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 published research more easily reproducible [24] [144] while offering users with a simple user interface for engaging with these environments. In 2022, brand-new advancements of Gym have been moved to the library Gymnasium. [145] [146]
Gym Retro
Released in 2018, Gym Retro is a platform for support learning (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing representatives to fix single jobs. Gym Retro provides the capability to generalize in between video games with similar ideas however different looks.
RoboSumo
Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have understanding of how to even walk, but are offered the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents discover how to adjust to changing conditions. When a representative is then removed from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could develop an intelligence "arms race" that might increase a representative's capability to work even outside the context of the competition. [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 learn to play against human players at a high ability level totally through experimental algorithms. Before becoming a group of 5, the first public demonstration took place at The International 2017, the yearly best championship tournament 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 found out by playing against itself for 2 weeks of actual time, and that the learning software application was an action in the direction of producing software that can handle complicated jobs like a surgeon. [152] [153] The system uses a kind of support 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 ability of the bots expanded to play together as a complete group of 5, and they had the ability to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The 2018, OpenAI Five played in two exhibition matches against professional gamers, however ended 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 appearance 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 mechanisms in Dota 2's bot gamer reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown making use of deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl
Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robot hand, wiki.dulovic.tech to manipulate physical items. [167] It discovers totally in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation problem by utilizing domain randomization, a simulation approach which exposes the learner to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB cams to permit the robotic to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robotic was able to resolve 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 toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating progressively harder environments. ADR differs from manual domain randomization by not requiring a human to specify randomization varieties. [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 developers call on it for "any English language AI job". [170] [171]
Text generation
The company has promoted generative pretrained transformers (GPT). [172]
OpenAI's initial GPT model ("GPT-1")
The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his colleagues, trademarketclassifieds.com and released 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 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 only limited demonstrative versions initially released to the public. The complete variation of GPT-2 was not instantly released due to concern about potential abuse, including applications for composing fake news. [174] Some specialists revealed uncertainty that GPT-2 posed a considerable hazard.
In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned of "the technology 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 launched the complete variation of the GPT-2 language design. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, shown by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not additional 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 prevents certain concerns encoding vocabulary with word tokens by using byte pair encoding. This allows 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 a without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million criteria were likewise trained). [186]
OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the function 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, and between English and German. [184]
GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or coming across the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the general public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month totally free private beta that started 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 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 model can create working code in over a lots programs languages, the majority of effectively in Python. [192]
Several concerns with glitches, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been accused of emitting copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would stop support 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), efficient in accepting text or image inputs. [199] They revealed that the updated technology 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 could likewise read, examine or create up to 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the version 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 modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal numerous technical details and data about GPT-4, such as the precise 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 modern results in 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) standard 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 particularly useful for enterprises, startups and designers seeking to automate services with AI agents. [208]
o1
On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been designed to take more time to believe about their actions, leading to higher accuracy. These models are particularly reliable 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 unveiled 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, pipewiki.org 2025, security and security scientists had the chance to obtain early access to these models. [214] The design is called o3 rather than o2 to prevent confusion with telecommunications companies O2. [215]
Deep research study
Deep research study is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform comprehensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image category
CLIP
Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic similarity 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 design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can create images of realistic items ("a stained-glass window with an image of a blue strawberry") along with things that do not exist in truth ("a cube with the texture of a porcupine"). As of March 2021, trademarketclassifieds.com 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 released on GitHub software application for Point-E, a new primary system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3
In September 2023, OpenAI revealed DALL-E 3, a more powerful design much better able to produce 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 create videos based upon short detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.
Sora's advancement team called it after the Japanese word for "sky", to symbolize its "unlimited imaginative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos licensed for that purpose, however did not expose the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it might produce videos up to one minute long. It likewise shared a technical report highlighting the methods used to train the design, and the design's abilities. [225] It acknowledged some of its drawbacks, including struggles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", however noted that they should have been cherry-picked and may not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have actually shown substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to generate reasonable video from text descriptions, citing its prospective to reinvent storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause plans for broadening his Atlanta-based film studio. [227]
Speech-to-text
Whisper
Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a large dataset of varied audio and is likewise a multi-task model that can perform multilingual speech acknowledgment as well as 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 tunes with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall under turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to create 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 genre, artist, and a bit of lyrics and outputs song samples. OpenAI mentioned the tunes "reveal regional musical coherence [and] follow standard chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable space" in between Jukebox and human-generated music. The Verge specified "It's highly impressive, even if the results seem like mushy versions of songs that may feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are appealing and sound legitimate". [234] [235] [236]
Interface
Debate Game
In 2018, OpenAI released the Debate Game, which teaches devices to discuss toy issues in front of a human judge. The function is to research study 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 nerve cell of eight neural network models which are typically studied in interpretability. [240] Microscope was produced to evaluate the functions that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT
Launched in November 2022, setiathome.berkeley.edu ChatGPT is an artificial intelligence tool built on top of GPT-3 that offers a conversational user interface that allows users to ask questions in natural language. The system then responds with an answer within seconds.
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The Verge Stated It's Technologically Impressive
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