nurseportal

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

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to solve single tasks. Gym Retro offers the ability to generalize between video games with similar ideas however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first do not have knowledge of how to even stroll, however are given the objectives of discovering to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives find out how to adapt to altering conditions. When a representative is then eliminated from this virtual environment and put in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could create an intelligence "arms race" that could increase a representative's ability to work even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human players at a high skill level entirely through trial-and-error algorithms. Before ending up being a team of 5, the very first public demonstration took place at The International 2017, the annual best champion tournament for the 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 discovered by playing against itself for two weeks of real time, which the knowing software application was a step in the direction of producing software that can deal with complicated tasks like a surgeon. [152] [153] The system utilizes a kind of support learning, as the bots learn gradually by playing against themselves numerous 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 broadened to play together as a full group of 5, and they were able to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional players, however 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 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 games. [165]
OpenAI 5's systems in Dota 2's bot player shows the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown making use of deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It discovers totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation issue by utilizing domain randomization, a simulation method which exposes the student to a variety of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has RGB video cameras to permit the robotic to control 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 demonstrated that Dactyl could fix a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating progressively more hard environments. ADR differs from manual domain randomization by not requiring a human to specify randomization varieties. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new AI models developed by OpenAI" to let designers get in touch with it for "any English language AI job". [170] [171]
Text generation

The company has actually promoted generative pretrained transformers (GPT). [172]
OpenAI's original GPT design ("GPT-1")

The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language could obtain world knowledge and process long-range dependencies by pre-training on a varied corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language design and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations initially released to the general public. The full variation of GPT-2 was not right away released due to concern about potential misuse, including applications for writing fake news. [174] Some specialists revealed uncertainty that GPT-2 presented a substantial hazard.

In reaction to GPT-2, pipewiki.org the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology 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 released the complete variation of the GPT-2 language model. [177] Several websites host interactive demonstrations of various circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, illustrated by GPT-2 attaining cutting edge accuracy 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain problems 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 without supervision 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] two orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 models with as few as 125 million specifications were likewise trained). [186]
OpenAI specified that GPT-3 was successful at certain "meta-learning" tasks and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and wavedream.wiki cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched to the general public for concerns of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month totally free personal beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed exclusively to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has 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 released in private beta. [194] According to OpenAI, the model can develop working code in over a lots shows languages, most effectively in Python. [192]
Several issues with problems, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been implicated of giving off copyrighted code, with no 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, engel-und-waisen.de OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar exam 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 likewise read, evaluate or produce as much as 25,000 words of text, and compose code in all major shows languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal various technical details and data about GPT-4, such as the precise size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced 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) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller variation 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 especially helpful for enterprises, start-ups and developers looking for 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 about their actions, resulting in higher precision. These models are especially efficient 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 unveiled o3, the successor of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecoms companies O2. [215]
Deep research study

Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform substantial web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, 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 design that is trained to analyze the semantic similarity 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 develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and create corresponding images. It can develop pictures of sensible things ("a stained-glass window with an image of a blue strawberry") along with 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 upgraded version of the model with more practical outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new fundamental 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 effective model better able to produce images from intricate 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 function in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can generate videos based on brief detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.

Sora's advancement group named it after the Japanese word for "sky", to symbolize its "limitless creative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos accredited for that function, but did not reveal 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, stating that it could create videos up to one minute long. It also shared a technical report highlighting the approaches used to train the design, and the model's abilities. [225] It acknowledged some of its imperfections, wakewiki.de including struggles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", however kept in mind that they need to have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have revealed substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to generate practical video from text descriptions, citing its potential to change storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause prepare for expanding 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 design that can carry out multilingual speech acknowledgment as well as speech translation and language recognition. [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 tunes with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to begin fairly however then fall under mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the web mental 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 bit of lyrics and outputs song samples. the songs "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a substantial space" in between Jukebox and human-generated music. The Verge mentioned "It's technologically impressive, even if the results sound like mushy versions of songs that may feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are catchy 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 function is to research whether such a method 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 substantial layer and neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various versions of Inception, and various 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 user interface that allows users to ask questions in natural language. The system then responds with an answer within seconds.