From 1400a7554b8df46a0e51f0bf4569e4ec8be2190c Mon Sep 17 00:00:00 2001 From: utecarpentier Date: Thu, 29 May 2025 09:56:44 +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..e08280d --- /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 reinforcement knowing algorithms. It aimed to standardize how [environments](http://gitlab.lvxingqiche.com) are specified in [AI](https://git.collincahill.dev) research, making published research study more easily reproducible [24] [144] while offering users with a basic interface for connecting with these environments. In 2022, brand-new advancements of Gym have actually been moved 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 on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on enhancing representatives to [solve single](https://zkml-hub.arml.io) tasks. Gym Retro provides the ability to generalize between video games with [comparable concepts](https://superappsocial.com) 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 at first do not have knowledge of how to even stroll, but are given the objectives of [learning](http://thegrainfather.com) to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives discover how to adjust to altering conditions. When an agent is then gotten rid of from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had found out how to balance in a generalized method. [148] [149] OpenAI's Igor [Mordatch argued](http://118.195.204.2528080) that competitors in between agents could produce an intelligence "arms race" that could increase an agent's capability to work even outside the context of the competitors. [148] +
OpenAI 5
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OpenAI Five is a team of five OpenAI-curated bots [utilized](https://git.biosens.rs) 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 experimental algorithms. Before becoming a group of 5, the very first public presentation happened at The International 2017, the yearly best championship competition for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for two weeks of [genuine](https://www.arztstellen.com) time, and that the knowing software was an action in the instructions of producing software application that can deal with complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots find out in time by playing against themselves numerous times a day for months, and are rewarded for actions such as [eliminating](https://code.dev.beejee.org) 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 beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against [professional](https://district-jobs.com) gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the video game at the time, 2:0 in a 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 video games. [165] +
OpenAI 5's systems in Dota 2's bot gamer shows the difficulties of [AI](https://winf.dhsh.de) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated making use of [deep support](https://video.igor-kostelac.com) learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It learns completely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by using domain randomization, a simulation method which [exposes](https://jobs.ahaconsultant.co.in) the [learner](http://www.becausetravis.com) to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB electronic cameras to enable the robotic to manipulate an approximate item by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate [physics](https://subemultimedia.com) that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating gradually harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169] +
API
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In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://talentup.asia) models established by OpenAI" to let developers call on it for "any English language [AI](https://www.lshserver.com:3000) job". [170] [171] +
Text generation
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The business has actually promoted generative pretrained transformers (GPT). [172] +
OpenAI's [initial GPT](https://www.yanyikele.com) model ("GPT-1")
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The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language could obtain world understanding and process 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 initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative variations initially released to the public. The full version of GPT-2 was not instantly launched due to concern about prospective misuse, including applications for composing fake news. [174] Some specialists revealed uncertainty that GPT-2 positioned a considerable threat.
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the technology to absolutely 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 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, illustrated by GPT-2 attaining state-of-the-art precision 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 a minimum of 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by [encoding](http://hjl.me) both private 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](http://lifethelife.com) language design and the successor to GPT-2. [182] [183] [184] OpenAI stated 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 couple of as 125 million [specifications](https://git.jamarketingllc.com) were likewise trained). [186] +
OpenAI specified that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning 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 designs might be approaching or encountering the basic capability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the general public for issues of possible abuse, although OpenAI prepared 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 licensed solely to Microsoft. [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://git.teygaming.com) 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 programming languages, a lot of successfully in Python. [192] +
Several problems with problems, style defects and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has actually been implicated of releasing copyrighted code, with no author attribution or license. [197] +
OpenAI revealed that they would discontinue 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), efficient in [accepting text](https://repo.farce.de) or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar examination 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, evaluate or generate up to 25,000 words of text, and write code in all major shows languages. [200] +
Observers reported that the version of ChatGPT utilizing GPT-4 was an [improvement](https://recruitment.transportknockout.com) on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems with earlier revisions. [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 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 create 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](https://git.jerl.dev) and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized 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 expects it to be especially useful for business, startups and designers looking for to automate services with [AI](http://www.xn--9m1b66aq3oyvjvmate.com) representatives. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been designed to take more time to think about their reactions, leading to greater accuracy. These models are especially effective in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211] +
o3
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On December 20, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:GalenSchoenheime) 2024, OpenAI revealed o3, the follower of the o1 reasoning design. OpenAI also revealed o3-mini, a lighter and faster version of OpenAI o3. Since 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, security and security researchers had the opportunity to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecommunications services supplier 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 model to perform substantial web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120] +
Image classification
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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 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 analyze natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can develop images of realistic things ("a stained-glass window with a picture of a blue strawberry") as well as things that do not exist in reality ("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 upgraded variation of the model with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new basic system for converting 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 model 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 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 generate videos based upon brief detailed prompts [223] along with [extend existing](https://git.teygaming.com) videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unknown.
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Sora's advancement group called it after the Japanese word for "sky", to [symbolize](https://jobsite.hu) its "endless creative potential". [223] Sora's technology is an adaptation of the technology behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos licensed for that function, but 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, mentioning that it could generate videos approximately one minute long. It likewise shared a technical report highlighting the methods used to train the design, and the model's capabilities. [225] It acknowledged a few of its imperfections, [consisting](https://git.cloud.exclusive-identity.net) of struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but noted that they must have been cherry-picked and may not represent Sora's normal output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have actually shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry [revealed](https://www.sintramovextrema.com.br) his astonishment at the technology's ability to produce sensible video from text descriptions, citing its potential to change storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided 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 on a big dataset of varied audio and is also a multi-task model that can carry out [multilingual speech](https://winf.dhsh.de) recognition in addition to 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 forecast subsequent [musical](http://www.grainfather.com.au) notes in MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to begin 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 web psychological thriller Ben Drowned to produce music for the titular [character](http://chillibell.com). [232] [233] +
Jukebox
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Released in 2020, Jukebox is an open-sourced algorithm to generate 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 stated the tunes "show regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" which "there is a significant space" in between Jukebox and [human-generated music](https://2workinoz.com.au). The Verge stated "It's technically remarkable, even if the results seem like mushy variations of tunes that might feel familiar", while Business Insider mentioned "remarkably, some of the resulting tunes are catchy and sound legitimate". [234] [235] [236] +
Interface
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Debate Game
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In 2018, OpenAI introduced the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The purpose is to research whether such a technique might assist in auditing [AI](https://www.naukrinfo.pk) [decisions](http://www.yfgame.store) and in establishing explainable [AI](http://gitlab.lvxingqiche.com). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of [visualizations](https://demo.wowonderstudio.com) of every significant layer and neuron of 8 neural network models which are often studied in interpretability. [240] Microscope was [produced](https://repo.farce.de) to analyze the features that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different versions of Inception, and different versions 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 supplies 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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