commit 97bc9a5a80f180522a11030307271726035c990b Author: charlipilgrim Date: Fri Feb 7 20:08:46 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..287aaaf --- /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 facilitate the [development](http://82.157.77.1203000) of support knowing algorithms. It aimed to standardize how environments are defined in [AI](https://bikapsul.com) research, making published research study more quickly reproducible [24] [144] while providing users with a basic interface for engaging with these environments. In 2022, brand-new advancements of Gym have actually 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 (RL) research on computer game [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to solve single jobs. Gym Retro provides the ability to generalize in between video games with similar principles but different looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack knowledge of how to even walk, however are provided the goals of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial [learning](https://cruzazulfansclub.com) process, the agents find out how to adjust to altering conditions. When a [representative](https://rca.co.id) is then gotten rid of from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might develop an intelligence "arms race" that might increase a [representative's capability](https://git.wisder.net) to operate even outside the context of the competition. [148] +
OpenAI 5
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OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that [discover](https://ai.ceo) to play against human gamers at a high ability level entirely through experimental algorithms. Before becoming a group of 5, the first public presentation happened at The International 2017, [yewiki.org](https://www.yewiki.org/User:WinifredHassell) the annual best championship competition for the game, where Dendi, a professional Ukrainian gamer, lost against a bot in a [live one-on-one](https://jobs.quvah.com) matchup. [150] [151] After the match, [CTO Greg](https://japapmessenger.com) Brockman explained that the bot had actually learned by playing against itself for 2 weeks of genuine time, which the learning software application was a step in the instructions of creating software application that can manage complex jobs like a surgeon. [152] [153] The system uses a form of support knowing, as the bots learn in time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and taking map goals. [154] [155] [156] +
By June 2018, the capability of the bots expanded to play together as a complete team of 5, and they were able to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated 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' final public appearance came later on that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games. [165] +
OpenAI 5's systems in Dota 2's bot player reveals the difficulties of [AI](http://publicacoesacademicas.unicatolicaquixada.edu.br) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated the usage of deep reinforcement learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It finds out entirely in simulation using the very same RL algorithms and training code as OpenAI Five. [OpenAI tackled](https://kewesocial.site) the item orientation issue by using domain randomization, a simulation method which exposes the student to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB cameras to allow the robot to manipulate an approximate item by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of creating progressively harder environments. ADR differs from manual domain randomization by not requiring a human to specify . [169] +
API
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In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](http://gitea.smartscf.cn:8000) designs developed by OpenAI" to let developers call on it for "any English language [AI](http://39.99.158.114:10080) job". [170] [171] +
Text generation
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The company has popularized generative pretrained transformers (GPT). [172] +
OpenAI's initial GPT design ("GPT-1")
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The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and published in preprint on [OpenAI's site](https://sing.ibible.hk) on June 11, 2018. [173] It revealed how a generative model of language could obtain world understanding and process long-range dependencies 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 an unsupervised transformer language model and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative variations initially released to the general public. The complete variation of GPT-2 was not instantly launched due to [concern](https://gitlab.innive.com) about possible misuse, including applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 posed a substantial danger.
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In [response](https://firstamendment.tv) to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "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 drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total variation of the GPT-2 language model. [177] Several sites host interactive demonstrations of various circumstances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue not being watched language models to be general-purpose students, shown by GPT-2 attaining advanced 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by [encoding](https://source.futriix.ru) 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 a not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as few as 125 million specifications were likewise trained). [186] +
OpenAI stated that GPT-3 was successful at certain "meta-learning" tasks and could generalize the purpose 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 in between English and German. [184] +
GPT-3 dramatically enhanced benchmark outcomes over GPT-2. [OpenAI warned](https://app.hireon.cc) that such scaling-up of language models could be approaching or experiencing the essential capability constraints of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately launched to the public for concerns 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 began in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed exclusively 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](http://39.100.93.187:2585) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the design can create working code in over a dozen programming languages, many effectively in Python. [192] +
Several concerns with glitches, design flaws and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has actually been accused of emitting copyrighted code, with no author [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:PHZIsis067429) attribution or license. [197] +
OpenAI announced that they would discontinue assistance 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 announced that the updated technology passed a simulated law school bar [examination](https://skytube.skyinfo.in) with a score around the top 10% of [test takers](https://deadlocked.wiki). (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 model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal numerous 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, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:KathrinSabella) OpenAI announced and launched GPT-4o, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:RamonitaSikes00) which can process and generate text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision benchmarks, setting brand-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 released GPT-4o mini, a smaller version of GPT-4o replacing GPT-3.5 Turbo on the [ChatGPT interface](https://careers.ebas.co.ke). 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 developers looking for to automate services with [AI](https://www.paradigmrecruitment.ca) agents. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been created to take more time to think about their reactions, leading to higher precision. These designs are particularly 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 thinking design. OpenAI likewise unveiled o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, [security](http://lty.co.kr) and security researchers had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecommunications providers O2. [215] +
Deep research study
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Deep research is an [agent established](https://www.trabahopilipinas.com) by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform substantial web browsing, information analysis, and synthesis, providing detailed [reports](http://150.158.93.1453000) within a timeframe of 5 to thirty minutes. [216] With searching and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE ([Humanity's](http://221.182.8.1412300) Last Exam) standard. [120] +
Image classification
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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 similarity between text and images. It can significantly be utilized 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 utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can produce pictures of practical things ("a stained-glass window with a picture of a blue strawberry") in addition to things 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 reasonable outcomes. [219] In December 2022, OpenAI [released](http://forum.moto-fan.pl) on GitHub software for Point-E, a new primary 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](https://forum.webmark.com.tr) better able to create images from complex descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was [launched](https://my-estro.it) 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 prompts [223] along with extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.
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[Sora's development](https://www.sedatconsultlimited.com) group named it after the Japanese word for "sky", to represent its "unlimited innovative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL ยท E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos licensed for that purpose, but did not reveal the number or the exact sources of the videos. [223] +
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it could create videos up to one minute long. It also shared a technical report highlighting the methods utilized to train the design, and the design's capabilities. [225] It acknowledged a few of its drawbacks, including battles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but kept in mind that they should have been cherry-picked and may not represent Sora's typical output. [225] +
Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have shown considerable interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to create practical video from text descriptions, mentioning its potential to change storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had decided to pause prepare for broadening his Atlanta-based movie 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 big dataset of varied audio and is likewise a multi-task design that can perform multilingual speech recognition 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 create songs with 10 instruments in 15 designs. According to The Verge, [89u89.com](https://www.89u89.com/author/sole7081199/) a song produced by MuseNet tends to begin fairly however then fall under turmoil the longer it plays. [230] [231] In popular culture, [preliminary applications](https://gold8899.online) of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to produce music for the titular character. [232] [233] +
Jukebox
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Released in 2020, Jukebox is an [open-sourced algorithm](http://test.wefanbot.com3000) to [generate](https://careerconnect.mmu.edu.my) 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 stated](https://stagingsk.getitupamerica.com) the songs "reveal local musical coherence [and] follow standard chord patterns" however [acknowledged](https://sso-ingos.ru) that the songs do not have "familiar bigger musical structures such as choruses that repeat" which "there is a considerable gap" between [Jukebox](http://120.79.157.137) and human-generated music. The Verge specified "It's technically excellent, even if the outcomes seem like mushy versions of songs that might feel familiar", while Business Insider stated "surprisingly, some of the resulting tunes are memorable and sound legitimate". [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 dispute toy issues in front of a human judge. The function is to research whether such a technique may help in auditing [AI](https://gogs.rg.net) choices and in developing explainable [AI](https://gitea.mierzala.com). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network designs which are often studied in [interpretability](https://aceme.ink). [240] Microscope was produced to analyze the features that form inside these neural networks quickly. The [designs included](https://infinirealm.com) are AlexNet, VGG-19, different versions of Inception, and different variations of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, [ChatGPT](https://mypungi.com) is an expert system tool built on top of GPT-3 that provides a conversational interface that permits users to ask questions in natural language. The system then responds with an answer within seconds.
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