From f9c92ffdbf98a512932f27cb019ad96672e67f95 Mon Sep 17 00:00:00 2001 From: Bruce Baldridge Date: Fri, 7 Feb 2025 13:50:07 +0000 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..8d38433 --- /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](https://gitlab.amatasys.jp) to help with the development of support learning algorithms. It aimed to standardize how environments are specified in [AI](http://famedoot.in) research, making published research study more easily reproducible [24] [144] while supplying users with a basic interface for engaging with these environments. In 2022, new advancements of Gym have actually been transferred to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for support learning (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing agents to resolve single tasks. Gym Retro gives the capability to generalize between video games with similar concepts however different appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack knowledge of how to even walk, however are offered the objectives of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adapt to changing conditions. When an agent is then eliminated 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 between [representatives](https://vcanhire.com) could develop an intelligence "arms race" that might increase an agent's capability to operate even outside the context of the competitors. [148] +
OpenAI 5
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OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against human players at a high ability level entirely through trial-and-error algorithms. Before ending up being a group of 5, the very first public demonstration took place at The International 2017, the annual premiere champion tournament for the video game, where Dendi, a [professional Ukrainian](https://cozwo.com) player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of real time, which the knowing software was a step in the instructions of creating software application that can handle intricate jobs like a [surgeon](https://ayjmultiservices.com). [152] [153] The system uses a type of support knowing, as the bots discover in time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156] +
By June 2018, the capability of the bots broadened to play together as a full team of 5, and they were able to [defeat teams](https://jobidream.com) of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat 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 look came later on that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games. [165] +
OpenAI 5's mechanisms in Dota 2's bot gamer shows the difficulties of [AI](http://170.187.182.121:3000) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown making use of deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 [matches](https://gitea.nongnghiepso.com). [166] +
Dactyl
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Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It discovers completely in simulation using the exact same RL algorithms and [training code](https://saksa.co.za) as OpenAI Five. OpenAI tackled the object orientation issue by [utilizing](http://148.66.10.103000) 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 motion tracking electronic cameras, also has RGB video cameras to permit the robot to control an approximate things by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168] +
In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of generating gradually harder environments. ADR varies from manual domain randomization by not requiring a human to define randomization ranges. [169] +
API
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In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](https://rna.link) designs developed by OpenAI" to let developers contact it for "any English language [AI](https://joydil.com) task". [170] [171] +
Text generation
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The company has promoted generative pretrained transformers (GPT). [172] +
OpenAI's original GPT model ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a [generative design](https://git.andy.lgbt) of language could obtain world understanding and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:KarolynShanahan) procedure long-range dependences 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 an unsupervised transformer language design and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions initially launched to the public. The complete variation of GPT-2 was not instantly launched due to concern about potential misuse, including applications for [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:DomingaEspinoza) writing fake news. [174] Some professionals expressed uncertainty that GPT-2 posed a considerable risk.
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In response 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 completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language model. [177] Several sites host interactive presentations of various instances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue without supervision language [designs](https://ifairy.world) to be general-purpose students, shown by GPT-2 attaining modern accuracy and perplexity 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain [concerns encoding](http://1.14.125.63000) vocabulary with word tokens by utilizing byte pair encoding. This permits 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 to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion specifications, [184] 2 orders of [magnitude larger](https://git.cloud.krotovic.com) than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were likewise trained). [186] +
OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and could generalize the function 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 dramatically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or [encountering](http://kuzeydogu.ogo.org.tr) the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately launched to the public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month totally [free private](https://asicwiki.org) beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed exclusively 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://pittsburghtribune.org) on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://lokilocker.com) powering the code autocompletion tool [GitHub Copilot](https://brightworks.com.sg). [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 effectively in Python. [192] +
Several concerns with problems, [design flaws](https://media.motorsync.co.uk) and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has actually been implicated of giving off 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), efficient in accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar exam with a rating around the [leading](https://git.whitedwarf.me) 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 as much as 25,000 words of text, and compose code in all significant programs languages. [200] +
Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to [expose numerous](https://git.joystreamstats.live) 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 released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art lead to 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 (MMLU) criteria compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI released GPT-4o mini, a smaller 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 useful for business, startups and designers seeking to automate services with [AI](http://120.79.157.137) agents. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been designed to take more time to think of their actions, resulting in greater accuracy. These models are particularly efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211] +
o3
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On December 20, 2024, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MacFalls93386606) OpenAI unveiled o3, the follower of the o1 reasoning model. OpenAI also revealed o3-mini, a lighter and of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing 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 services company O2. [215] +
Deep research study
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Deep research is a representative developed by OpenAI, unveiled 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 browsing and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120] +
Image category
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CLIP
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Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is [trained](http://git.agentum.beget.tech) to evaluate the semantic similarity in between text and images. It can notably 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](https://git.starve.space) language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can develop pictures of reasonable objects ("a stained-glass window with a picture of a blue strawberry") along with objects that do not exist in [reality](http://35.207.205.183000) ("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 announced DALL-E 2, an updated version of the design with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new rudimentary system for transforming a text description into a 3[-dimensional](https://whotube.great-site.net) model. [220] +
DALL-E 3
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In September 2023, OpenAI announced DALL-E 3, a more effective design better able to produce images from complicated descriptions without manual timely 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 create videos based upon short detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of created videos is unknown.
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Sora's advancement group called it after the Japanese word for "sky", to symbolize its "unlimited innovative potential". [223] [Sora's innovation](https://www.meetgr.com) 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 precise sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it might generate videos as much as one minute long. It likewise shared a technical report highlighting the approaches used to train the model, [ratemywifey.com](https://ratemywifey.com/author/christenaw4/) and the model's abilities. [225] It acknowledged a few of its drawbacks, consisting of struggles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they should have been cherry-picked and might not represent Sora's common output. [225] +
Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have revealed significant interest in the technology's capacity. In an interview, actor/[filmmaker Tyler](http://vk-mix.ru) Perry expressed his astonishment at the innovation's ability to create [reasonable](https://www.hyxjzh.cn13000) video from text descriptions, mentioning its potential to change storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause plans 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 recognition design. [228] It is [trained](http://120.26.108.2399188) on a big dataset of diverse audio and is also a multi-task model that can carry out multilingual speech acknowledgment as well as 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 anticipate subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a tune produced by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized 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 [produce music](https://linkin.commoners.in) 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 specified the songs "reveal regional musical coherence [and] follow conventional chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" which "there is a considerable space" between Jukebox and human-generated music. The Verge specified "It's technically remarkable, even if the outcomes sound like mushy variations of tunes that might feel familiar", while Business Insider mentioned "remarkably, some of the resulting tunes are catchy and sound genuine". [234] [235] [236] +
User user interfaces
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Debate Game
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In 2018, OpenAI released the Debate Game, which teaches makers to discuss toy issues in front of a human judge. The function is to research whether such a method might help in auditing [AI](https://projob.co.il) [choices](http://gitlab.dstsoft.net) and in establishing explainable [AI](https://candays.com). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of eight neural network designs which are frequently studied in interpretability. [240] Microscope was developed to examine the features that form inside these neural networks easily. The models included are AlexNet, VGG-19, different versions of Inception, and various versions 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 enables users to ask questions in natural language. The system then reacts with an answer within seconds.
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