From f73c5aba2eeba093952455e7350823853fc546ec Mon Sep 17 00:00:00 2001 From: madgetabor2870 Date: Fri, 30 May 2025 01:35:16 +0800 Subject: [PATCH] Update 'The Verge Stated It's Technologically Impressive' --- ...rge-Stated-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..aeb3586 --- /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 assist in the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](http://47.107.92.4:1234) research, making released research more quickly reproducible [24] [144] while offering users with an easy interface for interacting with these environments. In 2022, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:MilanCastro087) brand-new developments 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 knowing (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to solve single jobs. Gym Retro offers the ability to generalize in between games with [comparable ideas](http://120.55.164.2343000) but different looks.
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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, however are given the objectives of discovering to move and to press the opposing representative out of the ring. [148] Through this [adversarial learning](https://jollyday.club) process, the agents learn how to adapt to changing conditions. When a representative is then gotten rid of from this virtual environment and put in a brand-new virtual environment with high winds, the agent 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 between representatives might produce an intelligence "arms race" that could 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 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against human players at a high skill level entirely through trial-and-error [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) algorithms. Before ending up being a team of 5, the first public demonstration happened at The International 2017, the annual best champion tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of actual time, and that the knowing software was an action in the direction of producing software application that can deal with complex tasks like a cosmetic surgeon. [152] [153] The system uses a kind of support knowing, as the bots learn over time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent 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 were able to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert players, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 overall games in a four-day open online competitors, winning 99.4% of those video games. [165] +
OpenAI 5's systems in Dota 2's bot player shows the obstacles of [AI](https://www.gritalent.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown using deep reinforcement knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses device discovering to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It [discovers](https://www.dpfremovalnottingham.com) completely in [simulation utilizing](https://biiut.com) the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation issue by using domain randomization, a simulation approach which exposes the student to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB electronic cameras to allow the robot to control an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168] +
In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of creating gradually harder environments. ADR differs from manual domain randomization by not needing 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 new [AI](http://43.139.10.64:3000) models established by OpenAI" to let [developers](https://winf.dhsh.de) call on it for "any English language [AI](https://code.3err0.ru) job". [170] [171] +
Text generation
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The company has actually popularized generative pretrained transformers (GPT). [172] +
OpenAI's original GPT design ("GPT-1")
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The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his associates, and published in [preprint](https://careers.webdschool.com) on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world understanding and procedure long-range dependencies by pre-training on a varied 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 design and the successor to [OpenAI's initial](http://115.29.202.2468888) GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just minimal demonstrative versions at first released to the general public. The complete version of GPT-2 was not immediately launched due to issue about prospective abuse, consisting of applications for composing fake news. [174] Some professionals revealed uncertainty that GPT-2 posed a substantial threat.
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In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted 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 total version of the GPT-2 language design. [177] Several sites host interactive demonstrations of various 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 learners, [highlighted](https://se.mathematik.uni-marburg.de) by GPT-2 attaining cutting edge precision 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. 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 without supervision transformer [language](http://43.139.10.643000) design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models 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 might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer [learning](http://122.51.17.902000) between English and Romanian, and in between English and German. [184] +
GPT-3 significantly enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or encountering the essential capability constraints of predictive language designs. [187] Pre-training GPT-3 required 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 model](https://airsofttrader.co.nz) was not immediately released to the public for issues of possible abuse, although [OpenAI planned](https://git.yqfqzmy.monster) to enable 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 specifically to Microsoft. [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has actually additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://git.fandiyuan.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the design can create working code in over a lots shows languages, most effectively in Python. [192] +
Several problems with problems, style flaws and security vulnerabilities were pointed out. [195] [196] +
GitHub Copilot has actually been implicated of giving off copyrighted code, without any author attribution or license. [197] +
OpenAI revealed that they would [cease assistance](https://wikitravel.org) 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 innovation passed a simulated law school bar test with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise check out, examine or create up to 25,000 words of text, and write code in all major programming languages. [200] +
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has declined to reveal numerous technical details and statistics about GPT-4, such as the accurate size of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI announced and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision criteria, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the [Massive Multitask](https://git.joystreamstats.live) Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI released GPT-4o mini, a smaller 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, startups and designers seeking to automate services with [AI](https://gitea.namsoo-dev.com) agents. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, [wiki.whenparked.com](https://wiki.whenparked.com/User:OpalGmn46347349) which have actually been designed to take more time to believe about their actions, resulting in higher precision. These designs are especially reliable in science, coding, and [reasoning](https://dongochan.id.vn) 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, OpenAI revealed o3, the successor of the o1 reasoning model. OpenAI also revealed o3-mini, a [lighter](https://www.jobcheckinn.com) and quicker variation of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are checking 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 model is called o3 instead of o2 to prevent confusion with telecoms [services provider](https://music.michaelmknight.com) O2. [215] +
Deep research study
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Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It [leverages](https://www.bakicicepte.com) the abilities of OpenAI's o3 design to perform extensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching 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 design that is trained to [analyze](http://bolling-afb.rackons.com) the semantic resemblance between text and images. It can significantly be used for image category. [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 language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create [matching](http://8.140.229.2103000) images. It can produce pictures of realistic things ("a stained-glass window with a picture of a blue strawberry") as well as items that do not exist in reality ("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](https://snapfyn.com) DALL-E 2, an upgraded version of the model with more realistic outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new rudimentary system for transforming a text description into a 3-dimensional design. [220] +
DALL-E 3
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In September 2023, OpenAI announced DALL-E 3, a more [effective design](https://www.srapo.com) much better able to create images from intricate descriptions without manual timely engineering and render complicated details like hands and text. [221] It was [released](http://xrkorea.kr) to the general 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 on brief detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of [produced videos](https://boonbac.com) is unknown.
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Sora's development team called it after the Japanese word for "sky", to represent its "unlimited imaginative capacity". [223] Sora's technology 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 function, but did not reveal the number or the precise sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it could produce videos as much as one minute long. It also shared a technical report highlighting the techniques utilized to train the model, and the design's abilities. [225] It acknowledged a few of its imperfections, including battles replicating . [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but kept in mind that they must have been cherry-picked and may not [represent Sora's](http://okna-samara.com.ru) typical output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to generate sensible video from text descriptions, citing its possible to revolutionize storytelling and content production. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause prepare for broadening 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 also a multi-task design that can perform 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 produce tunes with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to begin fairly but then fall into mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the web psychological 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 to produce music with vocals. After [training](http://101.43.18.2243000) on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs tune samples. OpenAI mentioned the tunes "show local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" and that "there is a significant gap" in between Jukebox and human-generated music. The Verge mentioned "It's technically remarkable, even if the outcomes seem like mushy versions of tunes that might feel familiar", while Business Insider stated "surprisingly, a few of the resulting songs are memorable and sound genuine". [234] [235] [236] +
User 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](https://findschools.worldofdentistry.org) problems in front of a human judge. The function is to research whether such an approach might help in auditing [AI](http://118.195.226.124:9000) decisions and in establishing explainable [AI](http://xrkorea.kr). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and nerve cell of eight neural network models which are typically studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an artificial intelligence tool constructed on top of GPT-3 that provides a conversational interface that allows users to ask concerns in natural language. The system then responds with an answer within seconds.
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