Announced in 2016, Gym is an open-source Python library developed to assist in the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research study, making released research more easily reproducible [24] [144] while supplying users with an easy interface for engaging with these environments. In 2022, brand-new developments of Gym have actually been relocated to the library Gymnasium. [145] [146]
Gym Retro
Released in 2018, Gym Retro is a platform for support knowing (RL) research study on computer game [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to solve single tasks. Gym Retro offers the ability to generalize in between games with comparable principles but various appearances.
RoboSumo
Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack understanding of how to even stroll, but are offered the goals of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the agents learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents 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 team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against human players at a high skill level completely through experimental algorithms. Before ending up being a team of 5, the first public presentation took place at The International 2017, the annual best championship tournament for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of actual time, and that the learning software application was an action in the direction of producing software application that can deal with intricate tasks like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots find out over time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a complete 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 two exhibit matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 total 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 player reveals the obstacles of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually demonstrated 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 robotic hand, to manipulate physical things. [167] It finds out entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB cameras to permit the robotic to manipulate an arbitrary things by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could resolve a Rubik's Cube. The robotic was able to solve 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 effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing gradually 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 new AI models developed by OpenAI" to let developers contact it for "any English language AI task". [170] [171]
Text generation
The business has 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 design was written by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and process 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 a not being watched transformer language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions at first released to the public. The full variation of GPT-2 was not immediately released due to concern about potential misuse, including applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 positioned a significant threat.
In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush 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 websites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue not being watched language designs to be general-purpose learners, highlighted by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not further 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 problems 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 not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger 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" tasks and might generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184]
GPT-3 considerably enhanced benchmark outcomes 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 models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released 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 totally free private 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 furthermore 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, engel-und-waisen.de an API was launched in private beta. [194] According to OpenAI, the model can produce working code in over a lots programs languages, most efficiently in Python. [192]
Several issues with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of giving off copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would discontinue assistance 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 technology passed a simulated law school bar exam 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 could also read, examine or generate approximately 25,000 words of text, and write code in all major shows languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some 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 different technical details and statistics about GPT-4, such as the precise size of the design. [203]
GPT-4o
On May 13, 2024, OpenAI announced and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision criteria, 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 launched GPT-4o mini, a smaller sized version 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 beneficial for business, startups and designers looking for to automate services with AI agents. [208]
o1
On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been created to take more time to think of their actions, leading to higher precision. These models are particularly effective 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 usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecoms services service provider O2. [215]
Deep research
Deep research is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out substantial web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching 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 model that is trained to examine the semantic similarity in between text and images. It can notably be utilized 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 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 an unfortunate capybara") and create matching images. It can produce images of practical items ("a stained-glass window with a picture of a blue strawberry") in addition to things 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 variation of the design with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new simple system for converting a text description into a 3-dimensional model. [220]
DALL-E 3
In September 2023, OpenAI revealed DALL-E 3, a more effective model better able to generate images from intricate descriptions without manual timely engineering and render complicated 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 design that can generate 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 unidentified.
Sora's advancement group called it after the Japanese word for "sky", to signify its "unlimited innovative capacity". [223] Sora's technology is an adjustment 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 reveal the number or the specific sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, that it might create videos up to one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the design's capabilities. [225] It acknowledged some of its drawbacks, consisting of battles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however kept in mind that they should have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have actually shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to create realistic video from text descriptions, citing its potential to reinvent storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly prepare for broadening his Atlanta-based motion picture studio. [227]
Speech-to-text
Whisper
Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of diverse audio and is likewise a multi-task design that can carry out multilingual speech recognition as well as 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 generate songs with 10 instruments in 15 designs. According to The Verge, a tune created by MuseNet tends to start fairly however then fall into turmoil the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to produce music for the titular character. [232] [233]
Jukebox
Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs song samples. OpenAI specified the songs "show local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable space" in between Jukebox and human-generated music. The Verge mentioned "It's technologically outstanding, even if the outcomes sound like mushy versions of songs that may feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are appealing and sound genuine". [234] [235] [236]
Interface
Debate Game
In 2018, OpenAI introduced the Debate Game, which teaches machines to debate toy problems in front of a human judge. The function is to research whether such a method 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 neuron of eight neural network models which are often studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT
Launched in November 2022, ChatGPT is an expert system tool constructed on top of GPT-3 that provides a conversational user interface that allows users to ask questions in natural language. The system then responds with an answer within seconds.
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Benedict Blacklow edited this page 2025-02-21 16:07:29 +08:00