From 63cb46e9b911061264b04a571335e67143c06210 Mon Sep 17 00:00:00 2001 From: susannaerdmann Date: Wed, 9 Apr 2025 20:10:51 +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..ecda15d --- /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 reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](http://git.keliuyun.com:55676) research, making released research study more easily reproducible [24] [144] while offering users with a basic user interface for communicating 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 knowing (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to solve single jobs. Gym Retro provides the ability to generalize in between video games with comparable concepts however different looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have understanding of how to even stroll, however are given the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents find out how to adapt to altering conditions. When a representative is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives might produce an intelligence "arms race" that might increase a representative's ability to work even outside the context of the [competition](http://it-viking.ch). [148] +
OpenAI 5
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OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high skill level entirely through trial-and-error algorithms. Before ending up being a group of 5, the first public demonstration took place at The International 2017, the annual premiere championship tournament for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of actual time, which the knowing software application was a step in the direction of developing software that can deal with intricate tasks like a surgeon. [152] [153] The system uses a form of support knowing, as the bots find out gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives. [154] [155] [156] +
By June 2018, the ability of the bots broadened to play together as a complete group of 5, and they were able to defeat 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 ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall 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 gamer reveals the difficulties of [AI](http://doosung1.co.kr) systems in [multiplayer online](https://securityjobs.africa) [fight arena](https://evertonfcfansclub.com) (MOBA) games and how OpenAI Five has shown the use of [deep support](http://kodkod.kr) learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl utilizes [maker discovering](https://njspmaca.in) to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It finds out completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by using domain randomization, a simulation method which exposes the learner to a variety of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB electronic cameras to allow the [robotic](http://fcgit.scitech.co.kr) to control an arbitrary item by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an [octagonal prism](https://copyrightcontest.com). [168] +
In 2019, OpenAI showed that Dactyl could solve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of creating progressively harder environments. ADR varies from manual domain randomization by not requiring a human to specify randomization varieties. [169] +
API
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In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://gitea.cisetech.com) models developed by OpenAI" to let designers get in touch with it for "any English language [AI](https://dimension-gaming.nl) task". [170] [171] +
Text generation
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The business has actually popularized generative pretrained transformers (GPT). [172] +
OpenAI's original GPT design ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language could obtain world understanding and process long-range dependences by pre-training on a diverse 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 model](https://job.iwok.vn) and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative versions initially released to the public. The complete variation of GPT-2 was not instantly launched due to concern about prospective abuse, consisting of applications for [composing phony](https://rhabits.io) news. [174] Some specialists revealed uncertainty that GPT-2 posed a considerable hazard.
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In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language model. [177] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue not being watched language models to be general-purpose students, illustrated by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not more 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 problems [encoding vocabulary](https://source.coderefinery.org) with word tokens by using byte pair encoding. This allows representing any string of characters by encoding both private characters and [multiple-character](https://www.wikispiv.com) 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](https://oerdigamers.info) language model and the follower to GPT-2. [182] [183] [184] [OpenAI stated](http://121.40.209.823000) that the complete variation of GPT-3 contained 175 billion parameters, [184] 2 orders of [magnitude larger](https://tweecampus.com) than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were likewise trained). [186] +
OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184] +
GPT-3 dramatically enhanced benchmark outcomes over GPT-2. [OpenAI cautioned](https://usvs.ms) that such scaling-up of language designs could be approaching or encountering the fundamental capability [constraints](https://gitea.portabledev.xyz) of predictive language models. [187] Pre-training GPT-3 needed a number of 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 model was not right away launched to the public for issues of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month totally free private beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed specifically to [Microsoft](https://www.social.united-tuesday.org). [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://losangelesgalaxyfansclub.com) 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 produce working code in over a lots programs languages, the [majority](https://usvs.ms) of successfully in Python. [192] +
Several [concerns](http://39.98.253.1923000) with glitches, style flaws and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has actually been accused of releasing copyrighted code, without any 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](https://www.tqmusic.cn) Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They announced that the updated innovation passed a simulated law school bar exam 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 could likewise read, analyze or [produce](http://106.14.174.2413000) 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://anychinajob.com) on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal different technical details and stats about GPT-4, such as the accurate size of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and released GPT-4o, which can [process](https://gitea.cronin.one) and create text, images and audio. [204] GPT-4o attained advanced lead to voice, multilingual, and vision standards, 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 released GPT-4o mini, a smaller variation of GPT-4o changing GPT-3.5 Turbo on the [ChatGPT](https://dreamcorpsllc.com) 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 enterprises, startups and designers seeking to automate services with [AI](https://git.pawott.de) agents. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been developed to take more time to think about their reactions, resulting in greater accuracy. These designs are particularly [effective](https://www.cbtfmytube.com) in science, coding, and reasoning jobs, and were made available to [ChatGPT](https://repo.correlibre.org) Plus and Employee. [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 reasoning model. OpenAI likewise unveiled o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the [opportunity](https://higgledy-piggledy.xyz) to obtain early access to these models. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications [companies](https://thathwamasijobs.com) O2. [215] +
Deep research study
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Deep research is an agent established by OpenAI, revealed on February 2, 2025. It [leverages](https://securityjobs.africa) the capabilities of OpenAI's o3 model to carry out comprehensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, 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 design that is trained to evaluate the semantic resemblance in between text and images. It can notably 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 a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can create images of [practical items](http://116.236.50.1038789) ("a stained-glass window with a picture of a blue strawberry") as well as things that do not exist in truth ("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 sensible outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-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 announced DALL-E 3, a more effective design better able to produce images from complicated without manual prompt engineering and render complex 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 model that can generate videos based upon short detailed triggers [223] in addition to extend existing videos forwards or [89u89.com](https://www.89u89.com/author/dollie06z91/) in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.
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Sora's development team called it after the Japanese word for "sky", to signify its "endless creative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos licensed for that function, however did not expose the number or the specific sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it might produce videos up to one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the model's capabilities. [225] It acknowledged a few of its shortcomings, consisting of battles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but kept in mind that they must have been cherry-picked and may not represent Sora's common output. [225] +
Despite [uncertainty](https://hcp.com.gt) from some academic leaders 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 revealed his awe at the [technology's ability](https://iadgroup.co.uk) to create realistic video from text descriptions, mentioning its potential to revolutionize storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause prepare for expanding his Atlanta-based film studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, [Whisper](https://www.jobsalert.ai) is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of diverse audio and is also a multi-task model that can carry out multilingual speech recognition in addition to speech translation and language recognition. [229] +
Music generation
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MuseNet
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Released in 2019, [MuseNet](https://git.saidomar.fr) is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 [designs](https://grailinsurance.co.ke). According to The Verge, a song created by MuseNet tends to begin fairly however then fall into chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned 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 genre, artist, and a snippet of lyrics and outputs song 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 duplicate" and that "there is a significant gap" in between Jukebox and human-generated music. The Verge stated "It's highly excellent, even if the outcomes seem like mushy variations of songs that might feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are memorable and sound genuine". [234] [235] [236] +
User interfaces
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Debate Game
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In 2018, OpenAI released the Debate Game, which teaches machines to debate toy problems in front of a human judge. The [purpose](https://friendfairs.com) is to research whether such a method might assist in auditing [AI](https://newhopecareservices.com) decisions and in developing explainable [AI](https://www.e-vinil.ro). [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 designs which are frequently studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and different versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that provides a conversational user interface that allows users to ask questions in natural language. The system then responds with a response within seconds.
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