Announced in 2016, Gym is an open-source Python library created to help with the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research study, making published research study more easily reproducible [24] [144] while supplying users with a basic interface for communicating with these environments. In 2022, new advancements 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 study on computer game [147] using RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to resolve single tasks. Gym Retro offers the capability to generalize in between video games with comparable ideas but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first do not have understanding of how to even walk, links.gtanet.com.br however are offered the objectives of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to altering conditions. When a representative is then removed from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could create an intelligence "arms race" that might increase a representative's capability to function even outside the context of the competition. [148]

OpenAI 5

OpenAI Five is a team of 5 OpenAI-curated bots used 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 took place at The International 2017, the yearly premiere champion tournament for the 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 actually found out by playing against itself for 2 weeks of genuine time, and that the knowing software application was an action in the direction of producing software application that can manage complicated tasks like a surgeon. [152] [153] The system uses a form of support knowing, as the bots discover in time by playing against themselves hundreds of 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 full group 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 exhibition matches against expert gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later that month, where they played in 42,729 total video 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 reveals the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually demonstrated the use of deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]

Dactyl

Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It finds out completely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by using domain randomization, a simulation method which exposes the learner to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB cams to permit the robotic to manipulate an arbitrary item by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]

In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing progressively more difficult environments. ADR varies from manual domain randomization by not requiring 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 models 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 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 model of language might obtain world knowledge and procedure long-range dependences by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative variations initially released to the public. The full variation of GPT-2 was not immediately released due to concern about potential abuse, consisting of applications for writing fake news. [174] Some specialists expressed uncertainty that GPT-2 postured a significant danger.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be difficult 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 designs. [178] [179] [180]

GPT-2's authors argue without supervision language models to be general-purpose students, highlighted by GPT-2 attaining state-of-the-art accuracy 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both specific 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 design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full variation of GPT-3 contained 175 billion specifications, [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 few as 125 million criteria were also trained). [186]

OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" tasks and higgledy-piggledy.xyz could generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184]

GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or coming across the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly 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 complimentary private beta that started 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 launched in private beta. [194] According to OpenAI, the model can produce working code in over a dozen programs languages, many efficiently in Python. [192]

Several issues with glitches, style flaws and security vulnerabilities were cited. [195] [196]

GitHub Copilot has been implicated of emitting copyrighted code, with no author attribution or license. [197]

OpenAI announced that they would stop support for Codex API on March 23, 2023. [198]

GPT-4

On March 14, 2023, OpenAI announced 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 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 likewise read, analyze or create approximately 25,000 words of text, and write code in all significant shows languages. [200]

Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, 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 decreased to expose different technical details and data about GPT-4, such as the precise size of the design. [203]

GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting edge lead to 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) benchmark compared to 86.5% by GPT-4. [207]

On July 18, 2024, OpenAI launched GPT-4o mini, a smaller version of GPT-4o changing 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 anticipates it to be especially beneficial for enterprises, start-ups and developers seeking to automate services with AI representatives. [208]

o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been developed to take more time to consider their reactions, resulting in greater accuracy. These models are particularly efficient in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [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 reasoning design. OpenAI likewise revealed o3-mini, a lighter and faster version of OpenAI o3. Since 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 chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to avoid confusion with telecommunications services 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 carry out substantial web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]

Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design 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 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 a sad capybara") and create corresponding images. It can develop images of practical items ("a stained-glass window with a picture of a blue strawberry") in addition to 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 announced DALL-E 2, an updated variation of the model with more reasonable results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new simple system for transforming a text description into a 3-dimensional model. [220]

DALL-E 3

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

Text-to-video

Sora

Sora is a text-to-video model that can generate videos based upon short detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.

Sora's development team named it after the Japanese word for "sky", to signify its "unlimited innovative 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 as well as copyrighted videos licensed for that function, but 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 up to one minute long. It likewise shared a technical report highlighting the techniques used to train the design, and the design's capabilities. [225] It acknowledged some of its shortcomings, consisting of struggles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but noted that they should have been cherry-picked and may not represent Sora's normal output. [225]

Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to produce realistic video from text descriptions, mentioning its possible to change storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly plans for broadening his Atlanta-based motion picture studio. [227]

Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of diverse audio and is also a multi-task model that can carry out multilingual speech recognition as well as speech translation and language recognition. [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 songs with 10 instruments in 15 designs. According to The Verge, a tune created by MuseNet tends to begin 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 psychological 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 genre, artist, pipewiki.org 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 larger musical structures such as choruses that repeat" and that "there is a considerable space" in between Jukebox and human-generated music. The Verge mentioned "It's technically outstanding, even if the outcomes sound like mushy versions of songs that might feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are memorable and sound legitimate". [234] [235] [236]

Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers to dispute toy issues in front of a human judge. The purpose is to research study whether such a method might help in auditing AI choices and in establishing explainable AI. [237] [238]

Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of 8 neural network designs which are typically studied in interpretability. [240] Microscope was produced to analyze the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, and different variations of CLIP Resnet. [241]

ChatGPT

Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that supplies a conversational interface that allows users to ask concerns in natural language. The system then responds with a response within seconds.