Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library designed to [facilitate](https://careerportals.co.za) the advancement of [reinforcement learning](http://krzsyjtj.zlongame.co.kr9004) algorithms. It aimed to standardize how [environments](https://aquarium.zone) are defined in [AI](https://live.gitawonk.com) research, making published research more quickly reproducible [24] [144] while supplying users with an easy user interface for engaging with these environments. In 2022, brand-new developments of Gym have actually been relocated 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 support learning (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on [enhancing representatives](http://47.100.220.9210001) to resolve single tasks. Gym Retro offers the capability to generalize in between games with [comparable concepts](https://gamberonmusic.com) but different appearances.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, RoboSumo is a virtual world where [humanoid metalearning](http://106.55.3.10520080) robotic agents initially do not have understanding of how to even stroll, but are provided the goals of [discovering](https://www.genbecle.com) to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives discover how to adjust to altering conditions. When an agent is then removed from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually discovered how to stabilize in a generalized way. [148] [149] [OpenAI's Igor](https://jobpile.uk) Mordatch argued that competition between agents could produce an intelligence "arms race" that could increase a representative's capability to operate even outside the context of the [competition](http://82.19.55.40443). [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a team of five 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 very first public demonstration happened at The International 2017, the annual premiere championship tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of genuine time, and that the learning software application was a step in the instructions of creating software application that can deal with complicated jobs like a surgeon. [152] [153] The system utilizes a kind of support knowing, as the bots find out in 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]
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<br>By June 2018, the capability of the [bots expanded](http://gsrl.uk) to play together as a full team of 5, and they had the [ability](http://drive.ru-drive.com) to beat groups of [amateur](https://voggisper.com) and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against [professional](https://kanjob.de) players, however wound up losing both 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 exhibition match in San Francisco. [163] [164] The bots' last 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]
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<br>OpenAI 5's mechanisms in Dota 2's bot player shows the challenges of [AI](https://cinetaigia.com) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated using deep reinforcement knowing (DRL) agents 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 uses machine learning to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It learns totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a range of [experiences](http://media.nudigi.id) instead of [attempting](https://www.pkjobs.store) to fit to reality. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has [RGB video](https://kition.mhl.tuc.gr) cameras to permit the robotic to control an approximate item by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an [octagonal prism](https://natgeophoto.com). [168]
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<br>In 2019, OpenAI showed 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 complex physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach 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](http://124.129.32.66:3000) models developed by OpenAI" to let developers get in touch with it for "any English language [AI](http://lifethelife.com) task". [170] [171]
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<br>Text generation<br>
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<br>The company has actually promoted generative pretrained transformers (GPT). [172]
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<br>[OpenAI's original](https://git.opskube.com) GPT model ("GPT-1")<br>
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<br>The [initial paper](http://47.106.205.1408089) on generative pre-training of a [transformer-based language](http://39.100.93.1872585) model was written by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It showed how a [generative design](https://git.danomer.com) of [language](http://tian-you.top7020) might obtain world knowledge and process long-range dependencies by pre-training on a varied corpus with long stretches of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative [versions initially](https://natgeophoto.com) released to the public. The full variation of GPT-2 was not instantly launched due to issue about prospective misuse, consisting of applications for composing phony news. [174] Some experts expressed uncertainty that GPT-2 presented a significant threat.<br>
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake news". [175] Other scientists, such as Jeremy Howard, warned 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 version of the GPT-2 language design. [177] Several sites host interactive presentations of various circumstances of GPT-2 and other transformer models. [178] [179] [180]
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<br>GPT-2's authors argue unsupervised language designs to be general-purpose learners, illustrated by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not additional trained on any task-specific input-output examples).<br>
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<br>The corpus it was trained on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of [characters](http://106.39.38.2421300) 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 not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were likewise trained). [186]
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<br>[OpenAI stated](https://gurjar.app) that GPT-3 was successful at certain "meta-learning" jobs and might generalize the function of a [single input-output](https://git.apps.calegix.net) pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning in between English and Romanian, and in between English and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:Melaine2328) German. [184]
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<br>GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI warned that such [scaling-up](https://hub.bdsg.academy) of language designs might be approaching or encountering the essential ability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous 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 instantly released to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month free private beta that started in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was certified solely to Microsoft. [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 furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://www.jimtangyh.xyz:7002) powering the code autocompletion [tool GitHub](https://test.manishrijal.com.np) Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the model can develop working code in over a lots programs languages, the majority of effectively in Python. [192]
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<br>Several problems with glitches, design defects and security vulnerabilities were mentioned. [195] [196]
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<br>GitHub Copilot has actually been implicated of producing copyrighted code, without any author attribution or license. [197]
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<br>OpenAI announced that they would discontinue 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 announced the [release](https://www.tmip.com.tr) of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar exam 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 could also read, examine or create approximately 25,000 words of text, and compose code in all significant shows languages. [200]
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<br>[Observers](https://mcn-kw.com) reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose different technical details and stats about GPT-4, such as the exact size of the model. [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 produce text, images and audio. [204] GPT-4o attained state-of-the-art outcomes in voice, multilingual, and vision criteria, [setting](https://git.i2edu.net) new records in audio speech recognition 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 launched GPT-4o mini, a smaller variation of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user interface. Its [API costs](https://git.gumoio.com) $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 useful for business, startups and designers seeking to automate services with [AI](https://viraltry.com) agents. [208]
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<br>o1<br>
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<br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been created to take more time to consider their reactions, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11943978) leading to greater precision. These models are especially effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:SamualGuillen) Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
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<br>o3<br>
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<br>On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning model. OpenAI also revealed o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are evaluating o3 and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1103710) o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecoms providers O2. [215]
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<br>Deep research<br>
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<br>Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out comprehensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [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 design that is trained to evaluate the semantic similarity in between text and images. It can notably be utilized 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 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 purse formed like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can create images of practical objects ("a stained-glass window with an image of a blue strawberry") along with items that do not exist in truth ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.<br>
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<br>DALL-E 2<br>
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<br>In April 2022, [photorum.eclat-mauve.fr](http://photorum.eclat-mauve.fr/profile.php?id=261138) OpenAI announced DALL-E 2, [wavedream.wiki](https://wavedream.wiki/index.php/User:TerraPreiss5) an updated version of the model with more realistic results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new rudimentary system for transforming 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 announced DALL-E 3, a more powerful design much better able to generate images from complex descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched to the general public as a ChatGPT Plus 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 model that can generate videos based on brief detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.<br>
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<br>Sora's development group 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 model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that function, however did not expose the number or the exact sources of the videos. [223]
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<br>OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it could produce videos approximately one minute long. It also shared a technical report highlighting the techniques used to train the model, and the model's capabilities. [225] It acknowledged a few of its imperfections, including struggles replicating intricate physics. [226] Will [Douglas Heaven](https://raumlaborlaw.com) of the MIT Technology Review called the presentation videos "remarkable", but kept in mind that they need to have been cherry-picked and [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:LeonoreMuse6) might not represent Sora's common output. [225]
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<br>Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have shown significant interest in the innovation's potential. In an interview, actor/[filmmaker Tyler](https://www.stmlnportal.com) Perry expressed his astonishment at the innovation's capability to [generate practical](https://jobdd.de) video from text descriptions, mentioning its [potential](https://tmiglobal.co.uk) to reinvent storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly plans for broadening 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 recognition design. [228] It is trained on a large dataset of diverse audio and is also a multi-task design that can perform multilingual speech recognition along with speech translation and language identification. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<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 tune created by MuseNet tends to begin fairly however then fall into chaos 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]
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<br>Jukebox<br>
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<br>Released in 2020, Jukebox is an open-sourced algorithm to create music with vocals. After training on 1.2 million samples, the system [accepts](https://git.schdbr.de) a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the tunes "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" which "there is a considerable gap" between Jukebox and human-generated music. The Verge stated "It's technically outstanding, even if the outcomes seem like mushy variations of tunes that may feel familiar", while Business Insider stated "remarkably, a few of the resulting tunes are appealing 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 machines to discuss toy problems in front of a human judge. The purpose is to research whether such a [technique](https://yourfoodcareer.com) may assist in auditing [AI](https://git.freesoftwareservers.com) choices and in developing explainable [AI](https://dev.gajim.org). [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://gitea.lelespace.top) and neuron of eight neural network designs which are frequently studied in interpretability. [240] Microscope was produced to evaluate the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different variations of Inception, and various versions 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 [constructed](http://190.117.85.588095) on top of GPT-3 that provides a conversational interface that allows users to ask questions in natural language. The system then responds with an answer within seconds.<br>
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