From 2126afeac2693138f651535aa5d19d5c4ef6f88a Mon Sep 17 00:00:00 2001 From: darcimeston530 Date: Fri, 21 Feb 2025 22:04:28 +0000 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..cc74324 --- /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 help with the advancement of [support learning](http://116.203.108.1653000) algorithms. It aimed to standardize how environments are defined in [AI](https://www.yaweragha.com) research study, making released research more easily reproducible [24] [144] while offering users with a simple interface for interacting with these environments. In 2022, 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 knowing (RL) research study on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to resolve single tasks. Gym Retro provides the ability to generalize between games with similar concepts however different looks.
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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 walk, but are [offered](http://39.99.158.11410080) the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the agents find out how to adapt to changing conditions. When a representative 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 found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents might [produce](http://20.198.113.1673000) an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the competitors. [148] +
OpenAI 5
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OpenAI Five is a group of five OpenAI-curated bots utilized in the [competitive five-on-five](https://testgitea.educoder.net) computer game Dota 2, that learn to play against human players at a high ability level completely 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 player, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of real time, which the knowing software application was an action in the [direction](https://sebagai.com) of producing software that can manage complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a type of reinforcement knowing, as the bots learn with time by [playing](https://pierre-humblot.com) against themselves hundreds of times a day for months, and are rewarded for actions such as killing an opponent and taking map objectives. [154] [155] [156] +
By June 2018, the capability of the bots broadened to play together as a complete team 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](http://mao2000.com3000) against expert players, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the 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 total video games in a four-day open online competition, winning 99.4% of those games. [165] +
OpenAI 5['s mechanisms](http://git.szmicode.com3000) in Dota 2's bot player shows the difficulties of [AI](https://gitea.alexconnect.keenetic.link) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown the usage of deep reinforcement knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses machine learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It learns entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation problem by using domain randomization, a simulation approach which [exposes](https://git.trov.ar) the learner to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB electronic cameras to permit the robot to manipulate an arbitrary 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 showed that Dactyl might solve a Rubik's Cube. The [robotic](https://www.cbmedics.com) was able 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 improving the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of creating gradually harder environments. ADR differs 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 brand-new [AI](https://calciojob.com) models established by OpenAI" to let developers call on it for "any English language [AI](http://sp001g.dfix.co.kr) job". [170] [171] +
Text generation
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The business has popularized generative pretrained transformers (GPT). [172] +
[OpenAI's original](http://47.120.70.168000) GPT design ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language might obtain world understanding and process long-range reliances by pre-training on a [diverse corpus](https://git.marcopacs.com) with long [stretches](https://prime-jobs.ch) of adjoining text.
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GPT-2
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Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative variations at first released to the general public. The complete version of GPT-2 was not right away launched due to issue about potential misuse, including applications for writing fake news. [174] Some specialists revealed uncertainty that GPT-2 postured a considerable threat.
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In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural phony 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 drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language design. [177] Several [websites](http://60.205.104.1793000) host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not additional 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 at least 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 an unsupervised transformer [language](http://wcipeg.com) model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned 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 complete variation of GPT-2 (although GPT-3 models with as few as 125 million specifications were also trained). [186] +
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and might [generalize](https://supremecarelink.com) the purpose of a single input-output pair. The GPT-3 [release paper](https://bbs.yhmoli.com) provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184] +
GPT-3 significantly enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the essential capability constraints of predictive language models. [187] Pre-training GPT-3 required a number of 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 design was not immediately launched to the general public for issues of possible abuse, although OpenAI planned 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 to Microsoft. [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has actually in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://117.72.17.132:3000) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the model can produce working code in over a dozen programs languages, most [effectively](https://palsyworld.com) in Python. [192] +
Several problems with problems, style defects and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has actually been implicated of [releasing copyrighted](https://club.at.world) code, with no author attribution or license. [197] +
OpenAI announced that they would stop support for [Codex API](https://git.wyling.cn) 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 announced that the upgraded technology passed a [simulated law](http://60.209.125.23820010) 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, [evaluate](http://106.14.65.137) or create as much as 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 [enhancement](https://nakshetra.com.np) on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has [declined](http://carvis.kr) to [reveal numerous](https://video.spacenets.ru) [technical details](https://systemcheck-wiki.de) and stats about GPT-4, such as the 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 state-of-the-art results in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech acknowledgment 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 expects](http://fujino-mori.com) it to be particularly helpful for enterprises, start-ups and designers seeking to automate services with [AI](http://101.43.129.26:10880) agents. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been designed to take more time to think of their reactions, leading to greater precision. These designs are particularly 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 [changed](https://friendfairs.com) by o1. [211] +
o3
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On December 20, 2024, OpenAI revealed o3, the successor of the o1 reasoning design. OpenAI also unveiled 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 scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 instead of o2 to prevent confusion with telecommunications companies O2. [215] +
Deep research
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Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of [OpenAI's](https://media.motorsync.co.uk) o3 model to perform substantial web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [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 evaluate the semantic resemblance in between text and images. It can notably be [utilized](https://wheeoo.com) 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 images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can develop pictures of reasonable objects ("a stained-glass window with a picture of a blue strawberry") in addition to objects 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 updated variation of the model with more realistic results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new primary system for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) converting 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 model much better able to generate images from complex descriptions without manual prompt engineering and render complex details like hands and text. [221] It was launched to the public as a ChatGPT Plus feature 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] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.
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Sora's development team named it after the Japanese word for "sky", to represent its "limitless innovative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos accredited for that function, 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 might generate videos as much as one minute long. It also shared a technical report highlighting the techniques utilized to train the design, and the design's capabilities. [225] It acknowledged some of its shortcomings, including struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however noted that they should have been cherry-picked and might not [represent Sora's](http://xn--ok0b74gbuofpaf7p.com) common output. [225] +
Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have actually shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to [generate practical](http://www.gz-jj.com) video from text descriptions, citing its potential to reinvent storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly strategies 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 model. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task model that can perform multilingual speech acknowledgment along with 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 generate tunes with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly but then fall under chaos the longer it plays. [230] [231] In pop culture, preliminary 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 on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and [outputs tune](http://xrkorea.kr) samples. OpenAI stated the songs "show regional musical coherence [and] follow standard chord patterns" but acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" and that "there is a substantial space" in between Jukebox and human-generated music. The Verge mentioned "It's highly remarkable, even if the results seem like mushy variations of tunes that may feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are appealing and sound genuine". [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 discuss toy problems in front of a human judge. The function is to research whether such an approach might help in auditing [AI](https://securityjobs.africa) decisions and in developing explainable [AI](http://dev.icrosswalk.ru:46300). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was [produced](http://123.207.206.1358048) to evaluate the features that form inside these [neural networks](https://watch-wiki.org) quickly. The models consisted of are AlexNet, VGG-19, various versions of Inception, and different versions of [CLIP Resnet](https://reklama-a5.by). [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an expert system tool constructed 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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