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 defined in AI research study, making published research more easily reproducible [24] [144] while providing users with a basic user interface for communicating 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 knowing (RL) research on computer game [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to solve single tasks. Gym Retro provides the ability to generalize between games with similar concepts however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have knowledge of how to even stroll, but are given the objectives of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents find out how to adjust to changing conditions. When a representative is then gotten rid of from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents might produce an intelligence "arms race" that could increase an agent's capability to operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of 5 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 team of 5, the first public demonstration happened at The International 2017, the yearly best 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 learned by playing against itself for two weeks of genuine time, and that the learning software was a step in the direction of producing software application that can manage complicated jobs like a surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots learn with 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 ability of the bots expanded to play together as a full group of 5, and they were able to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional gamers, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated 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 appearance came later that month, where they played in 42,729 overall 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 reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown using deep reinforcement learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses device finding out to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It learns entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB cameras to enable the robot to control an approximate item 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 demonstrated that Dactyl might solve a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube complicated physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating progressively harder environments. ADR varies 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 developed by OpenAI" to let developers contact it for "any English language AI task". [170] [171]
Text generation

The company has actually promoted generative pretrained transformers (GPT). [172]
OpenAI's initial GPT model ("GPT-1")

The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language might obtain world understanding and procedure long-range reliances 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 design and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative versions at first released to the general public. The full variation of GPT-2 was not right away launched due to issue about possible abuse, including applications for composing fake news. [174] Some specialists expressed uncertainty that GPT-2 posed a considerable hazard.

In reaction to GPT-2, surgiteams.com the Allen Institute for Artificial Intelligence reacted with a tool to find "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 drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several sites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language designs to be general-purpose students, highlighted by GPT-2 attaining modern 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain problems 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 a without supervision 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] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186]
OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184]
GPT-3 significantly enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the basic ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, 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 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 totally free personal 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 additionally 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 create working code in over a dozen programming languages, a lot of efficiently in Python. [192]
Several problems with glitches, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of emitting copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would terminate 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 announced that the upgraded innovation passed a simulated law school bar test 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 likewise check out, analyze or produce as much as 25,000 words of text, and compose code in all major programs languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various technical details and stats about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, gratisafhalen.be which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision criteria, setting new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized 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 particularly beneficial for business, start-ups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been designed to take more time to think of their responses, resulting in higher accuracy. These models are particularly efficient in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 reasoning design. OpenAI also unveiled o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are checking 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 design is called o3 instead of o2 to avoid confusion with telecoms providers O2. [215]
Deep research

Deep research study is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing 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 resemblance in between text and images. It can significantly be used for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can create pictures of practical things ("a stained-glass window with a picture of a blue strawberry") as well as 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 announced DALL-E 2, an updated version of the model with more practical outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new fundamental 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 design much better able to produce images from complicated descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can generate videos based on short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 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 "limitless imaginative capacity". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos licensed 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 public on February 15, 2024, specifying that it could create videos approximately one minute long. It likewise shared a technical report highlighting the techniques utilized to train the model, and the design's abilities. [225] It acknowledged some of its shortcomings, consisting of struggles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but noted that they should have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have shown significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to produce reasonable video from text descriptions, citing its prospective to revolutionize storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause prepare for wiki.lafabriquedelalogistique.fr 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 large dataset of varied audio and is likewise a multi-task design that can carry out 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 anticipate subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to begin fairly but then fall under mayhem the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to develop music for the titular character. [232] [233]
Jukebox

Released in 2020, wiki.dulovic.tech 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 song samples. OpenAI specified the tunes "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" and that "there is a significant gap" between Jukebox and human-generated music. The Verge mentioned "It's technologically impressive, even if the results seem like mushy versions of songs that might feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are catchy and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers to discuss toy problems in front of a human judge. The purpose is to research study whether such an approach might assist 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 frequently studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

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