Announced in 2016, Gym is an open-source Python library developed to assist in the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research study, making published research study more easily reproducible [24] [144] while supplying users with a simple 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 knowing (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to fix single tasks. Gym Retro provides the capability to generalize between games with comparable ideas however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first do not have understanding of how to even walk, however are provided the objectives of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives discover how to adapt to changing conditions. When an agent is then gotten rid of from this virtual environment and positioned in a brand-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 competition between representatives could create an intelligence "arms race" that could increase a representative's ability to work even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of 5 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 completely 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, a professional Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of actual time, and that the learning software application was an action in the instructions of developing software that can handle intricate tasks like a surgeon. [152] [153] The system utilizes a type of support knowing, as the bots find out with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and wiki.snooze-hotelsoftware.de taking map objectives. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a complete team 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 two exhibit matches against expert gamers, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 overall video 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 difficulties of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown making use of deep support 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 manipulate physical items. [167] It finds out completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by using domain randomization, a simulation approach which exposes the student to a variety of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, also has RGB electronic cameras to permit the robotic to manipulate an arbitrary things by seeing it. In 2018, OpenAI revealed that the system was able to manipulate 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 design. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating progressively more difficult environments. ADR varies from manual domain randomization by not needing a human to define randomization varieties. [169]
API

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

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

The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language might obtain world knowledge and procedure long-range dependencies by pre-training on a varied corpus with long stretches of adjoining 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 revealed in February 2019, with only limited demonstrative variations initially launched to the public. The full variation of GPT-2 was not right away released due to issue about possible abuse, including applications for writing phony news. [174] Some experts expressed uncertainty that GPT-2 positioned a significant risk.

In reaction to GPT-2, wiki-tb-service.com the Allen Institute for Artificial Intelligence reacted with a tool to find "neural fake news". [175] Other scientists, 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 variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not additional trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains a little 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 permits 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 an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion criteria, [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 couple of as 125 million parameters were also trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks 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 between English and Romanian, and between English and German. [184]
GPT-3 dramatically enhanced outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or coming across the basic capability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the general public for issues of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified 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 launched in private beta. [194] According to OpenAI, the model can create working code in over a dozen shows languages, most successfully in Python. [192]
Several problems with glitches, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been implicated of giving off copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would stop assistance for Codex API on March 23, 2023. [198]
GPT-4

On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the updated innovation 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 might also read, evaluate or generate approximately 25,000 words of text, and write code in all major shows languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution 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 actually decreased to expose various technical details and stats about GPT-4, such as the exact size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, wiki.snooze-hotelsoftware.de and vision benchmarks, setting brand-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 variation 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 especially useful for business, start-ups and developers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been created to take more time to think about their reactions, causing greater accuracy. These designs are particularly reliable in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, engel-und-waisen.de OpenAI revealed o3, the follower of the o1 reasoning model. OpenAI also unveiled o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecoms providers O2. [215]
Deep research study

Deep research is a representative developed by OpenAI, unveiled on February 2, wiki.asexuality.org 2025. It leverages the capabilities of OpenAI's o3 design to carry out substantial web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty 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 evaluate the semantic similarity 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 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 handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can produce pictures of reasonable things ("a stained-glass window with an image of a blue strawberry") as well as items 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 version of the design with more practical outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new simple system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more effective model better able to create images from intricate descriptions without manual timely 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 design that can generate videos based on brief 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 maximal length of produced videos is unknown.

Sora's development team named it after the Japanese word for "sky", to signify its "endless innovative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos licensed for that function, however did not expose 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, stating that it might produce videos as much as one minute long. It also shared a technical report highlighting the techniques utilized to train the model, and the design's capabilities. [225] It acknowledged a few of its drawbacks, consisting of battles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", but 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 demonstration, noteworthy entertainment-industry figures have actually revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to create sensible video from text descriptions, mentioning its possible to reinvent storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause prepare for expanding his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of varied audio and is likewise a multi-task model that can perform multilingual speech acknowledgment 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 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 mental thriller Ben Drowned to create 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 category, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the tunes "show local musical coherence [and] follow standard chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a substantial gap" in between Jukebox and ratemywifey.com human-generated music. The Verge stated "It's technologically impressive, even if the outcomes sound like mushy versions of songs that might feel familiar", while Business Insider stated "surprisingly, some of the resulting songs are appealing and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches devices to discuss toy problems in front of a human judge. The purpose is to research study whether such a technique might assist in auditing AI decisions 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 8 neural network designs which are typically studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different versions 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 supplies a conversational interface that enables users to ask questions in natural language. The system then responds with an answer within seconds.