commit 650368f73201c6d56cf6dfcb79559405123466b4 Author: milagrosstine Date: Sun Apr 6 21:17:30 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..5e338d4 --- /dev/null +++ b/The-Verge-Stated-It%27s-Technologically-Impressive.md @@ -0,0 +1,76 @@ +
Announced in 2016, Gym is an open-source Python library developed to help with the development of support learning algorithms. It aimed to standardize how [environments](https://mcn-kw.com) are defined in [AI](http://test-www.writebug.com:3000) research study, [pediascape.science](https://pediascape.science/wiki/User:AugustusBickford) making released research more easily reproducible [24] [144] while [supplying](http://042.ne.jp) users with an easy user interface for interacting with these environments. In 2022, new advancements of Gym have been relocated to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for support knowing (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to solve single tasks. Gym Retro offers the ability to generalize in between video games with comparable ideas but various looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack understanding of how to even walk, but are provided the goals of discovering to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might develop an intelligence "arms race" that could increase a representative's ability to operate even outside the context of the [competition](https://jobsportal.harleysltd.com). [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 [ability](https://gitlab.kicon.fri.uniza.sk) level entirely through trial-and-error algorithms. Before becoming a group of 5, the first public [presentation](https://palsyworld.com) took place at The [International](https://git.cnpmf.embrapa.br) 2017, the yearly best championship tournament for the game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of real time, and that the [knowing software](https://git.daoyoucloud.com) application was a step in the direction of creating software application that can deal with complex tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of support learning, as the bots learn with time by playing against themselves numerous 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 broadened to play together as a complete team of 5, and they were able to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, however wound up losing both [video games](https://live.gitawonk.com). [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 total video games in a [four-day](http://tian-you.top7020) open online competitors, winning 99.4% of those video games. [165] +
OpenAI 5's systems in Dota 2's bot gamer shows the [difficulties](https://git.qingbs.com) of [AI](http://gungang.kr) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown using deep support knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, [Dactyl utilizes](https://worship.com.ng) machine discovering to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It discovers entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation problem by using domain randomization, a simulation technique which [exposes](http://193.30.123.1883500) the learner to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having [movement tracking](http://www.topverse.world3000) cams, likewise has RGB cams to allow the robot to manipulate an approximate item by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the [Rubik's Cube](https://wiki.snooze-hotelsoftware.de) present intricate physics that is harder to design. OpenAI did this by enhancing the toughness 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] +
API
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In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](https://labs.hellowelcome.org) models established by OpenAI" to let developers get in touch with it for "any English language [AI](http://119.23.214.109:30032) job". [170] [171] +
Text generation
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The company has actually pretrained transformers (GPT). [172] +
OpenAI's original GPT design ("GPT-1")
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The [initial](http://n-f-l.jp) 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 design of language might obtain world understanding and procedure long-range dependences by [pre-training](http://101.43.135.2349211) 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 model](https://links.gtanet.com.br) ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative variations at first launched to the general public. The complete version of GPT-2 was not right away released due to concern about possible abuse, including applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 posed a substantial hazard.
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In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the innovation to absolutely 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 launched the complete variation of the GPT-2 language model. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's [authors argue](https://git.qingbs.com) without supervision language models to be general-purpose students, shown by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not further 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 of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both private 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 without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion specifications, [184] two 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 criteria were also trained). [186] +
OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184] +
GPT-3 significantly improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or encountering the essential ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately launched to the general public for issues of possible abuse, although OpenAI prepared to enable gain access to through a paid [cloud API](https://www.finceptives.com) after a two-month free personal beta that began in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was certified specifically to Microsoft. [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has additionally been [trained](https://gold8899.online) on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://seekinternship.ng) powering the [code autocompletion](https://lab.chocomart.kz) tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can create working code in over a lots programs languages, many successfully in Python. [192] +
Several issues with problems, design flaws and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has actually been accused of giving off copyrighted code, without any author attribution or license. [197] +
OpenAI revealed that they would terminate assistance for Codex API on March 23, 2023. [198] +
GPT-4
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On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), [efficient](https://sss.ung.si) in accepting text or image inputs. [199] They revealed that the upgraded technology passed a simulated law school bar examination with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, evaluate or produce up to 25,000 words of text, and compose code in all significant shows languages. [200] +
[Observers](http://git.bzgames.cn) reported that the version of ChatGPT using GPT-4 was an [improvement](https://www.opad.biz) on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also [capable](https://git.zyhhb.net) of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose different technical details and data about GPT-4, such as the exact size of the model. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision criteria, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized variation of GPT-4o changing 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 particularly useful for enterprises, start-ups and designers seeking to automate services with [AI](https://codeh.genyon.cn) agents. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been developed to take more time to consider their reactions, causing higher precision. These models are especially reliable in science, coding, and reasoning tasks, and were made available to ChatGPT 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 revealed o3, the follower of the o1 reasoning design. OpenAI also [revealed](https://repo.myapps.id) o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and [security scientists](https://gurjar.app) had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecoms [services provider](https://jobs.campus-party.org) O2. [215] +
Deep research
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Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform extensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120] +
Image category
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CLIP
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Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze the semantic resemblance 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 in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can produce pictures of reasonable things ("a stained-glass window with an image of a blue strawberry") along with [objects](http://huaang6688.gnway.cc3000) that do not exist in truth ("a cube with the texture of a porcupine"). As of March 2021, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:RamonaSchultz41) 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 application for Point-E, a brand-new simple system for transforming a text description into a 3-dimensional model. [220] +
DALL-E 3
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In September 2023, OpenAI announced DALL-E 3, a more [effective model](https://adsall.net) better able to create images from complex descriptions without manual prompt engineering and render complex details like hands and text. [221] It was released to the 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 model that can create videos based upon brief detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can generate videos with [resolution](http://51.79.251.2488080) up to 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.
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Sora's development team called it after the Japanese word for "sky", to signify its "unlimited imaginative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system [utilizing publicly-available](https://starleta.xyz) videos along with copyrighted videos licensed for that purpose, however did not expose 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, specifying that it might create videos as much as one minute long. It also shared a technical report highlighting the approaches used to train the model, and the model's abilities. [225] It acknowledged some of its imperfections, including struggles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they must have been cherry-picked and might not represent Sora's normal output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have actually shown considerable interest in the innovation's potential. In an interview, actor/filmmaker [Tyler Perry](https://wiki.roboco.co) revealed his astonishment at the technology's capability to generate sensible video from text descriptions, mentioning its potential to change storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had decided to pause strategies for expanding his Atlanta-based movie studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task model that can perform multilingual speech acknowledgment as well as speech translation and language recognition. [229] +
Music generation
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MuseNet
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Released in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to start fairly but then fall under chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to produce music for the titular character. [232] [233] +
Jukebox
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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 snippet of lyrics and outputs tune samples. OpenAI mentioned the songs "show regional musical coherence [and] follow conventional chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" which "there is a significant space" between Jukebox and human-generated music. The Verge specified "It's technologically excellent, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider mentioned "remarkably, some of the resulting tunes are memorable and sound genuine". [234] [235] [236] +
User user interfaces
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Debate Game
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In 2018, OpenAI launched the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The purpose is to research study whether such a technique might assist in auditing [AI](http://116.62.118.242) choices and in establishing explainable [AI](http://39.106.223.11). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, various variations of Inception, and various variations of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is a synthetic intelligence tool developed on top of GPT-3 that provides a conversational interface that permits users to ask questions in natural language. The system then [responds](https://careers.synergywirelineequipment.com) with an answer within seconds.
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