stepstage

Announced in 2016, Gym is an open-source Python library developed to assist in the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research study, making published research more easily reproducible [24] [144] while offering users with a basic user interface for connecting with these environments. In 2022, brand-new developments 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 computer game [147] using RL algorithms and research study generalization. research study focused mainly on enhancing agents to resolve single jobs. Gym Retro gives the capability to generalize in between games with comparable ideas however various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially do not have knowledge of how to even walk, however are given the objectives of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, recommending it had discovered how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives might develop an intelligence "arms race" that might increase a representative'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 discover to play against human gamers at a high skill level totally through experimental algorithms. Before ending up being a team of 5, the first public demonstration happened at The International 2017, the annual premiere championship 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 actually found out by playing against itself for two weeks of real time, which the learning software application was an action in the instructions of creating software application that can deal with complex jobs like a surgeon. [152] [153] The system utilizes a kind of reinforcement learning, as the bots learn in 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 objectives. [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 groups 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 ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs 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 overall video games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5's systems in Dota 2's bot player shows the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated the use of deep reinforcement learning (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses device learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It discovers entirely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation issue by using domain randomization, a simulation approach 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 permit the robot to manipulate an approximate object by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might fix a Rubik's Cube. The robot was able to fix 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 effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing gradually harder environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI designs established by OpenAI" to let designers contact it for "any English language AI job". [170] [171]
Text generation

The company has popularized generative pretrained transformers (GPT). [172]
OpenAI's initial 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 released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language could obtain world knowledge and process long-range dependences by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative versions at first launched to the public. The full variation of GPT-2 was not right away launched due to issue about potential misuse, including applications for writing phony news. [174] Some experts revealed uncertainty that GPT-2 positioned a substantial threat.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted 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 impossible to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language design. [177] Several websites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose students, highlighted by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot jobs (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 concerns encoding vocabulary with word tokens by using 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 design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million criteria were also trained). [186]
OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately launched 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 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 personal beta. [194] According to OpenAI, the model can create working code in over a lots shows languages, most effectively in Python. [192]
Several problems with problems, design 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 discontinue 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), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar test 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 likewise read, analyze or generate up to 25,000 words of text, and compose code in all major programming languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose different technical details and data about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, setiathome.berkeley.edu 2024, OpenAI revealed and released GPT-4o, which can process and create 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 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 variation 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 particularly useful for enterprises, start-ups and designers 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 designed to take more time to believe about their responses, leading to greater precision. These models are particularly reliable in science, coding, and thinking tasks, 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 thinking design. OpenAI also revealed 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, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecommunications services service provider O2. [215]
Deep research study

Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web browsing, information analysis, and synthesis, 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) standard. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic similarity between text and images. It can especially be used for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can produce images of sensible items ("a stained-glass window with a picture of a blue strawberry") as well as objects that do not exist in truth ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an updated variation of the model with more practical results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new basic system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more powerful design 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 model that can create videos based upon brief detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.

Sora's development group named it after the Japanese word for "sky", to symbolize its "unlimited innovative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that function, but did not expose the number or the specific 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 generate videos approximately one minute long. It also shared a technical report highlighting the methods used to train the design, and the design's capabilities. [225] It acknowledged a few of its shortcomings, consisting of battles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they must 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 revealed substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to generate reasonable video from text descriptions, citing its prospective to reinvent storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause prepare for expanding 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 varied audio and is likewise a multi-task design that can carry out multilingual speech recognition as well as 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 styles. According to The Verge, a tune created by MuseNet tends to start fairly however then fall under mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to develop music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs song samples. OpenAI mentioned the songs "reveal local musical coherence [and] follow conventional chord patterns" however acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a substantial space" in between Jukebox and human-generated music. The Verge mentioned "It's highly impressive, even if the results sound like mushy variations of tunes that may feel familiar", while Business Insider mentioned "remarkably, a few of the resulting songs are appealing and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches devices to dispute toy issues in front of a human judge. The purpose is to research study whether such a technique may 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 substantial layer and nerve cell of eight neural network designs which are often studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, different variations of Inception, wiki.lafabriquedelalogistique.fr and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, kousokuwiki.org ChatGPT is an expert system tool constructed on top of GPT-3 that provides a conversational interface that allows users to ask questions in natural language. The system then reacts with a response within seconds.