Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of support learning algorithms. It aimed to standardize how environments are specified in AI research, making released research more easily reproducible [24] [144] while supplying users with an easy interface for interacting with these environments. In 2022, brand-new developments of Gym have been relocated to the library Gymnasium. [145] [146]

Gym Retro

Released in 2018, Gym Retro is a platform for support learning (RL) research study on computer game [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to solve single jobs. Gym Retro provides the capability to generalize in between games with similar ideas but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first do not have understanding of how to even stroll, but are given the objectives of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives learn how to adapt to changing conditions. When an agent is then removed from this virtual environment and placed in a new virtual environment with high winds, systemcheck-wiki.de the agent braces to remain upright, suggesting it had actually learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might create an intelligence "arms race" that could increase a representative's ability to operate even outside the context of the competitors. [148]

OpenAI 5

OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high ability level totally through trial-and-error algorithms. Before ending up being a group of 5, the first public presentation took place at The International 2017, the yearly best championship competition for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of genuine time, which the knowing software application was an action in the direction of producing software application that can manage complex jobs like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement learning, as the bots learn over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]

By June 2018, the capability of the bots expanded to play together as a full team of 5, and they had the ability to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert players, however ended up losing both video 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' last public appearance came later that month, where they played in 42,729 total video games in a four-day open online competition, 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 battle arena (MOBA) video games and how OpenAI Five has demonstrated making use of deep reinforcement learning (DRL) representatives to attain superhuman skills 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 completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB video cameras to permit the robot to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]

In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik's Cube. The robot had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating gradually harder environments. ADR varies from manual domain randomization by not needing a human to define randomization ranges. [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 designers call on it for "any English language AI job". [170] [171]

Text generation

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

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

The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative design of language could obtain world knowledge and procedure 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 an unsupervised transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations at first released to the general public. The complete variation of GPT-2 was not instantly released due to issue about potential misuse, consisting of applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 presented a considerable hazard.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural phony news". [175] Other researchers, 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 difficult to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]

GPT-2's authors argue unsupervised language models to be general-purpose students, shown by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more 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 concerns 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 model and the follower to GPT-2. [182] [183] [184] OpenAI specified that the complete 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 few as 125 million parameters were also trained). [186]

OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and might generalize the purpose 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 in between English and German. [184]

GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the essential ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to tens 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 general public for issues 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 certified 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 personal beta. [194] According to OpenAI, the model can create working code in over a lots programming languages, a lot of successfully in Python. [192]

Several problems with problems, design defects and security vulnerabilities were mentioned. [195] [196]

GitHub Copilot has been implicated of discharging copyrighted code, with no 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 technology passed a simulated law school bar exam with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also check out, analyze or produce up to 25,000 words of text, and write code in all significant shows languages. [200]

Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, 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 decreased to expose various technical details and statistics about GPT-4, such as the precise size of the design. [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 results in 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 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 beneficial for enterprises, start-ups and developers looking for to automate services with AI agents. [208]

o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been designed to take more time to think of their reactions, leading to higher accuracy. These models are particularly effective in science, coding, and reasoning jobs, 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 revealed o3, the follower of the o1 reasoning model. OpenAI also unveiled o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are evaluating 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 rather than o2 to avoid confusion with telecoms companies O2. [215]

Deep research study

Deep research study is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out comprehensive web surfing, 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) benchmark. [120]

Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic resemblance 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 design that creates 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 handbag shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can create images of realistic objects ("a stained-glass window with a picture of a blue strawberry") in addition to things that do not exist in truth ("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 upgraded variation of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new simple system for converting a text description into a 3-dimensional model. [220]

DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective design better able to create images from intricate descriptions without manual prompt engineering and render intricate 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 create videos based on brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.

Sora's advancement group called it after the Japanese word for "sky", to signify its "unlimited 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 licensed for that purpose, however did not reveal 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, mentioning that it might generate videos up to 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 shortcomings, consisting of struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", wavedream.wiki however noted that they should have been cherry-picked and may not represent Sora's normal output. [225]

Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have revealed considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to generate realistic video from text descriptions, citing its prospective to transform storytelling and material creation. 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 acknowledgment design. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task model that can perform 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 create songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to begin fairly however then fall into mayhem the longer it plays. [230] [231] In pop 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 create music with vocals. After training on 1.2 million samples, gratisafhalen.be the system accepts a category, artist, and a bit of lyrics and outputs song samples. OpenAI specified the songs "reveal regional 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 substantial gap" in between Jukebox and human-generated music. The Verge specified "It's technically excellent, even if the results sound like mushy variations of tunes that may feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting songs are appealing and sound genuine". [234] [235] [236]

Interface

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches machines to debate 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 significant layer and neuron of eight neural network models 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 variations of CLIP Resnet. [241]

ChatGPT

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