Announced in 2016, Gym is an open-source Python library created to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research study, making released research more easily reproducible [24] [144] while providing users with an easy user interface for interacting with these environments. In 2022, brand-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 research study generalization. Prior RL research study focused mainly on enhancing agents to fix single jobs. Gym Retro gives the ability to generalize between video games with comparable principles but different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first do not have knowledge of how to even stroll, but are offered the goals of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to changing conditions. When an agent is then removed from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could develop an intelligence "arms race" that might increase a representative's ability to function even outside the context of the competitors. [148]

OpenAI 5

OpenAI Five is a team of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high skill level totally through experimental algorithms. Before ending up being a group of 5, the first public demonstration took place 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 one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for two weeks of genuine time, which the knowing software application was an action in the instructions of developing software that can manage complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of support knowing, as the bots discover gradually by playing against themselves numerous 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 broadened to play together as a full group of 5, and they had the ability to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat 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 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 mechanisms in Dota 2's bot gamer reveals the obstacles of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown making use of deep reinforcement learning (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 robot hand, to control physical items. [167] It learns totally in simulation using the very same RL algorithms and forum.batman.gainedge.org training code as OpenAI Five. OpenAI tackled the item orientation problem by utilizing domain randomization, a simulation approach 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 electronic cameras, likewise has RGB electronic cameras to allow the robotic to manipulate 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 showed that Dactyl could fix a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of creating progressively more hard environments. ADR differs from manual domain randomization by not requiring a human to specify randomization ranges. [169]

API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI designs developed by OpenAI" to let developers call on it for "any English language AI task". [170] [171]

Text generation

The business has popularized 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 written by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might 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 an unsupervised transformer language design and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative variations at first launched to the general public. The full variation of GPT-2 was not instantly launched due to concern about prospective abuse, including applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 posed a considerable risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "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 hush all other speech and be difficult to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language model. [177] Several sites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]

GPT-2's authors argue not being watched language models to be general-purpose learners, 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 a minimum of 3 upvotes. It avoids certain problems 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 an unsupervised transformer language model and the successor 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 variation of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were also trained). [186]

OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184]

GPT-3 dramatically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly launched to the general public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month totally free private beta that started 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 released in personal beta. [194] According to OpenAI, the design can develop working code in over a lots shows languages, many successfully in Python. [192]

Several problems with glitches, style flaws and security vulnerabilities were mentioned. [195] [196]

GitHub Copilot has been implicated of giving off copyrighted code, without any author attribution or license. [197]

OpenAI revealed that they would stop assistance 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 test with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, analyze or pipewiki.org generate approximately 25,000 words of text, and compose code in all major 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 caveat 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 actually decreased to expose different 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 released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision benchmarks, setting new records in audio speech acknowledgment and 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 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 useful for enterprises, setiathome.berkeley.edu start-ups and developers looking for to automate services with AI representatives. [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 think of their responses, leading to higher precision. These designs are particularly effective in science, coding, and reasoning tasks, 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, the follower of the o1 reasoning model. OpenAI likewise unveiled o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and yewiki.org security scientists had the opportunity to obtain early access to these models. [214] The model is called o3 rather than o2 to prevent confusion with telecoms services provider O2. [215]

Deep research study

Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out extensive web surfing, data analysis, and synthesis, raovatonline.org providing detailed reports within a timeframe of 5 to 30 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 category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze the semantic similarity 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 produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can create pictures of realistic things ("a stained-glass window with an image of a blue strawberry") along with items 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 model with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new rudimentary system for converting a text description into a 3-dimensional model. [220]

DALL-E 3

In September 2023, hb9lc.org OpenAI announced DALL-E 3, a more effective model better able to produce images from intricate descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was released to the general public as a ChatGPT Plus function in October. [222]

Text-to-video

Sora

Sora is a text-to-video design that can produce videos based on short detailed prompts [223] as well as extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.

Sora's development team called it after the Japanese word for "sky", to represent its "unlimited imaginative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited for that purpose, however did not expose the number or the exact 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 generate videos approximately one minute long. It likewise shared a technical report highlighting the methods utilized to train the design, and the model's abilities. [225] It acknowledged a few of its drawbacks, including struggles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they need to have been cherry-picked and may not represent Sora's typical output. [225]

Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have actually revealed considerable interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to generate realistic video from text descriptions, citing its prospective to reinvent storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause strategies for expanding 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 big dataset of diverse audio and is likewise a multi-task design that can carry out 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 anticipate subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a tune produced by MuseNet tends to start fairly but 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 internet mental thriller Ben Drowned to develop music for the titular character. [232] [233]

Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs song samples. OpenAI stated the tunes "show regional musical coherence [and] follow standard chord patterns" but acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" and that "there is a significant gap" between Jukebox and human-generated music. The Verge specified "It's technologically remarkable, even if the outcomes sound like mushy variations of songs that might feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting tunes are appealing and sound genuine". [234] [235] [236]

Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches devices to discuss toy problems in front of a human judge. The purpose is to research whether such a method might help in auditing AI decisions and in establishing explainable AI. [237] [238]

Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of 8 neural network models which are typically studied in interpretability. [240] Microscope was produced to evaluate the functions that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various variations of Inception, and various 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 permits users to ask questions in natural language. The system then reacts with a response within seconds.