From 94991b5a218c402dde9e0bc67cf1adb709529050 Mon Sep 17 00:00:00 2001 From: kaligatenby24 Date: Mon, 7 Apr 2025 04:06:52 +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..6b02779 --- /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.rell.ru) research study, making [published](https://git.whitedwarf.me) research study more easily reproducible [24] [144] while supplying users with an easy interface for engaging with these environments. In 2022, new developments of Gym have actually been relocated 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 study generalization. Prior RL research focused mainly on enhancing agents to fix single tasks. Gym Retro offers the ability to generalize in between video games with similar [concepts](https://xn--9m1bq6p66gu3avit39e.com) but different looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have understanding of how to even walk, but are given the objectives of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and positioned in a new [virtual environment](http://git.papagostore.com) with high winds, the agent braces to remain upright, recommending it had found out how to balance in a generalized way. [148] [149] [OpenAI's Igor](https://www.empireofember.com) Mordatch argued that competition in between agents could create an [intelligence](http://47.119.27.838003) "arms race" that could [increase](https://git.becks-web.de) an agent's capability to work even outside the context of the competition. [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 learn to play against human gamers at a high skill level entirely through trial-and-error algorithms. Before becoming a group of 5, the very first public demonstration took place at The International 2017, the annual premiere championship competition for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for 2 weeks of genuine time, which the knowing software application was an action in the instructions of creating software that can handle intricate jobs like a surgeon. [152] [153] The system utilizes a type of reinforcement learning, as the bots learn gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156] +
By June 2018, the ability of the bots broadened to play together as a complete group of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 [exhibit matches](https://ou812chat.com) against [professional](https://10-4truckrecruiting.com) players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world [champions](https://virnal.com) 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 competitors, winning 99.4% of those video games. [165] +
OpenAI 5's mechanisms in Dota 2's bot player shows the obstacles of [AI](https://www.ssecretcoslab.com) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated the usage of deep support learning (DRL) agents to attain superhuman competence 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 robotic hand, to manipulate physical items. [167] It learns entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation problem by using domain randomization, a simulation technique which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, also has RGB electronic cameras to permit the robotic to manipulate an approximate item by seeing it. In 2018, [OpenAI revealed](http://plethe.com) that the system had the ability to manipulate a cube and an octagonal prism. [168] +
In 2019, [OpenAI demonstrated](https://git.getmind.cn) that Dactyl might solve a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated 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 creating progressively 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 announced a multi-purpose API which it said was "for accessing new [AI](https://drshirvany.ir) designs established by OpenAI" to let designers call on it for "any English language [AI](https://connect.taifany.com) job". [170] [171] +
Text generation
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The business has actually promoted generative pretrained transformers (GPT). [172] +
[OpenAI's initial](https://workmate.club) GPT 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 associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might 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 a not being watched transformer language model and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions initially released to the general public. The full variation of GPT-2 was not right away launched due to concern about potential misuse, [including applications](https://www.ministryboard.org) for [writing fake](http://118.190.145.2173000) news. [174] Some experts expressed uncertainty that GPT-2 posed a significant danger.
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In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to totally 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 launched the complete version of the GPT-2 language model. [177] Several sites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue unsupervised language to be general-purpose students, illustrated by GPT-2 attaining advanced precision 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 a minimum of 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters by encoding 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 a not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 [contained](https://jamesrodriguezclub.com) 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186] +
OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184] +
GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or experiencing the basic capability [constraints](http://git.hongtusihai.com) of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the general public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month free private beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed specifically 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](https://www.youtoonetwork.com) on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://47.108.182.66:7777) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the design can create working code in over a dozen shows languages, a lot of effectively in Python. [192] +
Several issues with problems, style flaws and security vulnerabilities were pointed out. [195] [196] +
GitHub Copilot has been implicated of emitting copyrighted code, without any author attribution or license. [197] +
OpenAI revealed that they would cease support for Codex API on March 23, 2023. [198] +
GPT-4
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On March 14, 2023, OpenAI [revealed](https://gitea.taimedimg.com) the release of Generative Pre-trained Transformer 4 (GPT-4), [efficient](http://media.nudigi.id) in accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar test with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise read, analyze 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 enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 [retained](https://pakkalljob.com) a few of the issues with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal different technical details and [statistics](https://www.paradigmrecruitment.ca) about GPT-4, such as the exact 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 create text, images and audio. [204] GPT-4o attained state-of-the-art 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 Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller variation of GPT-4o replacing GPT-3.5 Turbo on the [ChatGPT interface](https://ec2-13-237-50-115.ap-southeast-2.compute.amazonaws.com). 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 beneficial for enterprises, startups and developers looking for to automate services with [AI](https://www.grandtribunal.org) representatives. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been developed to take more time to consider their actions, resulting in higher accuracy. These models are especially effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was [changed](http://dev.ccwin-in.com3000) by o1. [211] +
o3
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On December 20, 2024, OpenAI revealed o3, the successor of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and [quicker variation](https://giftconnect.in) of OpenAI o3. As of December 21, 2024, this design is not available for [public usage](http://christianpedia.com). According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists 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 O2. [215] +
Deep research
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Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It [leverages](https://speeddating.co.il) the capabilities of OpenAI's o3 model to perform comprehensive web browsing, data analysis, and synthesis, delivering detailed [reports](http://xn--80azqa9c.xn--p1ai) within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it [reached](http://122.51.46.213) a precision 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 model that is trained to examine the semantic similarity in between text and images. It can significantly be utilized 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 creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can produce pictures of sensible [objects](https://tricityfriends.com) ("a stained-glass window with a picture of a blue strawberry") along with [objects](http://81.70.93.2033000) 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, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:FelishaJoy30) OpenAI announced DALL-E 2, an upgraded version of the model with more practical outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new simple system for transforming a text description into a 3-dimensional design. [220] +
DALL-E 3
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In September 2023, OpenAI revealed DALL-E 3, a more powerful model much better able to create images from complicated descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was [released](https://www.social.united-tuesday.org) 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 create videos based on brief detailed prompts [223] along with extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.
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Sora's advancement group called it after the Japanese word for "sky", to represent its "endless creative potential". [223] Sora's technology is an adjustment of the technology behind the DALL ยท E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as [copyrighted](http://83.151.205.893000) 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](http://hitbat.co.kr) videos to the public on February 15, 2024, stating that it might produce videos as much as one minute long. It likewise shared a technical report highlighting the techniques utilized to train the model, and the design's capabilities. [225] It acknowledged some of its shortcomings, including battles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but kept in mind that they should have been cherry-picked and may not represent Sora's normal output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have actually shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's ability to create practical video from text descriptions, citing its potential to change storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly plans for expanding 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 model. [228] It is trained on a large dataset of diverse audio and is also a multi-task design that can carry out multilingual speech [acknowledgment](http://gitlab.hanhezy.com) 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 notes in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall into turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben [Drowned](https://jobs.but.co.id) to create music for the titular character. [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 category, artist, and a snippet of lyrics and outputs tune samples. OpenAI stated the tunes "reveal regional musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" which "there is a substantial gap" in between Jukebox and [human-generated music](https://healthcarejob.cz). The Verge specified "It's highly excellent, even if the outcomes seem like mushy variations of songs that may feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are memorable and sound legitimate". [234] [235] [236] +
Interface
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Debate Game
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In 2018, OpenAI released the Debate Game, which teaches machines to debate toy issues in front of a human judge. The purpose is to research whether such an approach may assist in auditing [AI](https://www.arztsucheonline.de) decisions and in establishing explainable [AI](https://www.meditationgoodtip.com). [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 often studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different versions of Inception, and various 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 responds with an answer within seconds.
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