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Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research, making released research study more easily reproducible [24] [144] while providing users with a simple user interface for connecting with these environments. In 2022, new developments of Gym have actually been moved 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 focused mainly on optimizing agents to solve single tasks. Gym Retro offers the ability to generalize in between games with similar ideas however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially do not have understanding of how to even walk, but are provided the objectives of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt to changing conditions. When a representative is then eliminated from this virtual environment and put in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually learned how to balance in a generalized method. [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 operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots used 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 experimental algorithms. Before becoming 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 individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for two weeks of actual time, which the knowing software was an action in the direction of creating software application that can handle intricate jobs like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots discover gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a full team of 5, and bytes-the-dust.com 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 expert gamers, however ended up losing both . [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the 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 total 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 gamer reveals the challenges of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually demonstrated the use of deep reinforcement learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, disgaeawiki.info Dactyl uses machine learning to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation method which exposes the student to a variety of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, also has RGB video cameras to permit the robot to manipulate an approximate things by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robot was able to resolve 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 robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating gradually harder environments. ADR differs 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 brand-new AI models developed by OpenAI" to let developers call on it for "any English language AI task". [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 design was written by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world understanding and procedure long-range reliances by pre-training on a varied corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative versions at first released to the general public. The complete version of GPT-2 was not instantly launched due to concern about possible abuse, consisting of applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 presented a significant risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language design. [177] Several sites host interactive presentations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose learners, highlighted by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not further 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 prevents certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned 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 full version of GPT-2 (although GPT-3 models with as few as 125 million criteria were also trained). [186]
OpenAI specified 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 gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
GPT-3 drastically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or encountering the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month complimentary personal 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 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 develop working code in over a dozen programming languages, most successfully in Python. [192]
Several concerns with problems, design flaws and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been implicated of releasing 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 revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar examination with a score 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, examine or create as much as 25,000 words of text, and compose code in all significant programming languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal different technical details and stats about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, gratisafhalen.be which can process and generate text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision standards, setting 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 launched GPT-4o mini, a smaller sized variation 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 expects it to be particularly helpful for enterprises, 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 about their actions, leading to higher accuracy. These designs 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 follower of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and quicker variation of OpenAI o3. Since 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, security and security researchers had the opportunity to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecommunications providers O2. [215]
Deep research study

Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform substantial web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools made it possible for, it reached a precision 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 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 variation of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can create pictures of practical things ("a stained-glass window with an image of a blue strawberry") in addition to 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 upgraded variation of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new fundamental system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more powerful model better able to generate images from complex 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 design that can create videos based upon short detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.

Sora's advancement group named it after the Japanese word for "sky", to represent its "limitless innovative capacity". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos accredited for that function, but did not expose 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 also shared a technical report highlighting the methods used to train the model, and the design's abilities. [225] It acknowledged a few of its shortcomings, consisting of struggles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they need to have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have actually shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to generate reasonable video from text descriptions, citing its possible to revolutionize storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly strategies for broadening his Atlanta-based motion picture 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 also a multi-task model that can perform multilingual speech recognition in addition to speech translation and language identification. [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 produce songs with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to produce 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 snippet of lyrics and outputs song samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" which "there is a considerable space" between Jukebox and human-generated music. The Verge stated "It's highly impressive, even if the outcomes seem like mushy versions of songs that may feel familiar", while Business Insider stated "remarkably, some of the resulting songs are memorable and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The function is to research whether such an approach might assist in auditing AI choices and in developing 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 frequently studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an expert system tool built on top of GPT-3 that provides a conversational user interface that enables users to ask concerns in natural language. The system then responds with an answer within seconds.