From 26a41a564aae318554dab1139f2f78f324f2b91e Mon Sep 17 00:00:00 2001 From: staceywalck124 Date: Fri, 4 Apr 2025 09:43:35 +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..b6cd3d4 --- /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 facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://flowndeveloper.site) research study, making released research study more quickly reproducible [24] [144] while offering users with a basic user interface for communicating with these . In 2022, brand-new developments of Gym have actually been [relocated](https://howtolo.com) 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](http://www.thegrainfather.com.au) and research [study generalization](https://git.ffho.net). Prior RL research focused mainly on enhancing representatives to solve single tasks. Gym Retro offers the ability to generalize in between video games with comparable principles however various looks.
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RoboSumo
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[Released](https://xotube.com) in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack understanding of how to even stroll, but are given the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial learning procedure, the agents discover how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and put in a [brand-new](https://15.164.25.185) virtual environment with high winds, the representative braces to remain upright, recommending it had actually learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might produce an intelligence "arms race" that might increase a representative's ability to work even outside the context of the competition. [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 gamers at a high ability level totally through experimental algorithms. Before ending up being a group of 5, the first public presentation occurred at The International 2017, the annual best championship tournament 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 found out by playing against itself for 2 weeks of actual time, which the learning software application was a step in the instructions of producing software that can deal with intricate jobs like a surgeon. [152] [153] The system utilizes a form of support knowing, as the bots find out in time by [playing](https://jobs.but.co.id) against themselves hundreds of times a day for months, and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=12023507) are rewarded for actions such as eliminating an [opponent](http://47.121.132.113000) and taking map objectives. [154] [155] [156] +
By June 2018, the ability of the bots expanded to play together as a full team of 5, and they had the ability to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two [exhibit matches](https://friendfairs.com) against [professional](https://gitea.qi0527.com) players, but wound 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' last public appearance came later that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those [video games](http://106.15.48.1323880). [165] +
OpenAI 5's mechanisms in Dota 2's bot player reveals the obstacles of [AI](https://git.jackbondpreston.me) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has 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 finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It finds out entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a simulation method which exposes the learner to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, also has RGB cameras to allow the robot to manipulate an approximate object by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168] +
In 2019, OpenAI showed that Dactyl might solve a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of producing gradually harder environments. ADR varies from manual domain randomization by not needing a human to define 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](http://39.108.83.154:3000) models developed by OpenAI" to let developers get in touch with it for "any English language [AI](http://8.138.173.195:3000) job". [170] [171] +
Text generation
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The business has actually popularized generative pretrained transformers (GPT). [172] +
OpenAI's initial GPT model ("GPT-1")
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The initial paper on generative pre-training of a [transformer-based language](https://jobs.campus-party.org) model was written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and process long-range reliances 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 model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative variations at first [released](http://www.topverse.world3000) to the public. The full variation of GPT-2 was not right away launched due to concern about prospective misuse, including applications for composing phony news. [174] Some experts expressed uncertainty that GPT-2 posed a considerable danger.
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted 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 difficult to filter". [176] In November 2019, OpenAI released the total variation of the GPT-2 language model. [177] Several websites host interactive demonstrations of various circumstances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue unsupervised language models to be general-purpose students, shown by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further [trained](https://gitea.ws.adacts.com) 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](https://idaivelai.com). It prevents certain [concerns encoding](https://gitea.qi0527.com) vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both individual 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 design and the [follower](http://www.s-golflex.kr) to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million parameters were likewise trained). [186] +
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" jobs 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 drastically enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 required 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 model was not immediately launched to the public for issues of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month complimentary personal beta that began in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was certified exclusively 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://video.xaas.com.vn) 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 develop working code in over a dozen programming languages, a lot of effectively in Python. [192] +
Several concerns with glitches, style flaws and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has actually been accused of emitting copyrighted code, without any author attribution or license. [197] +
[OpenAI revealed](http://bedfordfalls.live) that they would discontinue support 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 revealed that the upgraded technology passed a [simulated law](https://farmwoo.com) school bar examination with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also read, examine or create as much as 25,000 words of text, and write 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 iteration, with the caveat that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and stats about GPT-4, such as the precise size of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision criteria, setting new records in audio speech recognition 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 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 anticipates it to be especially useful for enterprises, start-ups and developers seeking to automate services with [AI](https://gitlab.minet.net) agents. [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 believe about their actions, resulting in higher accuracy. These designs are particularly effective in science, coding, and thinking jobs, 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 successor of the o1 reasoning design. OpenAI also revealed o3-mini, a lighter and faster 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, security and [security scientists](http://archmageriseswiki.com) had the chance to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications companies O2. [215] +
Deep research study
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Deep research study is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web browsing, data analysis, and synthesis, delivering detailed reports within a [timeframe](https://wellandfitnessgn.co.kr) 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) standard. [120] +
Image classification
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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 analyze the semantic resemblance in 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 [develops](https://csmsound.exagopartners.com) 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 formed like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can develop images of reasonable things ("a stained-glass window with a picture of a blue strawberry") along with 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 announced DALL-E 2, an upgraded version of the model with more practical results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new primary 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 [effective model](https://1samdigitalvision.com) better able to generate images from intricate descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was launched 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 generate videos based upon brief detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.
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Sora's development group named it after the Japanese word for "sky", to represent its "limitless innovative potential". [223] Sora's innovation is an adaptation of the technology behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos licensed for that purpose, but did not expose the number or the exact sources of the videos. [223] +
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could create videos as much as one minute long. It likewise shared a technical report highlighting the approaches utilized to train the model, and the design's capabilities. [225] It acknowledged some of its shortcomings, [consisting](https://www.hue-max.ca) of battles replicating intricate physics. [226] Will [Douglas](https://www.jobs-f.com) Heaven of the MIT [Technology Review](http://139.199.191.19715000) called the presentation videos "outstanding", however kept in mind that they need to have been cherry-picked and might not represent Sora's normal output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have actually shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to produce reasonable video from text descriptions, mentioning its potential to reinvent storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had decided to pause plans for broadening his Atlanta-based movie studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of [varied audio](https://wiki.cemu.info) and is also a multi-task model that can carry out multilingual speech acknowledgment 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 tunes with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to begin fairly however then fall under mayhem the longer it plays. [230] [231] In pop culture, [preliminary applications](https://complexityzoo.net) of this tool were used as early as 2020 for the [internet mental](http://zhandj.top3000) thriller Ben Drowned to create 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 on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the tunes "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a considerable gap" in between Jukebox and human-generated music. The Verge specified "It's highly outstanding, even if the results 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] +
Interface
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Debate Game
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In 2018, OpenAI launched the Debate Game, which teaches makers to debate toy issues in front of a human judge. The function is to research study whether such an approach might help in auditing [AI](http://110.41.19.141:30000) choices and in [establishing explainable](http://jobpanda.co.uk) [AI](http://120.196.85.174:3000). [237] [238] +
Microscope
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Released in 2020, [Microscope](https://tv.goftesh.com) [239] is a collection of visualizations of every [considerable layer](http://142.93.151.79) and nerve cell of eight neural network models which are often studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. The designs 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 built on top of GPT-3 that offers a conversational interface that allows users to ask concerns in natural language. The system then reacts with a response within seconds.
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