petstory

Announced in 2016, Gym is an open-source Python library developed to help with the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research study, making released research study more quickly reproducible [24] [144] while offering users with a simple interface for connecting with these environments. In 2022, new advancements of Gym have been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on video games [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to solve single jobs. Gym Retro gives the capability to generalize in between video games with similar principles but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack understanding of how to even stroll, but are provided the objectives of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents discover how to adjust to altering conditions. When an agent is then removed from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might develop an intelligence "arms race" that might increase a representative's ability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high ability level completely through trial-and-error algorithms. Before ending up being a team of 5, the first public presentation occurred at The International 2017, the annual premiere champion tournament for the video game, where Dendi, a professional Ukrainian player, 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 discovered by playing against itself for 2 weeks of actual time, and that the learning software was a step in the direction of creating software that can manage intricate tasks like a surgeon. [152] [153] The system utilizes a type 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 eliminating an enemy and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a complete team of 5, and they had the ability to beat teams 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 games. [160] [161] [162] In April 2019, OpenAI Five beat 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 appearance came later that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5's mechanisms in Dota 2's bot player reveals the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated the usage of deep reinforcement learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It learns totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB cameras to allow the robot to control an approximate item by seeing it. In 2018, wiki.snooze-hotelsoftware.de OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complicated 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 generating gradually more challenging environments. ADR varies 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 new AI designs established by OpenAI" to let developers call on it for "any English language AI task". [170] [171]
Text generation

The company has 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 written by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world knowledge 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 an unsupervised transformer language model and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative variations at first released to the public. The complete version of GPT-2 was not immediately released due to concern about potential misuse, including applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 postured a substantial threat.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural phony 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 muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI released the total variation of the GPT-2 language design. [177] Several websites host interactive presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose learners, illustrated by GPT-2 attaining cutting edge 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of 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 without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as few as 125 million criteria were likewise trained). [186]
OpenAI specified 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 offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, gratisafhalen.be and in between English and German. [184]
GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or experiencing the essential capability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched 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 complimentary 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 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 personal beta. [194] According to OpenAI, the design can produce working code in over a dozen programming languages, many successfully in Python. [192]
Several issues with glitches, style flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has actually been accused of producing copyrighted code, with no author attribution or garagesale.es license. [197]
OpenAI announced that they would discontinue assistance for Codex API on March 23, bytes-the-dust.com 2023. [198]
GPT-4

On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the upgraded technology 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 could likewise check out, evaluate or produce up to 25,000 words of text, and write code in all significant programming languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal numerous technical details and data about GPT-4, 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 produce text, wiki.asexuality.org images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition 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 launched GPT-4o mini, a smaller sized 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 particularly helpful for business, startups and developers looking for to automate services with AI agents. [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 of their responses, causing higher accuracy. These models are particularly reliable in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking design. OpenAI also unveiled o3-mini, a lighter and quicker version 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, safety and security researchers had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to avoid confusion with telecommunications services supplier O2. [215]
Deep research

Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, 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 design that is trained to evaluate the semantic resemblance in between text and images. It can especially be utilized 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 uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can develop pictures of realistic 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, no API or code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, an upgraded variation of the design with more practical outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new simple system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful model better able to create images from intricate descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched to the general 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 on short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.

Sora's development team named it after the Japanese word for "sky", to symbolize its "limitless innovative capacity". [223] Sora's technology is an adjustment 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 certified for that function, but did not reveal the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it could create videos up to one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the design's abilities. [225] It acknowledged a few of its drawbacks, including struggles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however noted that they should have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry figures have revealed considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to create realistic video from text descriptions, citing its potential to transform storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause plans for expanding his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model that can perform 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 forecast subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall into turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to develop 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 category, artist, and a bit of lyrics and outputs song samples. OpenAI specified the songs "reveal local musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" which "there is a considerable gap" between Jukebox and human-generated music. The Verge mentioned "It's technically outstanding, even if the outcomes sound like mushy variations of songs that might feel familiar", while Business Insider stated "surprisingly, a few of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches devices to discuss 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 establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell 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 quickly. 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 expert system 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.