Announced in 2016, pediascape.science Gym is an open-source Python library developed to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research, making released research more quickly reproducible [24] [144] while providing users with an easy interface for connecting with these environments. In 2022, 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 support 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 capability to generalize between games with similar principles however different appearances.
RoboSumo
Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have understanding of how to even stroll, however are provided the goals of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives discover how to adapt to altering conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might develop an intelligence "arms race" that might increase a representative's ability to function even outside the context of the competition. [148]
OpenAI 5
OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high skill level totally through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation occurred 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 one-on-one matchup. [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 learning software application was an action in the direction of producing software application that can deal with complex tasks like a surgeon. [152] [153] The system uses a kind of reinforcement learning, as the bots find out over 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 ability of the bots broadened to play together as a complete group of 5, and they had the ability to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot player shows the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated the use of deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl
Developed in 2018, Dactyl utilizes device discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It finds out completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB cams to allow the robot 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 robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating gradually harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169]
API
In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI models developed by OpenAI" to let developers get in touch with it for "any English language AI job". [170] [171]
Text generation
The business has promoted generative pretrained transformers (GPT). [172]
OpenAI's original 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 website on June 11, 2018. [173] It demonstrated how a generative design of language might 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 model and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions initially released to the general public. The complete version of GPT-2 was not immediately launched due to issue about possible misuse, including applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 posed a substantial threat.
In reaction 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, cautioned of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI released the total variation of the GPT-2 language model. [177] Several sites host interactive presentations of various instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose learners, highlighted by GPT-2 attaining advanced precision 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain issues 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, systemcheck-wiki.de Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were likewise trained). [186]
OpenAI stated 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 offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
GPT-3 dramatically improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or coming across the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full 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 planned to permit gain access to through a paid cloud API after a two-month complimentary personal beta that began in June 2020. [170] [189]
On September 23, surgiteams.com 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 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 personal beta. [194] According to OpenAI, the design can produce working code in over a lots programs languages, most effectively in Python. [192]
Several concerns with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been implicated of emitting copyrighted code, without any author attribution or license. [197]
OpenAI revealed 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 announced that the updated 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 likewise check out, evaluate or generate up to 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement 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 actually declined to technical details and statistics 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 produce text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision standards, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria 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 expects it to be particularly helpful for enterprises, startups and designers looking for to automate services with AI agents. [208]
o1
On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been designed to take more time to think of their reactions, causing greater accuracy. These models are particularly reliable 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 revealed o3, the successor of the o1 thinking model. OpenAI also unveiled o3-mini, a lighter and quicker 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 researchers had the opportunity to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215]
Deep research
Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out substantial web surfing, data analysis, and synthesis, 89u89.com delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image category
CLIP
Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic resemblance between text and images. It can notably be used for image classification. [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 version of GPT-3 to interpret natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can develop images of reasonable items ("a stained-glass window with a picture 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 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 transforming a text description into a 3-dimensional model. [220]
DALL-E 3
In September 2023, OpenAI revealed DALL-E 3, a more effective design better able to generate images from complicated descriptions without manual timely engineering and render complex details like hands and text. [221] It was launched 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 upon brief detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.
Sora's advancement group named it after the Japanese word for "sky", to symbolize its "limitless imaginative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos licensed for that function, however did not expose the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it might produce videos approximately one minute long. It also shared a technical report highlighting the techniques utilized to train the design, and the model's abilities. [225] It acknowledged some of its imperfections, including battles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", however noted that they should have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to generate realistic video from text descriptions, citing its potential to change storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided 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 recognition design. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task design that can perform multilingual speech acknowledgment in addition to 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 created by MuseNet tends to begin fairly however then fall under turmoil the longer it plays. [230] [231] In pop culture, preliminary 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 create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI stated the tunes "reveal local musical coherence [and] follow standard 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 stated "It's technically excellent, even if the outcomes seem like mushy versions of tunes that may feel familiar", disgaeawiki.info while Business Insider mentioned "surprisingly, some of the resulting tunes are catchy and sound genuine". [234] [235] [236]
User user interfaces
Debate Game
In 2018, OpenAI launched the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The function is to research study whether such an approach 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 significant layer and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was created to examine the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, various variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT
Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that provides a conversational user interface that enables users to ask questions in natural language. The system then reacts with an answer within seconds.
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curtispool108 edited this page 2025-04-06 20:03:31 +08:00