Announced in 2016, Gym is an open-source Python library designed to help with the development of support learning algorithms. It aimed to standardize how environments are specified in AI research study, making published research more quickly reproducible [24] [144] while offering users with a simple interface for engaging with these environments. In 2022, brand-new developments of Gym have actually been moved to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to solve single jobs. Gym Retro gives the capability to generalize between video games with similar ideas but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack understanding of how to even stroll, but are given the goals of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents find out how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors 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 five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that learn to play against human players at a high ability level completely 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 gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for 2 weeks of real time, and that the knowing software application was a step in the instructions of developing software that can manage complicated jobs like a surgeon. [152] [153] The system uses a type of reinforcement learning, 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 opponent and wiki.dulovic.tech taking map objectives. [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 beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 overall 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 challenges of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown using deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It learns totally in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by using domain randomization, a simulation approach which exposes the learner to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB video cameras to permit 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 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 intricate 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 technique of generating progressively harder environments. ADR differs from manual domain randomization by not needing 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 established by OpenAI" to let designers contact it for "any English language AI job". [170] [171]
Text generation

The company has actually popularized generative (GPT). [172]
OpenAI's initial GPT model ("GPT-1")

The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and procedure long-range reliances 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 follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions initially released to the public. The full variation of GPT-2 was not immediately released due to issue about prospective misuse, including applications for composing fake news. [174] Some specialists revealed uncertainty that GPT-2 postured a significant risk.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural phony news". [175] Other researchers, such as Jeremy Howard, cautioned of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several sites host interactive demonstrations of various circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language designs 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 more 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 prevents certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 stated that the full variation of GPT-3 contained 175 billion specifications, [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 criteria were likewise trained). [186]
OpenAI specified 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 provided examples of translation and cross-linguistic transfer knowing in between English and Romanian, and 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 encountering the basic ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away 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 started 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 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 private beta. [194] According to OpenAI, the design can create working code in over a dozen shows languages, the majority of successfully in Python. [192]
Several problems with glitches, style flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of producing copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would cease 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), capable of accepting text or image inputs. [199] They revealed that the updated innovation passed a simulated law school bar test 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 read, examine or produce up to 25,000 words of text, and compose code in all major programming languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal numerous technical details and data about GPT-4, such as the exact size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced lead to voice, multilingual, and vision criteria, 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 sized version 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 anticipates it to be particularly useful for business, start-ups and developers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been designed to take more time to believe about their responses, causing higher accuracy. These models are particularly effective in science, coding, and reasoning 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 follower of the o1 thinking model. OpenAI also revealed o3-mini, a lighter and quicker version of OpenAI o3. Since December 21, 2024, pediascape.science this design is not available for public usage. According to OpenAI, they are evaluating 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 model is called o3 rather than o2 to avoid confusion with telecoms providers 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 carry out comprehensive web browsing, information 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 an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image classification

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 especially 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 interpret natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can produce pictures of realistic things ("a stained-glass window with an image of a blue strawberry") as well as things 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 announced DALL-E 2, an upgraded variation of the model with more practical results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new simple 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 effective design better able to produce images from complex descriptions without manual prompt engineering and render complex 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 produce videos based on short detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unidentified.

Sora's advancement team called it after the Japanese word for "sky", to represent its "endless innovative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos licensed for that purpose, however did not expose 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 techniques utilized to train the design, and the model's abilities. [225] It acknowledged a few of its drawbacks, consisting of battles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they must have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have shown significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's ability to create practical video from text descriptions, citing its potential to reinvent storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly plans for broadening his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task model that can perform multilingual speech acknowledgment as well as 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 generate songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly but then fall under turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to create 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 category, artist, and a snippet of lyrics and outputs tune samples. OpenAI mentioned the tunes "show local musical coherence [and] follow standard chord patterns" however acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" which "there is a significant gap" between Jukebox and human-generated music. The Verge specified "It's highly excellent, even if the results sound like mushy versions of tunes that might feel familiar", while Business Insider specified "surprisingly, some of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers to dispute toy issues in front of a human judge. The purpose is to research whether such an approach might assist in auditing AI choices and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and higgledy-piggledy.xyz nerve cell of eight neural network designs which are frequently studied in interpretability. [240] Microscope was produced to analyze the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various variations 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 offers a conversational interface that allows users to ask questions in natural language. The system then reacts with a response within seconds.