Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research, making published research more quickly reproducible [24] [144] while supplying users with a basic interface for engaging with these environments. In 2022, new advancements of Gym have actually been relocated 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 resolve single tasks. Gym Retro provides the ability to generalize in between video games with similar concepts however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first do not have understanding of how to even stroll, however 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 representatives discover how to adjust to altering 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 representative braces to remain upright, suggesting it had actually found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents might develop an intelligence "arms race" that might increase a representative's capability to operate 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 computer game Dota 2, that learn to play against human players at a high ability level totally through trial-and-error algorithms. Before becoming a team of 5, the first public demonstration occurred at The International 2017, the annual best 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, which the knowing software was an action in the instructions of developing software application that can deal with complicated jobs like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots find out gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a full group of 5, and they were able to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling 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 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 gamer reveals the challenges of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually shown using deep support learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, wiki.snooze-hotelsoftware.de Dactyl utilizes machine learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It finds out totally in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation problem by utilizing domain randomization, a simulation method which exposes the student to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB video cameras to allow the robot to control an approximate things by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might resolve a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complex 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 method of generating progressively harder environments. ADR differs 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 new AI designs developed by OpenAI" to let developers contact it for "any English language AI job". [170] [171]
Text generation

The company has 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 design was composed by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language could obtain world knowledge and process long-range reliances 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 follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative versions initially released to the general public. The complete variation of GPT-2 was not right away released due to concern about possible misuse, consisting of applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 presented a significant threat.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to completely 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 released the complete variation of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue not being watched language designs to be general-purpose students, illustrated by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model 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 prevents certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 not being watched transformer language model and the follower 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 full version of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were likewise trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning in between English and Romanian, and in between English and German. [184]
GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or coming across the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous 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 model was not right away released to the general 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 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 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 private beta. [194] According to OpenAI, the design can develop working code in over a dozen programming languages, a lot of efficiently in Python. [192]
Several concerns with problems, design flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has actually been accused of giving off 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 announced 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 rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also check out, evaluate or produce as much as 25,000 words of text, and compose code in all major programs languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose various technical details and stats about GPT-4, such as the precise size of the model. [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 state-of-the-art lead to voice, multilingual, and vision standards, setting brand-new records in audio speech acknowledgment 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 variation of GPT-4o replacing 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 helpful for enterprises, start-ups and designers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been designed to take more time to think about their actions, resulting in greater accuracy. These models are especially reliable 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, setiathome.berkeley.edu 2024, OpenAI unveiled o3, the successor of the o1 thinking design. OpenAI likewise 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 checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the opportunity to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid 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 capabilities of OpenAI's o3 design to carry out comprehensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, 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 evaluate the semantic resemblance between text and images. It can especially be used 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 uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can create pictures of sensible objects ("a stained-glass window with a picture of a blue strawberry") along with 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 revealed DALL-E 2, an updated variation of the model with more practical outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new primary system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3

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

Sora

Sora is a text-to-video model that can produce videos based upon brief detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.

Sora's advancement group called it after the Japanese word for "sky", to symbolize its "endless imaginative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos licensed for that function, however did not reveal the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it might generate videos up to one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and hb9lc.org the model's abilities. [225] It acknowledged some of its imperfections, consisting of struggles simulating complicated 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 demonstration, significant entertainment-industry figures have shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to generate practical video from text descriptions, mentioning its possible to transform storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly strategies for broadening his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a big dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition in addition to 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 songs with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to begin fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were used 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 a category, artist, and a bit of lyrics and outputs tune samples. OpenAI specified the tunes "reveal regional musical coherence [and] follow conventional chord patterns" however acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" and that "there is a substantial gap" between Jukebox and human-generated music. The Verge mentioned "It's highly remarkable, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider stated "remarkably, some of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches makers to debate toy issues in front of a human judge. The purpose is to research whether such an approach might assist in auditing AI choices 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 8 neural network designs which are often studied in interpretability. [240] Microscope was developed to examine the features that form inside these neural networks easily. The models included are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that provides a conversational interface that allows users to ask concerns in natural language. The system then reacts with a response within seconds.