From db27569b619a70ca982f01d67dc4016a04a37501 Mon Sep 17 00:00:00 2001 From: elsieharmon075 Date: Sun, 9 Feb 2025 19:13:27 +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..6a6bfe8 --- /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 assist in the advancement of [reinforcement learning](https://peopleworknow.com) algorithms. It aimed to standardize how environments are specified in [AI](https://login.discomfort.kz) research, making published research study more easily reproducible [24] [144] while offering users with a simple user interface for connecting with these environments. In 2022, brand-new developments 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 [support learning](https://albion-albd.online) (RL) research study on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to resolve single tasks. Gym Retro gives the capability to generalize in between games with comparable ideas 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, but are provided the goals of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents find out how to adjust to changing conditions. When a representative 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 actually learned how to [balance](http://124.70.149.1810880) in a [generalized](https://textasian.com) way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could create an intelligence "arms race" that might increase an agent's capability to work even outside the context of the competitors. [148] +
OpenAI 5
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OpenAI Five is a team of five OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that find out to play against human players at a high skill level totally through experimental algorithms. Before ending up being a group of 5, the very first public demonstration occurred at The International 2017, the yearly premiere champion tournament 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 found out by playing against itself for 2 weeks of real time, and that the knowing software application was a step in the instructions of creating software application that can handle complex jobs like a surgeon. [152] [153] The system uses a form of reinforcement learning, as the bots learn in time by playing against themselves [hundreds](https://www.frigorista.org) of times a day for months, and are rewarded for actions such as eliminating an enemy and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:AndreHeiden8) taking map objectives. [154] [155] [156] +
By June 2018, the [ability](https://xn--pm2b0fr21aooo.com) of the bots broadened to play together as a full group of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions 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 that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those games. [165] +
OpenAI 5's systems in Dota 2's bot gamer reveals the challenges of [AI](http://isarch.co.kr) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep reinforcement learning (DRL) representatives to attain superhuman [competence](http://www.buy-aeds.com) in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It learns entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences rather than trying to fit to truth. The set-up for [larsaluarna.se](http://www.larsaluarna.se/index.php/User:AlineCox0079049) Dactyl, aside from having motion tracking cams, likewise has RGB cams to enable the robotic to manipulate an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168] +
In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating gradually 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 new [AI](http://git.estoneinfo.com) models developed by OpenAI" to let developers contact it for "any English language [AI](https://movie.nanuly.kr) task". [170] [171] +
Text generation
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The business has 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 model was written by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative model of [language](http://1.13.246.1913000) might obtain world understanding and procedure long-range dependences by pre-training on a [diverse corpus](https://gitlab.econtent.lu) 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 initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative versions initially released to the public. The complete variation of GPT-2 was not immediately [launched](http://116.62.159.194) due to issue about prospective misuse, consisting of applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 presented a substantial risk.
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In reaction to GPT-2, the Allen [Institute](https://trackrecord.id) for Artificial Intelligence responded with a tool to identify "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned 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 impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue unsupervised language models to be general-purpose students, highlighted by GPT-2 edge accuracy and [perplexity](http://116.62.115.843000) on 7 of 8 zero-shot jobs (i.e. the design was not further trained on any task-specific input-output examples).
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The corpus it was trained on, called WebText, contains somewhat 40 [gigabytes](https://careers.jabenefits.com) of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain concerns 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](https://trackrecord.id) 3 (GPT-3) is a without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as few as 125 million parameters were also trained). [186] +
OpenAI specified that GPT-3 prospered at certain "meta-learning" jobs 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 in between English and German. [184] +
GPT-3 significantly improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or experiencing the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of compute, [compared](https://eet3122salainf.sytes.net) to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the general public for concerns of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month free personal beta that began in June 2020. [170] [189] +
On September 23, 2020, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:DominiqueCurmi) 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 furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://gitea.mierzala.com) powering the code autocompletion tool [GitHub Copilot](http://kiwoori.com). [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the design can create working code in over a lots programming languages, many successfully in Python. [192] +
Several concerns with glitches, design flaws and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has actually been implicated of giving off copyrighted code, with no [author attribution](https://btslinkita.com) or license. [197] +
OpenAI revealed that they would [terminate support](https://git.eugeniocarvalho.dev) 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), efficient in accepting text or image inputs. [199] They revealed that the updated innovation passed a simulated law [school bar](https://dandaelitetransportllc.com) 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 approximately 25,000 words of text, and compose code in all major programs languages. [200] +
Observers reported that the version of ChatGPT using GPT-4 was an [improvement](https://gitea.mpc-web.jp) 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 likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal different technical details and statistics 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 released GPT-4o, which can [process](http://www.hanmacsamsung.com) and [wiki.whenparked.com](https://wiki.whenparked.com/User:LatashaRutledge) create text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition 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 version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT 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 beneficial for business, startups and designers seeking to automate services with [AI](https://equipifieds.com) representatives. [208] +
o1
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On September 12, 2024, OpenAI launched the o1[-preview](https://www.tcrew.be) and o1-mini designs, which have actually been developed to take more time to consider their responses, leading to higher precision. These models are especially effective in science, coding, and thinking jobs, 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 reasoning design. OpenAI also unveiled o3-mini, a lighter and quicker version of OpenAI o3. Since December 21, 2024, this model 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 researchers had the chance to obtain early access to these designs. [214] The design 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 developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out comprehensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools enabled, 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](http://042.ne.jp) in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic similarity 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](https://rubius-qa-course.northeurope.cloudapp.azure.com) in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can produce pictures of sensible things ("a stained-glass window with a picture of a blue strawberry") along with 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 revealed DALL-E 2, an upgraded version of the design with more reasonable results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new primary 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 effective model better able to produce images from intricate descriptions without manual timely 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 model that can produce videos based on short [detailed triggers](https://video.invirtua.com) [223] in addition to extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.
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Sora's advancement team called it after the Japanese word for "sky", to symbolize its "endless innovative potential". [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 using publicly-available videos as well as copyrighted videos certified for that purpose, but did not reveal 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, specifying that it could generate videos as much as one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the [design's capabilities](http://101.43.135.2349211). [225] It acknowledged some of its drawbacks, including struggles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but kept in mind 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, noteworthy entertainment-industry figures have revealed significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to produce practical video from text descriptions, citing its prospective to change storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen 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 recognition 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 acknowledgment as well as 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 forecast subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web psychological thriller Ben [Drowned](https://repo.gusdya.net) to create 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 genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the tunes "reveal local musical coherence [and] follow standard chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" and that "there is a considerable gap" between Jukebox and human-generated music. The Verge specified "It's technically excellent, even if the outcomes seem like mushy variations of tunes that may feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are appealing and sound legitimate". [234] [235] [236] +
User user interfaces
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Debate Game
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In 2018, OpenAI introduced the Debate Game, which teaches devices to debate toy problems in front of a human judge. The function is to research study whether such an approach may help in auditing [AI](https://gitlab.donnees.incubateur.anct.gouv.fr) decisions and in developing explainable [AI](https://hiphopmusique.com). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every [considerable layer](https://ospitalierii.ro) and nerve cell of eight neural network models which are often studied in interpretability. [240] Microscope was created to analyze the functions that form inside these [neural networks](https://git.todayisyou.co.kr) easily. The designs included are AlexNet, VGG-19, different versions of Inception, and different versions of CLIP Resnet. [241] +
ChatGPT
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[Launched](http://www.gz-jj.com) in November 2022, ChatGPT is an artificial intelligence tool developed on top of GPT-3 that provides a conversational user interface that allows users to ask questions in [natural language](https://wiki.ragnaworld.net). The system then reacts with an answer within seconds.
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