Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research, making released research more easily reproducible [24] [144] while supplying users with a simple interface for connecting with these environments. In 2022, new advancements of Gym have been transferred 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] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to resolve single tasks. Gym Retro offers the capability to generalize between video games with comparable principles however various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially do not have knowledge of how to even walk, but are offered the objectives of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the agents learn how to adjust to altering conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could produce an intelligence "arms race" that might increase a representative's ability to function even outside the context of the competition. [148]

OpenAI 5

OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that learn to play against human players at a high skill level entirely through experimental algorithms. Before becoming a group of 5, the very first public demonstration took place at The International 2017, the yearly premiere champion competition for the game, where Dendi, an expert Ukrainian gamer, 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 learned by playing against itself for 2 weeks of real time, and that the knowing software was a step in the instructions of creating software application that can handle complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of support knowing, as the bots learn over 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 broadened to play together as a full team of 5, and they had the ability to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert gamers, but wound up losing both video games. [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 exhibit match in San Francisco. [163] [164] The bots' last public appearance 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 games. [165]

OpenAI 5's mechanisms in Dota 2's bot player shows the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated using deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]

Dactyl

Developed in 2018, Dactyl utilizes device finding out to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation problem by utilizing domain randomization, a simulation approach which exposes the learner to a variety of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB video cameras to enable the robotic to manipulate an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]

In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to design. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating gradually more challenging environments. ADR differs from manual domain randomization by not requiring a human to specify randomization ranges. [169]

API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new AI models developed by OpenAI" to let designers contact it for "any English language AI task". [170] [171]

Text generation

The business has actually popularized generative pretrained transformers (GPT). [172]

OpenAI's initial GPT model ("GPT-1")

The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and engel-und-waisen.de published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could 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 not being watched transformer language model and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative variations initially released to the general public. The full version of GPT-2 was not immediately released due to concern about possible abuse, consisting of applications for composing fake news. [174] Some professionals expressed uncertainty that GPT-2 postured a substantial threat.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several websites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]

GPT-2's authors argue not being watched language models to be general-purpose learners, highlighted by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model 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 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 not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were likewise trained). [186]

OpenAI stated 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 between English and German. [184]

GPT-3 considerably improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released to the general public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month free private beta that started 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 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 launched in private beta. [194] According to OpenAI, the design can produce working code in over a dozen programming languages, most efficiently in Python. [192]

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

GitHub Copilot has actually been implicated of producing copyrighted code, without any author attribution or license. [197]

OpenAI announced that they would terminate 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), capable of accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar examination with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, analyze or produce approximately 25,000 words of text, and write code in all significant programs languages. [200]

Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, 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 actually decreased to reveal various 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 released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision benchmarks, setting brand-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 sized version 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 anticipates it to be especially helpful for enterprises, start-ups and designers looking for to automate services with AI representatives. [208]

o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been developed to take more time to consider their actions, causing greater precision. These models are particularly reliable in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211]

o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 reasoning design. OpenAI likewise revealed 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, safety and security researchers had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215]

Deep research

Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools enabled, hb9lc.org 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 evaluate the semantic similarity between text and images. It can significantly be utilized for image category. [217]

Text-to-image

DALL-E

Revealed in 2021, wiki.vst.hs-furtwangen.de DALL-E is a Transformer design that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can produce images of reasonable objects ("a stained-glass window with a picture of a blue strawberry") as well as items 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 announced DALL-E 2, an updated version of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new basic 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 design much better able to create images from complex descriptions without manual prompt engineering and render intricate 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 generate videos based upon brief detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.

Sora's advancement team called it after the Japanese word for "sky", to symbolize its "unlimited imaginative 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 using publicly-available videos in addition to copyrighted videos licensed for that function, but did not reveal the number or the specific sources of the videos. [223]

OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, stating that it could generate videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the model, and the model's abilities. [225] It acknowledged some of its drawbacks, consisting of struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however noted that they need to have been cherry-picked and may not represent Sora's typical output. [225]

Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have actually revealed substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to generate practical video from text descriptions, mentioning its prospective to transform storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to stop briefly strategies for broadening his Atlanta-based movie 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 recognition along with 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 produce tunes with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to start fairly however then fall under turmoil the longer it plays. [230] [231] In popular culture, initial applications of this tool were used 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 create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI mentioned the tunes "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" and that "there is a substantial space" in between Jukebox and human-generated music. The Verge mentioned "It's highly impressive, even if the outcomes sound like mushy variations of songs that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting tunes are memorable and sound legitimate". [234] [235] [236]

User user interfaces

Debate Game

In 2018, OpenAI introduced 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 may 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 designs which are frequently studied in interpretability. [240] Microscope was developed to examine the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different variations of Inception, and different variations of CLIP Resnet. [241]

ChatGPT

Launched in November 2022, ChatGPT is an artificial intelligence tool constructed on top of GPT-3 that offers a conversational user interface that permits users to ask questions in natural language. The system then responds with a response within seconds.