Announced in 2016, Gym is an open-source Python library created to help with the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making released research more easily reproducible [24] [144] while offering users with an easy interface for connecting with these environments. In 2022, brand-new advancements of Gym have 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 video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to resolve single tasks. Gym Retro offers the ability to generalize in between video games with similar principles however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack knowledge of how to even walk, however are provided the goals of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and placed in a new virtual environment with high winds, the agent 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 might create 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 team of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that learn to play against human players at a high ability level entirely through trial-and-error algorithms. Before ending up being a group of 5, the first public presentation took place 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 actually found out by playing against itself for 2 weeks of actual time, and that the learning software application was a step in the instructions of producing software that can handle complicated tasks like a surgeon. [152] [153] The system utilizes a kind of support knowing, as the bots discover in 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 ability of the bots broadened to play together as a full 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 two exhibition matches against professional gamers, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public appearance came later on 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 obstacles of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep reinforcement learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It learns completely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB cameras to enable the robot to control an approximate item by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could solve a Rubik's Cube. The robotic was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating gradually more tough environments. ADR varies from manual domain randomization by not needing a human to define randomization ranges. [169]
API

In June 2020, OpenAI revealed 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 business has popularized generative pretrained transformers (GPT). [172]
OpenAI's initial GPT design ("GPT-1")

The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed 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 adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative variations initially launched to the general public. The full variation of GPT-2 was not instantly released due to issue about prospective misuse, consisting of applications for composing fake news. [174] Some specialists expressed uncertainty that GPT-2 presented a substantial threat.

In response 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, cautioned of "the technology to absolutely 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 complete variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language models to be general-purpose students, shown by GPT-2 attaining modern 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 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 using 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 not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion specifications, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as few as 125 million parameters were also trained). [186]
OpenAI mentioned that GPT-3 prospered 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 learning between English and Romanian, and in between English and German. [184]
GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched to the general public for wiki.snooze-hotelsoftware.de concerns of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary private 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 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 design can develop working code in over a lots shows languages, a lot of successfully in Python. [192]
Several concerns with glitches, design flaws and security vulnerabilities were pointed out. [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 terminate 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 upgraded innovation 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 could likewise check out, analyze or produce approximately 25,000 words of text, and compose code in all significant shows languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has declined to expose various 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 released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision benchmarks, yewiki.org 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 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 anticipates it to be especially useful for gratisafhalen.be enterprises, 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 been designed to take more time to think of their reactions, causing higher precision. These models are especially effective in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and trademarketclassifieds.com Staff member. [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 model. OpenAI likewise revealed o3-mini, a lighter and quicker variation of OpenAI o3. Since December 21, 2024, this model is not available for forum.pinoo.com.tr public use. According to OpenAI, they are checking 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 instead of o2 to prevent confusion with telecommunications companies O2. [215]
Deep research study

Deep research study is an agent developed by OpenAI, trademarketclassifieds.com unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform extensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to evaluate the semantic resemblance between text and images. It can especially be utilized 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 handbag shaped like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can create pictures of reasonable objects ("a stained-glass window with an image of a blue strawberry") in addition to 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 model with more realistic results. [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 announced DALL-E 3, a more powerful model much better able to produce images from complicated 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 feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can create videos based on brief detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.

Sora's development group named it after the Japanese word for "sky", to its "unlimited imaginative potential". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos licensed for that purpose, but did not expose the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it might generate 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 imperfections, including battles imitating complex 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 demonstration, notable entertainment-industry figures have actually shown substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's ability to create sensible video from text descriptions, mentioning its potential to change storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly 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 diverse audio and is likewise a multi-task model that can perform multilingual speech recognition in addition to speech translation and language recognition. [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 generate tunes with 10 instruments in 15 designs. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall under mayhem the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to create 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 genre, artist, and a bit of lyrics and outputs tune samples. OpenAI stated the tunes "reveal regional musical coherence [and] follow traditional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" which "there is a significant space" between Jukebox and human-generated music. The Verge mentioned "It's technically impressive, even if the results seem like mushy versions of songs that may feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are memorable and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers to debate toy problems in front of a human judge. The function is to research study whether such a technique might assist 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 nerve cell of 8 neural network models which are often studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

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