Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research study, making published research study more quickly reproducible [24] [144] while offering users with a basic user interface for engaging with these environments. In 2022, brand-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 support learning (RL) research study on video games [147] using RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to solve single jobs. Gym Retro offers the capability to generalize between video games with comparable principles but various appearances.

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, but are given the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents discover how to adjust to changing conditions. When an agent is then eliminated from this virtual environment and put in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had discovered how to stabilize in a generalized method. [148] [149] OpenAI’s Igor Mordatch argued that competition between representatives might develop an intelligence “arms race” that might increase a representative’s capability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high skill level entirely through experimental algorithms. Before ending up being a group of 5, the very first public demonstration occurred 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 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 setiathome.berkeley.edu that the knowing software was a step in the instructions of producing software application that can manage intricate tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of support learning, as the bots find out gradually 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 capability of the bots expanded to play together as a complete team 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 exhibition matches against professional players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the game at the time, higgledy-piggledy.xyz 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 games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5’s mechanisms in Dota 2’s bot gamer shows the challenges of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown using deep reinforcement knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It discovers completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation issue by using domain randomization, a simulation technique which exposes the student to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, likewise has RGB electronic cameras to allow the robot to manipulate an arbitrary item 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 robot had the ability to resolve 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 toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of generating progressively more challenging environments. ADR differs from manual domain randomization by not needing a human to define 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 designers get in touch with it for “any English language AI job”. [170] [171]
Text generation

The business has popularized 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 published in preprint on OpenAI’s website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and process 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 an unsupervised transformer language design and the follower to OpenAI’s original GPT design (“GPT-1”). GPT-2 was revealed in February 2019, with just minimal demonstrative versions at first launched to the public. The complete variation of GPT-2 was not instantly launched due to issue about possible misuse, consisting of applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 posed a significant threat.

In reaction 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 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 released the complete version of the GPT-2 language design. [177] Several websites host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2’s authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (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 a minimum of 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters by encoding both private 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 model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion specifications, [184] two orders of than the 1.5 billion [185] in the complete 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” tasks and 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 drastically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or encountering the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched to the public for concerns of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month complimentary 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 actually 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 launched in private beta. [194] According to OpenAI, the model can produce working code in over a dozen programming languages, many effectively in Python. [192]
Several issues with glitches, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been accused of releasing copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would discontinue 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 revealed that the updated innovation passed a simulated law school bar exam 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 could also read, analyze or produce as much as 25,000 words of text, and write code in all major programs languages. [200]
Observers reported that the version of ChatGPT using 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 modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal numerous technical details and statistics about GPT-4, such as the precise size of the model. [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 state-of-the-art lead to voice, multilingual, and vision benchmarks, setting brand-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 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 GPT-4o. OpenAI expects it to be especially beneficial for business, startups and developers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been created to take more time to consider their responses, leading to greater accuracy. These models are especially reliable in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, raovatonline.org 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 model is called o3 instead of o2 to avoid confusion with telecoms services service provider O2. [215]
Deep research

Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI’s o3 design to carry out extensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity’s Last Exam) standard. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic resemblance 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 uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as “a green leather handbag shaped like a pentagon” or “an isometric view of a sad capybara”) and produce corresponding images. It can produce pictures of practical objects (“a stained-glass window with a picture of a blue strawberry”) in addition to 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 upgraded version of the design with more reasonable results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new primary system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective model much better able to generate 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 design that can generate videos based on brief detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.

Sora’s development group called it after the Japanese word for “sky”, to symbolize its “unlimited imaginative capacity”. [223] Sora’s innovation is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that purpose, but did not reveal the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it could produce videos up to one minute long. It also shared a technical report highlighting the methods utilized to train the model, and the model’s abilities. [225] It acknowledged some of its shortcomings, consisting of battles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos “impressive”, but noted that they should have been cherry-picked and might not represent Sora’s typical output. [225]
Despite uncertainty from some scholastic leaders following Sora’s public demo, significant entertainment-industry figures have shown considerable 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, mentioning its potential to change storytelling and hb9lc.org content creation. He said that his enjoyment about Sora’s possibilities was so strong that he had decided to stop briefly prepare 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 diverse audio and is likewise a multi-task design that can perform multilingual speech recognition along with 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 tunes with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to begin fairly but then fall into mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to create 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 bit of lyrics and outputs tune samples. OpenAI specified the songs “show regional musical coherence [and] follow conventional chord patterns” however acknowledged that the tunes lack “familiar bigger musical structures such as choruses that repeat” and that “there is a substantial space” between Jukebox and human-generated music. The Verge stated “It’s technologically outstanding, even if the outcomes sound like mushy variations of tunes that may feel familiar”, while Business Insider mentioned “remarkably, some of the resulting tunes are appealing and sound genuine”. [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers to debate toy problems in front of a human judge. The purpose is to research 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 significant layer and neuron of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was developed to analyze the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, different versions of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

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