From c75f30dd194d47810970604e21321c19ea1e050c Mon Sep 17 00:00:00 2001 From: adriennegellat Date: Fri, 4 Apr 2025 23:33:56 +0800 Subject: [PATCH] Add The Verge Stated It's Technologically Impressive --- ...rge-Stated-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..8c0e90b --- /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 support knowing algorithms. It aimed to standardize how environments are [defined](https://novashop6.com) in [AI](http://162.19.95.94:3000) research, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:Princess3594) making released research study more quickly reproducible [24] [144] while providing users with a simple interface for communicating with these environments. In 2022, [brand-new advancements](http://1.14.71.1033000) 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 video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to resolve single tasks. Gym Retro provides the capability to generalize between games with similar concepts however various appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack knowledge of how to even stroll, however are [offered](https://kolei.ru) the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the agents learn how to adapt to changing conditions. When a representative is then [removed](https://dash.bss.nz) from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents might develop an intelligence "arms race" that could increase a representative's capability to function even outside the context of the competition. [148] +
OpenAI 5
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OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high ability level entirely through experimental algorithms. Before becoming a team of 5, the first public demonstration occurred at The International 2017, the annual premiere champion tournament for the video game, where Dendi, a professional 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 found out by [playing](http://www.chemimart.kr) against itself for 2 weeks of real time, and that the learning software was an action in the direction of producing software that can handle intricate tasks like a . [152] [153] The system utilizes a type of support knowing, as the bots learn in time by playing against themselves hundreds of times a day for months, and are rewarded for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11968903) actions such as killing an enemy and taking map objectives. [154] [155] [156] +
By June 2018, the capability of the bots expanded to play together as a full team of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the [video game](http://ja7ic.dxguy.net) 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 games in a four-day open online competition, winning 99.4% of those games. [165] +
OpenAI 5['s systems](https://social.engagepure.com) in Dota 2's bot gamer shows the obstacles of [AI](http://106.52.126.96:3000) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown making use of deep reinforcement learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses device learning to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It finds out entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by using domain randomization, a simulation approach which exposes the student to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB electronic cameras to permit the robotic to control an arbitrary item by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the [Rubik's Cube](http://gnu5.hisystem.com.ar) introduce complicated [physics](https://proputube.com) that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by [utilizing Automatic](https://git.jackbondpreston.me) Domain Randomization (ADR), a simulation method of creating gradually harder environments. ADR differs from manual domain randomization by not requiring a human to define randomization ranges. [169] +
API
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In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](https://meephoo.com) models developed by OpenAI" to let developers contact it for "any English language [AI](https://familyworld.io) job". [170] [171] +
Text generation
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The business has actually popularized 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 composed by Alec Radford and his colleagues, and 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 dependencies by pre-training on a varied 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 without supervision transformer language design and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations at first [released](http://adbux.shop) to the public. The full version of GPT-2 was not instantly launched due to concern about possible abuse, consisting of applications for writing phony news. [174] Some experts revealed uncertainty that GPT-2 [positioned](http://funnydollar.ru) a significant threat.
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural phony news". [175] Other scientists, 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 muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language model. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue not being watched language designs to be general-purpose learners, shown by GPT-2 attaining state-of-the-art precision and [perplexity](https://cyberbizafrica.com) on 7 of 8 zero-shot tasks (i.e. the design was not more 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 of text from URLs shared in Reddit submissions with a minimum of 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 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 without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were also trained). [186] +
OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and might 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 results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or encountering the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 required several 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 model was not instantly launched to the general 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 personal beta that started 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 additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://tiptopface.com) [powering](https://splink24.com) the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the model can create working code in over a dozen programs languages, a lot of successfully in Python. [192] +
Several problems with glitches, style flaws and security vulnerabilities were mentioned. [195] [196] +
GitHub Copilot has been implicated of releasing copyrighted code, with no [author attribution](https://www.ycrpg.com) or license. [197] +
OpenAI announced 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 [upgraded innovation](http://upleta.rackons.com) passed a simulated law school bar examination with a rating around the leading 10% of [test takers](https://mtglobalsolutionsinc.com). (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, evaluate or create up to 25,000 words of text, and write code in all [major programs](http://154.9.255.1983000) languages. [200] +
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also [efficient](https://complete-jobs.co.uk) in taking images as input on [ChatGPT](https://getquikjob.com). [202] OpenAI has actually declined to reveal numerous technical [details](https://git.cyu.fr) and stats about GPT-4, such as the precise size of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained advanced 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) standard compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller variation of GPT-4o replacing 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 especially useful for enterprises, start-ups and developers seeking to automate services with [AI](https://git.clicknpush.ca) 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 responses, resulting in higher precision. These models are particularly efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1[-preview](https://git.andy.lgbt) was replaced by o1. [211] +
o3
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On December 20, 2024, [OpenAI unveiled](https://pivotalta.com) o3, the successor of the o1 [reasoning design](http://175.27.215.923000). OpenAI also revealed o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing 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 model is called o3 rather than o2 to prevent confusion with telecoms services provider O2. [215] +
Deep research
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Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, 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 analyze the semantic similarity between text and images. It can especially 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 design 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 shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can develop pictures of realistic items ("a stained-glass window with a picture of a blue strawberry") as well as items that do not exist in truth ("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 announced DALL-E 2, an upgraded version of the model with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new rudimentary system for transforming a text description into a 3-dimensional design. [220] +
DALL-E 3
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In September 2023, OpenAI announced DALL-E 3, a more powerful model much better able to create images from [intricate descriptions](http://60.209.125.23820010) without manual prompt engineering and render intricate details like hands and text. [221] It was launched to the general 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 create videos based upon brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of produced videos is [unknown](http://code.hzqykeji.com).
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Sora's development group named it after the Japanese word for "sky", to symbolize its "endless innovative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos licensed for that purpose, but did not reveal the number or the [specific sources](https://drshirvany.ir) of the videos. [223] +
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could create videos approximately one minute long. It also shared a technical report highlighting the techniques utilized to train the model, and the model's capabilities. [225] It acknowledged some of its drawbacks, consisting of battles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but noted that they must have been cherry-picked and might not represent Sora's common output. [225] +
Despite uncertainty from some [academic leaders](https://10-4truckrecruiting.com) following Sora's public demonstration, notable [entertainment-industry figures](https://git.cyu.fr) have revealed considerable interest in the technology's capacity. In an interview, actor/filmmaker [Tyler Perry](https://gitea.dusays.com) expressed his astonishment at the technology's capability to generate realistic video from text descriptions, mentioning its potential to change storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly prepare for expanding his [Atlanta-based film](https://www.p3r.app) 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 big dataset of diverse audio and is also a multi-task model that can carry out 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 anticipate subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to begin fairly however then fall into chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the internet psychological 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 category, artist, and a bit of lyrics and outputs tune samples. OpenAI stated the songs "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" and that "there is a substantial space" in between Jukebox and human-generated music. The Verge mentioned "It's highly outstanding, even if the outcomes seem like mushy versions of tunes that may feel familiar", while Business Insider stated "surprisingly, 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 introduced the Debate Game, which teaches machines to debate toy issues in front of a human judge. The function is to research whether such a technique may assist in auditing [AI](https://www.genbecle.com) choices and in developing explainable [AI](https://losangelesgalaxyfansclub.com). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of 8 neural network designs which are typically studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, different variations of Inception, and various versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an artificial intelligence tool constructed on top of GPT-3 that supplies a conversational interface that permits users to ask questions in natural language. The system then responds with a response within seconds.
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