commit ee03904723d125d63099696521fd4e0e221e1430 Author: bernadinekime Date: Sat Feb 15 18:06:01 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..2377070 --- /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 assist in the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](http://git.keliuyun.com:55676) research study, making released research more easily reproducible [24] [144] while supplying users with a simple user interface for engaging with these environments. In 2022, [brand-new advancements](http://git.kdan.cc8865) 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 [reinforcement learning](http://okna-samara.com.ru) (RL) research on video games [147] using RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to resolve single jobs. Gym Retro offers the ability to generalize in between video games with comparable principles but various [appearances](https://addify.ae).
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RoboSumo
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Released in 2017, [RoboSumo](http://osbzr.com) is a virtual world where humanoid metalearning robotic representatives at first do not have knowledge of how to even stroll, however are provided the goals of discovering to move and to push the out of the ring. [148] Through this adversarial learning process, the representatives discover how to adapt to changing conditions. When a representative is then removed from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, [recommending](https://mastercare.care) it had found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might develop an intelligence "arms race" that could increase a representative's ability to function even outside the context of the competitors. [148] +
OpenAI 5
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OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against [human players](http://24insite.com) at a high skill level totally through trial-and-error algorithms. Before ending up being a group of 5, the first public [presentation](https://www.longisland.com) took place at The International 2017, the yearly best [championship tournament](https://git.intelgice.com) for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by playing against itself for 2 weeks of genuine time, which the knowing software [application](https://www.tmip.com.tr) was a step in the direction of producing software application that can manage complicated tasks like a cosmetic surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots find out over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking [map objectives](https://gitea.gumirov.xyz). [154] [155] [156] +
By June 2018, the ability of the [bots broadened](https://travelpages.com.gh) to play together as a full group of 5, and they were able to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert gamers, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated 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 appearance came later 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 gamer shows the challenges of [AI](http://219.150.88.234:33000) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated making use of [deep reinforcement](http://git.ningdatech.com) knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses maker finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It learns totally in simulation using the very same RL algorithms and [training](https://feleempleo.es) code as OpenAI Five. OpenAI took on the item orientation problem by utilizing domain randomization, a simulation method which exposes the student to a variety of [experiences](https://agora-antikes.gr) rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB electronic cameras to permit the robot to manipulate an arbitrary item by seeing it. In 2018, OpenAI revealed that the system had the ability to [manipulate](https://corerecruitingroup.com) a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. [Objects](https://www.noagagu.kr) like the Rubik's Cube introduce 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 producing gradually harder 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 announced a multi-purpose API which it said was "for accessing new [AI](https://www.virfans.com) models developed by OpenAI" to let designers contact it for "any English language [AI](http://43.138.57.202:3000) 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 pre-training of a transformer-based language design was written by [Alec Radford](http://a43740dd904ea46e59d74732c021a354-851680940.ap-northeast-2.elb.amazonaws.com) and his coworkers, and released in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language might obtain world knowledge and procedure long-range [reliances](https://weldersfabricators.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 initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations at first launched to the general public. The complete variation of GPT-2 was not right away launched due to concern about prospective abuse, including applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 positioned a [substantial threat](https://rca.co.id).
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language design. [177] Several websites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue unsupervised language models to be general-purpose learners, illustrated by GPT-2 attaining modern 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain problems 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 model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion parameters, [184] two 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 prospered 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 knowing in between English and Romanian, and between English and German. [184] +
GPT-3 significantly [improved benchmark](https://gst.meu.edu.jo) outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous 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 design was not instantly launched to the general 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 personal beta that began in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was certified solely to Microsoft. [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](https://gitlab.truckxi.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in [personal](https://takesavillage.club) beta. [194] According to OpenAI, the model can create working code in over a dozen shows languages, most successfully in Python. [192] +
Several issues with problems, style defects and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has been implicated of emitting copyrighted code, with no author attribution or license. [197] +
OpenAI revealed that they would cease assistance 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), efficient in accepting text or image inputs. [199] They announced that the updated innovation passed a [simulated law](https://www.dutchsportsagency.com) school bar exam 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 create approximately 25,000 words of text, and compose code in all major programs languages. [200] +
Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement 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 also [capable](http://clipang.com) of taking images as input on ChatGPT. [202] OpenAI has decreased to expose numerous 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 announced and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art results in voice, multilingual, and vision criteria, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) [benchmark compared](https://www.majalat2030.com) to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI released 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 anticipates it to be particularly useful for enterprises, start-ups and developers seeking to automate services with [AI](http://anggrek.aplikasi.web.id:3000) agents. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been developed to take more time to consider their actions, causing greater 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 unveiled o3, the successor of the o1 reasoning design. OpenAI also revealed o3-mini, a lighter and faster version of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecommunications companies O2. [215] +
Deep research study
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Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform substantial web browsing, information analysis, and synthesis, delivering detailed reports within a [timeframe](https://gitlab.donnees.incubateur.anct.gouv.fr) of 5 to 30 minutes. [216] With searching and Python tools enabled, 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 design that is trained to evaluate the semantic resemblance in between text and images. It can notably 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 creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and [produce](http://digitalmaine.net) corresponding images. It can create pictures of practical items ("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"). Since 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 updated version of the model with more practical results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new fundamental system for converting 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 effective model better able to generate images from complicated descriptions without manual prompt [engineering](https://wiki.tld-wars.space) and render complex 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 generate videos based upon brief detailed prompts [223] as well as extend existing videos [forwards](https://talentocentroamerica.com) or [wavedream.wiki](https://wavedream.wiki/index.php/User:LanSeyler65095) in reverse in time. [224] It can create videos with resolution up to 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 symbolize its "unlimited innovative potential". [223] Sora's technology is an adaptation of the technology behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos licensed for that purpose, however did not expose the number or the exact sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, [mentioning](https://jobz0.com) that it might create videos approximately one minute long. It likewise shared a technical report highlighting the methods used to train the model, and the design's abilities. [225] It acknowledged a few of its imperfections, including battles [simulating complex](https://semtleware.com) physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they need to have been [cherry-picked](https://gogs.es-lab.de) and might not represent Sora's normal output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have actually revealed substantial interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's ability to generate reasonable video from text descriptions, citing its potential to reinvent storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly prepare for expanding his [Atlanta-based film](http://120.79.218.1683000) studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, Whisper is a [general-purpose speech](https://aggm.bz) recognition model. [228] It is trained on a large dataset of varied audio and is also a multi-task design that can carry out multilingual speech acknowledgment along with speech translation and language identification. [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 songs with 10 instruments in 15 designs. According to The Verge, a tune created by MuseNet tends to start fairly however then fall into chaos the longer it plays. [230] [231] In [popular](http://git.mcanet.com.ar) culture, preliminary applications of this tool were used as early as 2020 for the web psychological thriller Ben [Drowned](https://nkaebang.com) to create music for the titular 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 bit of lyrics and outputs tune samples. OpenAI specified the songs "show local musical coherence [and] follow traditional chord patterns" however [acknowledged](https://www.yanyikele.com) that the tunes lack "familiar bigger musical structures such as choruses that repeat" which "there is a substantial space" in between Jukebox and human-generated music. The Verge stated "It's technically impressive, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider specified "surprisingly, some of the resulting tunes are memorable and sound genuine". [234] [235] [236] +
Interface
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Debate Game
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In 2018, OpenAI introduced the Debate Game, which teaches makers to debate toy problems in front of a human judge. The function is to research study whether such a technique might help in auditing [AI](https://git.komp.family) decisions and in developing explainable [AI](https://www.50seconds.com). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of 8 neural network models which are frequently studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different versions of Inception, and various versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, [ChatGPT](http://13.209.39.13932421) is an expert system tool built on top of GPT-3 that provides a [conversational interface](https://git.itk.academy) that allows users to ask concerns in natural language. The system then responds with an answer within seconds.
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