Announced in 2016, Gym is an open-source Python library developed to help with the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research, making published research study more quickly reproducible [24] [144] while offering users with a simple interface for engaging with these environments. In 2022, new developments of Gym have 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] using RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing agents to resolve single jobs. Gym Retro provides the capability to generalize between games with comparable principles however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack knowledge of how to even walk, but are offered the objectives of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the agents find out how to adapt to altering conditions. When a representative is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could produce an intelligence "arms race" that might increase an agent's ability to function 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 find out to play against human gamers at a high skill level totally through trial-and-error algorithms. Before becoming a group of 5, the very first public presentation happened at The International 2017, the annual premiere champion competition for the game, where Dendi, an expert 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 actually discovered by playing against itself for 2 weeks of real time, and that the knowing software application was an action in the direction of producing software that can manage complicated jobs like a cosmetic surgeon. [152] [153] The system uses a type of support knowing, as the bots discover over time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and taking map objectives. [154] [155] [156]

By June 2018, the capability of the bots broadened to play together as a full team of 5, and they had the ability to defeat groups of amateur and semi-professional gamers. [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, wiki.dulovic.tech 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' final public look came later that month, where they played in 42,729 total video games in a four-day open online competitors, winning 99.4% of those video games. [165]

OpenAI 5's systems 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 actually demonstrated using deep reinforcement learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]

Dactyl

Developed in 2018, Dactyl uses device discovering to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers entirely in simulation using the same RL algorithms and disgaeawiki.info training code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB video cameras to enable the robotic to control an arbitrary object by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]

In 2019, OpenAI showed that Dactyl could fix 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 design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of creating gradually harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169]

API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new AI models established by OpenAI" to let designers contact it for "any English language AI job". [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 written by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world understanding and procedure long-range reliances by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative versions initially released to the general public. The full variation of GPT-2 was not right away launched due to concern about prospective abuse, including applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 posed a substantial danger.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, bio.rogstecnologia.com.br OpenAI released the total variation of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180]

GPT-2's authors argue unsupervised language models to be general-purpose students, shown by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not more 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 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 specific 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] 2 orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million parameters were also trained). [186]

OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184]

GPT-3 significantly enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or experiencing the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, higgledy-piggledy.xyz compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the general public for issues of possible abuse, although OpenAI planned 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 exclusively to Microsoft. [190] [191]

Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually in addition 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 released in personal beta. [194] According to OpenAI, the model can develop working code in over a lots shows languages, many effectively in Python. [192]

Several concerns with glitches, style flaws and security vulnerabilities were pointed out. [195] [196]

GitHub Copilot has been implicated of discharging copyrighted code, with no author attribution or license. [197]

OpenAI announced that they would cease support 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), efficient in accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar exam 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 likewise check out, analyze or produce approximately 25,000 words of text, and write code in all significant shows languages. [200]

Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to expose different technical details and stats about GPT-4, such as the exact size of the design. [203]

GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art outcomes in voice, multilingual, and vision criteria, 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 released GPT-4o mini, a smaller version 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 business, start-ups and developers seeking to automate services with AI agents. [208]

o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been developed to take more time to think of their actions, causing greater accuracy. These models are especially reliable in science, coding, and thinking jobs, 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 revealed o3, the successor of the o1 thinking design. OpenAI likewise 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 evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications providers O2. [215]

Deep research

Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform extensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]

Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic resemblance between text and images. It can especially be utilized for image category. [217]

Text-to-image

DALL-E

Revealed in 2021, pipewiki.org DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can create images of practical items ("a stained-glass window with a picture of a blue strawberry") as well as things 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 version of the design with more realistic results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new fundamental 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 effective design better able to produce images from complicated descriptions without manual prompt engineering and render complex details like hands and text. [221] It was launched to the public as a ChatGPT Plus function in October. [222]

Text-to-video

Sora

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

Sora's development team named it after the Japanese word for "sky", to symbolize its "limitless innovative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited for that purpose, however did not reveal the number or the precise sources of the videos. [223]

OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it could produce videos approximately one minute long. It likewise shared a technical report highlighting the methods used to train the design, and the model's capabilities. [225] It acknowledged a few of its shortcomings, including battles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", however noted that they must have been cherry-picked and might not represent Sora's common output. [225]

Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to produce reasonable video from text descriptions, mentioning its possible to transform storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to stop briefly plans for broadening his Atlanta-based motion picture studio. [227]

Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of varied audio and is also a multi-task model that can carry out 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 forecast subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly but then fall under chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were used 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 create 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 "show regional musical coherence [and] follow standard chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a significant space" between Jukebox and human-generated music. The Verge stated "It's technically outstanding, even if the outcomes seem like mushy versions of songs that might feel familiar", while Business Insider specified "remarkably, some of the resulting tunes are appealing and sound legitimate". [234] [235] [236]

Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research study whether such an approach might assist in auditing AI decisions and in developing explainable AI. [237] [238]

Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and nerve cell of eight neural network designs which are frequently studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, different 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 allows users to ask concerns in natural language. The system then responds with an answer within seconds.