From 2b381329472f7ea4066e85e9b013d6db296bcc82 Mon Sep 17 00:00:00 2001 From: mindyashton31 Date: Fri, 7 Feb 2025 01:06:26 +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..98cd1de --- /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 created to help with the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://video.emcd.ro) research study, making released research more easily reproducible [24] [144] while offering users with an easy user interface for communicating with these environments. In 2022, new developments of Gym have been transferred to the library Gymnasium. [145] [146] +
Gym Retro
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[Released](https://phpcode.ketofastlifestyle.com) in 2018, [Gym Retro](http://121.199.172.2383000) is a platform for support knowing (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to resolve single jobs. Gym Retro gives the capability to generalize between games with similar principles however various 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 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 knowing process, the agents find out how to adapt to changing conditions. When a representative is then gotten rid of from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, recommending it had actually learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could produce an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the competitors. [148] +
OpenAI 5
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OpenAI Five is a group of 5 [OpenAI-curated bots](https://portalwe.net) used in the competitive five-on-five [video game](https://duyurum.com) Dota 2, that learn to play against human players at a high skill level completely through experimental algorithms. Before becoming a team of 5, the first public presentation took place at The International 2017, the yearly best champion competition for the game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, [gratisafhalen.be](https://gratisafhalen.be/author/dulcie01x5/) CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of real time, which the knowing software application was a step in the instructions of developing software application that can deal with complicated tasks like a surgeon. [152] [153] The system utilizes a kind of reinforcement learning, as the [bots learn](https://tageeapp.com) with 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 goals. [154] [155] [156] +
By June 2018, the capability of the bots expanded to play together as a full team of 5, and they were able 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 expert players, but 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 games in a four-day open online competitors, winning 99.4% of those video games. [165] +
OpenAI 5's systems in Dota 2's bot gamer reveals the obstacles of [AI](https://git.schdbr.de) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown using deep support learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl utilizes machine learning to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It learns entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation approach which exposes the learner to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB video cameras to enable the robotic to control an approximate things by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by [enhancing](https://www.meetyobi.com) the robustness of Dactyl to perturbations by [utilizing Automatic](http://git.baobaot.com) Domain Randomization (ADR), a simulation method of generating gradually more hard environments. ADR differs from manual domain randomization by not requiring a human to define randomization ranges. [169] +
API
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In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://ready4hr.com) models established by OpenAI" to let designers contact it for "any English language [AI](https://zomi.watch) task". [170] [171] +
Text generation
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The company has promoted generative pretrained transformers (GPT). [172] +
OpenAI's initial GPT design ("GPT-1")
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The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his associates, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world knowledge and procedure long-range reliances by pre-training on a varied corpus with long stretches of contiguous text.
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GPT-2
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[Generative](http://shop.neomas.co.kr) Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative versions initially released to the public. The full version of GPT-2 was not right away released due to concern about potential abuse, consisting of applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 postured a significant risk.
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to totally 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 total version of the GPT-2 language model. [177] Several websites [host interactive](https://sondezar.com) demonstrations of various instances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue unsupervised language models to be general-purpose learners, highlighted by GPT-2 attaining cutting edge precision and [perplexity](http://209.141.61.263000) on 7 of 8 zero-shot tasks (i.e. the model was not more 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 at least 3 upvotes. It avoids certain problems 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 an unsupervised transformer [language](https://www.huntsrecruitment.com) design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 [contained](https://notitia.tv) 175 billion specifications, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186] +
that GPT-3 prospered at certain "meta-learning" jobs and could [generalize](https://git.purwakartakab.go.id) the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer [learning](https://infinirealm.com) between English and Romanian, and in between English and German. [184] +
GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or encountering the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched 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 complimentary 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 actually in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://119.130.113.245:3000) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in [personal](https://musicplayer.hu) beta. [194] According to OpenAI, the design can create working code in over a lots shows languages, most effectively in Python. [192] +
Several problems with glitches, design flaws and security [vulnerabilities](http://codaip.co.kr) were cited. [195] [196] +
GitHub Copilot has actually been implicated of [emitting copyrighted](https://globalhospitalitycareer.com) code, with no [author attribution](https://www.h0sting.org) or license. [197] +
OpenAI announced that they would discontinue 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 in accepting text or image inputs. [199] They announced that the updated innovation 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 also check out, analyze or generate approximately 25,000 words of text, and [compose code](https://se.mathematik.uni-marburg.de) in all major shows languages. [200] +
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on [ChatGPT](https://gitlab.vp-yun.com). [202] OpenAI has declined to reveal various 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 announced and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language [Understanding](https://nytia.org) (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 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 particularly helpful for enterprises, startups and designers seeking to automate services with [AI](https://git.lewd.wtf) agents. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ShellieGenders) which have been designed to take more time to think about their responses, resulting in greater precision. These models are particularly effective in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1[-preview](https://git-dev.xyue.zip8443) 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 likewise revealed o3-mini, a lighter and much faster variation 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 to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215] +
Deep research
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Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive 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) standard. [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 examine the semantic similarity in between text and images. It can significantly 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 model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can produce pictures of practical items ("a stained-glass window with a picture of a blue strawberry") as well as items that do not exist in reality ("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 revealed DALL-E 2, an upgraded version of the model with more reasonable results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new basic 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 design much better able to generate images from complicated descriptions without manual timely engineering and render complicated details like hands and text. [221] It was launched 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 create videos based on short detailed prompts [223] as well as extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of [generated videos](http://stackhub.co.kr) is unknown.
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Sora's advancement group called it after the Japanese word for "sky", to symbolize its "unlimited creative potential". [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 in addition to copyrighted videos certified for that function, but did not expose the number or the exact sources of the videos. [223] +
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might produce videos approximately one minute long. It also shared a technical report [highlighting](https://www.paknaukris.pro) the techniques utilized to train the model, and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:GLXKatrice) the design's capabilities. [225] It acknowledged some of its drawbacks, including battles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however noted that they must have been cherry-picked and might not [represent Sora's](https://gitea.scalz.cloud) common output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually [revealed](http://39.101.167.1953003) significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to produce reasonable video from text descriptions, mentioning its prospective to reinvent storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen 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 large dataset of diverse audio and is likewise a multi-task model that can perform multilingual speech recognition in addition to speech translation and language recognition. [229] +
Music generation
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MuseNet
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[Released](https://git.gilesmunn.com) 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 created by MuseNet tends to begin fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet mental thriller Ben [Drowned](http://45.45.238.983000) to create music for the titular character. [232] [233] +
Jukebox
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Released in 2020, Jukebox is an open-sourced algorithm to create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "show local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "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 excellent, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are appealing and sound legitimate". [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 debate toy issues in front of a human judge. The function is to research study whether such a method may assist in auditing [AI](https://www.vidconnect.cyou) decisions and in establishing explainable [AI](http://39.101.179.106:6440). [237] [238] +
Microscope
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Released in 2020, [fishtanklive.wiki](https://fishtanklive.wiki/User:KentonR156) Microscope [239] is a collection of visualizations of every substantial layer and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, different variations of Inception, and various versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that supplies a conversational interface that permits users to ask questions in natural language. The system then responds with a response within seconds.
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