Announced in 2016, Gym is an open-source Python library developed to assist in the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI research, making published research more quickly reproducible [24] [144] while offering users with a simple user 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, Gym Retro is a platform for support knowing (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to fix single tasks. Gym Retro provides the ability to generalize between games with similar ideas however different appearances.

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 provided the goals of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives 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 representative braces to remain upright, suggesting it had learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might develop an intelligence "arms race" that could 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 utilized in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high ability level totally through experimental algorithms. Before becoming a group of 5, the very first public presentation took place at The International 2017, the yearly premiere champion competition for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually matchup. [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, which the learning software application was a step in the instructions of developing software application that can manage intricate jobs like a cosmetic surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots learn with time by playing against themselves hundreds of 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 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 2 exhibition matches against professional players, but ended up losing both video 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 exhibition match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot player reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown making use of deep support learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robot hand, wiki.myamens.com to manipulate physical items. [167] It finds out completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation issue by utilizing domain randomization, a simulation approach which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB video cameras to allow the robotic to control an arbitrary item by seeing it. In 2018, bytes-the-dust.com OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, ratemywifey.com OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce 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 approach of producing progressively harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new AI designs developed 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 original GPT model ("GPT-1")

The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, bytes-the-dust.com 2018. [173] It showed how a generative model of language could obtain world understanding and procedure long-range dependencies by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions initially released to the public. The full version of GPT-2 was not right away launched due to issue about potential misuse, including applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 positioned a considerable threat.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned of "the innovation 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 presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language models to be general-purpose learners, illustrated by GPT-2 attaining modern precision 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues 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 not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were also trained). [186]
OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
GPT-3 dramatically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, 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 immediately launched to the public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary personal beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed 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 personal beta. [194] According to OpenAI, the model can produce working code in over a dozen shows languages, most successfully in Python. [192]
Several concerns with problems, design defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been implicated of discharging copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would cease 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), efficient in accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar examination 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 also check out, evaluate or produce approximately 25,000 words of text, and write code in all major shows languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caution 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 actually decreased to expose numerous technical details and statistics about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can and produce text, images and audio. [204] GPT-4o attained cutting edge outcomes 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) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized version of GPT-4o changing 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 expects it to be particularly helpful for enterprises, start-ups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been developed to take more time to think of their responses, leading to greater precision. These designs are especially efficient in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and quicker variation 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, 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 prevent confusion with telecommunications services service provider O2. [215]
Deep research study

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

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 notably be utilized for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model 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 a sad capybara") and produce corresponding images. It can produce images of reasonable items ("a stained-glass window with an image of a blue strawberry") as well as objects that do not exist in truth ("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 revealed DALL-E 2, an updated version of the model with more reasonable results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new primary 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 better able to generate images from complicated descriptions without manual timely engineering and render complex details like hands and text. [221] It was released to the 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 short detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.

Sora's development team called it after the Japanese word for "sky", to represent its "unlimited imaginative capacity". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that function, but 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, mentioning that it might produce videos up to one minute long. It also shared a technical report highlighting the approaches used to train the model, bio.rogstecnologia.com.br and the model's abilities. [225] It acknowledged a few of its drawbacks, including struggles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", however kept in mind that they should have been cherry-picked and might not represent Sora's typical 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 capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to generate realistic video from text descriptions, mentioning its potential to transform storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually decided to stop briefly strategies for broadening his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of varied audio and is likewise a multi-task design 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 anticipate subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly but then fall into turmoil the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the web mental 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 category, artist, and a bit of lyrics and outputs tune samples. OpenAI stated the songs "reveal local musical coherence [and] follow standard chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" which "there is a substantial gap" between Jukebox and human-generated music. The Verge specified "It's technologically outstanding, even if the outcomes sound like mushy variations of songs that might feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are catchy and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches makers to discuss toy issues in front of a human judge. The function is to research whether such an approach may 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 neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks quickly. The models included are AlexNet, VGG-19, systemcheck-wiki.de different versions of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an expert system tool built on top of GPT-3 that supplies a conversational interface that allows users to ask concerns in natural language. The system then reacts with an answer within seconds.