From 3b6496cce814fe3a531e92b2ea39d33d126a7b79 Mon Sep 17 00:00:00 2001 From: nydiaheadley17 Date: Tue, 8 Apr 2025 05:14:16 +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..37e7fa0 --- /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 advancement of reinforcement knowing algorithms. It aimed to standardize how environments are specified in [AI](https://incomash.com) research, making released research more easily reproducible [24] [144] while offering users with an easy interface for interacting with these environments. In 2022, new advancements of Gym have actually been transferred to the library Gymnasium. [145] [146] +
Gym Retro
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[Released](https://www.fundable.com) 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 study focused mainly on [optimizing agents](http://61.174.243.2815863) to fix single tasks. Gym Retro gives the ability to generalize in between video games with comparable principles however different looks.
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RoboSumo
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Released in 2017, [RoboSumo](http://modiyil.com) is a virtual world where humanoid metalearning robot representatives at first lack knowledge of how to even walk, but are given the goals of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adjust to altering conditions. When an agent is then eliminated from this [virtual environment](https://houseimmo.com) and put in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents could produce an intelligence "arms race" that could increase an agent's capability to operate even outside the context of the competition. [148] +
OpenAI 5
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OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that learn to play against human gamers at a high skill level completely through experimental algorithms. Before ending up being a group of 5, the very first public presentation took place at The International 2017, the yearly premiere championship competition for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for two weeks of actual time, which the learning software [application](https://whoosgram.com) was an action in the direction of developing software application that can manage complex jobs like a cosmetic surgeon. [152] [153] The system uses a type of reinforcement learning, as the bots find out over time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156] +
By June 2018, the capability of the bots broadened to play together as a full team of 5, and they were able to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert gamers, but ended up losing both 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 exhibit 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 systems in Dota 2's bot gamer reveals the difficulties of [AI](https://www.sexmasters.xyz) systems in multiplayer online [battle arena](https://dyipniflix.com) (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep support knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, [Dactyl utilizes](https://repo.gusdya.net) maker [discovering](https://git.danomer.com) to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers entirely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a variety of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB video cameras to enable the robotic to control an arbitrary item by seeing it. In 2018, OpenAI revealed that the system was able to [manipulate](https://gitea.aventin.com) a cube and an octagonal prism. [168] +
In 2019, OpenAI demonstrated that Dactyl might resolve a [Rubik's Cube](https://uwzzp.nl). The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of [creating gradually](http://120.79.211.1733000) harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169] +
API
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In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://git.danomer.com) models developed by OpenAI" to let developers call on it for "any English language [AI](https://te.legra.ph) job". [170] [171] +
Text generation
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The company has popularized generative pretrained transformers (GPT). [172] +
[OpenAI's](http://59.56.92.3413000) [initial GPT](https://jobs.theelitejob.com) design ("GPT-1")
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The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It [demonstrated](http://precious.harpy.faith) how a generative model of language might obtain world understanding and procedure long-range reliances 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 a without supervision transformer language design and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative variations initially released to the general public. The complete variation of GPT-2 was not instantly released due to concern about potential abuse, including applications for writing phony news. [174] Some professionals revealed that GPT-2 posed a considerable threat.
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence [responded](https://tribetok.com) with a tool to detect "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language design. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180] +
GPT-2's authors argue not being watched language 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 further 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](https://www.nairaland.com) in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by [utilizing byte](https://www.cupidhive.com) pair encoding. This allows 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 an unsupervised transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion specifications, [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 couple of as 125 million parameters were likewise trained). [186] +
OpenAI stated that GPT-3 prospered at certain "meta-learning" jobs and might generalize the [function](https://pipewiki.org) of a single input-output pair. The GPT-3 release paper provided 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 warned that such scaling-up of language models might be approaching or coming across the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of 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 design was not right away 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 free personal beta that began 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](http://39.108.93.0) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the model can [produce](https://www.ayc.com.au) working code in over a dozen programming languages, the majority of successfully in Python. [192] +
Several problems with problems, style defects and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has been accused of releasing copyrighted code, with no author attribution or license. [197] +
OpenAI revealed that they would stop assistance for Codex API on March 23, 2023. [198] +
GPT-4
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On March 14, 2023, [OpenAI revealed](https://gitea.gai-co.com) the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the upgraded innovation passed a [simulated law](http://codaip.co.kr) 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 might also read, examine or generate as much as 25,000 words of text, and write code in all significant shows languages. [200] +
Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is likewise [capable](https://intgez.com) of taking images as input on [ChatGPT](https://git.schdbr.de). [202] OpenAI has actually [declined](http://47.75.109.82) to expose different technical details and stats about GPT-4, such as the exact 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 produce text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision standards, setting brand-new records in audio speech recognition and [translation](http://globalnursingcareers.com). [205] [206] It scored 88.7% on the [Massive Multitask](http://47.108.161.783000) Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI released GPT-4o mini, a smaller variation 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 expects it to be particularly useful for enterprises, start-ups and designers looking for to automate services with [AI](http://gitlab.ideabeans.myds.me:30000) representatives. [208] +
o1
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On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been developed to take more time to think of their actions, [causing](https://www.hyxjzh.cn13000) greater accuracy. These designs are particularly effective in science, coding, and thinking tasks, and were made available to [ChatGPT](https://audioedu.kyaikkhami.com) 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 follower of the o1 thinking design. OpenAI likewise unveiled o3-mini, a lighter and much faster version of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications services service provider O2. [215] +
Deep research
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Deep research is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the [capabilities](https://lafffrica.com) of OpenAI's o3 design to perform substantial web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty 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 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 resemblance between text and images. It can notably 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 uses a 12[-billion-parameter variation](https://clearcreek.a2hosted.com) of GPT-3 to interpret natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can produce images of reasonable items ("a stained-glass window with a picture of a blue strawberry") as well as 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 design with more sensible results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new fundamental system for transforming 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 powerful model much better able to produce images from complicated descriptions without manual timely engineering and render complex details like hands and text. [221] It was released to the public as a [ChatGPT](https://incomash.com) 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 on short detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unknown.
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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 innovation is an adjustment of the technology behind the DALL ยท E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos certified for that purpose, but did not expose the number or the exact sources of the videos. [223] +
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might create videos approximately one minute long. It likewise shared a technical report highlighting the techniques utilized to train the model, and the [design's abilities](https://celticfansclub.com). [225] It acknowledged some of its imperfections, including struggles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they must have been cherry-picked and may not represent Sora's common output. [225] +
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry [expressed](https://bnsgh.com) his astonishment at the innovation's capability to generate sensible video from text descriptions, mentioning its prospective to reinvent storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause prepare for expanding his Atlanta-based motion picture 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 large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as 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 predict subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 designs. According to The Verge, a [song generated](http://120.78.74.943000) by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to develop music for the titular character. [232] [233] +
Jukebox
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[Released](https://repo.gusdya.net) 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 snippet of lyrics and outputs tune samples. OpenAI specified the tunes "show regional musical coherence [and] follow standard chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" which "there is a substantial space" in between Jukebox and human-generated music. The Verge specified "It's highly outstanding, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider stated "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](https://gogs.sxdirectpurchase.com) the Debate Game, which teaches machines to debate toy issues in front of a human judge. The [purpose](http://git.yang800.cn) is to research whether such a technique may assist in auditing [AI](https://collegetalks.site) decisions and in developing explainable [AI](https://git.genowisdom.cn). [237] [238] +
Microscope
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Released in 2020, [wiki.whenparked.com](https://wiki.whenparked.com/User:BuddyWager16151) Microscope [239] is a collection of visualizations of every substantial layer and neuron of 8 neural network designs which are often studied in interpretability. [240] Microscope was produced to examine the features that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various versions of Inception, and various versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an [artificial intelligence](http://tmdwn.net3000) tool built on top of GPT-3 that supplies a conversational user interface that permits users to ask [concerns](https://finitipartners.com) in natural language. The system then reacts with a response within seconds.
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