Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research study, making published research more easily reproducible [24] [144] while offering users with an easy user interface for communicating with these environments. In 2022, brand-new advancements of Gym have actually been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on video games [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to resolve single jobs. Gym Retro offers the ability to generalize in between games with similar ideas but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have understanding of how to even walk, however are given the goals of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents find out how to adjust to changing conditions. When an agent is then removed from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives could develop an intelligence "arms race" that might increase a representative's ability to function 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 computer game Dota 2, that find out to play against human players at a high ability level entirely through experimental algorithms. Before becoming a group of 5, the very first public presentation occurred at The International 2017, the annual best champion competition for the game, where Dendi, setiathome.berkeley.edu a professional Ukrainian player, 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, which the knowing software application was a step in the instructions of developing software application that can manage intricate tasks like a surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots learn with 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 expanded to play together as a complete group of 5, and they had the to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert gamers, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, 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 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 challenges of AI systems in multiplayer online battle 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 utilizes device discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It learns entirely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation issue by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB cameras to enable the robotic to manipulate an arbitrary item by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could resolve a Rubik's Cube. The robotic had the ability 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 improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of creating progressively harder environments. ADR differs from manual domain randomization by not requiring a human to define randomization ranges. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI models developed by OpenAI" to let developers 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 design was written by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language might obtain world understanding and process long-range reliances by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative variations at first released to the general public. The full version of GPT-2 was not instantly launched due to issue about prospective misuse, consisting of applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 presented a significant danger.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned 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 impossible to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language design. [177] Several sites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose learners, shown by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not further 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 avoids certain issues encoding vocabulary with word tokens by using 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 not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were also trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" jobs and might 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 in between English and German. [184]
GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or encountering the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, 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 instantly released to the public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month free personal beta that began 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 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 private beta. [194] According to OpenAI, the model can develop working code in over a lots programs languages, a lot of efficiently in Python. [192]
Several issues with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been implicated of discharging copyrighted code, without any 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 announced the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar exam 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 might likewise read, examine or create as much as 25,000 words of text, and write code in all major shows languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and data about GPT-4, such as the accurate size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced 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 standards, setting brand-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 launched 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 anticipates it to be particularly helpful for enterprises, start-ups and developers 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 been created to take more time to consider their reactions, causing greater precision. These designs are particularly reliable in science, coding, and reasoning tasks, 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 revealed o3, the successor of the o1 thinking design. OpenAI also revealed o3-mini, a lighter and faster version of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the opportunity to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecoms companies O2. [215]
Deep research study

Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out extensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools made it possible for, it reached a precision 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 design 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 produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can create images of sensible items ("a stained-glass window with an image of a blue strawberry") along with 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 outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new simple system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful model much better able to produce images from intricate descriptions without manual prompt engineering and render complicated 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 generate videos based on short detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.

Sora's advancement team named it after the Japanese word for "sky", to symbolize its "unlimited imaginative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos accredited for that function, however did not expose the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it could create videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the design, and the model's capabilities. [225] It acknowledged a few of its shortcomings, including battles 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 might not represent Sora's normal output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to generate practical video from text descriptions, citing its potential to change storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly strategies for expanding his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of diverse audio and is also a multi-task design 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 produce songs with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to begin fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to develop music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow traditional chord patterns" however acknowledged that the songs lack "familiar bigger musical structures such as choruses that duplicate" which "there is a considerable gap" in between Jukebox and human-generated music. The Verge mentioned "It's highly excellent, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are appealing and sound genuine". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers to debate toy issues in front of a human judge. The purpose is to research study whether such a technique might help 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 neuron of 8 neural network designs which are typically studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, various versions of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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