From 413aaf0ae96781b8691c0f09cd86ceabc9e2a369 Mon Sep 17 00:00:00 2001 From: hermelindabril Date: Thu, 27 Feb 2025 14:04:42 +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..140d1ea --- /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 assist in the development of support knowing algorithms. It aimed to standardize how environments are defined in [AI](https://git.thomasballantine.com) research, making published research more easily reproducible [24] [144] while providing users with a basic interface for communicating with these environments. In 2022, brand-new developments 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 support knowing (RL) research on video games [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to solve single tasks. Gym Retro offers the capability to generalize in between video games with comparable ideas but various looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where [humanoid metalearning](https://just-entry.com) [robot representatives](https://theboss.wesupportrajini.com) at first do not have understanding of how to even stroll, but are provided the goals of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the [agents learn](http://60.209.125.23820010) how to adapt to altering conditions. When a representative is then removed from this virtual environment and placed in a new [virtual environment](http://hmkjgit.huamar.com) with high winds, the agent braces to remain upright, recommending it had learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could create an intelligence "arms race" that could increase an agent's ability to operate even outside the context of the competition. [148] +
OpenAI 5
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OpenAI Five is a group of 5 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 ability level completely through experimental algorithms. Before ending up being a group of 5, the very first public demonstration occurred at The International 2017, the annual premiere [championship competition](https://jobs.ahaconsultant.co.in) for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of actual time, which the knowing software was a step in the instructions of producing software application that can deal with complicated tasks like a cosmetic [surgeon](https://remnantstreet.com). [152] [153] The system utilizes a form of [support](http://christianpedia.com) learning, as the bots learn with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives. [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 defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 [exhibit matches](https://gitea.lihaink.cn) against expert players, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 overall [video games](https://www.tippy-t.com) in a four-day open online competition, winning 99.4% of those video games. [165] +
OpenAI 5's systems in Dota 2's bot gamer [reveals](https://hankukenergy.kr) the challenges of [AI](https://opedge.com) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown using deep reinforcement knowing (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, maker discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It finds out entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by using domain randomization, a simulation approach which exposes the student to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:MurrayAuricht37) aside from having movement tracking cams, also has RGB [video cameras](https://39.98.119.14) to permit the robot to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl could [resolve](https://elsingoteo.com) 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](http://repo.fusi24.com3000) that is harder to model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of generating gradually more hard environments. ADR differs from manual domain randomization by not requiring a human to define randomization [varieties](http://47.75.109.82). [169] +
API
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In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://git.magicvoidpointers.com) models established by OpenAI" to let developers contact it for "any English language [AI](http://47.107.92.4:1234) job". [170] [171] +
Text generation
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The business has actually promoted generative pretrained transformers (GPT). [172] +
OpenAI's initial [GPT model](http://christianpedia.com) ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language might obtain world understanding and procedure long-range reliances 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 without supervision transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions initially launched to the general public. The full variation of GPT-2 was not instantly released due to issue about potential misuse, including applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 presented a significant risk.
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In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted of "the innovation 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 released](https://git.opskube.com) the complete variation of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other [transformer designs](https://git.caraus.tech). [178] [179] [180] +
GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining state-of-the-art 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 at least 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both individual characters and [multiple-character](https://playvideoo.com) 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 follower to GPT-2. [182] [183] [184] [OpenAI mentioned](https://www.ontheballpersonnel.com.au) that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186] +
OpenAI stated that GPT-3 was successful at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 [release paper](https://posthaos.ru) provided examples of translation and cross-linguistic transfer learning between English and Romanian, and in between [English](https://wathelp.com) and German. [184] +
GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or encountering the essential capability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, 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 immediately released to the public for concerns of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month totally free personal beta that began in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was [certified exclusively](http://124.222.48.2033000) to Microsoft. [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](https://raovatonline.org) powering the code autocompletion 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 lots shows languages, a lot of efficiently in Python. [192] +
Several issues with glitches, style flaws and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has actually been implicated of emitting copyrighted code, with no author attribution or license. [197] +
OpenAI announced that they would stop 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), capable of accepting text or image inputs. [199] They revealed that the upgraded innovation passed a simulated law school bar test with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also check out, examine or generate as much as 25,000 words of text, and compose code in all major shows languages. [200] +
Observers reported that the model 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 revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal numerous technical details and stats about GPT-4, such as the accurate size of the model. [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 cutting edge lead to voice, multilingual, and vision benchmarks, setting brand-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 anticipates it to be particularly beneficial for enterprises, startups and designers looking for to automate services with [AI](http://rackons.com) 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 about their responses, resulting in greater [accuracy](https://forum.petstory.ge). These models are especially effective in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211] +
o3
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On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning model. OpenAI also revealed o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The design is called o3 rather than o2 to avoid [confusion](https://rna.link) with telecommunications providers O2. [215] +
Deep research study
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Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the [capabilities](http://128.199.125.933000) of [OpenAI's](https://git.phyllo.me) o3 design to carry out substantial web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty 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 design that is trained to analyze the semantic similarity 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 design that develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can develop pictures of [reasonable objects](http://apps.iwmbd.com) ("a stained-glass window with a picture of a blue strawberry") along with 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, OpenAI revealed DALL-E 2, an updated variation of the design with more practical outcomes. [219] In December 2022, OpenAI published on [GitHub software](https://busanmkt.com) 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 announced DALL-E 3, a more [powerful design](https://lifeinsuranceacademy.org) better able to create images from intricate 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](http://203.171.20.943000) 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 on brief detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 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 signify its "limitless creative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos accredited 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 general public on February 15, 2024, specifying that it could generate videos as much as one minute long. It likewise shared a technical report highlighting the techniques used to train the design, and the design's abilities. [225] It acknowledged a few of its shortcomings, including battles mimicing complicated 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](https://www.jobsires.com) following Sora's public demonstration, notable entertainment-industry figures have shown substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to generate reasonable video from text descriptions, mentioning its possible to revolutionize storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had actually decided to stop briefly plans for broadening his Atlanta-based motion picture studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:KevinChu177) Whisper is a general-purpose speech recognition model. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model that can perform multilingual speech recognition 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 predict subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to begin fairly however then fall under mayhem 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 create music for the titular character. [232] [233] +
Jukebox
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[Released](http://220.134.104.928088) in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system [accepts](https://platform.giftedsoulsent.com) a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI mentioned the songs "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a significant gap" in between Jukebox and human-generated music. The Verge mentioned "It's technologically remarkable, even if the results sound like mushy versions of tunes that may feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are appealing 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 devices to discuss toy issues in front of a human judge. The purpose is to research study whether such an approach might assist in auditing [AI](https://www.empireofember.com) decisions and in developing explainable [AI](https://platform.giftedsoulsent.com). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network models which are often studied in interpretability. [240] Microscope was developed to examine the functions that form inside these [neural networks](https://wakeuptaylor.boardhost.com) easily. The designs included are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that provides a conversational interface that permits users to ask questions in natural language. The system then responds with an answer within seconds.
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