commit d5530633aa6bb174f4846788fb6cc01aacdd2264 Author: eleanorem24143 Date: Fri Feb 7 01:57:55 2025 +0000 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..b686d4f --- /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://git.tool.dwoodauto.com) library [developed](https://job.iwok.vn) to assist in the development of support knowing algorithms. It aimed to how environments are defined in [AI](https://git.gocasts.ir) research, making published research more quickly reproducible [24] [144] while [supplying](https://rsh-recruitment.nl) users with an easy interface for engaging with these environments. In 2022, [brand-new developments](http://codaip.co.kr) of Gym have been transferred to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to resolve single tasks. Gym Retro offers the ability to generalize between 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 knowledge of how to even stroll, but are given the goals of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents discover how to adjust to changing conditions. When a representative is then gotten rid of from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, recommending it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives could produce 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 team 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 entirely through [trial-and-error algorithms](http://xn--950bz9nf3c8tlxibsy9a.com). Before becoming a group of 5, the first public demonstration occurred at The International 2017, the yearly best championship competition for the game, where Dendi, an [expert Ukrainian](https://moontube.goodcoderz.com) gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for two weeks of actual time, which the learning software application was a step in the direction of producing software application that can deal with intricate jobs like a surgeon. [152] [153] The system utilizes a form of support learning, as the bots discover in time by playing against themselves hundreds of 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 complete team of 5, and they were able to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later 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 mechanisms](http://metis.lti.cs.cmu.edu8023) in Dota 2's bot player shows the challenges of [AI](http://122.112.209.52) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually shown using deep support knowing (DRL) agents to attain superhuman proficiency in Dota 2 [matches](https://playvideoo.com). [166] +
Dactyl
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Developed in 2018, Dactyl utilizes maker discovering to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It learns entirely in simulation utilizing the very same RL algorithms and [training](https://cloudsound.ideiasinternet.com) code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB cameras to enable the robot to manipulate an arbitrary object by seeing it. In 2018, OpenAI revealed that the system had the ability 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 fix the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing gradually harder environments. ADR varies 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](https://gayplatform.de) designs developed by OpenAI" to let developers contact it for "any English language [AI](http://kandan.net) task". [170] [171] +
Text generation
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The business has actually 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 model was composed by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language might obtain world understanding and process long-range reliances by pre-training on a diverse 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](https://gitea.fcliu.net) language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative variations at first released to the general public. The full version of GPT-2 was not right away launched due to issue about prospective misuse, including applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 postured a considerable risk.
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In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, cautioned of "the technology 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 released the total variation of the GPT-2 language design. [177] Several sites host interactive presentations of various [instances](https://wiki.vifm.info) of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue unsupervised language designs to be general-purpose students, shown by GPT-2 attaining cutting edge [precision](https://git.danomer.com) and perplexity on 7 of 8 zero-shot jobs (i.e. the design 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](https://www.oemautomation.com8888) of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain problems 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 design and the successor [fishtanklive.wiki](https://fishtanklive.wiki/User:GiuseppeXve) to GPT-2. [182] [183] [184] OpenAI specified that the full 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 few as 125 million criteria were also trained). [186] +
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184] +
GPT-3 considerably enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or encountering the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous 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 design was not immediately [launched](https://www.so-open.com) 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 free personal beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was certified exclusively to [Microsoft](https://nusalancer.netnation.my.id). [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://www.vmeste-so-vsemi.ru) [powering](https://abileneguntrader.com) the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can [produce](https://git.chir.rs) working code in over a dozen programs languages, many successfully in Python. [192] +
Several issues with glitches, style flaws and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has been accused of emitting copyrighted code, [wavedream.wiki](https://wavedream.wiki/index.php/User:KarlBeardsley7) with no author attribution or license. [197] +
OpenAI revealed that they would terminate assistance for Codex API on March 23, 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 revealed that the updated technology passed a simulated law school bar test with a rating around the top 10% of [test takers](http://47.116.130.49). (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, examine or generate up to 25,000 words of text, and write code in all significant programs languages. [200] +
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to expose various technical details and data about GPT-4, such as the exact size of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller 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 anticipates it to be particularly helpful for business, start-ups and developers looking for to automate services with [AI](http://getthejob.ma) representatives. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been developed to take more time to consider their actions, resulting in higher precision. These designs are especially reliable in science, coding, and reasoning jobs, 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, [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:AnkeStarnes867) OpenAI revealed o3, the successor of the o1 reasoning design. OpenAI likewise revealed o3-mini, a lighter and much 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, safety and security researchers had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecoms services company O2. [215] +
Deep research
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Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out extensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it reached 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 evaluate the [semantic resemblance](https://git.bwnetwork.us) 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 develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can produce images of [realistic objects](https://careers.mycareconcierge.com) ("a stained-glass window with a picture of a blue strawberry") in addition to things 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, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:GLXKatrice) OpenAI announced DALL-E 2, an updated version of the model 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 revealed DALL-E 3, a more effective model much better able to generate images from complex descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched to the general 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 that can generate videos based on short detailed triggers [223] along with [extend existing](http://www.boutique.maxisujets.net) videos forwards or backwards in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.
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Sora's development group called it after the Japanese word for "sky", to represent its "limitless innovative potential". [223] Sora's technology is an adaptation of the technology behind the DALL ยท E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos certified for that purpose, but did not expose the number or the specific sources of the videos. [223] +
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it might produce videos as much as one minute long. It likewise shared a technical report highlighting the methods utilized to train the design, and the design's abilities. [225] It acknowledged some of its shortcomings, consisting of battles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however noted that they need to have been cherry-picked and may not represent Sora's typical output. [225] +
Despite uncertainty from some academic leaders following Sora's public demonstration, notable entertainment-industry figures have shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to create realistic video from text descriptions, citing its possible to change storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause strategies 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 model. [228] It is [trained](https://nsproservices.co.uk) on a big dataset of varied audio and is also a multi-task model that can perform multilingual speech recognition in addition to speech translation and language identification. [229] +
Music generation
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MuseNet
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Released in 2019, [gratisafhalen.be](https://gratisafhalen.be/author/dulcie01x5/) MuseNet is a deep neural net trained to predict subsequent musical notes in [MIDI music](https://watch-wiki.org) files. It can create songs with 10 instruments in 15 designs. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall into turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were used 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 create 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 mentioned the songs "show local musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" and that "there is a considerable space" between Jukebox and [human-generated music](http://www.shopmento.net). The Verge specified "It's highly excellent, even if the results sound like mushy versions of songs that may feel familiar", while [Business Insider](https://www.sewosoft.de) stated "surprisingly, a few of the resulting tunes are catchy and sound genuine". [234] [235] [236] +
Interface
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Debate Game
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In 2018, OpenAI released the Debate Game, which teaches makers to discuss toy problems in front of a [human judge](https://git.k8sutv.it.ntnu.no). The function is to research whether such a technique might assist in auditing [AI](https://www.hijob.ca) choices and in developing explainable [AI](https://git.j4nis05.ch). [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 8 neural network models which are frequently studied in interpretability. [240] Microscope was [produced](https://signedsociety.com) to [analyze](https://ransomware.design) the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, various variations of Inception, and different versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that offers a conversational interface that allows users to ask questions in natural language. The system then responds with a response within seconds.
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