commit 69fb97f599b0f16aa21071bb96407bc2b06ae8b4 Author: arliepoulin73 Date: Fri May 30 05:42:33 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..7c70278 --- /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 facilitate the development of support knowing [algorithms](https://sansaadhan.ipistisdemo.com). It aimed to standardize how environments are defined in [AI](http://www.ipbl.co.kr) research, making released research more quickly reproducible [24] [144] while supplying users with a basic interface for communicating with these environments. In 2022, new developments of Gym have actually 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 computer game [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to resolve single tasks. Gym Retro gives the ability to generalize between games with similar concepts but 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 understanding of how to even stroll, but are offered the objectives of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning procedure, the agents find out how to adjust to changing 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 found out how to [stabilize](https://tiktack.socialkhaleel.com) in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents could develop an intelligence "arms race" that could increase an agent's ability 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 learn to play against human players at a high ability level totally through trial-and-error algorithms. Before becoming a group of 5, the very first public presentation occurred at The International 2017, the annual best championship competition for the game, where Dendi, an [expert Ukrainian](https://gitea.ci.apside-top.fr) gamer, lost against a bot in a live individually 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, and that the knowing software application was a step in the instructions of producing software application that can handle complex tasks like a surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots discover over time by playing against themselves numerous 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 broadened to play together as a full group of 5, and they had the ability 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, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 total games in a [four-day](https://shankhent.com) open online competition, winning 99.4% of those video games. [165] +
OpenAI 5's mechanisms in Dota 2's bot player shows the obstacles of [AI](https://git.xiaoya360.com) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated the use of deep support learning (DRL) representatives to attain superhuman competence 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 control physical objects. [167] It learns entirely in simulation utilizing the same RL algorithms and [training](https://www.dynamicjobs.eu) code as OpenAI Five. OpenAI took on the item orientation problem by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having [movement tracking](https://tubechretien.com) electronic cameras, likewise has RGB cameras to permit the robot to manipulate an approximate object by seeing it. In 2018, OpenAI showed that the system was able to [manipulate](https://empregos.acheigrandevix.com.br) a cube and an octagonal prism. [168] +
In 2019, OpenAI showed that Dactyl could resolve a [Rubik's Cube](https://www.codple.com). The robot was able to fix 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 effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating progressively more hard [environments](http://120.55.59.896023). 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 new [AI](http://kacm.co.kr) models developed by OpenAI" to let developers call on it for "any English language [AI](https://trabaja.talendig.com) task". [170] [171] +
Text generation
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The business has actually popularized generative pretrained transformers (GPT). [172] +
OpenAI's original GPT model ("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 colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language might obtain world knowledge and process long-range dependences by pre-training on a varied 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 follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal [demonstrative variations](https://cv4job.benella.in) initially released to the general public. The complete variation of GPT-2 was not instantly launched due to concern about prospective abuse, including applications for writing fake news. [174] Some professionals expressed uncertainty that GPT-2 postured a substantial risk.
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In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned 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 difficult to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 [language](http://szfinest.com6060) model. [177] Several websites host interactive presentations of various circumstances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue unsupervised language designs to be general-purpose learners, shown by GPT-2 attaining cutting edge 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This allows 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](http://bingbinghome.top3001) design and the successor to GPT-2. [182] [183] [184] 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 complete version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were likewise trained). [186] +
OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and might generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184] +
GPT-3 considerably enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 required several 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 design was not instantly released to the general public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month free private beta that started 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 furthermore been [trained](https://www.thewaitersacademy.com) on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://chaakri.com) powering the code autocompletion tool GitHub [Copilot](https://www.xtrareal.tv). [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the model can create working code in over a dozen shows languages, the majority of efficiently in Python. [192] +
Several concerns with glitches, design defects and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has actually been accused of discharging copyrighted code, without any author attribution or license. [197] +
OpenAI announced 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), capable of [accepting text](http://43.139.182.871111) 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 could also check out, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:MauricioDoughert) evaluate or produce approximately 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 on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose numerous technical details and stats about GPT-4, such as the [accurate size](https://newtheories.info) of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision standards, 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 especially helpful for business, start-ups and designers seeking to automate services with [AI](https://signedsociety.com) representatives. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been created to take more time to consider their responses, resulting in higher precision. These models are especially effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, [yewiki.org](https://www.yewiki.org/User:EarnestineHiatt) o1-preview was changed by o1. [211] +
o3
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On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning design. OpenAI also unveiled o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are checking 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 instead of o2 to prevent confusion with telecoms services provider O2. [215] +
Deep research study
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Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of [OpenAI's](https://www.wikispiv.com) o3 design to perform extensive web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools enabled, 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 model that is trained to examine the semantic similarity in between text and images. It can especially 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 produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can develop pictures of realistic things ("a stained-glass window with an image of a blue strawberry") along with [objects](http://43.136.17.1423000) that do not exist in [reality](http://kodkod.kr) ("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 variation of the model with more sensible results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a 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 powerful design much better able to produce images from intricate descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched to the public as a ChatGPT Plus feature in October. [222] +
Text-to-video
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Sora
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Sora is a [text-to-video design](https://livy.biz) that can create videos based on brief detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The [optimum length](https://optimaplacement.com) of created videos is unknown.
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Sora's advancement team called it after the Japanese word for "sky", to symbolize its "unlimited innovative capacity". [223] Sora's technology is an adjustment of the innovation behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that purpose, but did not reveal the number or the precise sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could create videos as much as one minute long. It likewise shared a technical report highlighting the approaches utilized to train the model, and the design's capabilities. [225] It acknowledged a few of its imperfections, including struggles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however noted that they should have been cherry-picked and may not represent Sora's common output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to generate realistic video from text descriptions, mentioning its potential to transform storytelling and material creation. He said that his [excitement](http://git.papagostore.com) about Sora's possibilities was so strong that he had chosen to stop briefly prepare for broadening his Atlanta-based movie studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, [Whisper](http://195.58.37.180) is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of [varied audio](http://www.stardustpray.top30009) and is likewise a multi-task design that can carry out multilingual speech acknowledgment in addition to [speech translation](https://younivix.com) 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 predict subsequent [musical notes](https://www.loupanvideos.com) in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly however then fall under chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the internet psychological 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](https://git.vhdltool.com) music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and [outputs song](https://gitea.dokm.xyz) samples. OpenAI stated the songs "reveal regional musical coherence [and] follow standard chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" which "there is a considerable gap" between Jukebox and human-generated music. The Verge mentioned "It's technically excellent, even if the results seem like mushy variations of tunes that may feel familiar", while Business Insider stated "remarkably, a few of the resulting songs are memorable 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 devices to debate toy problems in front of a human judge. The function is to research study whether such an approach might help in auditing [AI](http://encocns.com:30001) choices and in establishing explainable [AI](https://gitea.linuxcode.net). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight neural network models which are typically studied in interpretability. [240] Microscope was developed to analyze the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:KristenSwartz09) different variations of Inception, and different variations of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an artificial intelligence tool built on top of GPT-3 that supplies a conversational user interface that enables users to ask questions in natural language. The system then reacts with a response within seconds.
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