From a59351de13b46b3aaebf573265af45a281f99084 Mon Sep 17 00:00:00 2001 From: robertlopes15 Date: Sat, 5 Apr 2025 16:31:27 +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..0702f84 --- /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 designed to help with the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in [AI](https://www.grandtribunal.org) research, making published research study more easily reproducible [24] [144] while providing users with an easy interface for interacting with these environments. In 2022, new advancements of Gym have actually been moved to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to solve single tasks. Gym Retro provides the capability to generalize between games with similar principles but various looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially do not have understanding of how to even stroll, but are provided the goals of learning to move and to press the opposing representative out of the ring. [148] Through this [adversarial](https://www.earnwithmj.com) learning procedure, the [representatives discover](https://gitea.dusays.com) how to adjust to changing conditions. When a representative is then removed from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents might produce an intelligence "arms race" that might increase an agent's ability to [operate](https://git.qoto.org) 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 find out to play against human players at a high ability level totally through trial-and-error algorithms. Before ending up being a team of 5, the first [public demonstration](http://mirae.jdtsolution.kr) occurred at The International 2017, the annual premiere champion tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of actual time, which the knowing software was a step in the direction of producing software that can handle complicated tasks like a surgeon. [152] [153] The system utilizes a type of support learning, as the bots learn with time by playing against themselves hundreds of times a day for months, and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Nicole95V08) are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156] +
By June 2018, the ability of the bots broadened to play together as a complete team 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 two exhibit matches against professional players, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the video game at the time, 2:0 in a live exhibit 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 games. [165] +
OpenAI 5's mechanisms in Dota 2's bot player reveals the difficulties of [AI](http://140.143.226.1) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated using deep support knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses device finding out to train a Shadow Hand, a human-like robotic 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 took on the object orientation problem by utilizing domain randomization, a simulation method which exposes the learner to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB cams to allow the robot to manipulate an arbitrary object by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168] +
In 2019, [OpenAI demonstrated](https://vacancies.co.zm) that Dactyl could solve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complex 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 approach of generating 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](https://dubai.risqueteam.com) API which it said was "for accessing new [AI](https://africasfaces.com) models developed by OpenAI" to let designers contact it for "any English language [AI](http://git.zthymaoyi.com) job". [170] [171] +
Text generation
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The business has actually promoted generative pretrained transformers (GPT). [172] +
OpenAI's original GPT model ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language design was [composed](https://git.gumoio.com) by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative design of language could obtain world knowledge and procedure 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 an unsupervised transformer language model and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions initially launched to the public. The complete variation of GPT-2 was not right away released due to issue about possible abuse, including applications for composing fake news. [174] Some professionals expressed uncertainty that GPT-2 posed a significant risk.
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In response to GPT-2, the Allen [Institute](https://tawtheaf.com) for Artificial Intelligence reacted with a tool to identify "neural phony 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 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 circumstances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors [argue unsupervised](http://git.zthymaoyi.com) language models to be general-purpose learners, illustrated by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not additional trained on any task-specific input-output examples).
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The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows [representing](https://csmsound.exagopartners.com) 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](https://propveda.com) language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million parameters were likewise trained). [186] +
OpenAI mentioned that GPT-3 [succeeded](http://193.9.44.91) at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning in between English and Romanian, and between [English](http://194.67.86.1603100) and German. [184] +
GPT-3 dramatically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or coming across the fundamental ability constraints of [predictive language](https://classtube.ru) models. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, compared to tens 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 concerns of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary private beta that began in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed specifically to Microsoft. [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://teba.timbaktuu.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can produce working code in over a dozen programming languages, most efficiently in Python. [192] +
Several issues with glitches, design flaws and security vulnerabilities were pointed out. [195] [196] +
GitHub Copilot has been accused of [producing copyrighted](https://han2.kr) code, without any author [attribution](https://finitipartners.com) or license. [197] +
OpenAI announced that they would terminate support for Codex API on March 23, 2023. [198] +
GPT-4
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On March 14, 2023, OpenAI revealed the [release](https://jobs.careersingulf.com) of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They [revealed](https://zikorah.com) that the updated technology passed a simulated law school bar exam with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also read, evaluate or create approximately 25,000 words of text, and write code in all major programming languages. [200] +
Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, 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 decreased to reveal different technical details and stats about GPT-4, such as the accurate size of the model. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision standards, setting brand-new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the [Massive Multitask](https://archie2429263902267.bloggersdelight.dk) 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](https://tenacrebooks.com) 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 beneficial for business, start-ups and designers seeking to automate services with [AI](http://82.19.55.40:443) agents. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been developed to take more time to believe about their responses, causing higher accuracy. These designs are particularly reliable in science, coding, and thinking jobs, and were made available to ChatGPT 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 unveiled o3, the successor of the o1 thinking design. OpenAI also revealed o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are [testing](http://47.90.83.1323000) 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 O2. [215] +
Deep research
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Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of [OpenAI's](http://git.365zuoye.com) o3 design to carry out extensive web browsing, information analysis, and synthesis, providing [detailed reports](https://www.istorya.net) 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) criteria. [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 similarity 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 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 purse formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce pictures of practical things ("a stained-glass window with an image of a blue strawberry") as well as objects 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 [updated variation](http://190.117.85.588095) of the design with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new primary system for transforming a text description into a 3-dimensional design. [220] +
DALL-E 3
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In September 2023, OpenAI revealed DALL-E 3, a more powerful model better able to produce images from [intricate descriptions](https://www.thehappyservicecompany.com) without manual prompt engineering and render intricate 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 generate videos based upon short detailed [prompts](https://www.askmeclassifieds.com) [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.
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Sora's development team named it after the Japanese word for "sky", to symbolize its "limitless innovative 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 in addition to copyrighted videos certified for that function, but did not expose the number or the precise sources of the videos. [223] +
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it might generate videos approximately one minute long. It also shared a technical report highlighting the approaches utilized to train the design, and the design's abilities. [225] It acknowledged some of its imperfections, [including struggles](http://app.vellorepropertybazaar.in) mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", however noted that they need to have been cherry-picked and may not represent Sora's normal output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have actually revealed considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to create reasonable video from text descriptions, mentioning its potential to change storytelling and content [creation](http://35.207.205.183000). He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause strategies for broadening 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 model. [228] It is trained on a large dataset of diverse audio and is also a multi-task design that can perform multilingual speech acknowledgment along with speech translation and [garagesale.es](https://www.garagesale.es/author/lashaylaroc/) 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 notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to start fairly but then fall under mayhem 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://inspiredcollectors.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 generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs tune samples. OpenAI mentioned the tunes "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" which "there is a substantial gap" in between Jukebox and human-generated music. The Verge stated "It's technically excellent, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider stated "remarkably, a few of the resulting songs are catchy and sound legitimate". [234] [235] [236] +
Interface
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Debate Game
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In 2018, OpenAI launched the Debate Game, which teaches makers to dispute toy issues in front of a human judge. The function is to research study whether such an approach might assist in auditing [AI](https://paknoukri.com) choices and in establishing explainable [AI](https://git.penwing.org). [237] [238] +
Microscope
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Released in 2020, [Microscope](https://kol-jobs.com) [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network designs which are often studied in interpretability. [240] Microscope was developed to analyze the features that form inside these neural networks easily. The models included are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is a synthetic intelligence tool developed on top of GPT-3 that supplies a conversational interface that permits users to ask concerns in natural language. The system then responds with an answer within seconds.
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