Announced in 2016, Gym is an open-source Python library created to assist in the development of support learning algorithms. It aimed to standardize how environments are specified in AI research, making published research study more easily reproducible [24] [144] while supplying users with a basic user interface for connecting with these environments. In 2022, new developments of Gym have actually been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support learning (RL) research study on computer game [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to resolve single tasks. Gym Retro offers the capability to generalize between video games with comparable principles however various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first lack understanding of how to even walk, however are given the goals of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to changing conditions. When an agent is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives could produce an intelligence "arms race" that could increase a representative's capability to operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against human gamers at a high ability level completely through experimental algorithms. Before ending up being a group of 5, the very first public presentation happened at The International 2017, the annual premiere championship competition 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 actually found out by playing against itself for 2 weeks of real time, and that the learning software was an action in the direction of producing software application that can manage complex tasks like a surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots discover in time by playing against themselves numerous 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 team of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs 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 competition, winning 99.4% of those video games. [165]
OpenAI 5's systems in Dota 2's bot player shows the challenges of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually demonstrated using deep reinforcement learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker finding out to train a Shadow Hand, a human-like robotic hand, to control physical objects. [167] It finds out completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cams, also has RGB cameras to allow the robot to control an arbitrary things by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of producing gradually more difficult environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

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

The business has actually 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 colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world knowledge and procedure 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 without supervision transformer language design and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative variations initially launched to the public. The complete variation of GPT-2 was not immediately released due to issue about possible abuse, consisting of applications for composing phony news. [174] Some specialists revealed uncertainty that GPT-2 presented a substantial hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake 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 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 sites host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language models to be general-purpose learners, illustrated by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not additional 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 at least 3 upvotes. It avoids certain problems 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 an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were likewise trained). [186]
OpenAI stated that GPT-3 was successful 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 between English and Romanian, and between English and German. [184]
GPT-3 significantly improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or experiencing the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 needed 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 concerns of possible abuse, although OpenAI prepared to enable 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 solely 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 launched in personal beta. [194] According to OpenAI, the design can produce working code in over a lots programming languages, many successfully in Python. [192]
Several problems with problems, design 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 discontinue 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 updated technology passed a simulated law school bar examination with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also read, analyze or produce approximately 25,000 words of text, and write 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 caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal numerous technical details and data about GPT-4, such as the precise size of the design. [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 cutting edge outcomes in voice, multilingual, and vision benchmarks, setting brand-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 sized variation of GPT-4o changing 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 forum.altaycoins.com GPT-4o. OpenAI anticipates it to be particularly useful for business, startups and designers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been developed to take more time to think about their actions, resulting in higher accuracy. These designs are particularly efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

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

Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform extensive web surfing, 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 analyze the semantic resemblance between text and images. It can notably be used for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E uses 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 produce matching images. It can produce images of sensible items ("a stained-glass window with an image of a blue strawberry") in addition to 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 announced DALL-E 2, an updated version of the design with more realistic outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new primary system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

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

Sora

Sora is a text-to-video model that can produce videos based on brief detailed prompts [223] along with extend existing videos forwards or backwards 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 team called it after the Japanese word for "sky", to represent its "endless 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 in addition to copyrighted videos accredited for that purpose, however did not reveal the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it might produce videos as much as one minute long. It likewise shared a technical report highlighting the techniques used to train the design, and the model's capabilities. [225] It acknowledged some of its shortcomings, including battles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but kept in mind that they must have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have actually shown substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to generate realistic video from text descriptions, mentioning its possible to revolutionize storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually 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 large dataset of varied audio and is also a multi-task design that can carry out multilingual speech acknowledgment along with speech translation and language recognition. [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 song created by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the internet mental thriller Ben Drowned to create music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to generate 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 stated the songs "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar bigger musical structures such as choruses that duplicate" which "there is a significant gap" between Jukebox and . The Verge mentioned "It's technologically impressive, even if the outcomes seem like mushy versions of songs that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting songs are memorable and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research whether such a technique may assist in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was created to examine the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various versions of Inception, and different versions 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 interface that enables users to ask concerns in natural language. The system then reacts with an answer within seconds.