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

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

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack knowledge of how to even walk, however are offered the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the agents find out how to adjust to altering conditions. When an agent is then gotten rid of from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, recommending it had learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might develop an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the competitors. [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 find out to play against human gamers at a high ability level completely through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation happened at The International 2017, the yearly premiere champion competition 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 found out by playing against itself for 2 weeks of real time, which the learning software application was a step in the instructions of developing software application that can deal with complex tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of reinforcement knowing, as the bots find out over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
By June 2018, the ability 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 2 exhibit matches against professional gamers, 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' last public look came later on 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 systems in Dota 2's bot player reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep reinforcement learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It learns completely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation approach 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 electronic cameras, also has RGB cams to enable the robot to control an approximate item 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 could resolve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating progressively more tough environments. ADR varies from manual domain randomization by not needing a human to define randomization varieties. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing 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 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, it-viking.ch 2018. [173] It showed how a generative design of language might obtain world and process long-range dependencies 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 design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions at first released to the public. The complete variation of GPT-2 was not right away released due to issue about prospective misuse, consisting of applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 presented a significant hazard.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted 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 total version of the GPT-2 language design. [177] Several websites host interactive presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose students, shown by GPT-2 attaining state-of-the-art precision and yewiki.org 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain issues 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 design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million specifications were also trained). [186]
OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and might generalize the function 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 wiki.myamens.com in between English and German. [184]
GPT-3 drastically improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the basic ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly launched to the general public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month free personal 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 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 released in personal beta. [194] According to OpenAI, the design can create working code in over a lots programs languages, a lot of successfully in Python. [192]
Several issues with problems, design defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been implicated of giving off copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would stop assistance 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 updated technology passed a simulated law school bar test 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, evaluate or generate up to 25,000 words of text, and write code in all significant programs languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal numerous technical details and data about GPT-4, such as the exact size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained modern outcomes in voice, multilingual, and vision standards, setting brand-new records in audio speech recognition 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 released GPT-4o mini, a smaller sized 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 expects it to be especially helpful for enterprises, startups 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 developed to take more time to think of their reactions, causing greater precision. These designs are particularly effective in science, coding, and thinking jobs, wiki.myamens.com 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 unveiled o3, disgaeawiki.info the follower of the o1 reasoning model. OpenAI also unveiled o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecoms services supplier O2. [215]
Deep research study

Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools enabled, it reached a precision 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 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 produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can create pictures of sensible things ("a stained-glass window with a picture of a blue strawberry") along with objects 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 revealed DALL-E 2, an updated variation of the model with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new rudimentary system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more powerful model 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 feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can generate videos based upon brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.

Sora's development team called it after the Japanese word for "sky", to signify its "limitless imaginative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos certified for that purpose, genbecle.com however did not expose the number or the precise sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it could create videos approximately one minute long. It likewise shared a technical report highlighting the methods utilized to train the model, and the design's capabilities. [225] It acknowledged a few of its drawbacks, consisting of struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but noted that they must have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have actually revealed significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to produce realistic video from text descriptions, mentioning its potential to change storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly plans 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 large dataset of varied audio and is likewise a multi-task design that can perform multilingual speech recognition along with 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 generate tunes with 10 instruments in 15 designs. 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 popular culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to produce 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 snippet of lyrics and outputs tune samples. OpenAI specified the tunes "show regional musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" which "there is a substantial space" between Jukebox and human-generated music. The Verge mentioned "It's technologically impressive, even if the results seem like mushy versions of songs that may feel familiar", while Business Insider stated "remarkably, a few of the resulting tunes are catchy and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches machines to discuss toy problems in front of a human judge. The function is to research whether such an approach may assist in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks quickly. The designs included 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 developed on top of GPT-3 that provides a conversational interface that allows users to ask questions in natural language. The system then reacts with a response within seconds.