commit 9ccd63a333433d15c94cd57a41dd5baa4381858f Author: dylandawe61621 Date: Thu Feb 13 08:11:02 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..d8dc5d8 --- /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 development of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](http://jolgoo.cn:3000) research, making published research study more quickly reproducible [24] [144] while supplying users with an [easy interface](https://meeting2up.it) for engaging with these environments. In 2022, new developments of Gym have been moved to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, [Gym Retro](http://39.106.177.1608756) is a platform for reinforcement learning (RL) research study on video games [147] utilizing [RL algorithms](https://www.cdlcruzdasalmas.com.br) and research study generalization. Prior RL research study focused mainly on enhancing representatives to resolve single jobs. Gym Retro provides the ability to [generalize](https://grace4djourney.com) in between games with comparable concepts however various appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have knowledge of how to even stroll, but are offered the objectives of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, recommending it had actually found out how to stabilize in a generalized method. [148] [149] [OpenAI's Igor](https://www.elcel.org) Mordatch argued that competitors in between agents might produce an intelligence "arms race" that might increase an agent'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 video game Dota 2, that find out to play against human gamers at a high skill level completely through trial-and-error algorithms. Before becoming a group of 5, the first public demonstration occurred at The International 2017, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:LindseyWalstab9) the yearly premiere championship competition for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of actual time, and that the knowing software application was an action in the direction of developing software that can manage complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of support learning, as the bots learn gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an [opponent](http://gitlab.zbqdy666.com) and taking map objectives. [154] [155] [156] +
By June 2018, the capability of the [bots broadened](https://thecodelab.online) to play together as a complete team of 5, and they had the ability to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The [International](http://47.56.181.303000) 2018, OpenAI Five played in 2 [exhibition matches](http://www.origtek.com2999) against expert gamers, but wound up losing both 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 exhibition match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those games. [165] +
OpenAI 5's systems in Dota 2's bot gamer shows the difficulties of [AI](https://dev.clikviewstorage.com) systems in [multiplayer online](https://code.in-planet.net) battle arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep reinforcement knowing (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 learns totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation problem by utilizing domain randomization, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MaurineMyers) a simulation approach which exposes the student to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, likewise has RGB cameras to enable the robot to control an arbitrary things by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robot had the ability to resolve 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 toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating gradually 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 announced a multi-purpose API which it said was "for accessing new [AI](http://g-friend.co.kr) designs established by OpenAI" to let developers call on it for "any English language [AI](https://centerdb.makorang.com) task". [170] [171] +
Text generation
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The business has promoted generative pretrained transformers (GPT). [172] +
[OpenAI's](https://bvbborussiadortmundfansclub.com) original GPT model ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language could obtain world understanding and [process long-range](http://121.40.209.823000) dependencies by pre-training on a varied corpus with long stretches of contiguous text.
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GPT-2
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Generative 2 ("GPT-2") is an unsupervised transformer language design and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just minimal demonstrative variations at first launched to the general public. The complete variation of GPT-2 was not instantly launched due to concern about prospective misuse, including applications for [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:Alexandria39G) writing fake news. [174] Some experts revealed uncertainty that GPT-2 postured a considerable risk.
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In [reaction](https://wiki.openwater.health) to GPT-2, the Allen [Institute](https://letsstartjob.com) for Artificial Intelligence reacted with a tool to discover "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned 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 released the complete version of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue not being watched language designs to be general-purpose learners, highlighted by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not additional trained on any task-specific input-output examples).
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The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both specific 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 model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million parameters were likewise trained). [186] +
OpenAI specified that GPT-3 prospered at certain "meta-learning" tasks and could generalize the purpose of a [single input-output](http://git.szchuanxia.cn) pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184] +
GPT-3 dramatically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or encountering the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous 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 right away launched to the general public for concerns of possible abuse, although OpenAI prepared to permit 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 certified exclusively 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://www.ayc.com.au) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the design can develop working code in over a dozen programs languages, the majority of successfully in Python. [192] +
Several concerns with problems, [style defects](http://gitlab.ds-s.cn30000) and security vulnerabilities were pointed out. [195] [196] +
GitHub Copilot has been accused of giving off copyrighted code, with no author attribution or license. [197] +
OpenAI revealed that they would terminate 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 updated technology passed a simulated law school bar examination with a rating 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 produce as much as 25,000 words of text, and write code in all major shows languages. [200] +
Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on [ChatGPT](http://47.103.91.16050903). [202] OpenAI has decreased to reveal various technical details and stats about GPT-4, such as the [exact size](https://orka.org.rs) of the model. [203] +
GPT-4o
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On May 13, 2024, [OpenAI revealed](http://66.85.76.1223000) and launched GPT-4o, which can process and [produce](https://gogs.2dz.fi) text, images and audio. [204] GPT-4o [attained modern](http://175.24.176.23000) lead to voice, multilingual, and vision criteria, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:AndyDana123) setting brand-new records in audio speech recognition 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 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 anticipates it to be particularly useful for business, startups and developers looking for to automate services with [AI](http://58.87.67.124:20080) agents. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been designed to take more time to consider their responses, resulting in higher precision. These models are particularly reliable in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [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 successor of the o1 reasoning model. OpenAI likewise unveiled o3-mini, a lighter and faster version of OpenAI o3. Since 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 researchers had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215] +
Deep research
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Deep research study is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform extensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it [reached](https://git.thetoc.net) an accuracy 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 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 design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can develop images of realistic items ("a stained-glass window with a picture of a blue strawberry") along with things that do not exist in [reality](https://git.valami.giize.com) ("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 design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new primary 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 generate images from complex descriptions without manual prompt engineering and render intricate 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 design that can generate videos based on short [detailed prompts](https://www.ndule.site) [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.
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Sora's development team named it after the Japanese word for "sky", to symbolize its "endless innovative potential". [223] Sora's technology is an adaptation of the innovation behind the DALL ยท E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that purpose, but did not reveal the number or the specific sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it could generate videos up to one minute long. It also shared a technical report highlighting the methods used to train the model, and the model's abilities. [225] It acknowledged some of its shortcomings, consisting of battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", however noted that they need to have been cherry-picked and may not represent Sora's [typical output](http://xn--950bz9nf3c8tlxibsy9a.com). [225] +
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to generate realistic video from text descriptions, citing its prospective to transform storytelling and content production. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause prepare for expanding 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 design. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model that can perform multilingual [speech recognition](https://adrian.copii.md) in addition to 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 forecast subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to start fairly but then fall into chaos 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 to produce 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 bit of lyrics and outputs song samples. OpenAI mentioned the tunes "show local musical coherence [and] follow standard chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" which "there is a significant gap" in between Jukebox and human-generated music. The Verge stated "It's technically impressive, even if the results seem like mushy versions of songs that might feel familiar", while Business Insider stated "surprisingly, a few of the resulting songs are memorable and sound legitimate". [234] [235] [236] +
User user interfaces
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Debate Game
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In 2018, OpenAI introduced the Debate Game, which teaches devices to discuss toy issues in front of a human judge. The function is to research study whether such a method might assist in auditing [AI](https://palkwall.com) choices and in [establishing explainable](https://lastpiece.co.kr) [AI](https://www.paknaukris.pro). [237] [238] +
Microscope
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[Released](https://losangelesgalaxyfansclub.com) in 2020, Microscope [239] is a collection of visualizations of every substantial layer and [nerve cell](https://crmthebespoke.a1professionals.net) of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various variations of Inception, and different variations of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an expert system tool constructed on top of GPT-3 that offers a conversational user interface that allows users to ask questions in natural language. The system then responds with a response within seconds.
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