From 9d54f2ed23dfedfaa92530aea915598025b98834 Mon Sep 17 00:00:00 2001 From: alma6946741367 Date: Sun, 9 Feb 2025 02:56:58 +0800 Subject: [PATCH] Add 'The Verge Stated It's Technologically Impressive' --- ...tated-It%27s-Technologically-Impressive.md | 55 +++++++++++++++++++ 1 file changed, 55 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..4d5975b --- /dev/null +++ b/The-Verge-Stated-It%27s-Technologically-Impressive.md @@ -0,0 +1,55 @@ +
Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in [AI](https://git.eisenwiener.com) research, making published research study more quickly reproducible [24] [144] while providing users with a simple interface for connecting with these environments. In 2022, brand-new developments of Gym have been [relocated](https://sebeke.website) to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:ArleenBabbidge) Gym Retro is a platform for support learning (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to fix single jobs. Gym Retro offers the ability to generalize between video games with comparable principles but various appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have understanding of how to even stroll, but are provided the goals of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives find out how to adapt to altering conditions. When an agent is then removed from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between agents might create an intelligence "arms race" that could increase a representative's capability to work even outside the context of the competitors. [148] +
OpenAI 5
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OpenAI Five is a group of five OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that find out to play against [human players](http://gogs.funcheergame.com) at a high skill level totally through trial-and-error algorithms. Before ending up being a group of 5, the first [public demonstration](https://tv.lemonsocial.com) took place at The International 2017, the yearly best champion tournament for the 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 actually found out by playing against itself for two weeks of actual time, and that the knowing software was a step in the direction of creating software application that can deal with complex jobs like a surgeon. [152] [153] The system uses a kind of reinforcement learning, as the bots learn over time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and taking map objectives. [154] [155] [156] +
By June 2018, the ability of the bots broadened to play together as a full group of 5, and they were able to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the game at the time, 2:0 in a [live exhibit](http://wp10476777.server-he.de) match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those games. [165] +
OpenAI 5's systems in Dota 2's bot player shows the difficulties of [AI](https://www.behavioralhealthjobs.com) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep reinforcement learning (DRL) representatives to attain superhuman competence 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 manipulate physical things. [167] It [discovers totally](https://jobs.campus-party.org) in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation issue by utilizing domain randomization, a simulation method which exposes the learner to a variety of experiences instead of [attempting](https://www.sedatconsultlimited.com) to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB electronic cameras to allow the robot to manipulate an arbitrary item 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 showed that Dactyl might fix a Rubik's Cube. The robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated [physics](http://8.211.134.2499000) that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain [Randomization](http://git.rabbittec.com) (ADR), a [simulation technique](http://47.104.6.70) of creating gradually more challenging environments. ADR differs from manual domain randomization by not needing a human to define randomization varieties. [169] +
API
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In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://www.designxri.com) designs developed by OpenAI" to let [designers](https://karmadishoom.com) call on it for "any English language [AI](https://fmstaffingsource.com) task". [170] [171] +
Text generation
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The business has promoted generative pretrained transformers (GPT). [172] +
OpenAI's original GPT model ("GPT-1")
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The original paper on generative [pre-training](https://sss.ung.si) of a transformer-based language model was written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world understanding and process long-range dependences by pre-training on a varied 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 without supervision transformer language design and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative variations at first [launched](https://git.tool.dwoodauto.com) to the public. The complete variation of GPT-2 was not right away released due to issue about possible misuse, including applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 positioned a significant danger.
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, warned 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 difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several sites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining modern accuracy 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 a little 40 [gigabytes](https://mastercare.care) of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain issues encoding vocabulary with word tokens by utilizing 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 not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were also trained). [186] +
OpenAI stated that GPT-3 prospered at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper provided 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 cautioned that such scaling-up of language designs could be approaching or coming across the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the public for concerns of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month totally free personal beta that began in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed 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 on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://git.cnpmf.embrapa.br) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the design can create working code in over a dozen shows languages, a lot of successfully in Python. [192] +
Several concerns with problems, design flaws and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has actually been implicated of [producing copyrighted](https://git.chocolatinie.fr) code, with no author attribution or license. [197] +
OpenAI announced that they would discontinue support 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), capable of accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar examination 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 check out, examine or create as much as 25,000 words of text, and compose code in all significant programming languages. [200] +
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is likewise [capable](http://193.105.6.1673000) of taking images as input on ChatGPT. [202] OpenAI has declined to reveal different technical details and statistics about GPT-4, such as the precise 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 generate text, images and audio. [204] GPT-4o attained modern lead to voice, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:LindseyWalstab9) multilingual, and vision criteria, setting new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the [Massive Multitask](https://crossdark.net) Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI released 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 expects it to be especially useful for enterprises, start-ups and designers seeking to automate services with [AI](https://121gamers.com) agents. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been created to take more time to consider their actions, leading to higher precision. These models are particularly reliable in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced 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](http://106.52.215.1523000) o3-mini, a [lighter](http://122.51.46.213) and faster version of OpenAI o3. Since December 21, 2024, this model is not available for [public usage](http://chillibell.com). According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to prevent confusion with [telecommunications](https://git.mikecoles.us) providers O2. [215] +
Deep research
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Deep research study is a representative developed by OpenAI, [unveiled](https://foris.gr) on February 2, 2025. It leverages the abilities 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 allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120] +
Image category
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CLIP
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Revealed in 2021, CLIP ([Contrastive Language-Image](https://udyogseba.com) Pre-training) is a model that is trained to analyze the 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 in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can produce pictures of reasonable 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 sensible outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new simple system for transforming a text description into a 3-dimensional design. [220] +
DALL-E 3
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In September 2023, OpenAI announced DALL-E 3, a more powerful model better able to create images from complex descriptions without manual timely engineering and render intricate details like hands and [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile \ No newline at end of file