Announced in 2016, Gym is an open-source Python library developed to assist in the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI research study, making released research more easily reproducible [24] [144] while providing users with a simple user interface for communicating with these environments. In 2022, brand-new advancements of Gym have been transferred to the library Gymnasium. [145] [146]
Gym Retro

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

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have understanding of how to even stroll, but are given the objectives of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial learning procedure, the agents discover how to adapt to changing conditions. When an agent is then removed from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might create an intelligence "arms race" that could increase a representative's ability to work even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high ability level completely through trial-and-error algorithms. Before becoming a group of 5, the first public presentation took place at The International 2017, the yearly best champion tournament for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of actual time, genbecle.com which the knowing software application was a step in the instructions of developing software that can deal with complex tasks like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots discover gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an opponent and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a complete group of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, but ended 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 exhibit match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot player shows the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated making use of deep reinforcement learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robot hand, ratemywifey.com to control physical items. [167] It discovers completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation issue by using domain randomization, a simulation approach which exposes the student to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB cams to permit the robot to manipulate an arbitrary item by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might solve a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating progressively more hard environments. ADR differs from manual domain randomization by not requiring a human to specify randomization varieties. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new AI designs established by OpenAI" to let developers get in touch with it for "any English language AI task". [170] [171]
Text generation

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

The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his associates, 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 a not being watched transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative variations at first released to the public. The complete version of GPT-2 was not right away released due to concern about possible misuse, consisting of applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 positioned a significant hazard.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of various instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose learners, highlighted by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not more 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 prevents 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 not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million criteria were also trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning in between English and wiki.asexuality.org Romanian, and between English and German. [184]
GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the essential capability constraints of predictive language models. [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 permit gain access to through a paid cloud API after a two-month free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed solely to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has furthermore 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 model can develop working code in over a dozen shows languages, most effectively in Python. [192]
Several problems with glitches, design flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of producing copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would stop 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), efficient in accepting text or image inputs. [199] They revealed that the upgraded technology passed a simulated law school bar exam 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 likewise read, examine or produce approximately 25,000 words of text, and compose code in all significant programming languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose different technical details and data about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text, surgiteams.com images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision benchmarks, setting new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly helpful for business, start-ups and developers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been designed to take more time to believe about their reactions, causing higher precision. These designs are especially effective 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 revealed o3, the successor of the o1 reasoning design. OpenAI likewise revealed o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215]
Deep research

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

CLIP

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

DALL-E

Revealed in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can develop pictures of reasonable things ("a stained-glass window with an image of a blue strawberry") as well as objects that do not exist in truth ("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 sensible results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new rudimentary system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

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

Sora

Sora is a text-to-video model that can create videos based on brief detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of created videos is unknown.

Sora's development team called it after the Japanese word for "sky", to represent its "limitless creative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos certified for that purpose, however did not expose the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it might produce videos approximately one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the design's capabilities. [225] It acknowledged some of its imperfections, consisting of struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but noted that they should have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have shown considerable interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to produce realistic video from text descriptions, mentioning its possible to change storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause prepare for broadening his Atlanta-based film 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 diverse audio and is likewise a multi-task design that can perform 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 anticipate subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to start fairly however then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized 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 create music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs song samples. OpenAI mentioned the songs "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a substantial space" between Jukebox and human-generated music. The Verge stated "It's technologically outstanding, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider stated "surprisingly, some of the resulting songs are catchy and sound genuine". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The function is to research study whether such a method might 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 nerve cell of 8 neural network models which are often studied in interpretability. [240] Microscope was produced to analyze the features that form inside these neural networks easily. The models included are AlexNet, VGG-19, different variations of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an expert system tool constructed on top of GPT-3 that supplies a conversational user interface that permits users to ask questions in natural language. The system then reacts with a response within seconds.