ai ｂｙ Concepcion 投稿日：2025/04/06(Sun) 03:09 No.4363567 
<br>Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI (http://www.haimimedia.cn:3001/abbiewrixon926) research study, making released research study more quickly reproducible [24] [144] while offering users with an easy user interface for connecting (https://picturegram.app/jettaainsworth) with these environments (http://103.77.166.1983000/dontewhiting3). In 2022, new advancements (http://81.68.246.1736680/claudiobillups) of Gym have actually been moved to the library Gymnasium (http://qiriwe.com/@adelinenewcomb?page=about). [145] [146]
<br>Gym Retro<br>
<br>Released in 2018, Gym Retro is a platform for reinforcement learning (https://video.etowns.ir/@uwlpaulette274?page=about) (RL) research on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to resolve single jobs. Gym Retro offers the ability to generalize between video games with similar ideas however various appearances.<br>
<br>RoboSumo<br>
<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially do not have understanding of how to even stroll, but are given the goals of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the agents learn how to adjust to changing conditions. When an agent is then gotten rid of from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents could create an intelligence "arms race" that could increase a representative's ability to function even outside the context of the competition (http://121.37.166.03000/anneliesekim85). [148]
<br>OpenAI 5<br>
<br>OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that find out to play against human players at a high ability level entirely through trial-and-error algorithms. Before becoming a group of 5, the first public presentation took place at The International 2017, the yearly best championship tournament 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 discovered by playing against itself for 2 weeks of actual time, and that the knowing software was an action in the instructions of producing software that can manage complex jobs like a surgeon. [152] [153] The system uses a type of reinforcement learning, 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 enemy and taking map goals. [154] [155] [156]
<br>By June 2018, the ability of the bots broadened to play together as a full team of 5, and they were able to defeat teams (https://try.gogs.io/louanngooch286) of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions (https://git.arachno.de/adelaidasoe577) of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance (https://www.wtfbellingham.com/lukegovett8374) came later that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those video games. [165]
<br>OpenAI 5's systems in Dota 2's bot gamer shows the obstacles of AI (https://git.sortug.com/aidenosmond27) systems in multiplayer online fight arena (MOBA) video games (https://svn.youshengyun.com3000/alex7324018853) and how OpenAI Five has demonstrated using deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
<br>Dactyl<br>
<br>Developed in 2018, Dactyl utilizes device finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It finds out completely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation issue by utilizing domain randomization, a simulation method which exposes the learner to a variety of experiences (https://git.penwing.org/adriana20x6720) rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB cameras to enable the robot to control (https://igita.ir/antwankeb55116) an arbitrary things by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
<br>In 2019, OpenAI demonstrated 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 present complex physics that is harder to design. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing gradually more challenging environments. ADR varies from manual domain randomization by not needing a human to define randomization varieties. [169]
<br>API<br>
<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI (https://54.165.237.249/melvakluge2458) designs established by OpenAI" to let designers contact it for "any English language AI (http://caxapok.space/milot071659012) task". [170] [171]
<br>Text generation<br>
<br>The company has actually promoted generative (http://repo.sprinta.com.br3000/almacothran28) pretrained (https://git.karma-riuk.com/veraopas25195) transformers (GPT). [172]
<br>OpenAI's original GPT model ("GPT-1")<br>
<br>The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language (https://git.lazyka.ru/altonmceachern) might obtain world understanding and procedure long-range reliances by pre-training on a varied corpus with long stretches of adjoining text.<br>
<br>GPT-2<br>
<br>Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower (http://192.241.211.111/rexbutterfield) to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions at first launched to the general public. The full variation of GPT-2 was not instantly released due to concern about prospective misuse, consisting of applications for writing phony news. [174] Some experts expressed uncertainty that GPT-2 presented a significant danger.<br>
<br>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 innovation 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 design (https://git.pxlbuzzard.com/mistymancia33). [177] Several websites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180]
<br>GPT-2's authors argue without supervision language models to be general-purpose students, shown by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not further trained on any task-specific input-output examples).<br>
<br>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 problems encoding vocabulary (http://gitlab.awcls.com/pcclamar840273) 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]
<br>GPT-3<br>
<br>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 mentioned that the full variation of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as few as 125 million parameters were likewise trained). [186]
<br>OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184]
<br>GPT-3 significantly improved benchmark (http://qiriwe.com/@darrenthomason?page=about) results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the basic capability constraints (http://140.82.32.174/adriannahillgr) of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, 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 right away released to the general public for issues of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month free private beta that began in June 2020. [170] [189]
<br>On September 23, 2020, GPT-3 was licensed exclusively (https://103.1.12.176/angelamize310) to Microsoft. [190] [191]
<br>Codex<br>
<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the AI (https://hebrewconnect.tv/@manuelapapst91?page=about) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the model can produce working code in over a lots programming languages, many successfully in Python. [192]
<br>Several issues with glitches, style defects and security vulnerabilities were cited. [195] [196]
<br>GitHub Copilot has actually been accused of giving off copyrighted code, without any author attribution or license. [197]
<br>OpenAI revealed that they would discontinue support for Codex API on March 23, 2023. [198]
<br>GPT-4<br>
<br>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 (https://git.andrewnw.xyz/alexandermonta) passed a simulated law school bar examination with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise read, examine or generate up to 25,000 words of text, and compose code in all major shows languages. [200]
<br>Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has declined to expose different technical details and stats about GPT-4, such as the accurate size of the model. [203]
<br>GPT-4o<br>
<br>On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced outcomes 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) criteria compared to 86.5% by GPT-4. [207]
<br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized version of GPT-4o changing (http://13.209.39.13932421/cooperpittmann) 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 enterprises, start-ups and developers seeking to automate services with AI (http://47.100.220.92:10001/cassandra2738) representatives. [208]
<br>o1<br>
<br>On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been developed to take more time to consider their reactions, causing greater precision. These models are particularly reliable in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
<br>o3<br>
<br>On December 20, 2024, OpenAI unveiled o3, the successor (https://freedomlovers.date/@amadolandsboro) of the o1 thinking model. OpenAI also revealed o3-mini, a lighter and quicker version of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215]
<br>Deep research<br>
<br>Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive (https://git.wisder.net/avisclowes5031) web browsing, information analysis, and synthesis, providing 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]
<br>Image category<br>
<br>CLIP<br>
<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic resemblance between text and images. It can notably be used for image classification. [217]
<br>Text-to-image<br>
<br>DALL-E<br>
<br>Revealed in 2021, DALL-E is a Transformer design that creates 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 an unfortunate capybara") and generate corresponding images. It can create images of reasonable things ("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"). Since March 2021, no API or code is available.<br>
<br>DALL-E 2<br>
<br>In April 2022, OpenAI announced DALL-E 2, an upgraded (http://106.14.65.137/bettebellingsh) version of the design with more practical results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new fundamental system for converting a text description into a 3-dimensional design (http://101.132.182.1013000/fosteralderman). [220]
<br>DALL-E 3<br>
<br>In September 2023, OpenAI announced DALL-E 3, a more powerful model better able to generate images from complex descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was released to the public as a ChatGPT Plus feature in October. [222]
<br>Text-to-video<br>
<br>Sora<br>
<br>Sora is a text-to-video design that can create videos based upon brief detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.<br>
<br>Sora's development group named it after the Japanese word for "sky", to symbolize its "unlimited creative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL ｷ E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos accredited for that purpose, but did not reveal the number or the specific sources of the videos. [223]
<br>OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it could produce videos up to one minute long. It likewise shared a technical report (https://git.pm-gbr.de/domingo88z3478) highlighting the approaches utilized to train the model, and the model's capabilities. [225] It acknowledged a few of its shortcomings, including struggles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but noted that they must have been cherry-picked and might not represent Sora's typical output. [225]
<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have revealed considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to produce reasonable (https://chatgay.webcria.com.br/@abbeygammon17) video from text descriptions, mentioning its prospective to revolutionize storytelling and content creation (https://git.lazyka.ru/alfredbelcher). He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly prepare for broadening his Atlanta-based motion picture studio. [227]
<br>Speech-to-text<br>
<br>Whisper<br>
<br>Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset (https://video.etowns.ir/@uwlpaulette274?page=about) of diverse audio and is also a multi-task model that can perform multilingual (http://81.70.24.14/alan0047487586) speech acknowledgment as well as speech translation and language identification. [229]
<br>Music generation<br>
<br>MuseNet<br>
<br>Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to start fairly but then fall into turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to develop music for the titular character. [232] [233]
<br>Jukebox<br>
<br>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 category, artist, and a bit of lyrics and outputs song samples. OpenAI stated the tunes "show regional musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" which "there is a significant gap" in between Jukebox and human-generated music. The Verge specified "It's highly remarkable, even if the outcomes seem like mushy variations of tunes that may feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are catchy and sound legitimate". [234] [235] [236]
<br>User interfaces<br>
<br>Debate Game<br>
<br>In 2018, OpenAI launched the Debate Game, which teaches devices to debate toy problems in front of a human judge. The purpose is to research whether such an approach may assist in auditing AI (http://58.34.54.46:9092/eleanore10b296) decisions and in developing explainable AI (https://121.36.226.23/abbietiffany68). [237] [238]
<br>Microscope<br>
<br>Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and nerve cell of 8 neural network models which are typically studied in interpretability. [240] Microscope was produced to examine the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241]
<br>ChatGPT<br>
<br>Launched in November 2022, ChatGPT is an artificial intelligence tool built on top of GPT-3 that provides a conversational interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.<br> |
