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

Released in 2018, Gym Retro is a platform for support knowing (RL) research on video games [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing agents to fix single jobs. Gym Retro offers the ability to generalize between games with similar ideas however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first do not have knowledge of how to even walk, but are provided the objectives of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives find out how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had discovered 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 could increase an agent's capability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group 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 completely through trial-and-error algorithms. Before ending up being a team of 5, the very first public demonstration occurred at The International 2017, the annual premiere championship tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by playing against itself for 2 weeks of real time, which the knowing software was an action in the direction of producing software application that can handle complex tasks like a surgeon. [152] [153] The system utilizes a form of reinforcement learning, 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 opponent and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a full group of 5, and they were able to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional gamers, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last 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 mechanisms in Dota 2's bot gamer shows the obstacles of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually shown making use of deep support knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It finds out completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB cameras to enable the robotic to control an arbitrary object by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might fix a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing gradually more challenging 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 new AI models developed by OpenAI" to let designers contact it for "any English language AI job". [170] [171]
Text generation

The company 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 design was written by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative model of language could obtain world knowledge 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 a without supervision transformer language design and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative versions initially released to the public. The full variation of GPT-2 was not instantly launched due to issue about prospective abuse, consisting of applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 posed a substantial hazard.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language design. [177] Several sites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose students, highlighted by GPT-2 attaining state-of-the-art precision and larsaluarna.se 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 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 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 not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated 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 designs with as couple of as 125 million parameters were likewise trained). [186]
OpenAI specified that GPT-3 was successful at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184]
GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the general public for issues of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month complimentary private beta that started 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 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 model can develop working code in over a dozen shows languages, most successfully in Python. [192]
Several problems with problems, style defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of emitting copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would stop 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), capable of accepting text or image inputs. [199] They announced that the updated innovation passed a simulated law school bar test 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, analyze or generate as much as 25,000 words of text, and compose code in all major programs languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement 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 likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose different technical details and data about GPT-4, such as the exact size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge results 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 launched GPT-4o mini, a smaller sized variation 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 anticipates it to be particularly useful for enterprises, start-ups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been created to take more time to think of their actions, causing higher accuracy. These designs are particularly effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the successor of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The design is called o3 rather than o2 to prevent confusion with telecoms providers O2. [215]
Deep research

Deep research is an agent established by OpenAI, unveiled 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 thirty minutes. [216] With browsing 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 model that is trained to analyze the semantic similarity between text and images. It can notably be used for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, 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 a sad capybara") and produce corresponding images. It can create images of realistic objects ("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 upgraded variation of the design with more reasonable outcomes. [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 model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more powerful model better able to create images from complicated descriptions without manual timely engineering and render complex 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 generate videos based on short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can produce 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 represent its "endless creative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos certified for that purpose, however did not reveal the number or the specific sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it could produce videos up to one minute long. It likewise shared a technical report highlighting the approaches utilized to train the model, and the model's capabilities. [225] It acknowledged a few of its shortcomings, consisting of struggles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but noted that they should have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have shown substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to create sensible video from text descriptions, mentioning its prospective to revolutionize storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to stop briefly prepare for broadening his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model 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 predict subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to start fairly but then fall into turmoil the longer it plays. [230] [231] In pop 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 produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "reveal local musical coherence [and] follow conventional chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge mentioned "It's highly remarkable, even if the outcomes sound like mushy versions of tunes that may feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are catchy and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research study whether such a technique may 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 neuron of 8 neural network models which are typically studied in interpretability. [240] Microscope was produced to examine the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different versions of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool developed on top of GPT-3 that supplies a conversational interface that permits users to ask concerns in natural language. The system then responds with a response within seconds.