fleeped

Announced in 2016, Gym is an open-source Python library designed to help with the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI research study, making published research more easily reproducible [24] [144] while supplying users with a simple user interface for connecting with these environments. In 2022, new advancements of Gym have actually been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing representatives to resolve single tasks. Gym Retro provides the ability to generalize between video games with similar concepts but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have understanding of how to even walk, however are given the objectives of learning 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 changing 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, recommending it had actually discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could produce 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 five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against human players at a high ability level totally through trial-and-error algorithms. Before becoming a team of 5, the first public demonstration happened at The International 2017, the annual best champion tournament for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for two weeks of real time, and that the learning software was a step in the direction of producing software that can handle complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots find out 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 goals. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a full team of 5, and they were able to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot player shows the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown the use of deep reinforcement learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It learns entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation problem by using domain randomization, a simulation approach which exposes the student to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has RGB cameras to allow the robot to control an arbitrary things 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 could resolve a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of producing gradually harder environments. ADR varies from manual domain by not needing a human to specify 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 get in touch with it for "any English language AI task". [170] [171]
Text generation

The business 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 model was written by Alec Radford and his associates, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world knowledge and process long-range dependencies 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 follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative versions at first launched to the general public. The complete version of GPT-2 was not instantly released due to concern about possible misuse, including applications for composing fake news. [174] Some professionals revealed uncertainty that GPT-2 positioned a significant hazard.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language design. [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 designs to be general-purpose learners, illustrated by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot jobs (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 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 model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were also trained). [186]
OpenAI stated that GPT-3 prospered at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or coming across the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 needed 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 design was not immediately released to the public for issues of possible abuse, although OpenAI prepared to permit 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 licensed solely 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 personal beta. [194] According to OpenAI, the design can develop working code in over a dozen programming languages, the majority of effectively in Python. [192]
Several concerns with glitches, design defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been implicated of giving off copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would cease 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 revealed that the updated technology 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 also read, evaluate or create approximately 25,000 words of text, and compose code in all major shows languages. [200]
Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal different technical details and statistics about GPT-4, such as the accurate size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision benchmarks, setting brand-new records in audio speech recognition 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, hb9lc.org 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, startups and developers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been developed to take more time to think of their responses, resulting in greater accuracy. These models are particularly efficient in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the successor of the o1 thinking design. OpenAI also revealed o3-mini, a lighter and much faster version of OpenAI o3. As of 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, safety and security scientists had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215]
Deep research study

Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform comprehensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image category

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 classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can produce images of sensible objects ("a stained-glass window with a picture of a blue strawberry") in addition to items that do not exist in reality ("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 upgraded version of the model with more sensible results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new fundamental 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 effective model much better able to create images from complicated descriptions without manual prompt 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 design that can create videos based upon brief detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.

Sora's development team named it after the Japanese word for "sky", to symbolize its "endless innovative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos accredited 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 general public on February 15, 2024, specifying that it might create videos approximately one minute long. It likewise shared a technical report highlighting the methods used to train the model, and the design's abilities. [225] It acknowledged some of its shortcomings, consisting of struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but 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, noteworthy entertainment-industry figures have actually shown significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to produce realistic video from text descriptions, mentioning its potential to reinvent storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had decided 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 design. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment 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 styles. According to The Verge, a song produced by MuseNet tends to start fairly but then fall under chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to develop 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 bit of lyrics and outputs song samples. OpenAI stated the tunes "reveal regional musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" and that "there is a significant space" between Jukebox and human-generated music. The Verge specified "It's technically outstanding, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are catchy and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The purpose is to research whether such a technique might 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 nerve cell of 8 neural network designs which are typically studied in interpretability. [240] Microscope was developed to analyze the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that offers a conversational interface that enables users to ask concerns in natural language. The system then reacts with a response within seconds.