Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library designed to help with the advancement of support learning algorithms. It aimed to standardize how environments are specified in [AI](https://www.pkjobs.store) research, making published research more quickly [reproducible](https://giaovienvietnam.vn) [24] [144] while [supplying](http://129.211.184.1848090) users with an easy interface for connecting with these environments. In 2022, brand-new advancements of Gym have been relocated to the library Gymnasium. [145] [146]
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<br>Gym Retro<br>
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<br>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 study focused mainly on optimizing agents to solve single tasks. Gym Retro provides the capability to generalize between video games with similar concepts however different appearances.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first do not have understanding of how to even stroll, however are offered the goals of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents discover how to adapt to changing conditions. When an agent is then removed from this virtual environment and put in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might [produce](https://gitlab.ineum.ru) an intelligence "arms race" that might increase a representative's ability to function even outside the [context](https://gitea.imwangzhiyu.xyz) of the competition. [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that discover to play against human players at a high ability level entirely through experimental [algorithms](http://caxapok.space). Before becoming a team of 5, the first public presentation happened at The International 2017, the annual best championship tournament for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually match. [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, and that the knowing software was a step in the instructions of developing software that can manage intricate tasks like a cosmetic surgeon. [152] [153] The system utilizes a type of support knowing, as the [bots learn](https://paknoukri.com) gradually by playing against themselves numerous times a day for [surgiteams.com](https://surgiteams.com/index.php/User:VictorWalls) months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the ability of the bots broadened to play together as a full group of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the 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 total games in a four-day open online competition, winning 99.4% of those games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot player shows the difficulties of [AI](https://forum.elaivizh.eu) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep reinforcement knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl uses device learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It learns completely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB video [cameras](https://www.kenpoguy.com) to permit the robot to manipulate an approximate object by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI showed that Dactyl might solve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of generating progressively harder environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
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<br>API<br>
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<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://www.arztstellen.com) models developed by OpenAI" to let designers call on it for "any English language [AI](http://121.41.31.146:3000) task". [170] [171]
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<br>Text generation<br>
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<br>The business has popularized generative pretrained transformers (GPT). [172]
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<br>OpenAI's initial GPT design ("GPT-1")<br>
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<br>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 revealed how a generative model of language might obtain world understanding and process long-range dependencies by pre-training on a diverse corpus with long stretches of adjoining text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations at first launched to the public. The complete version of GPT-2 was not right away due to issue about potential misuse, including applications for composing phony news. [174] Some professionals revealed uncertainty that GPT-2 postured a considerable risk.<br>
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "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 drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI launched 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]
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<br>GPT-2's authors argue not being watched language designs to be general-purpose learners, shown by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not additional trained on any task-specific input-output examples).<br>
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<br>The corpus it was [trained](https://lifeinsuranceacademy.org) 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 with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both private characters and multiple-character tokens. [181]
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<br>GPT-3<br>
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<br>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 larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as few as 125 million criteria were also trained). [186]
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<br>OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper [offered examples](https://gitlab.rails365.net) of translation and [cross-linguistic transfer](https://git.gumoio.com) knowing in between English and Romanian, and between English and German. [184]
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<br>GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI warned 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 needed a number of thousand petaflop/s-days [b] of compute, [surgiteams.com](https://surgiteams.com/index.php/User:LatanyaZiegler) compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the general public for issues of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month totally free private beta that began in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was certified solely to Microsoft. [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has actually [additionally](https://acrohani-ta.com) been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://gogs.xinziying.com) powering the [code autocompletion](https://pk.thehrlink.com) tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can produce working code in over a [dozen programs](http://58.87.67.12420080) languages, most successfully in Python. [192]
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<br>Several concerns with glitches, design flaws and security vulnerabilities were mentioned. [195] [196]
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<br>GitHub Copilot has actually been implicated of producing copyrighted code, with no author attribution or license. [197]
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<br>OpenAI announced that they would stop [assistance](https://teba.timbaktuu.com) for Codex API on March 23, 2023. [198]
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<br>GPT-4<br>
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<br>On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the upgraded technology passed a simulated law [school bar](https://git.skyviewfund.com) exam 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 could also check out, examine or create approximately 25,000 words of text, and write code in all significant programs languages. [200]
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<br>Observers reported that the version of ChatGPT using 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 modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal various technical details and data about GPT-4, such as the precise size of the model. [203]
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<br>GPT-4o<br>
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<br>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 criteria, setting new records in audio speech recognition and [translation](https://www.tcrew.be). [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1009888) OpenAI launched GPT-4o mini, a smaller sized version of GPT-4o changing 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 anticipates it to be particularly helpful for enterprises, startups and designers looking for to automate services with [AI](https://tobesmart.co.kr) representatives. [208]
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<br>o1<br>
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<br>On September 12, 2024, OpenAI released the o1[-preview](https://git.nazev.eu) and o1-mini designs, which have actually been developed to take more time to consider their actions, leading to greater precision. These designs are particularly efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was [changed](https://www.pickmemo.com) by o1. [211]
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<br>o3<br>
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<br>On December 20, 2024, OpenAI revealed o3, the successor of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and quicker variation 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](https://laborando.com.mx) and security researchers had the opportunity to obtain early access to these models. [214] The design is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215]
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<br>Deep research<br>
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<br>Deep research is a representative established by OpenAI, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:AndreHeiden8) revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out extensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it [reached](https://vacancies.co.zm) a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
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<br>Image category<br>
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<br>CLIP<br>
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic similarity between text and images. It can especially be used for image category. [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>Revealed in 2021, DALL-E is a Transformer design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can produce images of sensible items ("a stained-glass window with an image of a blue strawberry") as well as objects that do not exist in truth ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.<br>
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<br>DALL-E 2<br>
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<br>In April 2022, OpenAI announced DALL-E 2, an upgraded version of the design with more sensible results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new basic system for converting a [text description](http://flexchar.com) into a 3-dimensional design. [220]
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<br>DALL-E 3<br>
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<br>In September 2023, OpenAI announced DALL-E 3, a more effective design much better able to produce images from complex descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was released to the general public as a ChatGPT Plus function in October. [222]
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<br>Text-to-video<br>
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<br>Sora<br>
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<br>Sora is a text-to-video design that can generate videos based on short detailed prompts [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 generated videos is unknown.<br>
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<br>Sora's advancement group named it after the Japanese word for "sky", to represent its "endless creative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos certified for that function, however did not expose the number or the exact sources of the videos. [223]
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<br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might produce videos up to one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the design's abilities. [225] It acknowledged a few of its drawbacks, consisting of battles simulating [intricate](https://mmatycoon.info) physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they need to have been cherry-picked and might not represent Sora's normal output. [225]
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<br>Despite uncertainty from some academic leaders following [Sora's public](https://support.mlone.ai) demo, noteworthy entertainment-industry figures have shown substantial interest in the [innovation's capacity](https://abstaffs.com). In an interview, actor/filmmaker Tyler Perry [revealed](https://ruraltv.co.za) his awe at the technology's capability to create practical video from text descriptions, citing its prospective to change storytelling and content production. He said that his [excitement](http://8.141.83.2233000) about Sora's possibilities was so strong that he had decided to pause strategies for broadening his Atlanta-based motion picture studio. [227]
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<br>Speech-to-text<br>
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<br>Whisper<br>
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<br>Released in 2022, [wiki.eqoarevival.com](https://wiki.eqoarevival.com/index.php/User:WallaceMarkley2) Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task model that can perform multilingual speech acknowledgment as well as speech translation and language recognition. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>Released in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a tune generated by MuseNet tends to [start fairly](http://47.107.153.1118081) but then fall into turmoil the longer it plays. [230] [231] In pop 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]
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<br>Jukebox<br>
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<br>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 snippet of lyrics and outputs tune samples. OpenAI specified the tunes "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar bigger 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 technologically outstanding, even if the outcomes sound like mushy variations of tunes that might feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are catchy and sound genuine". [234] [235] [236]
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<br>User interfaces<br>
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<br>Debate Game<br>
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<br>In 2018, OpenAI launched the Debate Game, which teaches devices to debate toy problems in front of a human judge. The [function](http://8.137.89.263000) is to research study whether such an approach might assist in auditing [AI](http://sintec-rs.com.br) choices and in developing explainable [AI](https://gitlab.ineum.ru). [237] [238]
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<br>Microscope<br>
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<br>Released in 2020, Microscope [239] is a collection of [visualizations](https://municipalitybank.com) of every substantial layer and neuron of eight neural network models which are frequently studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different variations of Inception, and various versions of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, ChatGPT is an expert system tool constructed on top of GPT-3 that supplies a conversational user interface that enables users to ask questions in natural language. The system then reacts with a response within seconds.<br>
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