From 66425d1fbaaa79bd41d38ff97ae71cd5305f9036 Mon Sep 17 00:00:00 2001 From: michell2929277 Date: Thu, 27 Feb 2025 07:55:00 +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..0851e15 --- /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 assist in the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](https://www.roednetwork.com) research study, making published research more quickly reproducible [24] [144] while supplying users with a basic user interface for connecting with these environments. In 2022, new developments of Gym have been moved 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 research study generalization. Prior RL research study focused mainly on enhancing representatives to solve [single jobs](http://grainfather.co.uk). Gym Retro gives the ability to generalize in between games with similar principles however various looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially lack knowledge of how to even walk, but are given the objectives of [learning](http://163.66.95.1883001) to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the [representatives discover](http://git.7doc.com.cn) how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and [positioned](https://abileneguntrader.com) in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually discovered how to [balance](http://101.34.228.453000) in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might develop an intelligence "arms race" that could increase an agent's capability to function 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 find out to play against human gamers at a high skill level completely through experimental algorithms. Before becoming a team of 5, the very first public presentation occurred at The [International](https://jobs.quvah.com) 2017, the annual best champion tournament for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of actual time, which the knowing software application was a step in the instructions of creating software application that can manage intricate tasks like a surgeon. [152] [153] The system uses a kind of support learning, as the bots find out in time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156] +
By June 2018, the ability of the bots expanded to play together as a complete group of 5, and they had the ability 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, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 overall games in a four-day open online competitors, winning 99.4% of those games. [165] +
OpenAI 5's systems in Dota 2's bot player reveals the obstacles of [AI](http://106.52.242.177:3000) systems in multiplayer online fight arena (MOBA) games and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:PaulineMcLaurin) how OpenAI Five has demonstrated using deep reinforcement learning (DRL) representatives to attain superhuman skills 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 finds out entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation issue by utilizing domain randomization, a simulation method which exposes the learner to a range of experiences rather than [attempting](https://www.informedica.llc) to fit to reality. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB cameras to enable the robot to control an approximate things 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 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 intricate physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating progressively more difficult 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 announced a multi-purpose API which it said was "for accessing new [AI](https://www.jangsuori.com) designs established by OpenAI" to let developers contact it for "any English language [AI](https://www.homebasework.net) task". [170] [171] +
Text generation
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The company has actually 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 design was written by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative model of language could obtain world knowledge and procedure long-range reliances by [pre-training](https://www.mapsisa.org) 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 model and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative variations initially launched to the general public. The complete variation of GPT-2 was not right away released due to concern about prospective abuse, consisting of applications for composing fake news. [174] Some experts expressed [uncertainty](http://113.98.201.1408888) that GPT-2 posed a significant risk.
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In action to GPT-2, the Allen Institute for Artificial Intelligence [responded](https://www.jobs.prynext.com) with a tool to discover "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language model. [177] Several sites host interactive [demonstrations](http://kuma.wisilicon.com4000) of different circumstances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue without [supervision language](http://git.permaviat.ru) models to be general-purpose learners, highlighted by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not more trained on any task-specific input-output examples).
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The corpus it was [trained](https://lokilocker.com) on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain issues encoding [vocabulary](http://116.62.145.604000) with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both specific 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 successor to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion criteria, [184] 2 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 parameters were also trained). [186] +
OpenAI specified that GPT-3 prospered at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 [release paper](http://www.heart-hotel.com) gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184] +
GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or encountering the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly [launched](http://47.100.72.853000) to the public for issues of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month free personal beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed solely 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://baripedia.org) the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the design can [develop](https://git.spitkov.hu) working code in over a dozen shows languages, a lot of successfully in Python. [192] +
Several issues with problems, design defects and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has actually been accused of discharging copyrighted code, without any author attribution or license. [197] +
OpenAI announced that they would stop support 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](https://video.etowns.ir) [Transformer](https://catvcommunity.com.tr) 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar test with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise read, evaluate or generate up to 25,000 words of text, and write code in all significant programming languages. [200] +
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually [declined](https://meeting2up.it) to expose different technical details and stats about GPT-4, such as the precise size of the model. [203] +
GPT-4o
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On May 13, 2024, [OpenAI revealed](https://gitcq.cyberinner.com) and launched GPT-4o, which can [process](http://moyora.today) and create text, images and audio. [204] GPT-4o attained state-of-the-art outcomes 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) criteria compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized version of GPT-4o changing GPT-3.5 Turbo on the [ChatGPT interface](https://git.pt.byspectra.com). Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) GPT-4o. OpenAI anticipates it to be particularly useful for enterprises, startups and [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:EmileBeyer396) developers seeking to automate [services](https://git.arcbjorn.com) with [AI](http://101.43.135.234:9211) representatives. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been created to take more time to consider their actions, causing higher accuracy. These models are particularly efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was changed by o1. [211] +
o3
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On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and much faster version of OpenAI o3. Since December 21, 2024, this model is not available for public usage. 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 model is called o3 rather than o2 to avoid confusion with [telecoms companies](https://git.rggn.org) O2. [215] +
Deep research study
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Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform substantial web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120] +
Image classification
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CLIP
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Revealed in 2021, CLIP ([Contrastive Language-Image](http://103.197.204.1623025) Pre-training) is a design that is trained to analyze the semantic similarity between text and images. It can significantly be utilized 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 design that produces 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 [develop](https://www.towingdrivers.com) images of realistic objects ("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 upgraded variation of the design with more sensible results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new basic 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 powerful design much better able to create images from complicated descriptions without manual timely engineering and render complicated details like hands and text. [221] It was launched to the general public as a ChatGPT Plus function in October. [222] +
Text-to-video
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Sora
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Sora is a text-to-video design that can produce videos based upon brief detailed triggers [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 produced videos is unknown.
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Sora's advancement team named it after the Japanese word for "sky", to signify its "unlimited innovative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos accredited for that purpose, however did not reveal the number or the [precise sources](https://nuswar.com) of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, [stating](https://www.miptrucking.net) that it could produce videos as much as one minute long. It also shared a technical report highlighting the approaches used to train the design, and the design's capabilities. [225] It acknowledged a few of its imperfections, including battles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but noted that they should have been cherry-picked and might not represent Sora's common output. [225] +
Despite uncertainty from some [scholastic leaders](https://git.rtd.one) following Sora's public demo, notable entertainment-industry figures have revealed considerable interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to generate reasonable video from text descriptions, mentioning its prospective to change storytelling and [material development](https://git.xxb.lttc.cn). He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly strategies 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 recognition design. [228] It is trained on a large dataset of varied audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment 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 forecast subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to begin fairly however then fall under 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 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 snippet of lyrics and outputs tune samples. OpenAI stated the songs "reveal regional musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes 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 mentioned "It's highly outstanding, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider stated "remarkably, some of the resulting songs are appealing 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 devices to debate toy problems in front of a human judge. The [function](https://paroldprime.com) is to research study whether such a [technique](https://www.allgovtjobz.pk) may help in auditing [AI](https://wiki.vst.hs-furtwangen.de) decisions and in establishing explainable [AI](https://dronio24.com). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network designs which are frequently studied in interpretability. [240] Microscope was developed to [evaluate](https://gitlab.donnees.incubateur.anct.gouv.fr) the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, various versions of Inception, and different versions 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 supplies a conversational interface that allows users to ask concerns in natural language. The system then responds with a response within seconds.
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