From 99b6316b25493ab3735314588d9b9bbe60a8ca15 Mon Sep 17 00:00:00 2001 From: serenasweat790 Date: Wed, 28 May 2025 21:02:41 +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..84d5bf4 --- /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 advancement of support knowing algorithms. It aimed to standardize how environments are defined in [AI](http://gnu5.hisystem.com.ar) research, making published research more quickly reproducible [24] [144] while offering users with a simple user interface for engaging with these environments. In 2022, brand-new advancements 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 reinforcement learning (RL) research study on video games [147] using RL algorithms and research study generalization. Prior RL research focused mainly on enhancing representatives to resolve single jobs. Gym Retro provides the capability to generalize in between games with comparable ideas but various appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first [lack knowledge](http://betterlifenija.org.ng) of how to even stroll, however are offered the goals 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 adjust to altering conditions. When an agent is then eliminated from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could produce an intelligence "arms race" that could increase a representative's capability to work even outside the context of the competition. [148] +
OpenAI 5
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OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human players at a high ability level totally through trial-and-error algorithms. Before ending up being a group of 5, the very first public demonstration occurred at The International 2017, the annual premiere championship competition for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of real time, which the learning software was a step in the direction of developing software application that can deal with complex tasks like a surgeon. [152] [153] The system uses a form of support learning, as the bots learn gradually by playing 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 ability of the bots expanded 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 2 [exhibit matches](http://www.xn--he5bi2aboq18a.com) against expert gamers, but ended 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 appearance came later on that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those video games. [165] +
OpenAI 5's mechanisms in Dota 2's bot player shows the difficulties of [AI](https://pakfindjob.com) systems in multiplayer online battle arena (MOBA) [video games](https://git.thetoc.net) and how OpenAI Five has demonstrated using deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses [maker learning](http://krzsyjtj.zlongame.co.kr9004) to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It finds out totally in simulation utilizing the very same RL algorithms and training code as OpenAI Five. [OpenAI tackled](http://tktko.com3000) the object [orientation issue](https://cats.wiki) by using domain randomization, a simulation technique which exposes the learner to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, likewise has RGB cameras to permit the robotic to manipulate an arbitrary things 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 demonstrated that Dactyl might fix a Rubik's Cube. The robot had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing progressively 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://elitevacancies.co.za) models developed by OpenAI" to let developers call on it for "any English language [AI](https://talentrendezvous.com) task". [170] [171] +
Text generation
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The company has actually popularized generative pretrained transformers (GPT). [172] +
OpenAI's original GPT model ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a [generative](http://copyvance.com) model of language might obtain world knowledge 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 design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative variations initially released to the general public. The full version of GPT-2 was not right away released due to concern about prospective abuse, including applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 presented a substantial risk.
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In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted 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 released the complete version of the GPT-2 language design. [177] Several sites host interactive demonstrations of different instances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue not being watched language designs to be general-purpose learners, shown by GPT-2 attaining modern accuracy and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:VIRCarmela) perplexity on 7 of 8 [zero-shot tasks](https://www.hirecybers.com) (i.e. the model 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 issues encoding vocabulary with word tokens by using byte pair encoding. This [permits representing](https://heartbeatdigital.cn) 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 design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 [contained](https://git.wo.ai) 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were likewise trained). [186] +
OpenAI specified that GPT-3 prospered at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and [yewiki.org](https://www.yewiki.org/User:EdithKyj05) German. [184] +
GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or experiencing the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the general public for concerns of possible abuse, although [OpenAI planned](http://tobang-bangsu.co.kr) to allow gain access to through a paid cloud API after a two-month totally free private beta that began 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 furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://www.origtek.com:2999) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the design can [produce](https://git.gilesmunn.com) working code in over a lots [programming](http://101.43.248.1843000) languages, many successfully in Python. [192] +
Several issues with problems, design flaws and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has actually been accused 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 revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of [accepting text](https://www.vfrnds.com) or image inputs. [199] They revealed that the updated innovation passed a simulated law school bar examination with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, analyze or create approximately 25,000 words of text, and compose code in all major shows languages. [200] +
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also [efficient](https://www.mk-yun.cn) in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal different technical details and stats about GPT-4, such as the precise size of the model. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o [attained cutting](http://f225785a.80.robot.bwbot.org) edge 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) benchmark 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 helpful for business, start-ups and developers looking for to automate services with [AI](http://gitlab.code-nav.cn) agents. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been created to take more time to consider their actions, resulting in higher [precision](https://git.xiaoya360.com). These models are especially reliable in science, coding, and reasoning jobs, 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, 2024, OpenAI unveiled o3, the follower of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecoms services service O2. [215] +
Deep research study
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Deep research study is an agent developed by OpenAI, [unveiled](https://www.dcsportsconnection.com) on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform extensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached a precision 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 design that is trained to examine the [semantic resemblance](https://www.fionapremium.com) in between text and images. It can significantly be used for image classification. [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 version of GPT-3 to interpret 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 produce images of [reasonable items](https://signedsociety.com) ("a stained-glass window with a picture of a blue strawberry") in addition to 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 DALL-E 2, an upgraded variation of the model with more sensible results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new basic system for transforming a text description into a 3[-dimensional design](https://git.lodis.se). [220] +
DALL-E 3
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In September 2023, OpenAI announced DALL-E 3, a more powerful model much better able to generate images from complicated descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched to the general 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 design that can produce videos based on brief [detailed](https://juryi.sn) prompts [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.
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Sora's development team called it after the Japanese word for "sky", to represent its "endless innovative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos [licensed](http://mangofarm.kr) for that function, however did not reveal the number or the precise sources of the videos. [223] +
OpenAI showed some [Sora-created high-definition](https://atomouniversal.com.br) videos to the public on February 15, 2024, mentioning that it could produce videos up to one minute long. It also shared a technical report highlighting the methods used to train the model, and the [design's abilities](https://sahabatcasn.com). [225] It acknowledged some of its shortcomings, consisting of struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT [Technology Review](http://energonspeeches.com) called the [presentation](https://gitea.linuxcode.net) videos "excellent", but noted that they must have been cherry-picked and might not represent Sora's common output. [225] +
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually revealed significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's ability to create reasonable video from text descriptions, mentioning its possible to revolutionize storytelling and material creation. He said that his [excitement](https://candays.com) about Sora's possibilities was so strong that he had actually decided to pause plans for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:NataliaPrater2) expanding 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 acknowledgment model. [228] It is trained on a big dataset of diverse audio and is also a multi-task model that can carry out 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 predict subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to begin fairly however then fall into turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to produce 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 song samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow standard chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" which "there is a significant space" in between Jukebox and human-generated music. The Verge stated "It's technically impressive, even if the results seem like mushy versions of tunes that might feel familiar", while Business Insider stated "surprisingly, some of the resulting tunes are memorable and sound genuine". [234] [235] [236] +
Interface
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Debate Game
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In 2018, OpenAI released the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The function is to research whether such a technique might help in auditing [AI](http://128.199.125.93:3000) choices and in developing explainable [AI](http://47.108.94.35). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different variations of Inception, and various versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an artificial intelligence tool developed on top of GPT-3 that supplies a conversational user interface that allows users to ask concerns in natural language. The system then responds with a response within seconds.
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