mfactory

Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research, making published research study more quickly reproducible [24] [144] while providing users with an easy interface for communicating with these environments. In 2022, brand-new developments of Gym have actually been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support knowing (RL) research on computer game [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to resolve single jobs. Gym Retro offers the ability to generalize in between games with similar ideas 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 provided the objectives of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents find out how to adjust to changing conditions. When a representative is then eliminated from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives might produce an intelligence "arms race" that could increase an agent's ability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots utilized in the computer game Dota 2, that discover to play against human gamers at a high ability level entirely through experimental algorithms. Before becoming a team of 5, the first public presentation took place at The International 2017, the annual premiere championship competition for 135.181.29.174 the 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 found out by playing against itself for 2 weeks of genuine time, which the learning software was an action in the direction of creating software that can handle complicated jobs like a cosmetic surgeon. [152] [153] The system utilizes a kind of support knowing, as the bots learn with time by playing against themselves hundreds of 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 ability of the bots broadened to play together as a complete team of 5, and they were able to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional players, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated making use of deep support 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 control physical items. [167] It finds out completely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation issue by utilizing domain randomization, a simulation method which exposes the student to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB electronic cameras to enable the robot to control an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might solve a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce 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 producing progressively more challenging environments. ADR differs from manual domain randomization by not requiring 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 developers call on it for "any English language AI job". [170] [171]
Text generation

The business has actually 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 model 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 understanding 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 successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions initially launched to the general public. The full variation of GPT-2 was not instantly launched due to issue about prospective abuse, including applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 positioned a substantial risk.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake 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 hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several sites host interactive presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose students, shown by GPT-2 attaining cutting edge 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 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 using 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 not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as few as 125 million parameters were also trained). [186]
OpenAI mentioned that GPT-3 prospered 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 knowing in between English and Romanian, and in between English and German. [184]
GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or coming across the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the general public for concerns of possible abuse, although OpenAI planned to allow 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 certified specifically 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 launched in private beta. [194] According to OpenAI, the design can develop working code in over a lots shows languages, the majority of effectively in Python. [192]
Several concerns with glitches, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been accused of discharging copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would terminate 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 updated innovation 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, examine or create as much as 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose numerous 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 produce text, images and audio. [204] GPT-4o attained cutting edge results 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 sized version 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 particularly helpful for enterprises, start-ups and designers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been created to take more time to think about their reactions, resulting in higher precision. These designs are particularly effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and quicker 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, security and security scientists had the chance 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

Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out substantial web browsing, data analysis, and synthesis, delivering 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 classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze the semantic similarity in between text and images. It can notably 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 interpret 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 produce images of reasonable items ("a stained-glass window with a picture of a blue strawberry") in addition to things that do not exist in reality ("a cube with the texture of a porcupine"). As of March 2021, pipewiki.org no API or code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, an updated version of the design with more practical results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new primary 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 model much better able to produce images from complex 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 feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can produce videos based on short detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can create 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 creative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that function, but did not expose the number or the precise sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it might produce videos up to one minute long. It likewise shared a technical report highlighting the techniques utilized to train the design, and the model's capabilities. [225] It acknowledged a few of its imperfections, including battles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however noted that they need to have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have actually shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to create realistic video from text descriptions, citing its possible to change storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause 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 carry out multilingual speech acknowledgment along with speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 designs. According to The Verge, pipewiki.org a song generated by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to produce 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 genre, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "show local musical coherence [and] follow standard chord patterns" however acknowledged that the songs do not have "familiar bigger 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 technologically excellent, even if the outcomes seem like mushy variations of tunes that may feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches machines to debate toy issues in front of a human judge. The purpose is to research study whether such a method 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 typically studied in interpretability. [240] Microscope was created to analyze the features that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an artificial intelligence tool developed on top of GPT-3 that provides a conversational user interface that permits users to ask questions in natural language. The system then responds with a response within seconds.