commit 7da66846b014c7a99bb52001db409e4b75580438 Author: kayt5010123392 Date: Sun Feb 9 12:06:35 2025 +0800 Add The Verge Stated It's Technologically Impressive 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..fff9fc9 --- /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 help with the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://git.todayisyou.co.kr) research, making published research study more easily reproducible [24] [144] while supplying users with a simple user interface for [communicating](https://oliszerver.hu8010) with these environments. In 2022, new advancements of Gym have 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 study on video games [147] using [RL algorithms](https://git.dev-store.ru) and research study generalization. Prior RL research focused mainly on optimizing agents to fix single tasks. Gym Retro offers the ability to generalize in between video games with similar concepts but different appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack understanding of how to even stroll, but are offered the objectives of discovering to move and [yewiki.org](https://www.yewiki.org/User:ElenaGrenda45) to press the opposing representative out of the ring. [148] Through this adversarial learning procedure, the agents learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could develop an intelligence "arms race" that might increase an agent's capability to function even outside the context of the competitors. [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 discover to play against human players at a high skill level entirely through experimental algorithms. Before becoming a team of 5, the first public demonstration happened at The International 2017, the annual best champion tournament for the game, where Dendi, an expert [Ukrainian](http://43.138.236.39000) 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 discovered by playing against itself for 2 weeks of actual time, which the learning software application was a step in the direction of producing software that can deal with intricate tasks like a surgeon. [152] [153] The system uses a form of support knowing, as the bots discover over time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156] +
By June 2018, the ability of the bots expanded to play together as a complete team of 5, and they had the ability to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional players, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games. [165] +
OpenAI 5's mechanisms in Dota 2's bot player shows the challenges of [AI](https://dalilak.live) systems in [multiplayer online](http://124.223.100.383000) fight arena (MOBA) games and how OpenAI Five has actually shown using deep reinforcement [knowing](http://logzhan.ticp.io30000) (DRL) agents to attain superhuman competence in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1322040) Dactyl uses machine discovering to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the [object orientation](https://smartcampus-seskoal.id) problem by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB electronic cameras to enable the robotic to control an approximate object by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168] +
In 2019, [OpenAI demonstrated](http://101.42.90.1213000) that Dactyl might fix a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of creating progressively more tough environments. ADR differs from manual domain randomization by not requiring a human to define randomization varieties. [169] +
API
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In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](http://private.flyautomation.net:82) models developed by OpenAI" to let developers contact it for "any English language [AI](https://www.yohaig.ng) job". [170] [171] +
Text generation
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The business has popularized generative pretrained transformers (GPT). [172] +
OpenAI's original GPT model ("GPT-1")
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The initial paper on [generative](https://forum.webmark.com.tr) pre-training of a transformer-based language model was written by Alec Radford and his coworkers, and released in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language could obtain world knowledge and procedure long-range [reliances](http://xn--ok0b74gbuofpaf7p.com) 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 without supervision transformer language design and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative variations at first launched to the general public. The complete variation of GPT-2 was not instantly launched due to concern about prospective abuse, including applications for writing phony news. [174] Some experts revealed uncertainty that GPT-2 posed a substantial threat.
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language model. [177] Several websites host interactive demonstrations of various instances of GPT-2 and other [transformer models](http://candidacy.com.ng). [178] [179] [180] +
GPT-2's authors argue without [supervision language](https://deprezyon.com) models to be general-purpose students, illustrated by GPT-2 attaining advanced 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 a minimum of 3 upvotes. It prevents certain issues encoding vocabulary with word tokens by using 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 model](https://git.mbyte.dev) and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full version 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 could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and [cross-linguistic transfer](https://asg-pluss.com) learning in between English and Romanian, and in between English and German. [184] +
GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or coming across the fundamental capability [constraints](https://sudanre.com) of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released to the general public for issues of possible abuse, although OpenAI planned 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 certified exclusively to Microsoft. [190] [191] +
Codex
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Announced in mid-2021, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Margareta19E) 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://www.pakgovtnaukri.pk) [powering](https://textasian.com) the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the model can develop working code in over a dozen programming languages, many effectively in Python. [192] +
Several issues with glitches, design flaws and [security](http://git.foxinet.ru) vulnerabilities were cited. [195] [196] +
GitHub Copilot has been implicated of emitting copyrighted code, without any author attribution or license. [197] +
OpenAI revealed that they would for Codex API on March 23, 2023. [198] +
GPT-4
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On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the updated innovation passed a [simulated law](https://www.eticalavoro.it) 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 produce approximately 25,000 words of text, and compose code in all major programming languages. [200] +
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal different technical details and data about GPT-4, such as the precise size of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained state-of-the-art outcomes in voice, multilingual, and vision criteria, setting new records in audio speech acknowledgment 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 released 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 expects it to be particularly useful for enterprises, startups and developers seeking to automate services with [AI](https://miggoo.com.br) representatives. [208] +
o1
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On September 12, 2024, [OpenAI launched](https://git.foxarmy.org) the o1-preview and o1-mini models, which have been developed to take more time to think of their actions, leading to higher accuracy. These models are particularly efficient in science, coding, and thinking tasks, 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 revealed o3, the follower of the o1 thinking 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 evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these models. [214] The design is called o3 rather than o2 to prevent confusion with telecommunications [companies](https://careers.cblsolutions.com) O2. [215] +
Deep research
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Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform extensive web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools made it possible for, it reached an accuracy 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 analyze the [semantic resemblance](https://www.sewosoft.de) between text and images. It can [notably](https://career.agricodeexpo.org) 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 design that develops images from textual descriptions. [218] DALL-E [utilizes](https://code.agileum.com) a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and [generate matching](https://24cyber.ru) images. It can create images of sensible items ("a stained-glass window with a picture of a blue strawberry") as well as objects 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 announced DALL-E 2, an upgraded variation of the model with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new simple system for converting 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 design 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 [function](https://service.aicloud.fit50443) 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 triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of produced videos is [unidentified](https://www.proathletediscuss.com).
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Sora's development group called it after the Japanese word for "sky", to represent its "unlimited imaginative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL ยท E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that purpose, however did not expose the number or the specific sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it could [generate videos](https://repo.gusdya.net) approximately one minute long. It likewise shared a technical report highlighting the techniques utilized to train the model, and the design's abilities. [225] It acknowledged some of its drawbacks, consisting of struggles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they need to have been cherry-picked and might not represent Sora's common output. [225] +
Despite uncertainty from some [academic leaders](https://connect.taifany.com) following Sora's public demo, notable entertainment-industry figures have actually revealed significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to create reasonable video from text descriptions, citing its possible to change storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly prepare 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 [acknowledgment](https://git.numa.jku.at) design. [228] It is trained on a large dataset of varied audio and is likewise a multi-task design that can perform multilingual speech recognition 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](http://nas.killf.info9966) tunes with 10 instruments in 15 designs. 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, [initial applications](https://friendfairs.com) of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to produce music for the [titular](http://git.ningdatech.com) character. [232] [233] +
Jukebox
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Released in 2020, Jukebox is an open-sourced algorithm to create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and [garagesale.es](https://www.garagesale.es/author/chandaleong/) outputs song samples. OpenAI mentioned the tunes "reveal regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" and that "there is a significant space" in between Jukebox and human-generated music. The Verge specified "It's highly impressive, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are catchy 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 dispute toy problems in front of a human judge. The function is to research whether such an approach may help in auditing [AI](https://gitlab.zogop.com) decisions and in developing explainable [AI](https://dev.nebulun.com). [237] [238] +
Microscope
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[Released](http://aiot7.com3000) in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network designs which are frequently studied in interpretability. [240] Microscope was created to examine the features that form inside these [neural networks](https://www.jobspk.pro) quickly. The designs included 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 a [synthetic intelligence](https://git.howdoicomputer.lol) tool developed on top of GPT-3 that provides a conversational user interface that permits users to ask concerns in natural language. The system then responds with a response within seconds.
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