Announced in 2016, Gym is an open-source Python library created to assist in the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making published research study more quickly reproducible [24] [144] while supplying users with a basic user interface for connecting with these environments. In 2022, new developments of Gym have actually been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, engel-und-waisen.de Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to solve single tasks. Gym Retro provides the ability to generalize between video games with similar principles however various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack understanding of how to even walk, however are offered the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives learn how to adapt to changing conditions. When a representative is then gotten rid of from this virtual environment and put in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could create an intelligence "arms race" that might increase an agent's ability 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 video game Dota 2, that learn to play against human gamers at a high ability level completely through trial-and-error algorithms. Before ending up being a team of 5, the first public presentation took place at The International 2017, the yearly best championship competition for the video game, where Dendi, 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 learned by playing against itself for 2 weeks of real time, which the knowing software application was an action in the instructions of producing software application that can manage complex tasks like a surgeon. [152] [153] The system utilizes a kind of support learning, as the bots find out 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 group of 5, and they were able to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5's mechanisms 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 demonstrated using deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device discovering to train a Shadow Hand, a human-like robotic hand, pediascape.science to manipulate physical items. [167] It finds out entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by using domain randomization, a simulation approach which exposes the student to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, also has RGB electronic cameras to enable the robotic to manipulate an arbitrary things by seeing it. In 2018, hb9lc.org OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating progressively harder environments. ADR varies 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 designs developed by OpenAI" to let designers call on it for "any English language AI job". [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 written by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world understanding and process long-range dependences by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative variations at first launched to the public. The complete variation of GPT-2 was not right away released due to concern about possible abuse, including applications for composing fake news. [174] Some experts 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 spot "neural phony news". [175] Other researchers, such as Jeremy Howard, warned 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 difficult to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language design. [177] Several websites host interactive demonstrations of various instances 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, shown by GPT-2 attaining cutting edge precision 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 a minimum of 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI specified 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 full variation of GPT-2 (although GPT-3 models with as few as 125 million criteria were also trained). [186]
OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing between English and wiki.eqoarevival.com Romanian, and between English and German. [184]
GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or coming across the basic capability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched 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 personal beta that began 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 actually furthermore 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 produce working code in over a lots programs languages, a lot of effectively in Python. [192]
Several concerns with problems, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been accused of releasing copyrighted code, with no author attribution or license. [197]
OpenAI revealed 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), efficient in accepting text or image inputs. [199] They revealed that the upgraded innovation passed a simulated law school bar examination with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also read, evaluate or create approximately 25,000 words of text, and compose code in all significant programs languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based model, 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 various technical details and data about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art results in 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 variation of GPT-4o replacing 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 especially useful for enterprises, startups and designers 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 designed to take more time to think of their actions, resulting in higher precision. These designs are especially efficient in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking design. OpenAI likewise unveiled 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 scientists had the opportunity to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications companies 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 comprehensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, 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 analyze the semantic similarity in between text and images. It can especially be utilized for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design 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 handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can produce images of practical items ("a stained-glass window with an image of a blue strawberry") as well as objects 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 upgraded variation of the model with more reasonable 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 revealed DALL-E 3, a more powerful model much better able to from complicated 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 model that can produce videos based upon short detailed prompts [223] as well as extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or it-viking.ch 1080x1920. The maximal length of created videos is unknown.

Sora's development team named it after the Japanese word for "sky", to symbolize its "unlimited creative potential". [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 as well as copyrighted videos licensed for that purpose, but did not expose 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, stating that it could generate videos as much as one minute long. It likewise shared a technical report highlighting the techniques utilized to train the design, and the model's abilities. [225] It acknowledged a few of its drawbacks, consisting of battles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but kept in mind that they must have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to produce practical video from text descriptions, citing its possible to reinvent 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 expanding his Atlanta-based movie 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 diverse audio and is also a multi-task model that can perform multilingual speech recognition along with speech translation and language identification. [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 songs with 10 instruments in 15 designs. According to The Verge, a song produced 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 internet 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 genre, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the tunes "show local musical coherence [and] follow standard chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" which "there is a significant space" in between Jukebox and human-generated music. The Verge specified "It's highly impressive, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider stated "surprisingly, a few of the resulting songs are memorable and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches devices to discuss toy problems in front of a human judge. The purpose is to research whether such a method might help in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of eight neural network designs which are frequently studied in interpretability. [240] Microscope was developed to examine the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that offers a conversational user interface that enables users to ask concerns in natural language. The system then reacts with a response within seconds.