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<br>Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how [environments](https://intgez.com) are specified in [AI](https://lms.jolt.io) research, making published research more easily reproducible [24] [144] while providing users with a basic user interface for communicating with these environments. In 2022, brand-new developments of Gym have been transferred to the library Gymnasium. [145] [146] |
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<br>Gym Retro<br> |
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<br>Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research study on computer game [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to fix single tasks. Gym Retro offers the ability to generalize in between video games with similar ideas however various looks.<br> |
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<br>RoboSumo<br> |
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially do not have knowledge of how to even stroll, but are offered the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the [representatives](https://macphersonwiki.mywikis.wiki) learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually discovered how to [stabilize](http://119.3.9.593000) in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could create an intelligence "arms race" that might increase an agent's ability to work even outside the context of the [competitors](https://consultoresdeproductividad.com). [148] |
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<br>OpenAI 5<br> |
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<br>OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that find out to play against human gamers at a high ability level completely through experimental algorithms. Before becoming a team of 5, the very first public presentation happened at The International 2017, the annual premiere championship tournament for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of genuine time, which the learning software application was an action in the [instructions](https://gitcode.cosmoplat.com) of [creating software](https://www.outletrelogios.com.br) that can deal with complex tasks like a surgeon. [152] [153] The system uses a type of reinforcement learning, as the bots learn with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156] |
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<br>By June 2018, the capability of the bots broadened to play together as a full group of 5, and they were able to beat groups of amateur and [semi-professional gamers](https://thecodelab.online). [157] [154] [158] [159] At The International 2018, OpenAI Five played in two [exhibition matches](http://121.36.62.315000) against expert players, however ended up losing both [video games](http://43.138.57.2023000). [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world [champions](http://118.190.88.238888) of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those games. [165] |
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<br>OpenAI 5['s mechanisms](https://gitea.phywyj.dynv6.net) in Dota 2's bot player shows the challenges of [AI](https://gitea.winet.space) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown the use of deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166] |
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<br>Dactyl<br> |
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<br>Developed in 2018, Dactyl utilizes machine learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It finds out totally in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the things [orientation](http://152.136.126.2523000) problem by using domain randomization, a simulation approach which exposes the learner to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking electronic cameras, likewise has RGB cameras to allow the robotic to control 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] |
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<br>In 2019, OpenAI showed that Dactyl could solve a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present [complex physics](https://wiki.ragnaworld.net) that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating progressively harder environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169] |
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<br>API<br> |
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<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](https://youtubegratis.com) models developed by OpenAI" to let developers get in touch with it for "any English language [AI](http://tanpoposc.com) job". [170] [171] |
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<br>Text generation<br> |
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<br>The company has popularized generative pretrained transformers (GPT). [172] |
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<br>OpenAI's initial GPT model ("GPT-1")<br> |
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<br>The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world understanding and procedure long-range dependences by pre-training on a diverse corpus with long stretches of contiguous text.<br> |
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<br>GPT-2<br> |
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<br>[Generative Pre-trained](https://www.jobseeker.my) Transformer 2 ("GPT-2") is a not being watched transformer language model and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions at first launched to the public. The full version of GPT-2 was not right away launched due to concern about possible misuse, including applications for composing phony news. [174] Some professionals expressed uncertainty that GPT-2 positioned a considerable hazard.<br> |
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence [responded](https://git.rungyun.cn) with a tool to detect "neural phony 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 hush all other speech and be impossible to filter". [176] In November 2019, OpenAI released the total variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of various instances of GPT-2 and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:FloyRumsey3359) other transformer designs. [178] [179] [180] |
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<br>GPT-2's authors argue not being watched language models to be general-purpose students, shown by GPT-2 attaining modern precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not further trained on any task-specific input-output examples).<br> |
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<br>The corpus it was trained on, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:PeggyHocking58) 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](https://www.indianpharmajobs.in) pair encoding. This allows representing any string of characters by encoding both individual characters and multiple-character tokens. [181] |
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<br>GPT-3<br> |
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<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were likewise trained). [186] |
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<br>OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and could 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 in between English and German. [184] |
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<br>GPT-3 [considerably improved](https://www.jobseeker.my) benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or encountering the [fundamental capability](https://talento50zaragoza.com) constraints of predictive language designs. [187] [Pre-training](https://www.tvcommercialad.com) GPT-3 needed several thousand petaflop/s-days [b] of calculate, [compared](https://thewerffreport.com) to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched to the general public for issues of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month totally [free private](https://redmonde.es) beta that started in June 2020. [170] [189] |
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<br>On September 23, 2020, GPT-3 was licensed exclusively to [Microsoft](http://git.lai-tech.group8099). [190] [191] |
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<br>Codex<br> |
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<br>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](http://121.37.208.192:3000) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in [personal](https://git.137900.xyz) beta. [194] According to OpenAI, the design can create working code in over a lots shows languages, many in Python. [192] |
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<br>Several problems with problems, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) design flaws and security vulnerabilities were pointed out. [195] [196] |
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<br>GitHub Copilot has actually been implicated of releasing copyrighted code, with no [author attribution](http://142.93.151.79) or license. [197] |
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<br>OpenAI revealed that they would terminate support for Codex API on March 23, 2023. [198] |
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<br>GPT-4<br> |
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<br>On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of [accepting text](https://git.kansk-tc.ru) or image inputs. [199] They announced that the updated technology passed a simulated law school bar exam with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise read, analyze or produce as much as 25,000 words of text, and write code in all major shows languages. [200] |
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<br>Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on [ChatGPT](https://onsanmo.co.kr). [202] OpenAI has actually [declined](https://careers.express) to expose various technical details and stats about GPT-4, such as the precise size of the design. [203] |
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<br>GPT-4o<br> |
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<br>On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art lead to 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) benchmark compared to 86.5% by GPT-4. [207] |
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<br>On July 18, 2024, [OpenAI released](http://ncdsource.kanghehealth.com) 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 anticipates it to be especially helpful for enterprises, start-ups and developers looking for to automate services with [AI](http://gitlab.nsenz.com) agents. [208] |
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<br>o1<br> |
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<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been developed to take more time to believe about their actions, leading to higher precision. These models are especially reliable in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211] |
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<br>o3<br> |
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<br>On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning design. OpenAI also revealed o3-mini, a lighter and [quicker variation](https://forum.petstory.ge) of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to avoid confusion with telecoms services company O2. [215] |
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<br>Deep research study<br> |
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<br>Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out comprehensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120] |
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<br>Image classification<br> |
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<br>CLIP<br> |
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to evaluate the semantic resemblance between text and images. It can notably be used for image classification. [217] |
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<br>Text-to-image<br> |
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<br>DALL-E<br> |
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<br>Revealed in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) create corresponding images. It can create images of sensible items ("a stained-glass window with a picture of a blue strawberry") along with things that do not exist in truth ("a cube with the texture of a porcupine"). Since March 2021, no API or [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:Izetta33L4) code is available.<br> |
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<br>DALL-E 2<br> |
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<br>In April 2022, OpenAI announced DALL-E 2, an upgraded variation of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new primary system for converting a text description into a 3-dimensional design. [220] |
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<br>DALL-E 3<br> |
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<br>In September 2023, OpenAI revealed DALL-E 3, a more powerful model much better able to produce images from complex descriptions without manual timely engineering and render [complicated details](https://git.alenygam.com) like hands and text. [221] It was released to the public as a ChatGPT Plus function in October. [222] |
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<br>Text-to-video<br> |
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<br>Sora<br> |
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<br>Sora is a text-to-video design that can create videos based upon short detailed prompts [223] along with extend existing [videos forwards](https://git.kawen.site) or backwards in time. [224] It can [generate videos](http://124.129.32.663000) with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.<br> |
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<br>Sora's development team called it after the Japanese word for "sky", to represent its "limitless imaginative capacity". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos accredited for that purpose, however did not expose the number or [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:DustinThow82333) the [exact sources](http://120.55.164.2343000) of the videos. [223] |
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<br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it might produce videos as much as one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the model's capabilities. [225] It acknowledged a few of its drawbacks, consisting of struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but noted that they need to have been cherry-picked and may not represent Sora's normal output. [225] |
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<br>Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually revealed significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to produce practical video from text descriptions, citing its possible to change storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause prepare for expanding his Atlanta-based motion picture studio. [227] |
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<br>Speech-to-text<br> |
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<br>Whisper<br> |
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<br>Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task design that can carry out multilingual speech [recognition](https://tenacrebooks.com) as well as speech translation and language recognition. [229] |
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<br>Music generation<br> |
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<br>MuseNet<br> |
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<br>[Released](https://heli.today) in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly however then fall under mayhem the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the internet mental thriller Ben Drowned to produce music for the titular character. [232] [233] |
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<br>Jukebox<br> |
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<br>Released in 2020, Jukebox is an [open-sourced algorithm](http://www.xn--739an41crlc.kr) to produce music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI specified 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 substantial space" between Jukebox and human-generated music. The Verge stated "It's technically outstanding, even if the outcomes sound like mushy versions of songs that may feel familiar", while [Business Insider](https://pakkalljob.com) mentioned "surprisingly, a few of the resulting songs are memorable and sound genuine". [234] [235] [236] |
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<br>Interface<br> |
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<br>Debate Game<br> |
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<br>In 2018, OpenAI launched the Debate Game, which teaches makers to discuss toy problems in front of a human judge. The purpose is to research whether such a method might assist in auditing [AI](http://207.148.91.145:3000) decisions and in establishing explainable [AI](https://feniciaett.com). [237] [238] |
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<br>Microscope<br> |
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<br>Released in 2020, Microscope [239] is a collection of visualizations of every [substantial layer](https://www.webthemes.ca) and nerve cell of 8 neural network designs which are often studied in interpretability. [240] Microscope was developed to examine the features that form inside these [neural networks](http://git.lai-tech.group8099) quickly. The models included are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241] |
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<br>ChatGPT<br> |
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<br>Launched in November 2022, ChatGPT is an artificial intelligence tool developed on top of GPT-3 that offers a conversational user interface that enables users to ask questions in natural language. The system then [responds](https://crmthebespoke.a1professionals.net) with an answer within seconds.<br> |
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