GitLab

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 released research study more easily reproducible [24] [144] while supplying users with a basic interface for communicating with these environments. In 2022, new developments of Gym have been moved 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] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to solve single jobs. Gym Retro provides the capability to generalize between video games with similar ideas however different appearances.

RoboSumo

Released in 2017, yewiki.org RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have understanding of how to even stroll, but are given the goals of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives find out how to adjust to altering conditions. When a representative is then removed from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives might develop an intelligence "arms race" that might increase an agent's capability to work even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that find out to play against human players at a high ability level entirely through experimental algorithms. Before becoming a group of 5, the first public demonstration took place at The International 2017, the annual premiere champion competition for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg that the bot had discovered by playing against itself for 2 weeks of actual time, and that the knowing software application was a step in the direction of creating software application that can manage complex jobs like a cosmetic surgeon. [152] [153] The system uses a kind of support knowing, as the bots learn with time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a complete group of 5, and they had the ability to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the game at the time, 2:0 in a live exhibit 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 video games. [165]
OpenAI 5's systems in Dota 2's bot player reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually demonstrated making use of deep reinforcement knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine learning to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It learns totally in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, also has RGB electronic cameras to permit the robot to control an approximate item by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might resolve a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating gradually harder environments. ADR differs 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 models established by OpenAI" to let designers contact it for "any English language AI task". [170] [171]
Text generation

The company has promoted generative pretrained transformers (GPT). [172]
OpenAI's original GPT model ("GPT-1")

The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might 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 a without supervision transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative variations at first launched to the public. The full version of GPT-2 was not instantly released due to issue about possible abuse, consisting of applications for writing fake news. [174] Some specialists revealed uncertainty that GPT-2 presented a substantial danger.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "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 variation of the GPT-2 language design. [177] Several sites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, illustrated by GPT-2 attaining cutting edge accuracy 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 problems encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both individual 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 model and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were also trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks 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 between English and German. [184]
GPT-3 significantly enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released 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 personal beta that began 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 actually 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 launched in private beta. [194] According to OpenAI, forum.batman.gainedge.org the design can produce working code in over a lots shows languages, a lot of effectively in Python. [192]
Several issues with glitches, design defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been implicated of emitting copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would terminate 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 innovation passed a simulated law school bar examination 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 might also check out, analyze or create up to 25,000 words of text, and compose code in all major programs languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has declined to reveal various technical details and stats about GPT-4, wiki.myamens.com 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 results in voice, multilingual, and vision criteria, setting brand-new records in audio speech acknowledgment 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 version of GPT-4o replacing 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 especially helpful for business, startups 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 consider their reactions, resulting in greater precision. These designs are especially 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 also revealed o3-mini, a lighter and faster variation 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 researchers had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecoms companies 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 model to perform substantial web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy 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 resemblance between text and images. It can notably be used for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation 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 matching images. It can develop pictures of realistic objects ("a stained-glass window with an image of a blue strawberry") as well as 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 revealed DALL-E 2, an upgraded variation of the model with more sensible outcomes. [219] In December 2022, higgledy-piggledy.xyz OpenAI released on GitHub software application for Point-E, a new fundamental system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful design much better able to generate images from complicated descriptions without manual timely engineering and render complex 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] as well as extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or setiathome.berkeley.edu 1080x1920. The optimum length of generated videos is unidentified.

Sora's development team named it after the Japanese word for "sky", to represent its "limitless innovative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos accredited for that purpose, however did not reveal the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it might produce videos approximately one minute long. It likewise shared a technical report highlighting the approaches utilized to train the design, and the model's capabilities. [225] It acknowledged a few of its shortcomings, including battles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", however kept in mind that they must have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have shown substantial interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to generate sensible video from text descriptions, mentioning its possible to reinvent storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had chosen 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 big dataset of varied audio and is also a multi-task model that can carry out multilingual speech recognition as well as speech translation and setiathome.berkeley.edu language identification. [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 tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to create 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 snippet of lyrics and outputs song samples. OpenAI stated the tunes "reveal local musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" which "there is a substantial gap" between Jukebox and human-generated music. The Verge mentioned "It's technologically excellent, even if the outcomes seem like mushy variations of songs that might 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 launched the Debate Game, which teaches devices to dispute toy problems 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 significant layer and neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was developed to examine the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, and various variations 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 allows users to ask questions in natural language. The system then reacts with an answer within seconds.