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With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs and with an original score by Academy Award nominee Hauschka, AlphaGo had its premiere at the Tribeca Film Festival. It has since gone on to win countless awards and near universal praise for a story that chronicles a journey from the halls of Oxford, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity? Best documentary winner: Denver International Film Festival (2017), Warsaw International Film Festival (2017), and Traverse City Film Festival (2017). Official selection at Tribeca Film Festival (2017), BFI London Film Festival (2017), and Critics' Choice Documentary Awards (2017). Find out more: 🤍 "I want my style of Go to be something different, something new, my own thing, something that no one has thought of before." Lee Sedol, Go Champion (18 World Titles). "We think of DeepMind as kind of an Apollo program effort for AI. Our mission is to fundamentally understand intelligence and recreate it artificially." Demis Hassabis, Co-Founder & CEO, DeepMind. "The Game of Go is the holy grail of artificial intelligence. Everything we've ever tried in AI, it just falls over when you try the game of Go." Dave Silver, Lead Researcher for AlphaGo.
🤍 Watch on Google Play Movies → 🤍 AlphaGo chronicles a journey from the halls of Cambridge, through the backstreets of Bordeaux, past the coding terminals of DeepMind, to Seoul, where a legendary Go master faces an unproven AI challenger. As the drama unfolds, questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What will it teach us about humanity?
The AlphaGo Zero paper "Mastering the Game of Go without Human Knowledge" is available here: 🤍 🤍 Our Patreon page with the details: 🤍 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Andrew Melnychuk, Brian Gilman, Christoph Jadanowski, Dave Rushton-Smith, Dennis Abts, Eric Haddad, Esa Turkulainen, Evan Breznyik, Kaben Gabriel Nanlohy, Malek Cellier, Michael Albrecht, Michael Jensen, Michael Orenstein, Steef, Steve Messina, Sunil Kim, Torsten Reil. 🤍 Two Minute Papers Merch: US: 🤍 EU/Worldwide: 🤍 Photo credits: Watson - AP Photo/Jeopardy Productions, Inc. Fan Hui match photo - Google DeepMind - 🤍 Go board image credits (all CC BY 2.0): Renato Ganoza - 🤍 Jaro Larnos (changes were applied, mostly recoloring) - 🤍 Luis de Bethencourt - 🤍 Go ratings: 🤍 Music: Antarctica by Audionautix is licensed under a Creative Commons Attribution license (🤍 Artist: 🤍 Thumbnail background image credit: 🤍 Splash screen/thumbnail design: Felícia Fehér - 🤍 Károly Zsolnai-Fehér's links: Facebook: 🤍 Twitter: 🤍 Web: 🤍
Lee Sedol vs AlphaGo Move 37 reactions and analysis . Reactions from Lee Sedol, reactions from AlphaGo team, reactions from commentators, analysis on move 37 . Google DeepMind Challenge. With more board configurations than there are atoms in the universe, the ancient Chinese game of 'Go' has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The Google DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Cambridge, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and, ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity? —AlphaGo Credit: AlphaGo Movie 🤍
AlphaGo beat the Go World Champion 4-1. Why do the creators not know how? Brais Martinez is a Research Fellow & Deep Learning expert at the University of Nottingham. 🤍 🤍 This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: 🤍 Computerphile is a sister project to Brady Haran's Numberphile. More at 🤍
One of the greatest ‘Go’ players of all time just retired from the game, saying that artificial intelligence programmes can play at a level humans aren’t able to. His decision has reignited discussions about AI and how we define human achievement. #AI #Go #LeeSedol #SouthKorea Beyond The Game 👉 🤍 Panama Papers 👉 🤍 Subscribe: 🤍
Go is an ancient, aristocratic Chinese board game that’s reputed to have as many possible moves as there are atoms in the universe. And Google recently trained an artificial intelligence computer to play against one of the best human players in the world. The computer won. At Google’s Future of Go Summit, 19-year-old Chinese Go prodigy Ke Jie was defeated by the AI AlphaGo in a three-match series. AI evangelists are happy with the win, but AI doomsayers are worried it’s coming for our jobs next. And China is just mad that an American company beat the world at a Chinese game. VICE News reports on what the competition really means for AI development. Subscribe to VICE News here: 🤍 Check out VICE News for more: 🤍 Follow VICE News here: Facebook: 🤍 Twitter: 🤍 Tumblr: 🤍 Instagram: 🤍 More videos from the VICE network: 🤍
The hand of God move from Lee Sedol in match 4 against AlphaGo. This move 78 changed the dynamics of the match and Lee Sedol wins against AlphaGo. With more board configurations than there are atoms in the universe, the ancient Chinese game of 'Go' has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The Google DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Cambridge, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and, ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity? —AlphaGo Credit: AlphaGo Movie 🤍
DeepMind's Professor David Silver describes AlphaGo Zero, the latest evolution of AlphaGo, the first computer program to defeat a world champion at the ancient Chinese game of Go. Zero is even more powerful and is arguably the strongest Go player in history. Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0. Find out more here: 🤍
Watch DeepMind's program AlphaGo take on the legendary Lee Sedol (9-dan pro), the top Go player of the past decade, in a $1M 5-game challenge match in Seoul. This is the livestream for Match 1 to be played on: 9th March 13:00 KST (local), 04:00 GMT; note for US viewers this is the day before on: 8th March 20:00 PT, 23:00 ET. In October 2015, AlphaGo became the first computer program ever to beat a professional Go player by winning 5-0 against the reigning 3-times European Champion Fan Hui (2-dan pro). That work was featured in a front cover article in the science journal Nature in January 2016. Match commentary by Michael Redmond (9-dan pro) and Chris Garlock.
#AlphaGo #IA #IntelligenceArtificielle Débunkage et arguments scientifiques contre l'idée qu'AlphaGo serait une intelligence artificielle. Je vais vous expliquer pourquoi AlphaGo n'est pas une IA. Je vais prendre 2 arguments qui me sautent le plus aux yeux en tant que spécialiste. Mais il en existe d'autres. [1] Jeu de go et intelligence artificielle — À chaud ! #2 🤍 [2] AlphaGo et l'intelligence artificielle | Actu 1 🤍 [3] Lee Sedol vs AlphaGo Move 37 reactions and analysis 🤍 [4] Ce robot peut-il se faire du mal ? 🤍 50s à 2m30s: Ce qui me gêne particulièrement, c'est que l'apprentissage machine est présenté de la manière suivant: 1-Apprentissage sur un jeu de données Manquent: 2-la création aléatoire de 3-dizaines voires centaines de milliers de parcours 4-répartis en 3 jeux de données: Train 1, Train 2 et Test. 5-l'explication que le jeu de données d'apprentissage est partagé en 2 (pour la sélection et l'optimisation des paramètres d'apprentissage de tout classifieur). 6-que Train 1, Train 2 et Test ne doivent idéalement contenir aucun exemple identique. 7-la difficulté du calcul des performances: courbe ROC, subjectivité du seuil pour le calcul de la sensibilité, du rappel. L'impossibilité de pondérer les performances par classe en fonction des désiquilibre du nombre d'exemples d'apprentissage pour chaque classe. Et surtout, comment faire la "moyenne" (et quelle moyenne? artithmétique, pondérée, harmonique ...) de N rappels, sensibilités et Fscores? [5] Olivier Dufour, Benoît Gineste, Yves Bas, Matthieu Le Corre, Thierry Artières, First automatic passive acoustic tool for monitoring two species of procellarides (Pterodroma baraui and Puffinus bailloni) on Reunion Island, Indian Ocean, Ecological Informatics, Volume 35, 2016, Pages 55-60, ISSN 1574-9541, Keywords: Seabirds; Automatic detector; Acoustic monitoring; Procellarides; Autonomous recording unit; Barau's petrel 🤍 👨💻 Comment devenir chercheur en Intelligence Artificielle: 5 conseils 🤍 Pour vous former à l'intelligence artificielle et à l'apprentissage machine: Playlist de tous les épisodes de la chaîne dans l'ordre chronologique (écoutez-en 1 par jour par exemple!): 🤍 Références personnelles et mon profil: Références de mon sujet de thèse: 🤍 Un peu de biblio perso: 🤍 🤍 🤍 🤍 🤍 Les articles que j'ai reviewé pour la revue scientifique à comité de lecture "Ecological informatics": 🤍 🤍 🤍 🤍 F A C E B O O K 🤍 T W I T T E R 🤍 Je suis le Docteur Olivier Dufour. (Montpellier) Crédits photos miniature: 🤍 http://159.203.44.189/wp-content/uploads/2013/07/photoMoi.png 🤍 🤍
David Silver leads the reinforcement learning research group at DeepMind and was lead researcher on AlphaGo, AlphaZero and co-lead on AlphaStar, and MuZero and lot of important work in reinforcement learning. Support this podcast by signing up with these sponsors: - MasterClass: 🤍 - Cash App - use code "LexPodcast" and download: - Cash App (App Store): 🤍 - Cash App (Google Play): 🤍 EPISODE LINKS: Reinforcement learning (book): 🤍 PODCAST INFO: Podcast website: 🤍 Apple Podcasts: 🤍 Spotify: 🤍 RSS: 🤍 Full episodes playlist: 🤍 Clips playlist: 🤍 OUTLINE: 0:00 - Introduction 4:09 - First program 11:11 - AlphaGo 21:42 - Rule of the game of Go 25:37 - Reinforcement learning: personal journey 30:15 - What is reinforcement learning? 43:51 - AlphaGo (continued) 53:40 - Supervised learning and self play in AlphaGo 1:06:12 - Lee Sedol retirement from Go play 1:08:57 - Garry Kasparov 1:14:10 - Alpha Zero and self play 1:31:29 - Creativity in AlphaZero 1:35:21 - AlphaZero applications 1:37:59 - Reward functions 1:40:51 - Meaning of life CONNECT: - Subscribe to this YouTube channel - Twitter: 🤍 - LinkedIn: 🤍 - Facebook: 🤍 - Instagram: 🤍 - Medium: 🤍 - Support on Patreon: 🤍
Remember the excitement in 1997 when Deep Blue, the IBM computer, defeated chess grandmaster Gary Kasparov? That moment was considered a great leap forward for artificial intelligence. Now software designed to beat the best players in the world at “Go” is about to take thinking machines to a whole new level. Here’s why. Featuring former government tech adviser Rohan Silva. Newsnight is the BBC's flagship news and current affairs TV programme - with analysis, debate, exclusives, and robust interviews. Subscribe to our YouTube channel for highlights and bonus videos 🤍 Follow us on Twitter 🤍BBCNewsnight for the latest updates on #newsnight Add us on Snapchat - our username is 'BBCNewsnight' And follow on Facebook for our best material, an early heads up on what's coming up, and to join in our debates 🤍
In this episode I dive into the technical details of the AlphaGo Zero paper by Google DeepMind. This AI system uses Reinforcement Learning to beat the world's Go champion using only self-play, a remarkable display of clever engineering on the path to stronger AI systems. DeepMind Blogpost: 🤍 AlphaGo Zero paper: 🤍 If you want to support this channel, here is my patreon link: 🤍 - You are amazing!! ;) If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: 🤍
DeepMind's Professor David Silver describes AlphaGo Zero, the latest evolution of AlphaGo, the first computer program to defeat a world champion at the ancient Chinese game of Go. Zero is even more powerful and is arguably the strongest Go player in history. Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0. If similar techniques can be applied to other structured problems, such as protein folding, reducing energy consumption or searching for revolutionary new materials, the resulting breakthroughs have the potential to positively impact society. Find out more here: 🤍
Lex Fridman Podcast full episode: 🤍 Please support this podcast by checking out our sponsors: - SimpliSafe: 🤍 and use code LEX to get a free security camera - ExpressVPN: 🤍 and use code LexPod to get 3 months free - MasterClass: 🤍 to get 2 for price of 1 - BetterHelp: 🤍 to get 10% off PODCAST INFO: Podcast website: 🤍 Apple Podcasts: 🤍 Spotify: 🤍 RSS: 🤍 Full episodes playlist: 🤍 Clips playlist: 🤍 CONNECT: - Subscribe to this YouTube channel - Twitter: 🤍 - LinkedIn: 🤍 - Facebook: 🤍 - Instagram: 🤍 - Medium: 🤍 - Support on Patreon: 🤍
棋王3連敗沒對手AlphaGo退「弈」AlphaGo 三戰連勝柯潔，將退隱江湖不再挑戰人類 完勝棋王柯潔AlphaGo榮退 三戰AlphaGo全敗柯潔落淚：沒有希望差距太大 柯潔落淚：我和AlphaGo差距太大 中國大陸圍棋棋王柯潔和AlphaGo對戰連3敗後，柯潔今天在賽後記者會上一度哽咽。他說，「它太冷靜了，不給你任何希望。我跟它下棋特別痛苦，只能猜它一半的棋，差距太大」。 谷歌人工智慧系統AlphaGo攻勢凌厲，對上世界排名第一的圍棋好手柯潔順利取得3連勝。綜合澎湃新聞、中新社今天晚間報導，柯潔在賽後記者會的發言一度哽咽，現場則多次響起掌聲。 柯潔表示，AlphaGo表現實在太完美，沒有任何缺陷。他所擔心的每一步棋它都會下，還會下出他想不到的棋。「我只能猜出AlphaGo一半的棋，另一半我猜不到，差距實在太大」。 他坦言，和AlphaGo下棋實在太痛苦，它太冷靜，令人感受不到希望。雖然前2戰已經輸給AlphaGo，但柯潔強調，與AlphaGo下最後第3盤棋，這是一個正確的決定。 柯潔說，自己昨晚其實很緊張，也沒睡好，想著能用什麼招數對付AlphaGo。下棋本來應該是很快樂的一件事情，讓大家感覺到圍棋的魅力。他表示，但這種快樂，可能只有和人下棋才能感受到。
For a very long time Artificial Intelligence programs were using pure logic based bruit forcing. They managed to do lots of cool and useful stuff with that. However, with modern big data we need less rigid approach to make decisions, because we need huge amont of processing power to bruit force a probability tree and sometime it's practically impossible. That'w when we need a AI with approximated human intuition rather than strictly logical AI. In other words a Kirk rather than a Spock. The estimated time frame was around 2030 for us to build something like that. Fun fact! We have one now and that is AlphaGo built by DeepMind, a Alphabet company. Read More: 🤍 Follow TechTrack: 🤍 🤍 🤍 Follow Malinda: 🤍 🤍 🤍 🤍
🤍 – Bisher taten sich Computer schwer, auf Profi-Niveau Go zu spielen. Warum das so war und worin der Durchbruch besteht, weshalb AlphaGo durchaus Chancen gegen einen der weltstärksten Go-Spieler haben könnte, erklärt Harald Bögeholz. Mehr zu AlphaGo und KI im Artikel in c't 6/16: 🤍
A brief intro to the rules of Go. To learn more about Go please check out these other Udacity videos: Why Go is so Difficult for AI: 🤍 Go - Life & Death: 🤍
In 2016, Lee Sedol faced off against Google's AlphaGo for one final clash between man and machine. Though Lee Sedol lost the match 4-1, the one win he received was full of such insight and beauty, that many have called the turning point of the game Lee's Divine Move. In this video, I endeavored to bring the game to life - one last time in honor of one of the greatest Go players of our time! Support my work on Patreon! 🤍 Watch me play LIVE on Twitch here! 🤍 Follow me on Twitch 🤍in_sente Thanks to Annie, a good friend who helped give me the Influencie software which made this board visualization possible! Download the program here! 🤍 Thanks to Jota-R for the royalty free music! 🤍
The Tactics Kellin Used #ai #alphago #gogame Yesterday there was such an amazing news that A human player has defeated a top-ranked Go AI. Let's find out how Kellin Pelrine got to defeat AI. This is the link of games 🤍
好的电影 改变你的认知 欢迎大家回到【看电影了没】我是K猫。 我今天要跟大家讲人机大战《阿尔法围棋》 这是关于阿尔法狗背后不为人知的故事。 片名: 阿尔法围棋 AlphaGo (2017) 喜欢我们的频道内容，请记得点赞分享＋订阅！ ►🤍 下载K星球知道更多好看的电影►🤍 可以搜寻bilibili网站►🤍 #看电影了没 #講說者 #阿尔法围棋 #AlphaGo #解说 #讲评
On Google DeepMind Challenge Match 4, Lee Sedol (9-dan pro, the top Go player of the past decade) defeated DeepMind's program AlphaGo. The match took place in Seoul on 13 March 2016. Match commentary by Michael Redmond (9-dan pro) and Chris Garlock. Lee Sedol vs AlphaGo, Match 4 Credit: Google DeepMind
Originally aired on February 6, 2022 Produced by Eva-Dee Beech, Chris Garlock and Stephen Hu Thumbnail image of Rock 'em Sock 'em Robots by Lorie Shaull - Own work, CC BY-SA 4.0, 🤍
❤️ Become The AI Epiphany Patreon ❤️ ► 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ In this video, I cover the seminal AlphaGo paper - the first system to beat a professional Go player in the game of Go. A task previously considered beyond the reach of current AI systems and at least 10 years off into the future, but neural networks proved them wrong! You'll learn about: ✔️All of the nitty-gritty details around AlphaGo ✔️How MTCS and other subcomponents work ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ✅ AlphaGo movie: 🤍 ✅ Karpathy on AlphaGo: 🤍 ✅ Silver on UCB algo: 🤍 ✅ MTCS explained: 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ⌚️ Timetable: 00:00 Intro 00:37 Context behind the game of Go 04:10 High-level overview of components - SL policies 07:25 RL policy network 09:30 The value network 11:15 Going deeper 16:30 Details around value network 19:05 Understanding the search (MTCS) 27:10 Evaluation of AlphaGo 33:30 Older techniques 34:40 Even more detailed explanation of APV-MTCS 37:40 Virtual loss 41:00 Engineering 45:30 Neural networks and symmetries ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 💰 BECOME A PATREON OF THE AI EPIPHANY ❤️ If these videos, GitHub projects, and blogs help you, consider helping me out by supporting me on Patreon! The AI Epiphany ► 🤍 One-time donation: 🤍 Much love! ❤️ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 💡 The AI Epiphany is a channel dedicated to simplifying the field of AI using creative visualizations and in general, a stronger focus on geometrical and visual intuition, rather than the algebraic and numerical "intuition". ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 👋 CONNECT WITH ME ON SOCIAL LinkedIn ► 🤍 Twitter ► 🤍 Instagram ► 🤍 Facebook ► 🤍 👨👩👧👦 JOIN OUR DISCORD COMMUNITY: Discord ► 🤍 📢 SUBSCRIBE TO MY MONTHLY AI NEWSLETTER: Substack ► 🤍 💻 FOLLOW ME ON GITHUB FOR COOL PROJECTS: GitHub ► 🤍 📚 FOLLOW ME ON MEDIUM: Medium ► 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #alphago #deepmind #reinforcementlearning
直播: 柯洁与 AlphaGo 三番棋比赛第一局。与AlphaGo 和世界第一的围棋选手柯洁一同探索围棋背后的深远奥秘。本场直播将于2017年5月23日星期二 10:30 CST (当地时间)，03:30 BST 开始。
Com mais configurações de tabuleiro do que átomos no universo, o antigo jogo chinês de Go há muito é considerado um grande desafio para a Inteligência Artificial. Em 9 de março de 2016, os mundos do Go e da Inteligência Artificial colidiram na Coreia do Sul para uma extraordinária competição de melhor de cinco jogos, denominada The DeepMind Challenge Match. Centenas de milhões de pessoas em todo o mundo assistiram a um lendário mestre do Go enfrentar um desafiante de IA pela primeira vez na história. Dirigido por Greg Kohs e com trilha original do indicado ao Oscar Hauschka, AlphaGo teve sua estreia no Tribeca Film Festival. Desde então, ganhou inúmeros prêmios e elogios quase universais por uma história que narra uma jornada dos corredores de Oxford, pelas ruelas de Bordeaux, pelos terminais de codificação da DeepMind em Londres e, finalmente, pelo torneio de sete dias em Seul. À medida que o drama se desenrola, mais perguntas surgem: o que a Inteligência Artificial pode revelar sobre um jogo de 3.000 anos? O que ela pode nos ensinar sobre a humanidade? Melhor documentário: Denver International Film Festival (2017), Warsaw International Film Festival (2017) e Traverse City Film Festival (2017). Seleção oficial no Tribeca Film Festival (2017), BFI London Film Festival (2017) e Critics' Choice Documentary Awards (2017). Saiba mais: 🤍 Vídeo original (sem legendas em português): 🤍 Legenda por Guilherme Nunes "Quero que meu estilo de Go seja algo diferente, algo novo, algo meu, algo que ninguém tenha pensado antes." Lee Sedol, Campeão de Go (18 títulos mundiais). "Pensamos no DeepMind como uma espécie de esforço do programa Apollo para IA. Nossa missão é entender fundamentalmente a inteligência e recriá-la artificialmente." Demis Hassabis, cofundador e CEO da DeepMind. "O jogo de Go é o santo graal da Inteligência Artificial. Tudo o que já tentamos em IA, simplesmente falha quando você tenta o jogo Go." Dave Silver, pesquisador-chefe da AlphaGo.
In a paper published in Nature on 28th January 2016, we describe a new approach to computer Go. This is the first time ever that a computer program “AlphaGo” has defeated a human professional player. The game of Go is widely viewed as an unsolved “grand challenge” for artificial intelligence. Games are a great testing ground for inventing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. The first classic game mastered by a computer was noughts and crosses (also known as tic-tac-toe) in 1952. But until now, one game has thwarted A.I. researchers: the ancient game of Go. Despite decades of work, the strongest computer Go programs only played at the level of human amateurs. AlphaGo has won over 99% of games against the strongest other computer Go programs. It also defeated the human European champion by 5-0 in tournament games, a feat previously believed to be at least a decade away. In March 2016, AlphaGo will face its ultimate challenge: a 5-game challenge match in Seoul against the legendary Lee Sedol—the top Go player in the world over the past decade This video tells the story so far... With Demis Hassabis, Google DeepMind Deep Blue photo credit courtesy of International Business Machines Corporation, © International Business Machines Corporation.
Yapay zeka AlphaGo'nun, 18 kez dünya şampiyonu olmuş 9 dan sahibi Lee Sedol ile karşılaşmasının ve 5 serilik turnuvanın ikisinde tarihe nasıl tanıklık ettiğimizin hikayesi bu videoda. Go: Antik Çin’de 2.500 yıldan fazla tarihi ile dünyaca ünlü bir strateji oyunu. Amaç rakipten daha fazla alan sağlayarak onu alt etmek. Bu öyle bir oyun ki; board üzerindeki hamle olasılığı evrendeki atomların toplamından daha fazla. Bu oyunu son yıllarda bu kadar popüler yapan şey ise bir makine. AlphaGo'nun sıradışı hikayesini anlatmaya çalıştım. Abone Ol: 🤍 Blog: 🤍 Twitter: 🤍 Instagram: 🤍 Disclaimer for Fair Use: Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
Originally aired on March 12, 2020 More than 13,000 viewers — a new record for the AGA’s Twitch channel — tuned in for the March 12 Twitch broadcast featuring Michael Redmond 9P and Chris Garlock discussing their new book “Alphago to Zero” 🤍 and reviewing a game from the historic 2016 showdown between the world champion and the DeepMind AI. “Their passion for this game is infectious,” said CalmExit. The two talked about what it was like to provide the English-language commentary for the 2016 match for a global audience with intense media attention, and took questions from viewers. Chayashida wondered about “Michael and Chris’s thoughts, looking back, four years later…on the actual match as well as how AI has changed the game in the past 4 years.” And sam83a wondered “Does Michael plan writing other books using AI other than this 4-volume series? For example revisiting his san ren sei book, or analyzing historical classical games.” Check out the video to hear the answers to those questions and more, as well as Redmond’s review of Game 4 — chosen by a viewer poll — of the AlphaGo-Lee Sedol match. “I absolutely love how much Redmond loves the game,” said Trevoke. Stephen Hu produced the stream. PLUS: Check out Redmond's new YouTube channel, Michael Redmond's Go TV: 🤍
Subscribe here: 🤍 Become a Patreon!: 🤍 Visual animal AI: 🤍 Hi, welcome to ColdFusion (formally known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Sources: Why AlphaGo is NOT an "Expert System": 🤍 “Inside DeepMind” Nature video: 🤍 “AlphaGo and the future of Artificial Intelligence” BBC Newsnight: 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍forbes.com/sites/privacynotice/2014/02/03/inside-googles-mysterious-ethics-board/#5dc388ee4674 🤍 🤍 🤍 🤍 //Soundtrack// Disclosure - You & Me (Ft. Eliza Doolittle) (Bicep Remix) Stumbleine - Glacier Sundra - Drifting in the Sea of Dreams (Chapter 2) Dakent - Noon (Mindthings Rework) Hnrk - fjarlæg Dr Meaker - Don't Think It's Love (Real Connoisseur Remix) Sweetheart of Kairi - Last Summer Song (ft. CoMa) Hiatus - Nimbus KOAN Sound & Asa - This Time Around (feat. Koo) Burn Water - Hide » Google + | 🤍 » Facebook | 🤍 » My music | t.guarva.com.au/BurnWater 🤍 or » 🤍 » 🤍 » Collection of music used in videos: 🤍 Producer: Dagogo Altraide Editing website: 🤍cfnstudios.com Coldfusion Android Launcher: 🤍 » Twitter | 🤍ColdFusion_TV
This video explains the details behind AlphaGo! AlphaGo uses policy and value networks to reduce the search space in MCTS! Thanks for watching! Please Subscribe! Paper Link: 🤍
Originally aired on September 5, 2021 Produced by Eva-Dee Beech, Chris Garlock and Stephen Hu Thumbnail image of Rock 'em Sock 'em Robots by Lorie Shaull - Own work, CC BY-SA 4.0, 🤍
Watch DeepMind's program AlphaGo take on the legendary Lee Sedol (9-dan pro), the top Go player of the past decade, in a $1M 5-game challenge match in Seoul. This is the livestream for Match 2 to be played on: 10th March 13:00 KST (local), 04:00 GMT; note for US viewers this is the day before on: 9th March 20:00 PT, 23:00 ET. In October 2015, AlphaGo became the first computer program ever to beat a professional Go player by winning 5-0 against the reigning 3-times European Champion Fan Hui (2-dan pro). That work was featured in a front cover article in the science journal Nature in January 2016. Match commentary by Michael Redmond (9-dan pro) and Chris Garlock. Post Match Press Conference with Demis Hassabis and Lee Sedol
★ Để trân trọng việc Dưa Leo làm, hãy DONATE 💸 MoMo 0932539510 💸 VCB 0441003723804, Nguyễn Lê Minh Ngọc 💸Binance P2P: dualeone🤍gmail.com 💸 Paypal : Dualeone🤍gmail.com or 🤍 hoặc 🤍 search Dưa Leo Muốn THAY ĐỔI? Hãy subscribe và share clip CÔNG KHAI! ► Quảng cáo: dualeoteam🤍gmail.com ♥ Follow 🤍 🤍 🤍 🤍 🤍 Donate là gì: 🤍 ♥Channel của 🤍Gasensei Editor 🤍 🤍 ►►►Xin vui lòng không cut và re-up videos của kênh Dưa Leo dưới mọi hình thức. #Dualeo #AlphaGo #PhimHayNe
DeepMind's AlphaGo Zero algorithm beat the best Go player in the world by training entirely by self-play. It played against itself repeatedly, getting better over time with no human gameplay input. AlphaGo Zero was a remarkable moment in AI history, a moment that will always be remembered. Move 37 in particular is worthy of many philosophical debates. You'll see what I mean and get a technical overview of its neural components (code + animations) in this video. Enjoy! Code for this video: 🤍 Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: 🤍 instagram: 🤍 Facebook: 🤍 There are 2 errors in this video: 1. At the top of the residual network, it says value layer twice. One should say 'policy' layer. 2 The residual network is 40 layers, i say 20. This video is apart of my Machine Learning Journey course: 🤍 More Learning Resources: 🤍 🤍 🤍 🤍 🤍 🤍 Join us in the Wizards Slack channel: 🤍 Sign up for the next course at The School of AI: 🤍 And please support me on Patreon: 🤍 #AlphaGoZero #Deepmind #SirajRaval Signup for my newsletter for exciting updates in the field of AI: 🤍 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
... Победа компьютера над человеком в древней настольной игре Го заинтриговала и частично встревожила многих. Это свидетельство того, что машина вплотную приблизилась к постижению человеческой интуиции и превзошла человека, считающегося в этой игре величайшим мастером. Эксперты хором утверждали, что этого не случится еще как минимум лет десять. Это огромный шаг вперед для систем искусственного интеллекта и доказательство того, что они способны к самостоятельному обучению. Альфа-Го доказывает, что машины способны к самостоятельному обучению. Вместо того, чтобы рассматривать все возможные ходы методом перебора, что, собственно, делали все ее предшественники, Альфа Го использует обучение методом проб и ошибок и нейронные сети, чтобы имитировать процесс обучения, присущий человеку. Важно понимать, что даже по сравнению с шахматами количество ветвлений в Го просто немыслимо и даже теоретически не поддается расчету “в лоб”. Одних лишь позиций на доске десять в стосемидесятой степени, что больше, чем количество атомов во вселенной... Озвучено каналом Hello Robots. Фейсбук: 🤍 Вконтакте: 🤍 Видео с канала Cold Fusion. Оригинал видео здесь: 🤍
How Google Deepmind AlphaGo works. Extract from Movie Alphago. Thore Graepel is research scientist at Deepmind explains the AplhaGo logic. Three main components - Policy network, Value Network and tree search. Credit: AlphaGo movie
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![ALPHAGO - Khi TriTueNhanTao CHIẾN THẮNG CON NGƯỜI [KienThucNe - DLDBTT]](https://i.ytimg.com/vi/1YuX3UeSvYY/mqdefault.jpg "ALPHAGO - Khi TriTueNhanTao CHIẾN THẮNG CON NGƯỜI [KienThucNe - DLDBTT]")
