From 850df9e96cd2181af923d68253bea79e1d44d55c Mon Sep 17 00:00:00 2001 From: lawrencedennis Date: Wed, 28 May 2025 21:06:06 +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..8d9c9f1 --- /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 facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how [environments](https://gitea.dgov.io) are defined in [AI](https://property.listatto.ca) research, making released research more easily reproducible [24] [144] while offering users with a basic user interface for connecting with these [environments](http://gogs.black-art.cn). In 2022, new advancements of Gym have actually been transferred to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to solve single jobs. [Gym Retro](https://githost.geometrx.com) gives the [capability](https://www.usbstaffing.com) to generalize in between video games with similar concepts however different appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially do not have understanding of how to even walk, however are offered the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt 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, suggesting it had discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between [representatives](https://www.highpriceddatinguk.com) could create an intelligence "arms race" that could increase an agent's ability 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](https://www.ojohome.listatto.ca) computer game Dota 2, that find out to play against human players at a high skill level totally through trial-and-error algorithms. Before becoming a team of 5, the very first public demonstration took place at The International 2017, the annual best championship competition for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by playing against itself for two weeks of actual time, and that the learning software application was a step in the instructions of producing software that can manage complicated jobs like a surgeon. [152] [153] The system uses a form of reinforcement learning, as the bots find out over time by playing against themselves numerous times a day for months, and are rewarded for actions such as [eliminating](https://www.sexmasters.xyz) an opponent and taking map objectives. [154] [155] [156] +
By June 2018, the capability 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 exhibition matches against professional gamers, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the game at the time, 2:0 in a [live exhibit](https://finance.azberg.ru) match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those . [165] +
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the difficulties of [AI](http://web.joang.com:8088) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually [demonstrated](http://www.chemimart.kr) making use of deep reinforcement learning (DRL) agents to attain superhuman [proficiency](https://www.olsitec.de) in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl utilizes device discovering to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It learns entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, likewise has RGB video cameras to enable the robot to control an approximate item 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 could resolve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of creating gradually more difficult environments. ADR varies from manual domain randomization by not requiring a human to specify randomization ranges. [169] +
API
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In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](http://59.110.125.164:3062) designs established by OpenAI" to let designers get in touch with it for "any English language [AI](http://git.setech.ltd:8300) task". [170] [171] +
Text generation
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The company has promoted 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 design was written by [Alec Radford](http://128.199.161.913000) and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could 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 an unsupervised transformer language design and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions at first launched to the general public. The complete version of GPT-2 was not immediately released due to concern about prospective abuse, consisting of applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 presented a significant risk.
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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, alerted of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue unsupervised language designs to be general-purpose students, shown by GPT-2 attaining cutting edge 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 at least 3 upvotes. It prevents certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 follower to GPT-2. [182] [183] [184] OpenAI stated that the full variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were also trained). [186] +
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and could 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 between English and German. [184] +
GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or experiencing the basic capability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, [compared](http://101.200.33.643000) to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly [released](https://hiphopmusique.com) to the public for concerns of possible abuse, although OpenAI prepared to allow 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 actually in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://gitlab.ujaen.es) 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 lots programming languages, the majority of efficiently in Python. [192] +
Several concerns with problems, design defects and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has been implicated of releasing copyrighted code, without any author attribution or license. [197] +
OpenAI revealed that they would stop 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](https://51.75.215.219) or image inputs. [199] They announced that the updated innovation passed a simulated law school bar examination 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 also read, examine or create approximately 25,000 words of text, and compose code in all significant programs languages. [200] +
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal different technical details and data about GPT-4, such as the [exact size](http://git.nuomayun.com) of the design. [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 modern lead to voice, multilingual, and vision standards, setting 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 version of GPT-4o changing 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 business, start-ups and developers looking for to automate services with [AI](https://wathelp.com) representatives. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:BarneyQ75767094) which have been developed to take more time to think of their actions, leading to higher accuracy. These models are especially effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was changed by o1. [211] +
o3
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On December 20, 2024, OpenAI unveiled o3, the successor of the o1 [reasoning design](https://git.getmind.cn). OpenAI likewise unveiled o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this design 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 chance to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecoms companies O2. [215] +
Deep research
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Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to [perform substantial](https://choosy.cc) web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 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) criteria. [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 resemblance between text and images. It can significantly 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](http://git.setech.ltd8300) 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 bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can create pictures of sensible objects ("a stained-glass window with an image of a blue strawberry") along with items 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, OpenAI announced DALL-E 2, an updated version of the model with more realistic results. [219] In December 2022, OpenAI released on [GitHub software](http://gitlab.digital-work.cn) for Point-E, a brand-new basic system for transforming a text description into a 3-dimensional model. [220] +
DALL-E 3
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In September 2023, OpenAI revealed DALL-E 3, a more powerful model much better able to produce images from complicated descriptions without manual prompt engineering and render intricate 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 design that can create videos based on brief [detailed prompts](https://movie.nanuly.kr) [223] along with [extend existing](https://flowndeveloper.site) videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.
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Sora's development group named it after the Japanese word for "sky", to symbolize its "limitless innovative capacity". [223] Sora's innovation is an adjustment of the innovation behind the DALL ยท E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos accredited for that function, but did not expose the number or the precise sources of the videos. [223] +
OpenAI showed some [Sora-created high-definition](https://classtube.ru) videos to the general public on February 15, 2024, specifying that it could generate videos as much as one minute long. It also shared a technical report highlighting the [methods utilized](http://gkpjobs.com) to train the design, and the design's capabilities. [225] It acknowledged some of its drawbacks, including struggles imitating complex physics. [226] Will Douglas Heaven of the MIT [Technology](https://chosenflex.com) Review called the demonstration videos "impressive", but noted that they need to have been cherry-picked and may not represent Sora's common output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have actually shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to produce practical video from text descriptions, citing its potential to revolutionize storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had chosen 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 recognition model. [228] It is trained on a big dataset of [varied audio](https://xpressrh.com) and is also a multi-task model that can carry out multilingual speech recognition as well as speech translation and [language identification](http://119.3.70.2075690). [229] +
Music generation
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MuseNet
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Released in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to start 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 internet mental 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 generate music with vocals. After [training](https://www.vidconnect.cyou) on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and [outputs song](https://cats.wiki) samples. OpenAI specified the songs "reveal regional musical coherence [and] follow standard chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" which "there is a significant gap" between Jukebox and human-generated music. The Verge mentioned "It's technically impressive, even if the results sound like mushy variations of songs that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting songs are catchy 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 dispute toy problems in front of a human judge. The purpose is to research whether such an approach may assist in auditing [AI](https://myjobapply.com) decisions and in establishing explainable [AI](https://superblock.kr). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network models which are typically studied in interpretability. [240] [Microscope](https://www.top5stockbroker.com) was created to analyze the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different variations of Inception, and various variations of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, [ChatGPT](https://satitmattayom.nrru.ac.th) is a synthetic intelligence tool constructed on top of GPT-3 that supplies a conversational interface that allows users to ask questions in natural language. The system then responds with an answer within seconds.
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