commit 2816f73afc511086e8195b6b95a6e1cb7c2a8cce Author: arturojorgenso Date: Thu Feb 13 07:39:07 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..5d5ea4f --- /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 development of support knowing algorithms. It aimed to standardize how [environments](https://21fun.app) are specified in [AI](https://followmylive.com) research study, making published research more quickly reproducible [24] [144] while supplying users with a basic user interface for engaging with these environments. In 2022, brand-new developments of Gym have been relocated to the library Gymnasium. [145] [146] +
Gym Retro
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[Released](https://gitlab.thesunflowerlab.com) in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on computer game [147] using RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to fix single jobs. Gym Retro provides the ability to generalize in between video games with comparable ideas however different looks.
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RoboSumo
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Released in 2017, [RoboSumo](https://epcblind.org) is a virtual world where humanoid metalearning robot agents initially lack understanding of how to even stroll, but are given the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial learning procedure, the agents learn how to adjust to [changing conditions](http://luodev.cn). When an agent is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, recommending it had found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could produce an intelligence "arms race" that might increase an agent'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 gamers at a high ability level totally through experimental algorithms. Before ending up being a group of 5, the very first public presentation happened at The International 2017, the annual premiere champion competition for the video game, where Dendi, an expert 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 discovered by playing against itself for 2 weeks of actual time, and that the knowing software was a step in the instructions of developing software application that can manage complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a form of support learning, as the bots learn over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156] +
By June 2018, the capability of the bots broadened to play together as a full group 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 expert players, but ended up losing both video 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 exhibition match in San Francisco. [163] [164] The bots' last public look came later that month, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1321201) where they played in 42,729 overall games in a four-day open online competition, [winning](https://git.uzavr.ru) 99.4% of those games. [165] +
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the challenges of [AI](http://119.45.49.212:3000) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep support knowing (DRL) representatives to attain superhuman proficiency 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 manipulate physical things. [167] It finds out entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the things [orientation issue](https://praca.e-logistyka.pl) by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB cameras to allow the robotic to control 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 showed that Dactyl might fix a Rubik's Cube. The robotic was able to resolve 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 toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating gradually more challenging environments. ADR differs 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 new [AI](http://43.139.182.87:1111) models established by OpenAI" to let developers contact it for "any English language [AI](https://bewerbermaschine.de) task". [170] [171] +
Text generation
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The company has promoted generative pretrained transformers (GPT). [172] +
OpenAI's initial 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 [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:WBKJosef646) released in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative design of language could obtain world knowledge and process long-range dependences by pre-training on a diverse corpus with long stretches of contiguous 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 follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative versions at first released to the public. The full version of GPT-2 was not right away released due to issue about prospective abuse, consisting of applications for composing phony news. [174] Some professionals revealed uncertainty that GPT-2 posed a considerable risk.
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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 totally 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 released the total version of the GPT-2 language model. [177] Several websites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue not being [watched language](https://bcde.ru) models to be [general-purpose](https://nepaxxtube.com) students, illustrated by GPT-2 attaining cutting edge precision and perplexity 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 a little 40 [gigabytes](https://lastpiece.co.kr) of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain concerns 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 an unsupervised transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 [designs](https://hireblitz.com) with as couple of as 125 million [specifications](https://thenolugroup.co.za) 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 gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184] +
GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or encountering the basic capability constraints of predictive language designs. [187] [Pre-training](https://git.lain.church) GPT-3 needed several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the general public for issues of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a [two-month totally](http://westec-immo.com) free personal beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was certified solely to [Microsoft](https://www.goodbodyschool.co.kr). [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://destruct82.direct.quickconnect.to:3000) powering the code autocompletion [tool GitHub](http://revoltsoft.ru3000) Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the design can create working code in over a dozen programs languages, most effectively in Python. [192] +
Several issues with glitches, design flaws and vulnerabilities were pointed out. [195] [196] +
GitHub Copilot has actually been accused of releasing copyrighted code, with no author attribution or license. [197] +
OpenAI revealed that they would cease 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 or image inputs. [199] They revealed that the updated innovation passed a simulated law school bar test 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 model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, 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 decreased to reveal different technical details and statistics about GPT-4, such as the exact size 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 cutting edge results 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) standard 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 interface. Its API costs $0.15 per million [input tokens](http://124.70.149.1810880) and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly helpful for business, startups and developers seeking to automate services with [AI](https://kigalilife.co.rw) agents. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and [oeclub.org](https://oeclub.org/index.php/User:MauricioRdz) o1-mini designs, which have actually been developed to take more time to consider their actions, causing higher accuracy. These models are particularly efficient in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was [replaced](https://git.fanwikis.org) by o1. [211] +
o3
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On December 20, 2024, OpenAI revealed o3, the successor [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) of the o1 reasoning model. OpenAI also revealed o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this model 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 instead of o2 to prevent confusion with telecoms providers O2. [215] +
Deep research
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Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web browsing, data analysis, and synthesis, delivering detailed [reports](https://git.xhkjedu.com) 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 category
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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 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 model that creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can create pictures of reasonable things ("a stained-glass window with an image of a blue strawberry") in addition to things 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 realistic outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new fundamental system for converting a text description into a 3[-dimensional](https://lat.each.usp.br3001) model. [220] +
DALL-E 3
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In September 2023, OpenAI revealed DALL-E 3, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:Marcy4075626057) a more [effective model](https://grace4djourney.com) better able to create images from complex descriptions without manual timely engineering and render complex details like hands and text. [221] It was released 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 brief detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of [produced videos](https://code.dev.beejee.org) is unidentified.
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Sora's advancement team named it after the Japanese word for "sky", to represent its "endless innovative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL ยท E 3 [text-to-image model](https://ipen.com.hk). [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos certified for that function, but did not expose the number or the [precise sources](http://122.51.230.863000) of the videos. [223] +
OpenAI demonstrated some [Sora-created high-definition](https://nytia.org) videos to the general public on February 15, 2024, stating that it might create videos up to one minute long. It also shared a technical report highlighting the approaches used to train the model, and the design's abilities. [225] It acknowledged some of its imperfections, including battles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but kept in mind that they should have been cherry-picked and might not represent Sora's normal output. [225] +
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to create practical video from text descriptions, mentioning its possible to reinvent storytelling and content production. He said that his enjoyment about Sora's possibilities was so strong that he had decided to pause plans for expanding his Atlanta-based film studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task model that can carry out 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 anticipate subsequent [musical](https://nse.ai) notes in MIDI music files. It can generate tunes with 10 instruments in 15 designs. According to The Verge, a tune generated by MuseNet tends to begin fairly but then fall under chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the [web mental](https://git.gilgoldman.com) 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 produce music with vocals. After training on 1.2 million samples, the system [accepts](https://ideezy.com) a category, artist, and a bit of lyrics and outputs song samples. OpenAI mentioned the tunes "show local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a significant space" between Jukebox and human-generated music. The Verge stated "It's technologically remarkable, even if the results sound like mushy variations of tunes that might feel familiar", while Business Insider stated "surprisingly, some 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 introduced the Debate Game, which teaches machines to discuss toy problems in front of a human judge. The function is to research study whether such a method may assist in auditing [AI](http://94.224.160.69:7990) decisions and in establishing explainable [AI](https://git.muhammadfahri.com). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of 8 neural network models which are often studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various versions of Inception, and different variations 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 permits users to ask questions in natural language. The system then reacts with a response within seconds.
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