becoming a group of 5, the very first public presentation occurred at The International 2017, the annual premiere champion tournament for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of real time, and that the learning software was an action in the direction of developing software application that can manage intricate jobs like a surgeon. [152] [153] The system utilizes a kind of reinforcement learning, as the bots find out gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a full group of 5, and they had the ability to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert gamers, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot player shows the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown the usage of deep reinforcement knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It discovers completely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a simulation approach which exposes the learner to a variety of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, also has RGB electronic cameras to enable the robotic to manipulate an approximate object by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robotic was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating gradually harder environments. ADR differs from manual domain randomization by not needing a human to define randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new AI designs developed by OpenAI" to let developers call on it for "any English language AI job". [170] [171]
Text generation

The business has actually popularized generative pretrained transformers (GPT). [172]
OpenAI's original GPT design ("GPT-1")

The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world understanding and procedure long-range dependences by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions at first launched to the general public. The full variation of GPT-2 was not instantly launched due to concern about potential misuse, consisting of applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 presented a significant risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language model. [177] Several websites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not further trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains a little 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both individual characters and multiple-character tokens. [181]
GPT-3

First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were also trained). [186]
OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or coming across the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the public for issues of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month complimentary personal beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified specifically to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the AI powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the design can produce working code in over a lots programming languages, the majority of successfully in Python. [192]
Several concerns with problems, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been accused of discharging copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would terminate assistance for Codex API on March 23, 2023. [198]
GPT-4

On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar examination with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, evaluate or create up to 25,000 words of text, and write code in all significant programs languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to expose various technical details and stats about GPT-4, such as the exact size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision standards, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller variation of GPT-4o changing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million input tokens and setiathome.berkeley.edu $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be particularly beneficial for enterprises, startups and developers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been designed to take more time to consider their actions, resulting in greater accuracy. These designs are particularly reliable in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 thinking design. OpenAI also revealed o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215]
Deep research

Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out substantial web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic resemblance in between text and images. It can notably be utilized for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can develop images of practical things ("a stained-glass window with an image of a blue strawberry") as well as items that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, an upgraded version of the model with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new fundamental system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective model much better able to generate images from complicated descriptions without manual timely engineering and render complex details like hands and text. [221] It was released to the public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can produce videos based upon brief detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.

Sora's development group called it after the Japanese word for "sky", to signify its "limitless innovative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that purpose, but did not reveal the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might create videos up to one minute long. It likewise shared a technical report highlighting the approaches utilized to train the model, and the model's abilities. [225] It acknowledged some of its shortcomings, consisting of struggles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they must have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have revealed considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's ability to produce sensible video from text descriptions, mentioning its potential to change storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause plans for broadening his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a big dataset of varied audio and is also a multi-task model that can carry out multilingual speech recognition along with speech translation and language identification. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a tune produced by MuseNet tends to begin fairly however then fall into turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to develop music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs tune samples. OpenAI specified the songs "show regional musical coherence [and] follow standard chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" which "there is a significant space" in between Jukebox and human-generated music. The Verge specified "It's technically outstanding, even if the results seem like mushy versions of tunes that might feel familiar", while Business Insider stated "remarkably, a few of the resulting songs are appealing and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches devices to debate toy problems in front of a human judge. The purpose is to research whether such a method may help in auditing AI choices and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of 8 neural network models which are typically studied in interpretability. [240] Microscope was developed to examine the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an artificial intelligence tool developed on top of GPT-3 that provides a conversational user interface that enables users to ask questions in natural language. The system then responds with an answer within seconds.