From f2789c2c12b19b4133a2e52050663eaf93aa4a7f Mon Sep 17 00:00:00 2001 From: ronnybruntnell Date: Sun, 9 Feb 2025 13:59:08 +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..29473cd --- /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 created to assist in the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in [AI](https://git.isatho.me) research, making published research study more easily reproducible [24] [144] while offering users with an easy user interface for engaging with these environments. In 2022, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:RachelSantos0) new developments of Gym have been relocated to the [library Gymnasium](https://sudanre.com). [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for support learning (RL) research study on computer game [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 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 representatives initially do not have knowledge of how to even walk, however are offered the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives discover how to adjust to changing conditions. When a representative is then gotten rid of from this virtual environment and placed in a new virtual environment with high winds, the representative 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 competitors between representatives might produce an intelligence "arms race" that might increase an agent's capability to work 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 discover to play against human players at a high ability level completely through trial-and-error algorithms. Before ending up being a group of 5, the first public presentation happened at The International 2017, the yearly best champion competition for the video game, where Dendi, a professional 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 actually learned by playing against itself for two weeks of actual time, which the learning software application was a step in the instructions of [developing software](http://ev-gateway.com) application that can manage intricate tasks like a surgeon. [152] [153] The system uses a type of support learning, as the bots discover gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an opponent and taking map objectives. [154] [155] [156] +
By June 2018, the capability of the [bots expanded](http://vk-mix.ru) to play together as a full team of 5, and they had the ability to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last 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 video games. [165] +
OpenAI 5['s mechanisms](http://114.115.218.2309005) in Dota 2's bot player reveals the challenges of [AI](http://dancelover.tv) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown the use of deep reinforcement learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl utilizes machine [learning](http://221.238.85.747000) to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It finds out totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by using domain randomization, a simulation method which exposes the learner to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, likewise has RGB electronic cameras to allow the robot to manipulate an approximate things 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 that Dactyl might resolve a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to perturbations by using [Automatic Domain](http://www.lebelleclinic.com) Randomization (ADR), a simulation technique of generating progressively more hard 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 announced a multi-purpose API which it said was "for accessing brand-new [AI](https://git.xinstitute.org.cn) designs established by OpenAI" to let designers call on it for "any English language [AI](https://activeaupair.no) job". [170] [171] +
Text generation
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The company has actually [promoted generative](https://proputube.com) pretrained transformers (GPT). [172] +
OpenAI's initial GPT model ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and process 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](http://182.92.163.1983000) language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations at first released to the general public. The full variation of GPT-2 was not right away released due to concern about prospective abuse, including applications for writing fake news. [174] Some professionals [revealed uncertainty](http://git.ningdatech.com) that GPT-2 posed a substantial threat.
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In action to GPT-2, the Allen [Institute](http://gitlab.y-droid.com) for Artificial Intelligence responded with a tool to discover "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned of "the technology to totally 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 variation of the GPT-2 language design. [177] Several websites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue not being watched language models to be general-purpose students, shown by GPT-2 attaining advanced precision 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 on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain issues encoding vocabulary 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 a not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as few as 125 million parameters were also trained). [186] +
OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning between English and Romanian, and in between [English](https://sb.mangird.com) and German. [184] +
GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or encountering the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, 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 right away launched to the public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary [private](http://pyfup.com3000) beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was certified exclusively to Microsoft. [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://intgez.com) powering 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 efficiently in Python. [192] +
Several concerns with glitches, style defects and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has actually been implicated of emitting copyrighted code, with no author attribution or license. [197] +
OpenAI announced that they would discontinue 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 Transformer 4 (GPT-4), [efficient](http://47.101.207.1233000) in accepting text or image inputs. [199] They revealed that the [upgraded innovation](https://schoolmein.com) passed a simulated law school bar examination with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise check out, analyze or generate as much as 25,000 words of text, and compose code in all significant shows languages. [200] +
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on [ChatGPT](https://diskret-mote-nodeland.jimmyb.nl). [202] OpenAI has actually declined to expose numerous technical details and data about GPT-4, such as the precise size of the model. [203] +
GPT-4o
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On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced lead to 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 Language Understanding (MMLU) [benchmark compared](https://git.dadunode.com) to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI [released](https://willingjobs.com) GPT-4o mini, a smaller version of GPT-4o changing 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 especially beneficial for business, start-ups and designers seeking to automate services with [AI](http://ipc.gdguanhui.com:3001) agents. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been designed to take more time to think of their actions, leading to greater accuracy. These models are particularly reliable in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211] +
o3
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On December 20, 2024, OpenAI revealed 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 design is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and [security researchers](http://bolling-afb.rackons.com) had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215] +
Deep research
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Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform extensive web browsing, data analysis, and synthesis, delivering detailed reports within a [timeframe](https://gitea.jessy-lebrun.fr) of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached an accuracy 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 examine the [semantic similarity](https://open-gitlab.going-link.com) in 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 develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can produce images of practical things ("a stained-glass window with a picture of a blue strawberry") along with 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 upgraded variation of the model with more practical outcomes. [219] In December 2022, OpenAI published on [GitHub software](https://www.bongmedia.tv) for Point-E, a brand-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 revealed DALL-E 3, a more effective model better able to generate images from complicated descriptions without manual prompt engineering and 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 produce videos based on brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.
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Sora's advancement team named it after the [Japanese](https://www.jobsalert.ai) word for "sky", to signify its "endless innovative potential". [223] Sora's innovation 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 purpose, but did not expose the number or the specific sources of the videos. [223] +
OpenAI showed 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 techniques utilized to train the model, and the design's abilities. [225] It acknowledged a few of its shortcomings, consisting of battles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however kept in mind that they must have been cherry-picked and may not represent Sora's common output. [225] +
Despite uncertainty from some academic leaders following [Sora's public](http://update.zgkw.cn8585) demo, noteworthy entertainment-industry figures have actually shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to generate sensible video from text descriptions, mentioning its potential to change storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to pause prepare for expanding his [Atlanta-based motion](https://git.the.mk) picture studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, [Whisper](http://115.238.142.15820182) is a general-purpose speech recognition model. [228] It is trained on a large dataset of varied audio and is also a multi-task model that can perform multilingual speech recognition 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 anticipate subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to start fairly however then fall into chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the internet mental 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://101.34.211.1723000) 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 mentioned the tunes "show regional musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" which "there is a considerable space" between Jukebox and human-generated music. The Verge stated "It's highly impressive, even if the results sound like mushy variations of songs that may feel familiar", while Business Insider [mentioned](https://nse.ai) "surprisingly, a few of the resulting songs are appealing and sound legitimate". [234] [235] [236] +
Interface
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Debate Game
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In 2018, OpenAI released the Debate Game, which teaches devices to discuss toy problems in front of a human judge. The function is to research study whether such an approach may assist in auditing [AI](https://rhcstaffing.com) decisions and in establishing explainable [AI](https://www.social.united-tuesday.org). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network models which are often studied in [interpretability](http://git.chuangxin1.com). [240] Microscope was produced to examine the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241] +
ChatGPT
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[Launched](https://euvisajobs.com) in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that provides a conversational user interface that enables users to ask questions in natural language. The system then reacts with an answer within seconds.
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