commit 7c805937d542f28b4fe5f6f2d873ce3740c2c910 Author: ilana73d757620 Date: Wed Mar 5 13:56:25 2025 +0800 Add 'The Verge Stated It's Technologically Impressive' diff --git a/The-Verge-Stated-It%27s-Technologically-Impressive.md b/The-Verge-Stated-It%27s-Technologically-Impressive.md new file mode 100644 index 0000000..09711f5 --- /dev/null +++ b/The-Verge-Stated-It%27s-Technologically-Impressive.md @@ -0,0 +1,76 @@ +
Announced in 2016, Gym is an [open-source Python](https://www.linkedaut.it) [library designed](https://www.womplaz.com) to assist in the advancement of support knowing [algorithms](http://43.136.54.67). It aimed to standardize how [environments](https://git.j4nis05.ch) are specified in [AI](http://159.75.133.67:20080) research study, making published research study more easily reproducible [24] [144] while supplying users with a simple user interface for communicating with these environments. In 2022, brand-new developments of Gym have actually been moved to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for support 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 jobs. Gym Retro offers the ability to generalize between games with similar concepts but different looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first lack understanding of how to even walk, but are offered the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the agents discover how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and put in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that [competition](https://sso-ingos.ru) in between agents might develop an intelligence "arms race" that might increase an agent's capability to work even outside the context of the competition. [148] +
OpenAI 5
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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 gamers at a high skill level completely through trial-and-error algorithms. Before ending up being a group of 5, the very first public presentation occurred at The International 2017, the yearly premiere champion competition for the video game, where Dendi, a professional 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 learned by playing against itself for two weeks of actual time, which the learning software application was a step in the direction of developing software application that can manage intricate tasks like a surgeon. [152] [153] The system utilizes a type of support knowing, as the bots discover in time by [playing](https://git.j.co.ua) against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [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 gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against [professional](https://git.genowisdom.cn) players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated 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' final public look came later on that month, where they played in 42,729 total video games in a four-day open online competitors, winning 99.4% of those games. [165] +
OpenAI 5['s systems](http://tobang-bangsu.co.kr) in Dota 2's bot gamer reveals the obstacles of [AI](https://www.meetyobi.com) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually demonstrated using deep support knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It discovers entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has RGB video cameras to permit the robot to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168] +
In 2019, OpenAI [demonstrated](http://101.34.66.2443000) that Dactyl could fix a Rubik's Cube. The robot had the ability 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 robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of creating progressively more tough environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169] +
API
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In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](http://gogs.fundit.cn:3000) designs developed by OpenAI" to let developers get in touch with it for "any English language [AI](http://git.zhongjie51.com) job". [170] [171] +
Text generation
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The company has promoted generative pretrained transformers (GPT). [172] +
OpenAI's [initial GPT](https://www.globalshowup.com) design ("GPT-1")
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The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and procedure long-range dependencies by pre-training on a diverse corpus with long stretches of contiguous text.
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GPT-2
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Generative Pre-trained 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 announced in February 2019, with only restricted demonstrative versions at first released to the public. The complete variation of GPT-2 was not immediately released due to concern about prospective misuse, consisting of applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 positioned a considerable danger.
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In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology to totally 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 released the complete variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of various [circumstances](https://www.joboont.in) of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue unsupervised language designs to be [general-purpose](https://myclassictv.com) students, highlighted by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more trained on any task-specific input-output examples).
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The corpus it was trained on, called WebText, contains a little 40 [gigabytes](https://diversitycrejobs.com) of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both specific characters and multiple-character tokens. [181] +
GPT-3
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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 stated that the complete version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million specifications were also trained). [186] +
OpenAI stated that GPT-3 prospered at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184] +
GPT-3 considerably 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 models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched to the public for issues of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month complimentary private beta that began in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed exclusively to Microsoft. [190] [191] +
Codex
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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://repo.gusdya.net) 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 working code in over a lots shows languages, [ratemywifey.com](https://ratemywifey.com/author/hugocruse67/) most successfully in Python. [192] +
Several issues with glitches, style flaws and security vulnerabilities were pointed out. [195] [196] +
GitHub Copilot has been accused of giving off copyrighted code, with no [author attribution](https://repo.correlibre.org) or license. [197] +
OpenAI revealed that they would cease support for Codex API on March 23, 2023. [198] +
GPT-4
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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 innovation 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 might also check out, analyze or produce up to 25,000 words of text, and compose code in all significant programming languages. [200] +
Observers reported that the version of ChatGPT utilizing GPT-4 was an [improvement](https://worship.com.ng) on the previous GPT-3.5-based version, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose different technical details and stats about GPT-4, such as the exact size of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained modern results 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) criteria compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI released 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 expects it to be especially useful for enterprises, startups and developers seeking to automate services with [AI](http://slfood.co.kr) representatives. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been created to take more time to consider their responses, resulting in greater precision. These designs are particularly [efficient](https://repo.beithing.com) in science, coding, and thinking jobs, and were made available to [ChatGPT](https://ka4nem.ru) Plus and Staff member. [209] [210] In December 2024, o1-preview was changed by o1. [211] +
o3
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On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215] +
Deep research
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Deep research study is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform substantial web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools made it possible for, it reached an [accuracy](https://social.stssconstruction.com) of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120] +
Image classification
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CLIP
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Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic resemblance in between text and images. It can significantly be used for image classification. [217] +
Text-to-image
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DALL-E
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[Revealed](https://www.mk-yun.cn) in 2021, DALL-E is a Transformer model that creates images from textual descriptions. [218] DALL-E uses 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 an unfortunate capybara") and produce corresponding images. It can develop images of realistic things ("a stained-glass window with an image of a blue strawberry") as well as things that do not exist in [reality](https://gitea.xiaolongkeji.net) ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.
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DALL-E 2
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In April 2022, OpenAI announced DALL-E 2, an updated version of the design with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new simple system for converting a text description into a 3[-dimensional model](http://webheaydemo.co.uk). [220] +
DALL-E 3
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In September 2023, OpenAI revealed DALL-E 3, a more powerful design much better able to produce images from intricate descriptions without manual [prompt engineering](https://parissaintgermainfansclub.com) and render [complex details](https://www.tvcommercialad.com) like hands and text. [221] It was released to the general public as a ChatGPT Plus function in October. [222] +
Text-to-video
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Sora
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Sora is a text-to-video design that can generate videos based on brief detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.
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Sora's advancement group named it after the Japanese word for "sky", to symbolize its "limitless imaginative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos certified for that purpose, however did not reveal the number or the exact sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it could generate videos as much as one minute long. It also shared a technical report highlighting the methods used to train the model, and the model's capabilities. [225] It acknowledged some of its imperfections, including battles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but kept in mind that they need to have been cherry-picked and might not represent Sora's typical output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have revealed substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry [revealed](https://postyourworld.com) his astonishment at the technology's capability to create reasonable video from text descriptions, citing its prospective to transform storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly prepare for broadening his Atlanta-based film studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of diverse audio and is likewise a multi-task design that can perform multilingual speech acknowledgment as well as speech translation and [language identification](http://47.107.126.1073000). [229] +
Music generation
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MuseNet
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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 generated by MuseNet tends to begin fairly but then fall into mayhem the longer it plays. [230] [231] In popular culture, [initial applications](https://jobwings.in) of this tool were [utilized](http://www.scitqn.cn3000) as early as 2020 for the web psychological thriller Ben Drowned to develop music for the titular character. [232] [233] +
Jukebox
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Released in 2020, Jukebox is an open-sourced algorithm to [produce](http://123.60.97.16132768) music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the songs "show local musical coherence [and] follow standard chord patterns" however acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a significant gap" between Jukebox and human-generated music. The Verge specified "It's technically impressive, even if the outcomes seem like mushy versions of songs that may feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are appealing and sound genuine". [234] [235] [236] +
User interfaces
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Debate Game
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In 2018, OpenAI released the Debate Game, which teaches machines to discuss toy problems in front of a human judge. The function is to research study whether such a method may assist in auditing [AI](https://munidigital.iie.cl) choices and in establishing explainable [AI](http://106.14.125.169). [237] [238] +
Microscope
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[Released](https://busanmkt.com) in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was created to analyze the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241] +
ChatGPT
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[Launched](https://skillsvault.co.za) in November 2022, ChatGPT is a synthetic 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 responds with a response within seconds.
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