mark5803838894/kiwoori
1
0
Code Issues Actions Packages Projects Wiki

Add The Verge Stated It's Technologically Impressive

Mark Judge 2025-02-18 03:16:56 +08:00
commit d8458da1ba
1 changed files with 76 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

76
The-Verge-Stated-It%27s-Technologically-Impressive.md Normal file

@ -0,0 +1,76 @@
<br>Announced in 2016, Gym is an open-source Python library developed to help with the development of support knowing [algorithms](https://askcongress.org). It aimed to standardize how environments are specified in [AI](https://skillnaukri.com) research, making published research more quickly reproducible [24] [144] while providing users with an easy user interface for engaging with these environments. In 2022, brand-new developments of Gym have actually been transferred to the library Gymnasium. [145] [146]
<br>Gym Retro<br>
<br>Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to fix single jobs. Gym Retro offers the ability to generalize between video games with similar concepts but various appearances.<br>
<br>RoboSumo<br>
<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have knowledge of how to even stroll, but are provided the objectives of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the agents learn how to adapt to changing conditions. When a representative is then [removed](https://git.itk.academy) from this virtual environment and put in a new virtual [environment](http://forum.ffmc59.fr) with high winds, the agent braces to remain upright, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) recommending it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents might produce an [intelligence](https://copyright-demand-letter.com) "arms race" that could increase an agent's ability to operate even outside the [context](http://47.108.140.33) of the competitors. [148]
<br>OpenAI 5<br>
<br>OpenAI Five is a team of five OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that [discover](http://orcz.com) to play against human gamers at a high skill level completely through trial-and-error algorithms. Before becoming a group of 5, the very first public demonstration occurred at The International 2017, the yearly premiere champion [competition](https://git.dadunode.com) for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually [matchup](https://labs.hellowelcome.org). [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by playing against itself for 2 weeks of real time, which the knowing software was an action in the direction of developing software application that can handle complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a type of [support](https://www.celest-interim.fr) knowing, 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]
<br>By June 2018, the capability of the bots expanded to play together as a complete 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 exhibition matches against expert gamers, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the game at the time, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:WCTSteve75017) 2:0 in a live exhibit 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 competition, winning 99.4% of those video games. [165]
<br>OpenAI 5's mechanisms in Dota 2's bot player shows the challenges of [AI](http://team.pocketuniversity.cn) systems in multiplayer online battle arena (MOBA) games and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:NolanShropshire) how OpenAI Five has demonstrated making use of deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
<br>Dactyl<br>
<br>Developed in 2018, Dactyl uses maker discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It discovers completely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item [orientation](http://git.the-archive.xyz) problem by using domain randomization, a simulation method which exposes the [learner](https://repo.gusdya.net) to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has RGB cams to enable the robot to manipulate an approximate item by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]
<br>In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik's Cube. The robot had the to [resolve](https://www.anetastaffing.com) the puzzle 60% of the time. [Objects](https://source.lug.org.cn) like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually more hard environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169]
<br>API<br>
<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](http://60.250.156.230:3000) models developed by OpenAI" to let developers contact it for "any English language [AI](https://corerecruitingroup.com) job". [170] [171]
<br>Text generation<br>
<br>The business has popularized generative pretrained transformers (GPT). [172]
<br>OpenAI's initial GPT design ("GPT-1")<br>
<br>The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a [generative model](https://git.zyhhb.net) of language could obtain world knowledge and process long-range dependencies by pre-training on a varied corpus with long stretches of adjoining text.<br>
<br>GPT-2<br>
<br>Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative versions at first launched to the public. The full version of GPT-2 was not instantly launched due to concern about possible misuse, including applications for composing [fake news](https://gitea.qi0527.com). [174] Some specialists expressed uncertainty that GPT-2 posed a considerable danger.<br>
<br>In reaction to GPT-2, the Allen [Institute](http://xiaomu-student.xuetangx.com) for Artificial Intelligence responded with a tool to spot "neural phony news". [175] Other scientists, such as Jeremy Howard, cautioned of "the technology to completely 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 launched the complete version 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]
<br>GPT-2's authors argue unsupervised language designs to be general-purpose students, [oeclub.org](https://oeclub.org/index.php/User:RebekahOSullivan) shown by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not further trained on any task-specific input-output examples).<br>
<br>The corpus it was trained on, called WebText, contains slightly 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 specific characters and [multiple-character](https://wiki.rrtn.org) tokens. [181]
<br>GPT-3<br>
<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language model 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 designs with as few as 125 million specifications were also trained). [186]
<br>OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the function of a [single input-output](http://114.55.169.153000) pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning in between English and Romanian, and in between English and German. [184]
<br>GPT-3 drastically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Kenny57356) the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released to the public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month complimentary private beta that began in June 2020. [170] [189]
<br>On September 23, 2020, GPT-3 was certified solely to Microsoft. [190] [191]
<br>Codex<br>
<br>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](https://gitlab.ngser.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the model can develop working code in over a dozen shows languages, the majority of effectively in Python. [192]
<br>Several issues with problems, design defects and security vulnerabilities were pointed out. [195] [196]
<br>GitHub Copilot has actually been accused of discharging copyrighted code, with no author attribution or license. [197]
<br>OpenAI announced that they would terminate support for Codex API on March 23, 2023. [198]
<br>GPT-4<br>
<br>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 revealed that the updated innovation passed a simulated law school bar examination with a score around the top 10% of [test takers](https://sudanre.com). (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also check out, evaluate or generate up to 25,000 words of text, and write code in all significant programming languages. [200]
<br>Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has declined to expose various technical details and statistics about GPT-4, such as the accurate size of the design. [203]
<br>GPT-4o<br>
<br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision benchmarks, setting new [records](http://ncdsource.kanghehealth.com) 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]
<br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million [input tokens](http://60.250.156.2303000) 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, startups and designers looking for to automate services with [AI](https://git.cacpaper.com) representatives. [208]
<br>o1<br>
<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been designed to take more time to believe about their reactions, resulting in greater precision. These designs are especially effective 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]
<br>o3<br>
<br>On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and faster version of OpenAI o3. As of 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 scientists](https://git.mbyte.dev) had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications services company O2. [215]
<br>Deep research study<br>
<br>Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With [browsing](https://jobsleed.com) and Python tools enabled, it reached a [precision](https://celflicks.com) of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
<br>Image category<br>
<br>CLIP<br>
<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is [trained](https://git.jerrita.cn) to examine the semantic resemblance in between text and images. It can especially be used for image category. [217]
<br>Text-to-image<br>
<br>DALL-E<br>
<br>Revealed in 2021, DALL-E is a Transformer design that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can produce images of practical things ("a stained-glass window with an image of a blue strawberry") in addition to items that do not exist in reality ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.<br>
<br>DALL-E 2<br>
<br>In April 2022, OpenAI revealed DALL-E 2, an updated version of the design with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new basic system for converting a [text description](http://406.gotele.net) into a 3-dimensional model. [220]
<br>DALL-E 3<br>
<br>In September 2023, OpenAI revealed DALL-E 3, a more effective design better able to generate images from [intricate descriptions](https://gitea.linuxcode.net) without manual timely engineering and render intricate [details](https://www.ubom.com) like hands and text. [221] It was launched to the public as a ChatGPT Plus feature in October. [222]
<br>Text-to-video<br>
<br>Sora<br>
<br>Sora is a text-to-video design that can generate videos based upon short detailed triggers [223] as well as extend existing videos forwards or [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11861831) backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.<br>
<br>Sora's advancement team called it after the Japanese word for "sky", to symbolize its "endless imaginative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos certified for that purpose, but did not reveal the number or the precise sources of the videos. [223]
<br>OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:LoisHuntley) specifying that it might generate videos up to one minute long. It likewise shared a technical report highlighting the techniques utilized to train the model, and the design's abilities. [225] It acknowledged some of its imperfections, including battles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but noted that they need to have been [cherry-picked](http://175.27.189.803000) and may not represent Sora's normal output. [225]
<br>Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have actually [revealed](https://gitea.portabledev.xyz) significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's ability to generate realistic video from text descriptions, mentioning its possible to revolutionize storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly prepare for broadening his Atlanta-based movie studio. [227]
<br>Speech-to-text<br>
<br>Whisper<br>
<br>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 carry out multilingual speech acknowledgment along with speech translation and language recognition. [229]
<br>Music generation<br>
<br>MuseNet<br>
<br>Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly however then fall into turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to create music for the [titular character](https://git.viorsan.com). [232] [233]
<br>Jukebox<br>
<br>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 tunes "reveal regional musical coherence [and] follow standard chord patterns" but [acknowledged](http://104.248.138.208) that the tunes lack "familiar bigger musical structures such as choruses that repeat" which "there is a considerable space" in between Jukebox and human-generated music. The Verge mentioned "It's technologically remarkable, even if the results seem like mushy variations of tunes that may feel familiar", while Business Insider mentioned "surprisingly, some of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
<br>User interfaces<br>
<br>Debate Game<br>
<br>In 2018, OpenAI launched the Debate Game, which [teaches makers](https://vsbg.info) to discuss toy problems in front of a human judge. The function is to research whether such a method might assist in auditing [AI](https://xn--v69atsro52ncsg2uqd74apxb.com) choices and in establishing explainable [AI](https://gitea.namsoo-dev.com). [237] [238]
<br>Microscope<br>
<br>Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of eight neural network models which are typically studied in interpretability. [240] [Microscope](https://gogs.lnart.com) was produced to analyze the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, and various [variations](https://vydiio.com) of CLIP Resnet. [241]
<br>ChatGPT<br>
<br>Launched in November 2022, ChatGPT is a synthetic intelligence [tool constructed](http://personal-view.com) on top of GPT-3 that supplies a conversational interface that allows users to ask concerns in natural language. The system then reacts with a response within seconds.<br>