From 6ebe1841504c2a01ebd6c77c37a38e668429d7c2 Mon Sep 17 00:00:00 2001 From: Anne Fox Date: Fri, 21 Feb 2025 02:34:27 +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..21a9b69 --- /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 help with the advancement of support learning algorithms. It aimed to standardize how environments are defined in [AI](https://coverzen.co.zw) research, making released research more easily reproducible [24] [144] while providing users with a basic interface for interacting with these environments. In 2022, brand-new developments of Gym have been moved to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on computer game [147] using [RL algorithms](http://47.122.66.12910300) and study generalization. Prior RL research study focused mainly on optimizing representatives to solve single jobs. Gym Retro offers the capability to generalize between video games with comparable principles but different appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack understanding of how to even stroll, but are provided the goals of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents learn how to adjust to altering conditions. When an agent is then eliminated from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives might develop an intelligence "arms race" that might increase an agent's capability to function even outside the [context](http://shammahglobalplacements.com) of the competitors. [148] +
OpenAI 5
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OpenAI Five is a team of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that learn to play against human gamers at a high skill level completely through trial-and-error algorithms. Before ending up being a team of 5, the first public presentation occurred at The International 2017, the yearly best [championship competition](https://jr.coderstrust.global) for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for two weeks of genuine time, and that the learning software was an action in the instructions of creating software application that can handle intricate tasks like a surgeon. [152] [153] The system uses a kind of reinforcement learning, as the bots discover gradually by playing against themselves hundreds of 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 ability of the bots expanded to play together as a full group of 5, and they had the ability to [beat teams](https://git.panggame.com) of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those video games. [165] +
OpenAI 5's mechanisms in Dota 2's bot player shows the obstacles of [AI](http://47.121.121.137:6002) systems in multiplayer online battle arena (MOBA) [video games](http://117.50.100.23410080) and how OpenAI Five has shown 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 [machine finding](https://www.ahhand.com) out to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It finds out totally in simulation utilizing the same RL algorithms and [training code](http://gitlab.mints-id.com) as OpenAI Five. OpenAI took on the [item orientation](https://sapjobsindia.com) issue by utilizing domain randomization, a simulation approach which [exposes](http://dasaram.com) the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking electronic cameras, likewise has RGB cameras to permit the robot to control an arbitrary item 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 demonstrated that Dactyl could resolve a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating 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 new [AI](https://git.xaviermaso.com) models established by OpenAI" to let designers get in touch with it for "any English language [AI](https://electroplatingjobs.in) job". [170] [171] +
Text generation
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The business has actually promoted 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 coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language could obtain world knowledge and process long-range reliances by pre-training on a diverse corpus with long stretches of adjoining 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 original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions at first [launched](https://platform.giftedsoulsent.com) to the public. The full variation of GPT-2 was not immediately released due to concern about possible misuse, consisting of applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 positioned a substantial danger.
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In [reaction](http://anggrek.aplikasi.web.id3000) to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural phony news". [175] Other scientists, such as Jeremy Howard, cautioned of "the technology to completely 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 launched the total variation 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 models to be general-purpose students, shown by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (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 a minimum of 3 [upvotes](https://gitlabdemo.zhongliangong.com). It avoids certain concerns 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 a not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full 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" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184] +
GPT-3 [considerably improved](http://8.129.8.58) benchmark outcomes over GPT-2. OpenAI cautioned that such [scaling-up](https://cn.wejob.info) of language designs could be approaching or encountering the essential capability constraints of predictive language models. [187] [Pre-training](http://118.190.145.2173000) GPT-3 needed numerous thousand petaflop/s-days [b] of compute, 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 instantly released to the general public for issues 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](https://git.logicloop.io) 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 on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://cristianoronaldoclub.com) powering the [code autocompletion](http://47.121.132.113000) tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can create working code in over a dozen programs languages, the majority of effectively in Python. [192] +
Several concerns with glitches, style defects and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has been accused of emitting copyrighted code, with no author attribution or license. [197] +
OpenAI announced that they would stop support 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), capable of accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar exam with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise read, analyze or create approximately 25,000 words of text, and compose code in all major programming languages. [200] +
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to expose different 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](https://udyogseba.com) and create text, images and audio. [204] GPT-4o attained modern 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) standard compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI launched 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 especially useful for enterprises, start-ups and developers looking for to automate services with [AI](https://9miao.fun:6839) representatives. [208] +
o1
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On September 12, 2024, [OpenAI launched](https://nytia.org) the o1-preview and o1-mini models, which have been developed to take more time to think about their reactions, resulting in greater precision. These models are particularly reliable in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was changed by o1. [211] +
o3
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On December 20, 2024, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:LindaIsenberg91) OpenAI unveiled o3, the successor of the o1 reasoning model. OpenAI also unveiled o3-mini, a [lighter](http://221.238.85.747000) and [quicker](http://139.224.213.43000) version of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215] +
Deep research study
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Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out substantial web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, 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 examine the semantic resemblance 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 develops 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 purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can create images of realistic items ("a stained-glass window with a picture of a blue strawberry") as well as things that do not exist in [reality](http://124.70.58.2093000) ("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 upgraded variation of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new basic system for transforming a text description into a 3-dimensional model. [220] +
DALL-E 3
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In September 2023, OpenAI revealed DALL-E 3, a more powerful model much better able to create images from intricate descriptions without manual timely engineering and render complex 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 model that can produce videos based upon brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.
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Sora's advancement team named it after the Japanese word for "sky", to symbolize its "endless imaginative 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 utilizing publicly-available videos as well as copyrighted videos certified for that purpose, however did not reveal 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 create videos approximately one minute long. It also shared a technical report highlighting the methods used to train the model, and the design's capabilities. [225] It acknowledged a few of its imperfections, including struggles mimicing complicated physics. [226] Will [Douglas Heaven](https://vibefor.fun) of the MIT Technology Review called the presentation videos "outstanding", however noted that they should have been cherry-picked and may not represent Sora's normal output. [225] +
Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry figures have actually shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to create reasonable video from text descriptions, citing its potential to transform storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause plans for expanding his Atlanta-based motion picture 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 design that can carry out multilingual speech recognition along with speech translation and language recognition. [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 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 into mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the internet mental thriller Ben Drowned to develop music for the titular character. [232] [233] +
Jukebox
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Released in 2020, Jukebox is an [open-sourced algorithm](http://203.171.20.943000) to music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs tune [samples](https://gogs.zhongzhongtech.com). OpenAI stated the tunes "reveal regional musical coherence [and] follow standard chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" and that "there is a considerable gap" between Jukebox and human-generated music. The Verge stated "It's technologically outstanding, even if the results sound like mushy variations of tunes that may feel familiar", while Business Insider mentioned "remarkably, a few of the resulting songs are appealing and sound legitimate". [234] [235] [236] +
User interfaces
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Debate Game
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In 2018, OpenAI launched the Debate Game, which teaches devices to debate toy problems in front of a human judge. The purpose is to research whether such an approach might help in auditing [AI](https://moontube.goodcoderz.com) decisions and in establishing explainable [AI](http://mao2000.com:3000). [237] [238] +
Microscope
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Released in 2020, [Microscope](https://www.srapo.com) [239] is a collection of visualizations of every substantial layer and neuron of eight [neural network](https://justhired.co.in) models which are typically studied in interpretability. [240] Microscope was developed to analyze the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various versions of Inception, and different versions of CLIP Resnet. [241] +
ChatGPT
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[Launched](http://212.64.10.1627030) in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that provides a conversational user interface that allows users to ask concerns in natural language. The system then responds with a response within seconds.
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