Announced in 2016, Gym is an open-source Python library designed to assist in the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research, making published research study more easily reproducible [24] [144] while offering users with a basic interface for interacting with these environments. In 2022, new advancements of Gym have actually been moved to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, larsaluarna.se 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 focused mainly on enhancing agents to fix single tasks. Gym Retro provides the ability to generalize in between games with similar principles however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack understanding of how to even stroll, however are given the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the agents learn how to adapt to changing conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might produce an intelligence "arms race" that might increase an agent's capability to operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against human players at a high ability level entirely through trial-and-error algorithms. Before becoming a team of 5, the first public demonstration took place at The International 2017, the annual premiere championship competition for the game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for two weeks of real time, and that the knowing software application was a step in the instructions of developing software that can deal with complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a type of support knowing, as the bots learn in time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a full team of 5, and they were able 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 professional players, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later on 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 gamer shows the obstacles of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated the usage of deep support learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker discovering to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It finds out totally in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation issue by utilizing domain randomization, a simulation approach which exposes the learner to a variety of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB electronic cameras to allow the robot to manipulate an approximate item 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 could fix a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating progressively more difficult 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 designs established by OpenAI" to let developers contact it for "any English language AI task". [170] [171]
Text generation

The business has actually promoted generative pretrained transformers (GPT). [172]
OpenAI's initial GPT design ("GPT-1")

The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his coworkers, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language could obtain world understanding and procedure long-range dependences 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 successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative versions at first launched to the general public. The complete variation of GPT-2 was not instantly released due to issue about potential misuse, consisting of applications for writing phony news. [174] Some professionals expressed uncertainty that GPT-2 positioned a significant threat.

In action to GPT-2, the Allen Institute for larsaluarna.se Artificial Intelligence responded with a tool to discover "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 drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several sites 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 designs to be general-purpose students, illustrated by GPT-2 attaining modern 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 at least 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This allows 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 complete variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were also trained). [186]
OpenAI stated that GPT-3 prospered at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning in between English and Romanian, and in between English and German. [184]
GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or coming across the essential capability constraints of predictive language models. [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 design was not instantly released to the public for issues of possible abuse, although OpenAI planned to permit 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 solely to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually additionally been 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 create working code in over a lots programs languages, a lot of efficiently in Python. [192]
Several concerns with problems, style flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has actually been accused of discharging copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would discontinue 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 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 might also check out, examine or generate as much as 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to expose various technical details and statistics about GPT-4, such as the accurate 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 systemcheck-wiki.de audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision criteria, 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 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 particularly beneficial for enterprises, start-ups and developers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been created to take more time to consider their reactions, leading to higher accuracy. These designs are especially effective 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, ratemywifey.com OpenAI unveiled o3, the follower of the o1 thinking design. OpenAI also revealed o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this model is not available for 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 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 established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out substantial web browsing, information analysis, and synthesis, providing 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) 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 significantly be utilized for image classification. [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 analyze natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can produce images of sensible objects ("a stained-glass window with an image of a blue strawberry") in addition to 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 revealed DALL-E 2, an upgraded version of the design with more sensible results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new rudimentary 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 design much better able to produce images from complicated 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 function in October. [222]
Text-to-video

Sora

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

Sora's advancement group named it after the Japanese word for "sky", to symbolize its "endless creative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that purpose, however did not reveal 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, mentioning that it could create videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the model, and the design's capabilities. [225] It acknowledged some of its imperfections, consisting of struggles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but noted that they should have been cherry-picked and may not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have actually shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to generate reasonable video from text descriptions, mentioning its possible to revolutionize storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had actually 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 acknowledgment model. [228] It is trained on a large dataset of varied audio and is likewise a multi-task model that can perform multilingual speech acknowledgment along with 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 songs with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to start fairly but 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 web mental thriller Ben Drowned to produce 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 tune samples. OpenAI specified the tunes "show regional musical coherence [and] follow conventional chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" which "there is a substantial space" in between Jukebox and human-generated music. The Verge mentioned "It's highly outstanding, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, surgiteams.com OpenAI launched the Debate Game, which teaches devices to debate toy issues in front of a human judge. The purpose is to research whether such a method may 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 designs which are frequently studied in interpretability. [240] Microscope was developed to analyze the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

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