Announced in 2016, Gym is an open-source Python library created to assist in the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI research, making released research study more easily reproducible [24] [144] while supplying users with a simple interface for communicating with these environments. In 2022, brand-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 study generalization. Prior RL research study focused mainly on optimizing agents to resolve single jobs. 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 initially do not have knowledge of how to even stroll, however are given the goals of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents learn how to adjust to altering conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents could develop an intelligence "arms race" that could increase an agent's ability to operate even outside the context of the competition. [148]

OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against human players at a high skill level totally through experimental algorithms. Before becoming a group of 5, the very first public presentation took place at The International 2017, the yearly best championship competition for the video game, where Dendi, an expert Ukrainian player, 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 two weeks of actual time, and that the knowing software was a step in the direction of producing software application that can manage complex jobs like a cosmetic surgeon. [152] [153] The system uses a kind of support 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 goals. [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 defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional players, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the 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 total 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 gamer shows the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown the usage of deep support learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]

Dactyl

Developed in 2018, Dactyl uses maker discovering to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It discovers totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation issue by using domain randomization, a simulation technique which exposes the learner to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, also has RGB cams to enable the robotic to control an approximate item by seeing it. In 2018, trademarketclassifieds.com OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]

In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating progressively more hard environments. ADR differs from manual domain randomization by not needing a human to define randomization varieties. [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 developers call on it for "any English language AI job". [170] [171]

Text generation

The business has popularized 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 coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative model of language might obtain world knowledge and procedure long-range dependences by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

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

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "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 muffle 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 websites host interactive presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]

GPT-2's authors argue without supervision language designs to be general-purpose students, shown by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not more trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows 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 without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186]

OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and systemcheck-wiki.de cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]

GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or experiencing the basic capability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for engel-und-waisen.de the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately launched to the public for concerns of possible abuse, although OpenAI prepared to allow 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 exclusively 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 launched in personal beta. [194] According to OpenAI, the model can produce working code in over a dozen programming languages, the majority of efficiently in Python. [192]

Several problems with glitches, style defects and security vulnerabilities were pointed out. [195] [196]

GitHub Copilot has been implicated of producing copyrighted code, with no author attribution or license. [197]

OpenAI announced that they would discontinue 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 announced that the upgraded innovation 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 check out, analyze or produce approximately 25,000 words of text, and compose code in all significant programming languages. [200]

Observers reported that the iteration 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 revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal numerous technical details and statistics 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 standards, setting brand-new records in audio speech recognition 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 sized variation of GPT-4o replacing 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 expects it to be especially beneficial 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 developed to take more time to think about their actions, leading to greater accuracy. These models are especially efficient 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 unveiled o3, the follower of the o1 reasoning design. OpenAI also 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 evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215]

Deep research

Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform extensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]

Image category

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 especially be utilized for image classification. [217]

Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E uses 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 create matching images. It can create images of reasonable items ("a stained-glass window with a picture of a blue strawberry") as well as items that do not exist in truth ("a cube with the texture of a porcupine"). As of March 2021, no API or wiki.vst.hs-furtwangen.de code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, wavedream.wiki an updated variation of the model with more sensible 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 design much better able to generate images from complex descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the public as a ChatGPT Plus feature in October. [222]

Text-to-video

Sora

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

Sora's advancement team named it after the Japanese word for "sky", to represent its "unlimited imaginative potential". [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 as well as copyrighted videos certified for that function, however did not expose the number or the precise sources of the videos. [223]

OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might create videos as much as one minute long. It likewise shared a technical report highlighting the techniques utilized to train the model, and the design's abilities. [225] It acknowledged some of its drawbacks, including battles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration 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 demo, notable entertainment-industry figures have actually shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's ability to produce practical video from text descriptions, citing its potential to change storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had 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 recognition design. [228] It is trained on a large dataset of varied audio and is likewise a multi-task model that can perform multilingual speech acknowledgment along with speech translation and language recognition. [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 produce tunes with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to start fairly however then fall under turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to develop 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 category, artist, and a snippet of lyrics and outputs song samples. OpenAI mentioned the tunes "show local musical coherence [and] follow standard chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" which "there is a considerable gap" between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the outcomes seem like mushy versions of songs that may feel familiar", while Business Insider stated "remarkably, some of the resulting songs are memorable and sound genuine". [234] [235] [236]

Interface

Debate Game

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

Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of eight neural network models 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, different versions of Inception, and various versions of CLIP Resnet. [241]

ChatGPT

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