Announced in 2016, Gym is an open-source Python library developed to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research study, making published research more easily reproducible [24] [144] while supplying users with a basic interface for communicating with these environments. In 2022, brand-new developments of Gym have actually been transferred to the library Gymnasium. [145] [146]
Gym Retro
Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to fix single tasks. Gym Retro offers the capability to generalize between games with similar concepts but different appearances.
RoboSumo
Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially lack knowledge of how to even stroll, but are offered the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives learn how to adjust to changing conditions. When an agent 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 discovered how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could develop an intelligence "arms race" that might increase an agent's capability to function even outside the context of the competitors. [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 completely through experimental algorithms. Before ending up being a group of 5, the first public presentation took place at The International 2017, the annual best champion competition for setiathome.berkeley.edu the 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 learned by playing against itself for two weeks of actual time, which the knowing software was an action in the direction of developing software application that can handle complex jobs like a surgeon. [152] [153] The system uses a form of support 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 enemy and taking map objectives. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a full group of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert players, but ended up losing both video 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 exhibition match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot player shows the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown using deep reinforcement learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl
Developed in 2018, Dactyl uses device discovering to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It finds out entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, disgaeawiki.info aside from having movement tracking cams, likewise has RGB cameras to allow the robotic to control an arbitrary item by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually harder environments. ADR varies 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 new AI models developed by OpenAI" to let designers call on 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 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 released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and procedure long-range dependences by pre-training on a diverse corpus with long stretches of adjoining text.
GPT-2
Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative variations initially launched to the public. The full variation of GPT-2 was not right away released due to issue about potential misuse, including applications for writing phony news. [174] Some experts revealed uncertainty that GPT-2 presented a significant danger.
In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language model. [177] Several sites host interactive demonstrations of various instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose students, highlighted by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not additional 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 a minimum of 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 an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete variation of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million criteria were likewise trained). [186]
OpenAI stated that GPT-3 prospered 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 between English and Romanian, and in between English and German. [184]
GPT-3 significantly enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or coming across the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 required 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 model was not immediately launched to the public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month free personal beta that began 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 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 released in private beta. [194] According to OpenAI, the design can create working code in over a dozen programs languages, a lot of successfully in Python. [192]
Several concerns with problems, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of giving off copyrighted code, without any author bytes-the-dust.com 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 announced that the updated technology 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 could likewise read, analyze or generate approximately 25,000 words of text, and compose code in all significant programming languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement 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 declined to reveal various technical details and statistics about GPT-4, such as the precise size of the model. [203]
GPT-4o
On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller 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 expects it to be particularly beneficial for business, start-ups and developers seeking to automate services with AI representatives. [208]
o1
On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been designed to take more time to consider their actions, resulting in greater precision. These models are particularly effective in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3
On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The design is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215]
Deep research
Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 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 design that is trained to evaluate the 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 version of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can develop images of realistic items ("a stained-glass window with a picture of a blue strawberry") along with 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 announced DALL-E 2, an updated variation of the design with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new rudimentary system for transforming 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 generate images from complicated descriptions without manual timely engineering and larsaluarna.se render intricate details like hands and text. [221] It was launched to the general public as a ChatGPT Plus feature in October. [222]
Text-to-video
Sora
Sora is a text-to-video design that can produce videos based on brief detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.
Sora's development group called it after the Japanese word for "sky", to signify its "limitless imaginative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos licensed for that function, 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 might generate videos approximately one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the design's capabilities. [225] It acknowledged a few of its shortcomings, including battles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", however kept in mind that they should have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, notable entertainment-industry figures have revealed significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's capability to produce realistic video from text descriptions, mentioning its potential to reinvent storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly prepare for expanding his Atlanta-based motion picture studio. [227]
Speech-to-text
Whisper
Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of varied audio and is also a multi-task design 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 produce tunes with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to begin fairly however then fall under chaos 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 produce 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 bit of lyrics and outputs tune samples. OpenAI mentioned the songs "show regional musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" and that "there is a significant gap" in between Jukebox and human-generated music. The Verge stated "It's technologically excellent, even if the outcomes sound like mushy versions of songs that might feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are appealing and sound genuine". [234] [235] [236]
User user interfaces
Debate Game
In 2018, OpenAI released the Debate Game, which teaches makers to discuss toy issues in front of a human judge. The purpose is to research whether such a technique may assist in auditing AI decisions and in establishing 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 designs which are typically studied in interpretability. [240] Microscope was developed to analyze the features that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and various versions of CLIP Resnet. [241]
ChatGPT
Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that offers a conversational interface that enables users to ask concerns in natural language. The system then reacts with an answer within seconds.
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Eugene Pink edited this page 2025-03-15 08:19:09 +08:00