Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making published research more easily reproducible [24] [144] while offering users with a simple user interface for engaging with these environments. In 2022, new developments of Gym have actually been moved to the library Gymnasium. [145] [146]

Gym Retro

Released in 2018, Gym Retro is a platform for support learning (RL) research on video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to solve single tasks. Gym Retro provides the ability to generalize in between games with comparable concepts but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack understanding of how to even stroll, but are provided the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents could develop an intelligence "arms race" that could increase an agent's ability 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 video game Dota 2, that find out to play against human players at a high skill level completely through experimental algorithms. Before becoming a group of 5, the very first public demonstration occurred at The International 2017, the annual premiere champion competition for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of real time, and that the learning software was a step in the instructions of creating software application that can deal with complex tasks like a cosmetic surgeon. [152] [153] The system utilizes a kind of support learning, as the bots discover gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and taking map goals. [154] [155] [156]

By June 2018, the capability of the bots broadened to play together as a full team 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 exhibit matches against expert players, but wound 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' last public appearance 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 gamer shows the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown using deep support learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]

Dactyl

Developed in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It discovers entirely in simulation utilizing 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 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 cameras to enable the robot to control an approximate item by seeing it. In 2018, archmageriseswiki.com OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]

In 2019, OpenAI showed that Dactyl could 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 enhancing the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of producing progressively more challenging environments. ADR varies from manual domain randomization by not needing a human to specify randomization ranges. [169]

API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new AI models established by OpenAI" to let designers get in touch with it for "any English language AI task". [170] [171]

Text generation

The company has actually promoted generative pretrained transformers (GPT). [172]

OpenAI's original GPT model ("GPT-1")

The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative model of language might obtain world knowledge 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 without supervision transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions initially launched to the public. The full version of GPT-2 was not instantly launched due to issue about prospective misuse, including applications for writing fake 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 spot "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned 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 total variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of various circumstances of GPT-2 and other transformer models. [178] [179] [180]

GPT-2's authors argue unsupervised language models to be general-purpose learners, illustrated by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model 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 at least 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 wiki.vst.hs-furtwangen.de the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million parameters were likewise trained). [186]

OpenAI specified 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 provided examples of translation and cross-linguistic transfer learning between English and Romanian, pediascape.science and 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 coming across the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched to the public for concerns of possible abuse, although OpenAI planned to allow 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 licensed exclusively 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 private beta. [194] According to OpenAI, the model can produce working code in over a lots programs languages, the majority of efficiently in Python. [192]

Several issues with glitches, design defects and security vulnerabilities were pointed out. [195] [196]

GitHub Copilot has been accused of releasing 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 revealed 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 exam 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 approximately 25,000 words of text, and compose code in all significant programming languages. [200]

Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal various technical details and statistics 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 generate text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision criteria, setting new records in audio speech recognition and bytes-the-dust.com 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 particularly helpful for higgledy-piggledy.xyz business, startups and designers looking for to automate services with AI representatives. [208]

o1

On September 12, 2024, OpenAI released the o1-preview and systemcheck-wiki.de o1-mini designs, which have been designed to take more time to think about their reactions, 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 changed by o1. [211]

o3

On December 20, 2024, OpenAI revealed o3, the successor of the o1 thinking model. OpenAI also unveiled o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215]

Deep research

Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform extensive web surfing, information analysis, and synthesis, providing 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 examine the semantic similarity in between text and images. It can notably be used for image classification. [217]

Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model 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 formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can develop images of practical things ("a stained-glass window with an image 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 variation of the model with more realistic outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new simple system for converting a text description into a 3-dimensional design. [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 timely engineering and render intricate details like hands and text. [221] It was launched to the public as a ChatGPT Plus function in October. [222]

Text-to-video

Sora

Sora is a text-to-video design that can create videos based upon brief detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of created videos is unknown.

Sora's advancement team called it after the Japanese word for "sky", to signify its "unlimited creative potential". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that purpose, but did not expose the number or the specific sources of the videos. [223]

OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it could create videos approximately one minute long. It likewise shared a technical report highlighting the methods used to train the design, and the design's capabilities. [225] It acknowledged some of its shortcomings, including battles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but noted that they need to have been cherry-picked and might not represent Sora's common output. [225]

Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have revealed substantial interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to create sensible video from text descriptions, mentioning its possible to revolutionize storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had chosen 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 design. [228] It is trained on a big dataset of diverse audio and is likewise a multi-task model that can carry out multilingual speech recognition in addition to 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 songs with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly but then fall under chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to develop music for the titular character. [232] [233]

Jukebox

Released in 2020, forum.batman.gainedge.org 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 stated the tunes "reveal local musical coherence [and] follow traditional chord patterns" however acknowledged that the songs lack "familiar bigger musical structures such as choruses that duplicate" and that "there is a considerable gap" between Jukebox and human-generated music. The Verge stated "It's technologically impressive, even if the outcomes sound like mushy versions of songs that might feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are appealing and sound genuine". [234] [235] [236]

User interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches machines to debate toy issues in front of a human judge. The purpose is to research study whether such an approach may 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 nerve cell of 8 neural network designs which are often studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various variations of Inception, and different versions of CLIP Resnet. [241]

ChatGPT

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