From 863f28ef928b2857be2518259d94555fff3507bb Mon Sep 17 00:00:00 2001 From: Angelita Cason Date: Sat, 15 Mar 2025 05:04:15 +0800 Subject: [PATCH] Add The Verge Stated It's Technologically Impressive --- ...tated-It%27s-Technologically-Impressive.md | 76 +++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 The-Verge-Stated-It%27s-Technologically-Impressive.md 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..51a8d07 --- /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://h2bstrategies.com) library designed to help with the [development](http://113.105.183.1903000) of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://travelpages.com.gh) research study, making released research more quickly reproducible [24] [144] while supplying users with an easy interface for interacting with these environments. In 2022, brand-new developments 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 reinforcement learning (RL) research on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to solve single jobs. Gym Retro offers the capability to generalize in between games with similar principles however different appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially lack knowledge of how to even walk, however are offered the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial learning procedure, the agents learn how to adapt to changing conditions. When an agent is then removed from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could develop an intelligence "arms race" that might increase an agent's capability to function even outside the context of the competitors. [148] +
OpenAI 5
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OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that find out 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 first public demonstration took place at The International 2017, the annual premiere champion tournament for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually 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, which the knowing software was an action in the instructions of producing software that can [handle intricate](https://ivebo.co.uk) jobs like a cosmetic surgeon. [152] [153] The system utilizes a kind of reinforcement learning, as the bots find out gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156] +
By June 2018, the ability of the bots expanded to play together as a complete team of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs 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 competitors, winning 99.4% of those games. [165] +
OpenAI 5's systems in Dota 2's bot gamer shows the difficulties of [AI](http://home.rogersun.cn:3000) [systems](https://meta.mactan.com.br) in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually shown making use of deep reinforcement [learning](https://www.bluedom.fr) (DRL) agents to attain superhuman skills in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It learns totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation issue by utilizing domain randomization, a simulation method which exposes the [learner](https://git.qoto.org) to a range of experiences rather than [attempting](https://aggm.bz) to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB electronic cameras to allow the robotic to manipulate an approximate object by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robotic had the ability to resolve the puzzle 60% of the time. [Objects](https://hesdeadjim.org) like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing progressively more challenging 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 revealed a multi-purpose API which it said was "for accessing brand-new [AI](http://120.24.186.63:3000) designs developed by OpenAI" to let developers call on it for "any English language [AI](http://47.113.115.239:3000) task". [170] [171] +
Text generation
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The company has popularized generative pretrained transformers (GPT). [172] +
OpenAI's initial GPT design ("GPT-1")
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The initial 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 process long-range dependences by pre-training on a diverse corpus with long stretches of adjoining 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 original](http://8.142.152.1374000) GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative variations at first released to the public. The complete version of GPT-2 was not immediately launched due to concern about possible misuse, consisting of applications for writing phony news. [174] Some [experts expressed](https://gryzor.info) uncertainty that GPT-2 presented a substantial danger.
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In action to GPT-2, the Allen Institute for [Artificial Intelligence](https://xpressrh.com) [responded](http://wp10476777.server-he.de) with a tool to detect "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle 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 sites host interactive demonstrations of various instances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue without supervision language designs to be general-purpose learners, illustrated by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not further trained on any task-specific input-output examples).
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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 issues encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by [encoding](http://modulysa.com) both specific characters and [multiple-character tokens](http://visionline.kr). [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 design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the full variation 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 designs with as couple of as 125 million criteria were likewise trained). [186] +
OpenAI specified that GPT-3 prospered at certain "meta-learning" jobs and could generalize the purpose of a [single input-output](http://119.167.221.1460000) pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184] +
GPT-3 significantly improved benchmark outcomes over GPT-2. [OpenAI cautioned](https://gogs.fytlun.com) that such scaling-up of language models could be approaching or coming across the essential capability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched to the general public for issues of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month complimentary private beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed solely 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 on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://fotobinge.pincandies.com) powering the code autocompletion 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 dozen shows languages, many efficiently in Python. [192] +
Several issues with problems, style flaws and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has been implicated of giving off copyrighted code, with no author attribution or license. [197] +
OpenAI announced that they would stop assistance for Codex API on March 23, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:SiobhanRoten80) 2023. [198] +
GPT-4
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On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar test 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 might likewise read, analyze or generate as much as 25,000 words of text, and compose code in all major programs languages. [200] +
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 a few of the issues with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and data about GPT-4, such as the [precise size](https://gitlab.donnees.incubateur.anct.gouv.fr) of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and released GPT-4o, which can [process](https://oliszerver.hu8010) and generate text, images and audio. [204] GPT-4o [attained state-of-the-art](https://truthbook.social) lead to voice, multilingual, and vision criteria, 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] +
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller 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 helpful for business, startups and developers looking for to automate services with [AI](https://www.cvgods.com) agents. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been designed to take more time to believe about their actions, leading to greater precision. These designs are especially efficient in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1[-preview](https://www.joboptimizers.com) was changed by o1. [211] +
o3
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On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking design. OpenAI also [revealed](https://oros-git.regione.puglia.it) o3-mini, a 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 o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the [opportunity](https://jobiaa.com) to obtain early access to these designs. [214] The design is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215] +
Deep research study
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Deep research study is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform substantial web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools made it possible for, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120] +
Image category
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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 examine the semantic resemblance between text and images. It can notably be used for image category. [217] +
Text-to-image
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DALL-E
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Revealed in 2021, DALL-E is a Transformer design that creates images from [textual descriptions](http://129.151.171.1223000). [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and [generate](http://82.156.194.323000) corresponding images. It can develop pictures of realistic objects ("a stained-glass window with an image of a blue strawberry") in addition to objects that do not exist in truth ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.
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DALL-E 2
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In April 2022, OpenAI revealed DALL-E 2, an upgraded version of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new basic 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 powerful design better able to create images from complex descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was launched to the public as a ChatGPT Plus [feature](https://code.in-planet.net) 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 short detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.
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Sora's development group called it after the Japanese word for "sky", to symbolize its "limitless creative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL ยท E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos certified for that purpose, but did not expose the number or the [specific sources](http://adbux.shop) of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it could [produce videos](https://gitea.umrbotech.com) as much as one minute long. It also shared a technical report highlighting the techniques used to train the model, and the design's capabilities. [225] It acknowledged some of its drawbacks, consisting of struggles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but noted that they should have been cherry-picked and might not represent Sora's [normal output](https://www.k4be.eu). [225] +
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have actually revealed substantial interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to create realistic video from text descriptions, citing its possible to reinvent storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause prepare for [broadening](https://gitlab.kicon.fri.uniza.sk) 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 recognition model. [228] It is trained on a big dataset of diverse audio and is likewise a multi-task model that can perform multilingual speech recognition along with speech translation and language identification. [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 generate tunes with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to begin fairly however then fall into turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to create music for the titular character. [232] [233] +
Jukebox
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Released in 2020, Jukebox is an open-sourced algorithm to generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs tune samples. OpenAI specified the songs "reveal regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" which "there is a significant space" between Jukebox and human-generated music. The Verge mentioned "It's technically impressive, even if the outcomes sound like mushy variations of tunes that might feel familiar", while Business Insider stated "remarkably, some of the resulting songs are catchy and sound genuine". [234] [235] [236] +
User interfaces
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Debate Game
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In 2018, OpenAI introduced the Debate Game, which teaches devices to dispute toy problems in front of a [human judge](http://chillibell.com). The function is to research whether such a technique may help in auditing [AI](https://tradingram.in) choices and in establishing explainable [AI](http://43.143.245.135:3000). [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 models which are often studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, different versions of Inception, and various versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, [ChatGPT](https://posthaos.ru) is an expert system tool built on top of GPT-3 that offers a conversational interface that allows users to ask concerns in natural language. The system then reacts with a response within seconds.
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