Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of support knowing algorithms. It aimed to standardize how environments are specified in AI research study, making released research study more quickly reproducible [24] [144] while supplying users with a simple user interface for engaging with these environments. In 2022, brand-new developments of Gym have actually been transferred to the library Gymnasium. [145] [146]
Gym Retro

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

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack knowledge of how to even walk, however are offered the objectives of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to balance in a generalized way. [148] [149] OpenAI’s Igor Mordatch argued that competition in between agents might create an intelligence “arms race” that might increase a representative’s ability to work even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human players at a high ability level completely through trial-and-error algorithms. Before ending up being a group of 5, the first public demonstration took place at The International 2017, the yearly best championship competition for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of actual time, which the knowing software was an action in the instructions of developing software that can deal with intricate tasks like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots learn gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a complete team of 5, and they were able to beat 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, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots’ final public appearance came later that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5’s systems in Dota 2’s bot gamer shows the challenges of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown making use of deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It learns totally in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation problem by using domain randomization, a simulation technique which exposes the learner to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, also has RGB video cameras to enable the robot to control an arbitrary things by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might fix a Rubik’s Cube. The robot had the ability to resolve the puzzle 60% of the time. Objects like the Rubik’s Cube introduce complicated 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 creating gradually harder environments. ADR differs from manual domain randomization by not requiring a human to define randomization ranges. [169]
API

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

The company has 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 model was written by Alec Radford and his coworkers, and published in preprint on OpenAI’s website on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world knowledge and process long-range dependences by pre-training on a diverse 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 successor to OpenAI’s original GPT design (“GPT-1”). GPT-2 was revealed in February 2019, with just limited demonstrative versions initially released to the general public. The complete variation of GPT-2 was not immediately launched due to concern about possible abuse, consisting of applications for writing phony news. [174] Some professionals revealed uncertainty that GPT-2 positioned a significant danger.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify “neural phony news”. [175] Other researchers, such as Jeremy Howard, warned of “the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter”. [176] In November 2019, OpenAI released the complete variation of the GPT-2 language model. [177] Several websites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2’s authors argue without supervision language designs to be general-purpose students, shown by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not more trained on any task-specific input-output examples).

The corpus it was trained on, surgiteams.com 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 using byte pair encoding. This permits representing any string of characters by encoding both private 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 design and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million parameters were likewise trained). [186]
OpenAI stated that GPT-3 succeeded at certain “meta-learning” tasks and might 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 Romanian, and between English and German. [184]
GPT-3 drastically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away launched to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month totally free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified exclusively 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 produce working code in over a dozen shows languages, many efficiently in Python. [192]
Several concerns with glitches, style defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been implicated of releasing copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would stop support 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 updated technology passed a simulated law school bar examination with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, analyze or create as much as 25,000 words of text, and write code in all major programs languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose various technical details and data about GPT-4, ratemywifey.com such as the exact size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision benchmarks, 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 released GPT-4o mini, a smaller version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user 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 anticipates it to be especially useful for enterprises, startups and designers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been created to take more time to consider their reactions, causing greater precision. These models are especially effective in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking design. OpenAI also revealed o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecoms services service provider O2. [215]
Deep research

Deep research is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI’s o3 model to perform substantial web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity’s Last Exam) benchmark. [120]
Image classification

CLIP

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

DALL-E

Revealed in 2021, DALL-E is a Transformer design that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as “a green leather purse formed like a pentagon” or “an isometric view of an unfortunate capybara”) and create matching images. It can develop images of reasonable things (“a stained-glass window with a picture of a blue strawberry”) in addition to things that do not exist in reality (“a cube with the texture of a porcupine”). As of March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, an updated variation of the design with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new primary system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3

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

Sora

Sora is a text-to-video design that can generate videos based on short detailed prompts [223] along with extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.

Sora’s development team called it after the Japanese word for “sky”, to signify its “unlimited creative potential”. [223] Sora’s innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos certified for that function, however did not expose the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it could generate videos approximately one minute long. It also shared a technical report highlighting the techniques utilized to train the design, and the design’s abilities. [225] It acknowledged some of its drawbacks, consisting of struggles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos “excellent”, but noted that they must have been cherry-picked and may not represent Sora’s common output. [225]
Despite uncertainty from some academic leaders following Sora’s public demo, notable entertainment-industry figures have actually revealed significant interest in the innovation’s capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology’s capability to produce sensible video from text descriptions, mentioning its potential to transform storytelling and material production. He said that his enjoyment about Sora’s possibilities was so strong that he had chosen to pause strategies for broadening his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model that can perform multilingual speech acknowledgment as well as speech translation and language identification. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to start fairly however then fall into turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to create 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 genre, artist, and a bit of lyrics and outputs tune samples. OpenAI mentioned the tunes “reveal regional musical coherence [and] follow conventional chord patterns” but acknowledged that the songs do not have “familiar larger musical structures such as choruses that duplicate” and that “there is a considerable gap” between Jukebox and human-generated music. The Verge stated “It’s technically impressive, even if the results seem like mushy versions of tunes that may feel familiar”, while Business Insider mentioned “surprisingly, a few of the resulting songs are catchy and sound genuine”. [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, pediascape.science which teaches machines to discuss toy problems in front of a human judge. The function is to research study whether such a method may help in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network designs which are typically studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, different variations of Inception, and raovatonline.org various variations of CLIP Resnet. [241]
ChatGPT

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