Game native to Japan
POPULARITY
2 episodes with Shogi
2 episodes with Shogi
2 episodes with Shogi
2 episodes with Shogi
2 episodes with Shogi
2 episodes with Shogi
2 episodes with Shogi
2 episodes with Shogi
2 episodes with Shogi
Aujourd'hui, on parle des lauréats du prix Turing 2025, la plus haute distinction en informatique. Il vient d'être décerné à deux chercheurs pionniers de l'intelligence artificielle. Il s'agit de Andrew Barto et Richard Sutton.Mais alors, quelle est leur contribution au monde de l'informatique ? Il s'agit d'une technique dite d'apprentissage par renforcement. C'est cette une approche clé qui a permis à des IA comme AlphaZero et AlphaStar d'exceller dans des jeux complexes, comme les échecs.Mais avant d'aller plus loin, penchons nous sur ce qu'est l'apprentissage par renforcement.Qu'est ce que l'apprentissage par renforcement ?Imaginez une souris dans un labyrinthe. À chaque décision, à chaque direction qu'elle prend, elle peut être récompensée ou non en fonction de son avancée vers la sortie.Et bien l'apprentissage que peut effectuer un ordinateur fonctionne de la même manière. Il explore différentes options, apprend de ses erreurs et ajuste sa stratégie pour maximiser ses gains.Et cette méthode est devenue essentielle pour entraîner des systèmes intelligents, oui tout le monde dit intelligence artificielle désormais. Et elles sont à présent capables de prendre des décisions autonomes.Echecs, go et shogi comme terrains d'entraînementConcrètement, l'apprentissage par renforcement est devenue une technique clé pour réaliser les promesses de l'IA moderne.C'est cette approche qui a permis à AlphaZero, le programme de Google DeepMind, d'apprendre à jouer aux échecs, au go ou encore au shogi, qui est un jeu de société traditionnel japonais.Et le tout sans connaissance préalable. L'IA s'est en effet entraînée contre elle même sur ces trois jeux, jusqu'à devenir experte en la matière. De la même manière mais cette fois dans le domaine des jeux vidéos, le programme AlphaStar a atteint un niveau de "grand maître" dans le jeu Starcraft 2.La première véritable théorie computationnelle de l'intelligenceMais évidemment, la puissance de l'apprentissage par renforcement à désormais un impact bien au-delà des jeux.Richard Sutton et Andrew Barto affirment que leur vision de l'apprentissage par renforcement repose sur une idée plus profonde. Ils expliquent que l'apprentissage par renforcement pourrait être la première véritable théorie computationnelle de l'intelligence.Mais au-delà des algorithmes, ils insistent sur l'importance du jeu et de la curiosité comme moteurs fondamentaux de l'apprentissage, et ce aussi bien pour les humains que pour les machines.Le ZD Tech est sur toutes les plateformes de podcast ! Abonnez-vous !Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.
Professor Swarat Chaudhuri from the University of Texas at Austin and visiting researcher at Google DeepMind discusses breakthroughs in AI reasoning, theorem proving, and mathematical discovery. Chaudhuri explains his groundbreaking work on COPRA (a GPT-based prover agent), shares insights on neurosymbolic approaches to AI. Professor Swarat Chaudhuri: https://www.cs.utexas.edu/~swarat/ SPONSOR MESSAGES: CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/ TOC: [00:00:00] 0. Introduction / CentML ad, Tufa ad 1. AI Reasoning: From Language Models to Neurosymbolic Approaches [00:02:27] 1.1 Defining Reasoning in AI [00:09:51] 1.2 Limitations of Current Language Models [00:17:22] 1.3 Neuro-symbolic Approaches and Program Synthesis [00:24:59] 1.4 COPRA and In-Context Learning for Theorem Proving [00:34:39] 1.5 Symbolic Regression and LLM-Guided Abstraction 2. AI in Mathematics: Theorem Proving and Concept Discovery [00:43:37] 2.1 AI-Assisted Theorem Proving and Proof Verification [01:01:37] 2.2 Symbolic Regression and Concept Discovery in Mathematics [01:11:57] 2.3 Scaling and Modularizing Mathematical Proofs [01:21:53] 2.4 COPRA: In-Context Learning for Formal Theorem-Proving [01:28:22] 2.5 AI-driven theorem proving and mathematical discovery 3. Formal Methods and Challenges in AI Mathematics [01:30:42] 3.1 Formal proofs, empirical predicates, and uncertainty in AI mathematics [01:34:01] 3.2 Characteristics of good theoretical computer science research [01:39:16] 3.3 LLMs in theorem generation and proving [01:42:21] 3.4 Addressing contamination and concept learning in AI systems REFS: 00:04:58 The Chinese Room Argument, https://plato.stanford.edu/entries/chinese-room/ 00:11:42 Software 2.0, https://medium.com/@karpathy/software-2-0-a64152b37c35 00:11:57 Solving Olympiad Geometry Without Human Demonstrations, https://www.nature.com/articles/s41586-023-06747-5 00:13:26 Lean, https://lean-lang.org/ 00:15:43 A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play, https://www.science.org/doi/10.1126/science.aar6404 00:19:24 DreamCoder (Ellis et al., PLDI 2021), https://arxiv.org/abs/2006.08381 00:24:37 The Lambda Calculus, https://plato.stanford.edu/entries/lambda-calculus/ 00:26:43 Neural Sketch Learning for Conditional Program Generation, https://arxiv.org/pdf/1703.05698 00:28:08 Learning Differentiable Programs With Admissible Neural Heuristics, https://arxiv.org/abs/2007.12101 00:31:03 Symbolic Regression With a Learned Concept Library (Grayeli et al., NeurIPS 2024), https://arxiv.org/abs/2409.09359 00:41:30 Formal Verification of Parallel Programs, https://dl.acm.org/doi/10.1145/360248.360251 01:00:37 Training Compute-Optimal Large Language Models, https://arxiv.org/abs/2203.15556 01:18:19 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, https://arxiv.org/abs/2201.11903 01:18:42 Draft, Sketch, and Prove: Guiding Formal Theorem Provers With Informal Proofs, https://arxiv.org/abs/2210.12283 01:19:49 Learning Formal Mathematics From Intrinsic Motivation, https://arxiv.org/pdf/2407.00695 01:20:19 An In-Context Learning Agent for Formal Theorem-Proving (Thakur et al., CoLM 2024), https://arxiv.org/pdf/2310.04353 01:23:58 Learning to Prove Theorems via Interacting With Proof Assistants, https://arxiv.org/abs/1905.09381 01:39:58 An In-Context Learning Agent for Formal Theorem-Proving (Thakur et al., CoLM 2024), https://arxiv.org/pdf/2310.04353 01:42:24 Programmatically Interpretable Reinforcement Learning (Verma et al., ICML 2018), https://arxiv.org/abs/1804.02477
The Japanese government said Saturday it will give the Medal of Honor for autumn 2024 to 786 people and 26 organizations, including shogi player Akira Watanabe and 54 gold medalists at the Paris Olympic and Paralympic Games.
If I download the shogi app you know I'm cooked Podcast art by Joey Rizk
With this episode, it goes over a few variants that exist for the game shogi. Although we go over a few variants here in this episode, feel free to share other variants that you're aware of with us so that we can share them with the world. So with that said, we hope you enjoy. Credits Writer - Bradley P. Thomas Producer - Bradley P. Thomas Voice Talent - ElevenLabs: Taylor Editor - Bradley P. Thomas Copyright Disclaimer: 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, education, 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. https://www.copyright.gov/legislation/dmca.pdf
With this episode, it goes over the version of chess that is known as shogi and it originated from the country of Japan. Although it possesses a fair number of similarities to that of other versions of chess, except that the pieces are the same color and possess the same pentagonal shape with this shape indicating who is the owner of said piece. So with that said, we hope you enjoy. Credits Writer - Bradley P. Thomas Producer - Bradley P. Thomas Voice Talent - ElevenLabs: Taylor Editor - Bradley P. Thomas Copyright Disclaimer: 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, education, 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. https://www.copyright.gov/legislation/dmca.pdf
David Silver is a principal research scientist at DeepMind and a professor at University College London. This interview was recorded at UMass Amherst during RLC 2024. References Discovering Reinforcement Learning Algorithms, Oh et al -- His keynote at RLC 2024 referred to more recent update to this work, yet to be published Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, Silver et al 2017 -- the AlphaZero algo was used in his recent work on AlphaProof AlphaProof on the DeepMind blog AlphaFold on the DeepMind blog Reinforcement Learning Conference 2024 David Silver on Google Scholar
Xenia Bayer spielt nicht nur chinesisches Schach (Xiangqi), sondern neuerdings auch Shogi. In dieser Episode bringt sie uns alles über die Figuren im Shogi und die Besonderheiten dieses japanischen Brettspiels bei. Shogi wird in der Regel auf einem 9x9-Brett gespielt. Es gibt einige Ähnlichkeiten zum Schach, wie zum Beispiel das Ziel, den gegnerischen König zu besiegen. Die Figuren sind teilweise aber anders, z.B. gibt es eine Lanze. Wer mehr wissen will, horcht einfach rein! Folge direkt herunterladen Die beten Schachmaterialien im Chess Tigers Online Shop: Chess Tigers Shop Der Schach-Booster: Das Buch von Michael Busse mit den 10 besten Methoden zur Verbesserung ...Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen? Dann schaue auf www.kostenlos-hosten.de und informiere dich. Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.Gern unterstützen wir dich bei deiner Podcast-Produktion.
Xenia Bayer spielt nicht nur chinesisches Schach (Xiangqi), sondern neuerdings auch Shogi. In dieser Episode bringt sie uns alles über die Figuren im Shogi und die Besonderheiten dieses japanischen Brettspiels bei. Shogi wird in der Regel auf einem 9x9-Brett gespielt. Es gibt einige Ähnlichkeiten zum Schach, wie zum Beispiel das Ziel, den gegnerischen König zu besiegen. Die Figuren sind teilweise aber anders, z.B. gibt es eine Lanze. Wer mehr wissen will, horcht einfach rein! Folge direkt herunterladen ℹ Die beten Schachmaterialien im Chess Tigers Online Shop: Chess Tigers Shop
Xenia Bayer spielt nicht nur chinesisches Schach (Xiangqi), sondern neuerdings auch Shogi. In dieser Episode bringt sie uns alles über die Figuren im Shogi und die Besonderheiten dieses japanischen Brettspiels bei. Shogi wird in der Regel auf einem 9x9-Brett gespielt. Es gibt einige Ähnlichkeiten zum Schach, wie zum Beispiel das Ziel, den gegnerischen König zu besiegen. Die Figuren sind teilweise aber anders, z.B. gibt es eine Lanze. Wer mehr wissen will, horcht einfach rein! Folge direkt herunterladen Die beten Schachmaterialien im Chess Tigers Online Shop: Chess Tigers Shop Der Schach-Booster: Das Buch von Michael Busse mit den 10 besten Methoden zur Verbesserung ...Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen? Dann schaue auf www.kostenlos-hosten.de und informiere dich. Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.Gern unterstützen wir dich bei deiner Podcast-Produktion.
Japanese shogi star Sota Fujii on Monday became the youngest winner of "eisei" lifetime status for a shogi title by fending off a challenge for his "Kisei" title.
Japanese shogi sensation Sota Fujii was beaten by his challenger for his "Eio" title Thursday, losing one of the eight major titles for the first time since he dominated them in October last year.
Episode 454: This week the Summit Squad take on another recommendation from one of our awesome Patrons, Snowman, and he recommended us to watch March Comes In Like A Lion, the story of a reclusive 17 year old who lives on his own in Tokyo, and how upon meeting three sisters challenges his loneliness gained from his previous home life due to the immense pressure put on him as a Shogi player prodigy. --- Send in a voice message: https://podcasters.spotify.com/pod/show/anime-summit/message
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: LLMs seem (relatively) safe, published by JustisMills on April 26, 2024 on LessWrong. Post for a somewhat more general audience than the modal LessWrong reader, but gets at my actual thoughts on the topic. In 2018 OpenAI defeated the world champions of Dota 2, a major esports game. This was hot on the heels of DeepMind's AlphaGo performance against Lee Sedol in 2016, achieving superhuman Go performance way before anyone thought that might happen. AI benchmarks were being cleared at a pace which felt breathtaking at the time, papers were proudly published, and ML tools like Tensorflow (released in 2015) were coming online. To people already interested in AI, it was an exciting era. To everyone else, the world was unchanged. Now Saturday Night Live sketches use sober discussions of AI risk as the backdrop for their actual jokes, there are hundreds of AI bills moving through the world's legislatures, and Eliezer Yudkowsky is featured in Time Magazine. For people who have been predicting, since well before AI was cool (and now passe), that it could spell doom for humanity, this explosion of mainstream attention is a dark portent. Billion dollar AI companies keep springing up and allying with the largest tech companies in the world, and bottlenecks like money, energy, and talent are widening considerably. If current approaches can get us to superhuman AI in principle, it seems like they will in practice, and soon. But what if large language models, the vanguard of the AI movement, are actually safer than what came before? What if the path we're on is less perilous than what we might have hoped for, back in 2017? It seems that way to me. LLMs are self limiting To train a large language model, you need an absolutely massive amount of data. The core thing these models are doing is predicting the next few letters of text, over and over again, and they need to be trained on billions and billions of words of human-generated text to get good at it. Compare this process to AlphaZero, DeepMind's algorithm that superhumanly masters Chess, Go, and Shogi. AlphaZero trains by playing against itself. While older chess engines bootstrap themselves by observing the records of countless human games, AlphaZero simply learns by doing. Which means that the only bottleneck for training it is computation - given enough energy, it can just play itself forever, and keep getting new data. Not so with LLMs: their source of data is human-produced text, and human-produced text is a finite resource. The precise datasets used to train cutting-edge LLMs are secret, but let's suppose that they include a fair bit of the low hanging fruit: maybe 5% of publicly available text that is in principle available and not garbage. You can schlep your way to a 20x bigger dataset in that case, though you'll hit diminishing returns as you have to, for example, generate transcripts of random videos and filter old mailing list threads for metadata and spam. But nothing you do is going to get you 1,000x the training data, at least not in the short run. Scaling laws are among the watershed discoveries of ML research in the last decade; basically, these are equations that project how much oomph you get out of increasing the size, training time, and dataset that go into a model. And as it turns out, the amount of high quality data is extremely important, and often becomes the bottleneck. It's easy to take this fact for granted now, but it wasn't always obvious! If computational power or model size was usually the bottleneck, we could just make bigger and bigger computers and reliably get smarter and smarter AIs. But that only works to a point, because it turns out we need high quality data too, and high quality data is finite (and, as the political apparatus wakes up to what's going on, legally fraught). There are rumbling...
Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: LLMs seem (relatively) safe, published by JustisMills on April 26, 2024 on LessWrong. Post for a somewhat more general audience than the modal LessWrong reader, but gets at my actual thoughts on the topic. In 2018 OpenAI defeated the world champions of Dota 2, a major esports game. This was hot on the heels of DeepMind's AlphaGo performance against Lee Sedol in 2016, achieving superhuman Go performance way before anyone thought that might happen. AI benchmarks were being cleared at a pace which felt breathtaking at the time, papers were proudly published, and ML tools like Tensorflow (released in 2015) were coming online. To people already interested in AI, it was an exciting era. To everyone else, the world was unchanged. Now Saturday Night Live sketches use sober discussions of AI risk as the backdrop for their actual jokes, there are hundreds of AI bills moving through the world's legislatures, and Eliezer Yudkowsky is featured in Time Magazine. For people who have been predicting, since well before AI was cool (and now passe), that it could spell doom for humanity, this explosion of mainstream attention is a dark portent. Billion dollar AI companies keep springing up and allying with the largest tech companies in the world, and bottlenecks like money, energy, and talent are widening considerably. If current approaches can get us to superhuman AI in principle, it seems like they will in practice, and soon. But what if large language models, the vanguard of the AI movement, are actually safer than what came before? What if the path we're on is less perilous than what we might have hoped for, back in 2017? It seems that way to me. LLMs are self limiting To train a large language model, you need an absolutely massive amount of data. The core thing these models are doing is predicting the next few letters of text, over and over again, and they need to be trained on billions and billions of words of human-generated text to get good at it. Compare this process to AlphaZero, DeepMind's algorithm that superhumanly masters Chess, Go, and Shogi. AlphaZero trains by playing against itself. While older chess engines bootstrap themselves by observing the records of countless human games, AlphaZero simply learns by doing. Which means that the only bottleneck for training it is computation - given enough energy, it can just play itself forever, and keep getting new data. Not so with LLMs: their source of data is human-produced text, and human-produced text is a finite resource. The precise datasets used to train cutting-edge LLMs are secret, but let's suppose that they include a fair bit of the low hanging fruit: maybe 5% of publicly available text that is in principle available and not garbage. You can schlep your way to a 20x bigger dataset in that case, though you'll hit diminishing returns as you have to, for example, generate transcripts of random videos and filter old mailing list threads for metadata and spam. But nothing you do is going to get you 1,000x the training data, at least not in the short run. Scaling laws are among the watershed discoveries of ML research in the last decade; basically, these are equations that project how much oomph you get out of increasing the size, training time, and dataset that go into a model. And as it turns out, the amount of high quality data is extremely important, and often becomes the bottleneck. It's easy to take this fact for granted now, but it wasn't always obvious! If computational power or model size was usually the bottleneck, we could just make bigger and bigger computers and reliably get smarter and smarter AIs. But that only works to a point, because it turns out we need high quality data too, and high quality data is finite (and, as the political apparatus wakes up to what's going on, legally fraught). There are rumbling...
Welcome to Anime watch club, a bi-weekly group discussion and review where the hosts of the what do you say anime podcast, nominate and vote on shows either that we haven't seen or shows that will hopefully lead to a great discussion. On today's episode, we review the 2016 anime, March Comes in Like a Lion Socials/Discord - https://linktr.ee/whatdoyousayanime 0:00 - Intro 2:32 - First Impressions 12:56 - Establishing the cast in the early episodes (Ep. 1-5) 23:53 - The relationship between Rei and Kyoko 29:56 - The "Child of God" arc (Ep 6-9) 42:05 - Would a knowledge of Shogi help your enjoyment? 50:49 - Rei's slice of life, backstory, and new developments (Ep. 10-15) 1:01:27 - The shogi workshop and leading up to the Kyoto tournament (Ep. 16-18) 1:06:04 - Tournament arc and wrapping up season 1 (Ep. 19-22) 1:13:32 - The Kawamoto's and impact of found family 1:22:52 - Closing thoughts and scores 1:30:38 - What We're Watching Next --- Support this podcast: https://podcasters.spotify.com/pod/show/whatdoyousayanime/support
For Episode 29 we are back to our d20 random episode. The dice gods, by their grace, blessed us with March Comes in Like a Lion. Well regarded amongst the anime community, will this show be a hit or miss for the three weebs? If you enjoyed the show, please subscribe for future episodes every Wednesday. Additionally, we'd appreciate you following the podcast on Twitter @TheAnimeBacklog or leaving us a review on iTunes, Spotify, Stitcher, or wherever you get your podcasts. If you want to follow us individually on Twitter, our handles are - Dan: @Avarice77, Marcus: @Marcus R, Nick: @NickSpartz. Any questions or comments feel free to email us at TheAnimeBacklogPodcast@gmail.com Music: "Kawaii Friends" by Alexander Lisenkov
Surprise! Wir starten unsere dreistelligen Folgen direkt mit einer dicken Überraschung, nämlich einer Sonderfolge! Maxi und Fabi aka Shogoon haben sich mal wieder für eine neue Folge zusammengerafft und einfach darüber geredet, was die letzten drei Jahre so los war. ein guter Ort also für Emotionen, Spaß, ein bisschen Gossip und tonnenweise Anekdoten. Es wird geil!Tickets für die Shogoon Tour:https://shogoon.ticket.io/2ejxnnf7/ywej3s/?lang=deAlle weiteren Infos zu Shogi & seiner Musik:https://workingclassmonopoly.comTickets für Maxis Tour:Shops und Podcasts sind ja nice, aber ein Max ohne Bühne ist kein richtiger Max. Und irgendwo muss der Frust der letzten vier Jahre ja hin.Wer Bock hat, Max im Herbst auf Tour zu besuchen... hier gibts die Karten:https://krasserstoff.com/tours/max-rockstah-nachtsheim-762b4709-c2cd-46d8-a171-db22853ddaf8 Hosted on Acast. See acast.com/privacy for more information.
In this podcast episode, Gretta returns after a long hiatus to help Xan review a heartfelt manga series about adopted families and Shogi. Is it worth checking out? Well, Sit back and Find out as they review March Comes In Like A lion by Chica Umino. ----more---- As our hosts go over this mesmerizing manga series, Gretta also does a deep dive on the unique characters in the series and Xan goes over the latest manga releases for the week. Remember to Like, Share and Subscribe. Follow us @spiraken on Twitter and @spiraken on Instagram, subscribe to this podcast and our YouTube channel, Support our Patreon and if you would kindly, please go to www.tinyurl.com/helpxan and give us a great rating on Apple Podcasts. Also join our discord and Thank you, hope you enjoy this episode. #spiraken #mangareview #wheelofmanga #seinenmanga #dramamanga #sliceoflifemanga #chicaumino #shogimanga #marchcomesinlikealion #denpa #podcasthq #manga #spirakenreviewpodcast Music Used in This Episode: Closing Theme-Trendsetter by Mood Maze (Uppbeat) Music from Uppbeat (free for Creators!): https://uppbeat.io/t/mood-maze/trendsetter License code: YEPNB5COHX56JVES WHERE TO FIND US Our Instagram https://www.instagram.com/spiraken/ Our Email Spiraken@gmail.com Xan's Email xan@spiraken.com Our Patron https://www.patreon.podbean.com/spiraken or https://www.patreon.com/spiraken Our Discord https://tinyurl.com/spiradiscord Our Twitter https://twitter.com/spiraken Our Youtube Channel https://www.youtube.com/@spiraken Our Twitch https://www.twitch.tv/spiraken Our Amazon Store http://www.amazon.com/shops/spiraken Random Question of the Day: Have You Ever Played Shogi Before?
Pull up a chair (or cushion or something) and have a seat for a rousing game of shogi with The Best Boys! What is shogi you ask? You didn't?! Well Best Boys Dan and Justin are gonna tell you anyway! Some people refer to it as "Japanese Chess" while others hate that particular moniker. The truth, as is so often the case, lies somewhere between these extremes. So grab your drunken elephant and sit in a form-perfect seiza while The Best Boys breakdown the history and culture surrounding the game of shogi and its connections to anime! *Mobile Suit Gundam: Witch from Mercury spoilers start at 00:13:35 and end at 00:20:45* Follow The Best Boys on Instagram @bestboys_pod or send us an email at thebestboyspod@gmail.com. If you like what we do here, please go give us a review, it really helps us with the algorithm, especially on Apple Podcasts! Bubble Tea by Pikuseru (https://fanlink.to/bubbletea) No Copyright Breaking News Music (https://youtu.be/a4I0jlETu4g) Sayonara April by KODOMOi (https://soundcloud.com/kodomoimusic) Creative Commons - Attribution 3.0 Unported - CC BY 3.0 (https://creativecommons.org/licenses/…) Music promoted by Music Panda - Vlog No Copyright Free Music Video Link: https://youtu.be/jmuJp29d57Q
The Adult Improver Series returns with two insightful guests joining the podcast. WIM Natasha Regan is an author and actuary who among many other chess accomplishments recently became the British over 50 Women's National Champion! Natasha recently collaborated on a Chessable course with Matthew Ball, who is a chess dad and dedicated improver who has made significant rating progress since returning to competitive chess in recent years. Natasha and Matthew shared lots of helpful chess study tips covering topics ranging from The Woodpecker Method, to the Chess Steps series, to whether one should alter their approach to a game against a younger opponent. We also discussed their fun and instructive new course, Zwischenzug: A Comprehensive Guide to Intermediate Moves. You can find timestamps for all of the topics discussed below. 0:00- Perpetual Chess is brought to you in part by Chessable.com! Check out Natasha and Matthew's new CHessable course here: : https://www.chessable.com/zwischenzug-a-comprehensive-guide-to-intermediate-moves/course/139623/ You can check out some of my recommended courses here: https://go.chessable.com/perpetual-chess-podcast/ 0:03- Matthew Ball and Natasha discuss their shared background as junior players, and how their paths recrossed in recent years. 7:30- Patreon mailbag question- Does Natasha have any different strategies when playing against kids as compared to adults? 17:00- Matthew came back into chess a few years back and his seen some rating gain. He discusses his training regimen. Mentioned: Chess for Life, Chess Steps Books, Woodpecker Method 22:00- More on the Woodpecker Method Mentioned: Pump Up Your Rating by GM Axel Smith, Book Recap #6 on the Woodpecker Method 23:00- How does Natasha tune up for a tournament? 26:00- Natasha discusses some similarities between Shogi and Chess. Mentioned: Karolina Styczyńska of the Shogi Harbor Twitch Channel 32:00- Matthew shares a few more improvement recommendations. 35:00- Why did Natasha and Matthew decide to do a course on intermediate moves? 45:00- Natasha and Matthew discusses their approaches to openings 52:00- Do they work with coaches? 56:00- Natasha and Matt discuss their tournament and summer plans. Thanks so much to Natasha and Matt for joining the show! Check out their course here: https://www.chessable.com/zwischenzug-a-comprehensive-guide-to-intermediate-moves/course/139623/ If you would like to help support Perpetual Chess via Patreon, you can do so here: https://www.patreon.com/perpetualchess Learn more about your ad choices. Visit megaphone.fm/adchoices
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Podcast Review | Best of the Bet #4 Keishi Ohtomo (2017) Highschool student, Rei Kiriyama is ranked Shogi player. He lives alone, away from his adopted family because his foster sister like--hates him. He soon meets another family of sister that care for him. Kiriyama struggles to become the best Shogi player, facing the threatening high ranking player Goto. Tazza: The High Rollers (Choi Dong-hoon, 2007) The god of gamblers (Wong Jing, 1989) Kaiji the ultimate Gambler (Tôya Satô, 2009) March comes in like a lion (Keishi Ohtomo, 2017) YouTube | https://www.youtube.com/channel/UCzMwCEPYI47Mq7W997iJkbg?view_as=subscriber Support us on Patreon! https://www.patreon.com/pastthesubtitles?fan_landing=true Instagram| Pastthesubtitles Twitter| @PastTheSubtitle Episode analytics --- Support this podcast: https://anchor.fm/gps1/support
Today's broadcast is C1E65A for Wayback Wednesday, July 13th, 2022 Today's episode is part one of two in our "Best of 2020 / 2021" celebration – featuring St. John's picks, and unimaginitavely called "The Best of 2020 / 2021 – part 1: St. John's picks". Track# - Track – Game – System – Composer(s) - Original Episode – Originally selected by 01) Intro – 00:00:00 02) Funky Radio [In-Game vers] - Jet Grind Radio – Dreamcast – B.B. Rights – C1E51 – St. John – 00:09:44 03) Title – TMNT – NES – Jun Funahashi – C1E60 – Phillip Vaughn – 00:13:07 04) [REVERSE] Options – Daffy Duck in Hollywood – Genesis – Matt Furniss – Ch F: Backtracks: the OTHER 50 – St. John – 00:14:51 05) Roller Mobster – Hotline Miami – Multiplatform – Carpenter Brut – C1E60 – Adam Huisman – 00:17:10 06) Got Well Soon – Life is Strange – Multiplatform – Breton – C1E60 – Amber Pearey – 00:20:37 07) Signs of Love – Persona IV – PS2 – Shoji Meguro – C1E60 – Trey Johnson – 00:25:22 08) [SLOW] Can you Feel the Sunshine – Sonic R – Saturn – C1E57 - Richard Jacques – St. John – 00:28:16 09) [REVERSE] Mirage – Gran Turismo 5 – PS3 – Kemmei Adachi – C1E52 – St. John – 00:35:19 10) Star Guitar – Lumines II – PSP – The Chemical Bros – C1E51 – St. John – 00:39:24 11) Overworld Theme – Zelda II: Adventure of Link – NES – Akito Nakatsuka – C1E53/C1E58 - St. Jesse / St. John – 00:45:44 12) World D – Art of Balance – Multiplatform - Martin Schjøler - Ch F: C2S1 Complete – St. John – 00:47:13 13) Afternoon in Crossbell – Zero no Kiseki – PC / PSP – Takahiro Unisuga – C1E59 – Hugues Johnson – 00:49:32 14) [REVERSE] Cylic – Heavily Armored – Cosmic Carnage – 32X – Hikoshi Hashimoto – C1E52 – St. John – 00:51:51 15) Stage 02 – Contra III: The Alien Wars – SNES – Konami Kukeiha Club – C1E53 – St. John – 00:54:04 16) Illusion – Ys IV – PC Engine – Atsushi Shiakawa – C1E59 – Hugues Johnson – 00:56:55 17) Norfair – Super Smash Bros. Ultimate – Switch – c: Hirokazu ("Hip") Tanaka / a: Yuzo Koshiro – Ch F: C2S1 Complete – St. John – 00:58:58 18) Mystic Woods (aka Forest 2) - Grounseed (OPN vers) - PC98 – Daisuke Takahashi – C2E3 – St. John – 01:01:27 19) [REVERSE] Stage 4-3 – Bram Stoker's Dracula – Genesis – Matt Furniss – C1E52 – St. John – 01:05:03 20) [REVERSE] The Reverse Will – Silent Hill 2 – PS2 – Akira Yamaoka – C1E52 – St. John – 01:08:17 21) Voices of Urdak – DOOM Eternal – Multiplatform – Mick Gordon – C1E56 – St. John – 01:11:38 22) Anxious Heart – Final Fantasy VII – PS1 – Nobuo Uematsu – C1E58 – St. Jesse – 01:17:44 23) If you Open your Heart – Final Fantasy VII – PS1 – Nobuo Uematsu – C1E58 – St. Jesse – 01:21:37 24) Everything's Going to be Okay – Prey – Multiplatform – Mick Gordon – C1E51 – St. John – 01:24:31 25) [REVERSE] Process Control – Gran Turismo Sport – PS4 – Yasuhisa Inoue – C1E52 – St. John – 01:27:11 26) BGM 3 – Mario Paint – SNES – Hirokazu "Hip" Tanaka, Ryouji Yoshitomi, and/or Kazumi Totaka – C1E54 – St. John – 01:30:37 27) [SLOW] Fonction – n++ - Multiplatform – Broca – C1E57 – St. John – 01:33:57 28) Fonction – n++ - Multiplatform – Broca – Ch F: C2S1 Complete – St John – 01:43:31 29) Kara Kara Bazaar – LoZ: Breath of the Wild – WiiU / Switch – Hajime Wakai, Manaka Kataoka, and/or Yasuaki Iwata – C1E60 – St. John – 01:50:26 30) Space – Yoshi's Crafted World – Switch – Kazufumi Umeda – C1E54 – St. John – 01:52:32 31) Footlight Lane – Super Mario 3D World – WiiU / Switch – Mahito Yokota, Toru Minegishi, Koji Kondo, and/or Yasuaki Iwata – C1E54 – St. John – 01:55:38 32) Course World – Super Mario Maker 2 – Switch – Atsuko Asahi, Toru Minegishi, and/or Sayoka Doi – C1E54 – St. John – 01:57:38 33) Gomoku, Shogi, Mini-Shogi, Hanafuda – Clubhouse Games: 51 Worldwide Classics – Switch – Chamy Ishi, and/or Toshiki Aida – C1E56 – St. John – 01:59:52 34) Prime #5 – Echochrome - PSP / PS3 – Hideki Sakamoto – C1E60 – Electric Boogaloo – 02:01:53 35) Skytown – Metroid Prime 3 – Wii – Kenji Yamamoto, Minako Hamano, and/or Masaru Tajima – C1E51 – St. John – 02:05:06 36) [REVERSE] The Blazing Sands – Final Fantasy X – PS2 – Masashi Hamauzu – C1E52 – St. John – 02:08:28 37) [REVERSE] Stage 4 – Chip Chan Kick – PCFX – Yoshio Furukawa, Masahara Iwata, and/or Hitoshi Sakamoto – C1E52 – St. John – 02:11:26 38) Division – BrandishVT – PC – Naoki Kaneda – C1E59 – Hugues Johnson – 02:13:25 39) Snif City – Paper Mario: The Origami King – Switch – Shoh Marakami, Yoshiaki Kimura, Hiroki Moishita, and/or Fumihiro Isobe – C1E54 / C2E4 – St. John – 02:15:24 40) Marionette, Marionette – Ys IX – PS4 – Mitsuo Singa – C1E59 – Hugues Johnson – 02:18:19 41) Toad's Turnpike – Mario Kart 64 – N64 – Kenta Nagata, Taroh Bandoh, and/or Yoji Inagaki – C1E54 – St. John – 02:20:34 42) Guruguru Majin De Pon – Gurumin – PC / PSP / 3DS – Takahide Murayama – C1E59 – Hugues Johnson – 02:23:28 43) Results Theme (aka Record of Samus) - Metroid Prime – Gamecube / Wiii – Kenji Yamamoto (inspired by "Brinstar" from Metroid – NES – Hirokazu "Hip" Tanaka) - C1E60 – St. John – 02:27:08 44) Results Parade – Check Mii Out Channel – Wii – Kazumi Totaka – C1E56 – St. John –02:30:05 45) A Battle's Conclusion – Hyrule Warriors: Age of Calamity – Switch – Kumi Tanioka, Reo Uratai, Ryotaro Yagi, and/or Haruki Yamada – C1E56 – St. John – 02:32:09 46) Tear-Stained Eyes – Snatcher – Sega CD / PC Engine CD – Konami Kukeiha Club – C1E51 – St. John – 02:34:18 47) Inn / Game Over – Ironsword: Wizards and Warriors II – NES – David Wise – C1E53 – St. John – 02:39:09 48) [REVERSE] The Serpent Trench – Final Fantasy VI – SNES – Nobuo Uematsu – C1E52 – St. John – 02:39:59 49) [REVERSE] What Can you Do? - Gran Turismo Sport – PS4 – Lenny Ibizarre – C1E52 – St. John – 02:41:56 50) [SLOW] Jump Up Super Star – Super Mario Odyssey – Switch – Shiho Fujii, and/or Koji Kondo – C1E57 – St. John – 02:46:54 51) [REVERSE] Ending – Super Castlevania IV – SNES – Masanori Adachi and/or Taro Kudo – Ch F: Backtracks – The OTHER 50 – St. John – 02:52:41 52) Outro – 02:57:55 Music Block Runtime: 02:48:26, Total Episode Runtime: 03:17:52 Our Intro Music is Funky Radio, from Jet Grind Radio on the Sega Dreamcast, composed by BB Rights. Our Outro Music is Results Parade from the Check Mii Out Channel on the Wii, composed by Kazumi Totaka. Produced using Ardour 6 / Audacity 3 in Ubuntu Studio [Linux] 22.04 A few important milestones in 2020 / 2021 that I had failed to mention in the intro, but which deserve mentioning: A) we had our first Ch 1 episode by Hugues (which was also the first Ch 1 in the entire show's history that St. John had absolutely no hand whatsoever in producing. It was a focus on music fromFalcomgames, and was quite succinctly named "The Falcom Episode" B) We made the migration from producing Nerd Noise Radio in GarageBand via MacOS on a mac mini (and using primarily MP3s in production – mostly through YouTube-to-MP3 websites) to producing NNR in a mix of Audacity andArdourvia Ubuntu Studio (Linux) on a mix of a Dell Latitude Laptop and my custom-built gaming PC (and using primarily WAVs in production – mostly through the youtube-dl command in the Linux terminal). The last episode to be produced in Mac was C1E52: Backtracks (April 1st 2020) and the first episode to be produced in Linux was Channel F: Backtracks – the OTHER 50 (April or May 2020). I feel like the production value on balance is noticeably and appreciably better after the change. And since Hugues is also a Linux user, that means that both sides of our Channel 2 episodes are Linux productions. You can find the Google Sheets spreadsheet showing St. John's work in arriving at his tracklist for this episode here (WARNING: MILD SPOILERS! Contains track lists for potential future Archive-exclusive SUPER BONUS supplemental music collections: https://docs.google.com/spreadsheets/d/1X6JpkqiGADAF9sHAsSpgyImjvimVWJv2mOATf5_ZZGc/edit?usp=sharing You can also find all of our audio episodes on Archive.org as well as the occasional additional release only available there, such as remixes of previous releases and other content. Our YouTube Channel, for the time being is in dormancy, but will be returning with content, hopefully, in 2022. Meanwhile, all the old stuff is still there, and can be found here: https://www.youtube.com/user/NerdNoiseRadio Our episodes (and occasionally, other content, including expanded show notes) can be found on our blog here: nerdnoiseradio.blogspot.com. Nerd Noise Radio is also available on The Retro Junkies Network at www.theretrojunkies.com, and is a member of the VGM Podcast Fans community at https://www.facebook.com/groups/VGMPodcastFans/ Or, if you wish to connect with us directly, we have two groups of our own: Nerd Noise Radio - Easy Mode: https://www.facebook.com/groups/276843385859797/ for sharing tracks, video game news, or just general videogame fandom. Nerd Noise Radio - Expert Mode: https://www.facebook.com/groups/381475162016534/ for going deep into video game sound hardware, composer info, and/or music theory. You can also follow us on Twitter at @NerdNoiseRadio. And we are also now on Spotify, TuneIn, Pandora, iHeartRadio, Stitcher, and Vurbl. Thanks for listening! Join us again in August (dates TBD) for a special guest on Channel 1, and our desert biome focus, "Just Deserts" on Channel 2. Delicious VGM, as well as Tasty VGM and Talk on Nerd Noise Radio...and wherever you are....Fly the N!!! Cheers!
'March Comes in Like a Lion' is a masterpiece that beautifully delivers on character-driven stories via internal monologues, nuanced dialogue, and effective imagery centered around the world of shogi. Ionatan and Ravi dive deep into the series, discussing the strengths of Chica Umino's writing and Akiyuki Shinbo's adaptation, the realistic depictions of depression and bullying, and how the message of the series is ultimately optimistic in terms of personal growth and relationships.
In this episode, I share my experience learning to play shogi in Japan. Read the transcript on my website: https://teatimewithtayvinb.wordpress.com/2022/04/13/lets-go-for-shigi/ Talk with me on iTalki: www.italki.com/teacher/2889545 Follow me on Twitter: https://twitter.com/TEA_Time_Tayvin --- Support this podcast: https://anchor.fm/teatimewithtayvin/support
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Supervise Process, not Outcomes, published by stuhlmueller on April 5, 2022 on LessWrong. We can think about machine learning systems on a spectrum from process-based to outcome-based: Process-based systems are built on human-understandable task decompositions, with direct supervision of reasoning steps. Outcome-based systems are built on end-to-end optimization, with supervision of final results. This post explains why Ought is devoted to process-based systems. The argument is: In the short term, process-based ML systems have better differential capabilities: They help us apply ML to tasks where we don't have access to outcomes. These tasks include long-range forecasting, policy decisions, and theoretical research. In the long term, process-based ML systems help avoid catastrophic outcomes from systems gaming outcome measures and are thus more aligned. Both process- and outcome-based evaluation are attractors to varying degrees: Once an architecture is entrenched, it's hard to move away from it. This lock-in applies much more to outcome-based systems. Whether the most powerful ML systems will primarily be process-based or outcome-based is up in the air. So it's crucial to push toward process-based training now. There are almost no new ideas here. We're reframing the well-known outer alignment difficulties for traditional deep learning architectures and contrasting them with compositional approaches. To the extent that there are new ideas, credit primarily goes to Paul Christiano and Jon Uesato. We only describe our background worldview here. In a follow-up post, we'll explain why we're building Elicit, the AI research assistant. The spectrum Supervising outcomes Supervision of outcomes is what most people think about when they think about machine learning. Local components are optimized based on an overall feedback signal: SGD optimizes weights in a neural net to reduce its training loss Neural architecture search optimizes architectures and hyperparameters to have low validation loss Policy gradient optimizes policy neural nets to choose actions that lead to high expected rewards In each case, the system is optimized based on how well it's doing empirically. MuZero is an example of a non-trivial outcome-based architecture. MuZero is a reinforcement learning algorithm that reaches expert-level performance at Go, Chess, and Shogi without human data, domain knowledge, or hard-coded rules. The architecture has three parts: A representation network, mapping observations to states A dynamics network, mapping state and action to future state, and A prediction network, mapping state to value and distribution over next actions. Superficially, this looks like an architecture with independently meaningful components, including a “world model” (dynamics network). However, because the networks are optimized end-to-end to jointly maximize expected rewards and to be internally consistent, they need not capture interpretable dynamics or state. It's just a few functions that, if chained together, are useful for predicting reward-maximizing actions. Neural nets are always in the outcomes-based regime to some extent: In each layer and at each node, they use the matrices that make the neural net as a whole work well. Supervising process If you're not optimizing based on how well something works empirically (outcomes), then the main way you can judge it is by looking at whether it's structurally the right thing to do (process). For many tasks, we understand what pieces of work we need to do and how to combine them. We trust the result because of this reasoning, not because we've observed final results for very similar tasks: Engineers and astronomers expect the James Webb Space Telescope to work because its deployment follows a well-understood plan, and it is built out of well-understo...
Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Supervise Process, not Outcomes, published by stuhlmueller on April 5, 2022 on LessWrong. We can think about machine learning systems on a spectrum from process-based to outcome-based: Process-based systems are built on human-understandable task decompositions, with direct supervision of reasoning steps. Outcome-based systems are built on end-to-end optimization, with supervision of final results. This post explains why Ought is devoted to process-based systems. The argument is: In the short term, process-based ML systems have better differential capabilities: They help us apply ML to tasks where we don't have access to outcomes. These tasks include long-range forecasting, policy decisions, and theoretical research. In the long term, process-based ML systems help avoid catastrophic outcomes from systems gaming outcome measures and are thus more aligned. Both process- and outcome-based evaluation are attractors to varying degrees: Once an architecture is entrenched, it's hard to move away from it. This lock-in applies much more to outcome-based systems. Whether the most powerful ML systems will primarily be process-based or outcome-based is up in the air. So it's crucial to push toward process-based training now. There are almost no new ideas here. We're reframing the well-known outer alignment difficulties for traditional deep learning architectures and contrasting them with compositional approaches. To the extent that there are new ideas, credit primarily goes to Paul Christiano and Jon Uesato. We only describe our background worldview here. In a follow-up post, we'll explain why we're building Elicit, the AI research assistant. The spectrum Supervising outcomes Supervision of outcomes is what most people think about when they think about machine learning. Local components are optimized based on an overall feedback signal: SGD optimizes weights in a neural net to reduce its training loss Neural architecture search optimizes architectures and hyperparameters to have low validation loss Policy gradient optimizes policy neural nets to choose actions that lead to high expected rewards In each case, the system is optimized based on how well it's doing empirically. MuZero is an example of a non-trivial outcome-based architecture. MuZero is a reinforcement learning algorithm that reaches expert-level performance at Go, Chess, and Shogi without human data, domain knowledge, or hard-coded rules. The architecture has three parts: A representation network, mapping observations to states A dynamics network, mapping state and action to future state, and A prediction network, mapping state to value and distribution over next actions. Superficially, this looks like an architecture with independently meaningful components, including a “world model” (dynamics network). However, because the networks are optimized end-to-end to jointly maximize expected rewards and to be internally consistent, they need not capture interpretable dynamics or state. It's just a few functions that, if chained together, are useful for predicting reward-maximizing actions. Neural nets are always in the outcomes-based regime to some extent: In each layer and at each node, they use the matrices that make the neural net as a whole work well. Supervising process If you're not optimizing based on how well something works empirically (outcomes), then the main way you can judge it is by looking at whether it's structurally the right thing to do (process). For many tasks, we understand what pieces of work we need to do and how to combine them. We trust the result because of this reasoning, not because we've observed final results for very similar tasks: Engineers and astronomers expect the James Webb Space Telescope to work because its deployment follows a well-understood plan, and it is built out of well-understo...
Shogi! Game of champions. Get ready for an entire anime about one introverted Shogi player's journey through the horrible world of Society and having to have relationships with other people in your life. The horror! March Comes In Like A Lion! Follow Deer on twitter: https://twitter.com/DeerBits_ Follow Gamb on twitter: https://twitter.com/DrewGamblord Follow Spooky on twitter: https://twitter.com/SpoonkyTweets Don't forget we also make public our commentary track so you can watch along with us our just listen to us go insane: https://soundcloud.com/friendsforcingfriends/march-comes-in-like-a-lion-commentary Patreon where you can find commentary tracks, notes, early access to next week's episode! https://www.patreon.com/gamblord
¿con nuevas normas para el ajedrez? / ¿con hidroaviones? / ¿con drones recogiendo manzanas? / Sony compra Bungie / Más épica legal en Epic vs Apple / Vertido tóxico de Samsung / Los emojis 3D animados de Windows siguen en camino / OpenRAN en Málaga Patrocinador: La gente Colchón Morfeo vuelve con nosotros para que disfrutes del mejor colchón del universo https://www.colchonmorfeo.com/?utm_source=NL&utm_campaign=MIXX, que te dará el descanso y la vitalidad que necesitas para cumplir tus sueños. El envío es gratuito y en 24 horas https://www.colchonmorfeo.com/?utm_source=NL&utm_campaign=MIXX y tienes 100 días de prueba sin compromiso. — Ahórrate 100 euros https://www.colchonmorfeo.com/?utm_source=NL&utm_campaign=MIXX con el código MIXX100. ¿con nuevas normas para el ajedrez? / ¿con hidroaviones? / ¿con drones recogiendo manzanas? / Sony compra Bungie / Más épica legal en Epic vs Apple / Vertido tóxico de Samsung / Los emojis 3D animados de Windows siguen en camino / OpenRAN en Málaga ♟️ ¿Pueden los algoritmos mejorar las reglas del ajedrez? Mi conclusión después de leer este interesante estudio sobre AlphaZero jugando con diferentes normas es que acabaría empeorando el sistema tradicional https://cacm.acm.org/magazines/2022/2/258230-reimagining-chess-with-alphazero/fulltext al traer aún mayor ventaja para las piezas blancas.
This week, Dr. Green is joined by Dr. Yoichi Funabashi, chairman of the Tokyo-based think tank Asia Pacific Initiative, to discuss geopolitical and economic trends in the Indo-Pacific and Japanese grand strategy. Dr. Funabashi talks about the evolution of Japan's foreign policy strategy, from the Abe administration to the new Kishida administration, as well as the role of the U.S.-Japan alliance in Japan's strategic thinking. The two also touch on Japan's relationship with South Korea, economic security, and Japan's prospects for acquiring strike capabilities.
Today's Whiteboard & Poll きょうのホワイトボード https://www.azumiism.com/category/podcast/
Nos visitan los amigos (y en concreto Nichi) de Konichiwa desde Japón (un podcast que ya estáis tardando en escuchar y suscribiros) para sacarnos de dudas como ¿es verdad todo lo que sale de Japón en los videojuegos? ¿hay videojuegos en los baños públicos? ¿cómo se juega al puñetero Shogi? ¿lleva la gente los pelos de colores? Esto y mucho más ¡Pasad y descubridlo por alguien que vive en el país del sol naciente. ¡Qué lo disfrutéis!
Ah, Chess. This one's been around a long time. But what if you're looking for something similar, but with a different spin on it? In this episode, Dylan and Bill discuss some of the many variants that Chess has inspired, as well as a few games that are completely different but might scratch the same itch.
Director Kahlil Silver along with his brother Shogi Silver join Silas to talk about their newest film In The Company of Women. A film set in and shot in Seattle WA. They also answer the world famous Seven Questions. #SFCS
What if Hideo Kojima made a manga about Shogi that was a mashup of Fight Club and Joker? This episode we, and our guest Tim Batt from The Worst Idea of All Time discuss Kentarou Fukuda's Shonen Jump manga Double Taisei. Show Notes: You can reach us at shonenflop@gmail.com or on twitter @shonenflopcast Episode art by Shannon (IG: illuminyatea) You can find our guest on Twitter @Tim_batt, The Worst Idea of All Time: worstideaofalltime.com If you're enjoying Shonen Flop and want to support us or even just access exclusive content like bonus eps, warmup audio, or deleted scenes consider becoming a Patron patreon.com/shonenflop. Your support is extremely appreciated! Our merch store has new designs! Get Shonen Flop art, including this episode's cover art, on a shirt, mug, print, or whatever else might catch your eye teepublic.com/t-shirts?ref_id=22733 Become a member of our community by joining our Discord. You can hang out with us, play games, and even join our comic book discussion club! Find it at discord.gg/4hC3SqRw8r Shoutouts: Check out Newsly for free now from www.newsly.me and use promo code FL0P2021 to receive a 1-month free premium description Blake and Spencer Get Jumped: linktr.ee/bandsgetjumped The Broken Lords Tabletop RPG Podcast: linktr.ee/thebrokenlords 4 Eyes Academia: @4EyesAcademia
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Sailor Noob is the podcast where a Sailor Moon superfan and a total noob go episode by episode through the original Sailor Moon series!It's a game of wits this week as Ami faces off against Berthier! Ami is a chess prodigy, but when Berthier challenges her to a match she'll have to put her life on the line to save her friends!In this episode, we discuss chess in Japan, Shogi, the JCA, and Miyoko Watai. We also talk about Sailor ships, a tasty new OP, spoiler stew, eye boogers and nose boogers, playing to forget your loneliness, being mercurial, Japanese Jane Doe, warp speed chess, rubbing the goop, accepting Usagi into your heart, "Shogi's Gambit", dodgeball mercenaries, imaginary cake, and Tuxedo Mauve!Project much?We're on iTunes and your listening platform of choice! Please subscribe and give us a rating and a review! Arigato gozaimasu!https://podcasts.apple.com/us/podcast/sailor-noob/id1486204787Become a patron of the show and get access to even more Sailor Noob content!http://www.patreon.com/sailornoobSailor Noob is a part of the Just Enough Trope podcast network. Check out our other shows about your favorite pop culture topics and join our Discord!http://www.twitter.com/noob_sailorhttp://www.justenoughtrope.comhttp://www.instagram.com/noob_sailorhttps://discord.gg/MYg6YN7vBuy us a Kōhī on Ko-Fi!https://ko-fi.com/E1E01M2UA
In this episode, Zoe tells us about Yokai - The folklore surrounding them as well as specific examples of the Kappa and Kitsune. We also talk about the ship of Theseus, Mike's Badger beard, where (or when,) to avoid when time travelling through history, and more! Here's a rundown of the episode: 00:37 - Hello! 01:27 - Presented Piece - Yokai 09:49 - Promo - Strange Origins 10:51 - Discussion 41:15 - Thanks and Where You Can Find Us Online 44:27 - Fun Fact Total Runtime: 45:02 Links: Promo Our promo partner this week is Strange Origins podcast, which you can check out here: https://www.listennotes.com/podcasts/strange-origins-fascinating-productions-3rvREZzXdp2/amp/ Research Links https://en.wikipedia.org/wiki/Princess/_Mononoke (https://en.wikipedia.org/wiki/Princess/_Mononoke) https://en.wikipedia.org/wiki/Animism (https://en.wikipedia.org/wiki/Animism) https://en.wikipedia.org/wiki/Yōkai (https://en.wikipedia.org/wiki/Yōkai) https://en.wikipedia.org/wiki/Mononoke (https://en.wikipedia.org/wiki/Mononoke) https://yokaiwatchfans.com/wiki/Walkappa (https://yokaiwatchfans.com/wiki/Walkappa) https://en.wikipedia.org/wiki/Kappa/_(folklore) (https://en.wikipedia.org/wiki/Kappa/_(folklore)) https://www.tsunagujapan.com/6-famous-yokai-mystical-creatures-from-japan/ (https://www.tsunagujapan.com/6-famous-yokai-mystical-creatures-from-japan/) https://en.wikipedia.org/wiki/Shogi (https://en.wikipedia.org/wiki/Shogi) https://en.wikipedia.org/wiki/Kitsune (https://en.wikipedia.org/wiki/Kitsune) Our Links http://storiesofstrangeness.com (Website) - Sign up for email alerts, view the gallery, and other cool stuff. https://Instagram.com/storiesofstrangeness (Instagram) - Where we hang out the most. https://Facebook.com/storiesofstrangeness (Facebook) - We have a page and a group - come talk to us! https://Twitter.com/sostrangepod (Twitter) - Mike sends all our tweets - blame him! Our https://www.redbubble.com/people/ZoeandMike/shop?asc=u&ref=account-nav-dropdown (Redbubble) page, where you can find all of our designs and illustrations on loads of products. Our https://www.patreon.com/storiesofstrangeness?fan_landing=true (Patreon) page, if you'd like to support the show. We're starting with 2 tiers: £1 a month, to support the show, which is about $1.37, where we will thank you personally on the show - and £3 a month, which is about $4.11, that will enable you to listen to outtakes and other bonus content. More tiers may follow. Support this podcast
The Habibis talk about games, shogi, and haggling.Timestamps: Persona 5 Strikers (00:45) Loop Hero (06:37) Shogi (20:40) A Board With No Pieces (26:56) That's it for this episode. Follow us on Twitter at @the_habibis, e-mail us your stories, feedback, or suggestions at info@thehabibis.com, or join our (very young) Discord server at https://discord.thehabibis.com/. Shokran, and salaam!
Today’s broadcast is C1E56 for Mishmash Monday, February 22nd, 2021. Today’s episode will be Mishmash Monday, vol 10 – the inaugural episode of calendar 2021, and of our very non-standard "Season 4 – part 2". Track# – Track – Game – System – Composer(s) – Timestamp 01) Season 4 [pt. 2] Earcatcher – 00:00:00 02) Intro – 00:00:03 03) Drawing Game – Pictionary – NES – Tim Follin – 00:02:11 04) Menu 1 – Wimbledon Championship Tennis – Genesis – Crush-X – 00:04:50 05) In-Game Theme – Top Players Golf – Neo Geo MVS / AES – Kikuko Hataya – 00:06:36 06) 2dpp - Tetris Party Deluxe – Wii – Takashi Watanabe, Tomoya Yoshida, Keisuke Fukuhira, and/or Ichirō Shimakura – 00:08:54 07) Overworld Theme – Fester's Quest – NES – Naoki Kodaka – 00:11:46 08) Stage 3 – Journey to Silius – NES – Nobuyuki Hara, Naohisa Morota, Shinichi Seya, Naoki Kodaka, and/or Mabochan – 00:13:48 09) Misty Blue – Misty Blue – PC88 – Yuzo Koshiro – 00:15:35 10) Sand Hell – Lightening Force – Genesis – Toshiharu Yamanishi – 00:18:29 11) Cocoa Cave – Kirby Super Star – SNES – Jun Ishikawa, and/or Dan Miyakawa – 00:20:38 12) Results Parade – Check Mii Out Channel – Wii – Kazumi Totaka – 00:22:47 13) A North Breeze – Hover Racing – PS1 – Tooru Kawakami and/or Hiroaki Ohmori – 00:24:54 14) Sacred Warrior – Actraiser 2 – SNES – Yuzo Koshiro – 00:29:01 15) Track 12 – RyuuKyuu – PC Engine – Yasushi Kawasaki and/or Hiroto Saitoh – 00:30:49 XX) (Medley) – [color games) – Android/iOS – Bart Bonte 16) Yellow – 00:32:36 17) Red – 00:35:56 18) Black – 00:40:35 19) Blue – 00:43:47 20) Green – 00:47:19 21) Phazon Mine Ambience – Metroid Prime – GameCube – Kenji Yamamoto – 00:50:41 22) Voices of Urdak – DOOM Eternal – multi – Mick Gordon – 00:52:22 23) Apprehensive Moments (cutscene) / Stage 4-3 Ninja Gaiden – NES – Keiji Yamagishi, Ryuichi Nitta, and/or Ichiro Nakagawa – 00:58:39 24) Boss 1 – Golden Axe II – Genesis – Naofumi Hataya – 00:59:23 25) The Ing Attack – Metroid Prime 2 – GameCube – Kenji Yamamoto – 01:01:01 26) Shadow – Doomsday Warrior – SNES – Junta – 01:03:52 27) Story Sequence – Jurassic Park – Genesis – Sam Powell – 01:05:40 28) Hanako and Yorinobu – Cyberpunk 2077 – multi - P.T. Adamczyk, Marcin Przybyłowicz, Paul Leonard-Morgan, and/or Richard Devine – 01:06:33 29) Adore – Gran Turismo Sport – PS4 – i:Cube – 01:09:16 30) Gomoku, Shogi, Mini-Shogi, Hanafuda – Clubhouse Games: 51 Worldwide Classics – Switch – Chamy Ishi and/or Toshiki Aida – 01:16:06 31) Sunset Seaside – Kick Off – Arcade – Tsukasa Tawada – 01:18:08 32) In-Browser Theme (aka Addiction.mod, or "addicti.mod") – Wii Homebrew Browser – Wii – Noiseless (c1992) – 01:19:52 33) A Battle's Conclusion – Hyrule Warriors: Age of Calamity – Switch – Kumi Tanioka, Reo Uratani, Ryotaro Yagi, and/or Haruki Yamada – 01:24:53 34) Outro – 01:27:29 35) Season 4 [pt. 2] celebration sign-off – 01:38:33 Music Block Runtime: 01:25:22, Total Episode Runtime: 01:39:08 Our Intro and Outro Music is Funky Radio, from Jet Grind Radio on the Sega Dreamcast, composed by BB Rights. Produced using Ardour 6 / Audacity in Ubuntu Studio 20.04. Music Block produced 01/27/21. Final Episode produced 02/20/21. NOTICE: The "Nerd Noise Radio - RERUNS!" feed will be going away after December of 2021, and consolidating its output with the main feed. However, in the meanwhile, it will remain active with rerun content, and can be found here: https://www.buzzsprout.com/77944/ or here https://itunes.apple.com/us/podcast/nerd-noise-radios-podcast/id1191400767 You can also find all of our audio episodes on Archive.org as well as the occasional additional release only available there, such as remixes of previous releases and other content. Our YouTube Channel, for the time being is in dormancy, but will be returning with content, hopefully, in 2021. Meanwhile, all the old stuff is still there, and can be found here: https://www.youtube.com/user/NerdNoiseRadio Our episodes (and occasionally, other content, including expanded show notes) can be found on our blog here: nerdnoiseradio.blogspot.com. Nerd Noise Radio is also available on The Retro Junkies Network at www.theretrojunkies.com, and is a member of the VGM Podcast Fans community at https://www.facebook.com/groups/VGMPodcastFans/ Or, if you wish to connect with us directly, we have two groups of our own: Nerd Noise Radio - Easy Mode: https://www.facebook.com/groups/276843385859797/ for sharing tracks, video game news, or just general videogame fandom. Nerd Noise Radio - Expert Mode: https://www.facebook.com/groups/381475162016534/ for going deep into video game sound hardware, composer info, and/or music theory. You can also follow us on Twitter at @NerdNoiseRadio. And we are also now on Spotify, TuneIn, Pandora, iHeartRadio, Stitcher, and Vurbl. December may seem like a long ways away, but as a content creator, I can assure you it's not. Indeed, it feels like it's right around the corner. C1E60 will be a listener picks free play. So, please start getting me tracks - any tracks that move your heart, your feet, or your mind. You can get them to me by any available means, be that by e-mail at nerdnoiseradio@gmail.com, or on social media, or via the comments section on the blog. However you choose to get me tracks, just start sending them my way! And in December, we'll make an episode out of it! If you liked our medley today, then please check out Bart Bonte's other works at www.bontegames.com and at https://bartbonte.bandcamp.com. Tell him you heard him on Nerd Noise Radio! Thanks for listening. Join us again in March - date TBD for C2E3, that is, Nerd Noise Radio Channel 2, Episode 3: "Mishmash Monday - vol. 1 (not to be confused with C1E10 from 2017)….tasty VGM and talk on Nerd Noise Radio. And wherever you are….fly the N! Cheers!
An avid fan of Shogi—a strategy game sometimes known as Japanese chess—wants to popularize his hobby and get paid at the same time. Side Hustle School features a new episode EVERY DAY, featuring detailed case studies of people who earn extra money without quitting their job. This year, the show includes free guided lessons and listener Q&A several days each week. Share: #SideHustleSchool Show notes: SideHustleSchool.com Twitter: @chrisguillebeau Learn more about your ad choices. Visit podcastchoices.com/adchoices
Facciamo un ricapitolo di tutto quello che è successo nel 2020 nel campo dell'Intelligenza Artificiale.Anche se il lockdown globale ha - in buona sostanza - fermato il mondo, non è riuscito a fermare lo sviluppo dell'Intelligenza Artificiale, anzi…Come diciamo da alcuni mesi a questa parte, la pandemia ha dato una spinta molto forte al processo di trasformazione digitale.Diverse innovazioni sono venute fuori dal mondo dell'Intelligenza Artificiale, e di cui abbiamo voluto parlare nel corso del 2020...Perché secondo noi sono novità che hanno avuto un effettivo impatto sul business...Ne diciamo solo alcune, ne abbiamo parlato in una puntata apposta...►►Less Than One Shot Learning: un training di immagini che non utilizza immagini.►►AI Hacker Proof, un training fatto con una rete neurale che deve si irrobustisce proteggendosi dagli attacchi di un'altra rete neurale.►►MuZero, l'algoritmo inventato da DeepMind che riesce a vincere a Go, Shogi, Scacchi e Atari senza conoscere fondamentalmente le regole.►►Vokenizzazione, un sistema di apprendimento che unisce sia le parole scritte alle immagini per migliorare la comprensione della realtà.►►L'app dell'MIT che riconosce l'infezione da Covid, ascoltando il colpo di tosse di un utente.►►L'algoritmo al 100% affidabile che riesce a dire quanto è effettivamente corretta la previsione.E c'è stata anche una grossa, grossissima innovazione dell'anno scorso... ne abbiamo parlato per tutto il 2020...IL GPT-3, appunto.L'algoritmo di OpenAI ha segnato il nuovo gold standard degli algoritmi di linguaggio...E la sua uscita sul mercato ha dato uno slancio agli investimenti in AI, sia nel mondo del business che in quello della ricerca e dello sviluppo.Infatti, il GPT-3 ha spinto diversi gruppi di ricerca e sviluppo a sfruttarne il funzionamento del per creare nuovi algoritmi o migliorare quelli già presenti.E le novità di cui abbiamo voluto parlare nella puntata #42 del podcast sono molto legate al GPT-3, e avranno un innegabile impatto sul business.Per ora buona visione e ricordati che ci puoi ascoltare anche su:►GOOGLE PODCAST: https://bit.ly/32TDZei ►SPOTIFY: https://spoti.fi/2D3ttGF ►APPLE PODCAST: https://apple.co/2CBsSfz►SPREAKER: https://bit.ly/3f6D5PA #artificialintelligence #industry40 #machinelearning #deeplearning***P.S. Vuoi scoprire come implementare un sistema di Intelligenza Artificiale alla tua azienda?►► Telefona al numero verde 800-270-021►► Scrivi un'email a info@bluetensor.ai►► Visita il sito www.bluetensor.ai
A sort of Patrick & Eric in the Morning episode - but this episode we invited on Charles from the Things of No Interest Blog to discuss the 31 games released on Board Game Arena during the month of December. 12/01: Thurn & Taxis, from Andreas Seyfarth & Karen Seyfarth, 2006 Spiel des Jahres 12/02: Welcome to… / Welcome to New Vegas, from Alexis Allard & Benoît Tupin 12/03: 99, public domain 12/04: Flaming Pyramids, designed by Norbert Abel 12/05: Small Islands, designed by Alexis Allard 12/06: Map Maker, designed by Louis Lafair, Joshua Lafair, Becca Lafair 12/07: Solo Whist, public domain 12/08: Kami, adapted from Goïta Shogi 12/09: Luxor, from Rüdiger Dorn, 2018 Spiel des Jahres nominee 12/10: Hungarian Tarokk, public domain 12/11: Cacao, from Phil Walker-Harding, 2015 Spiel des Jahres Recommended 12/12: Piraten Kapern, from Haim Shafir 12/13: Off the Rails, from Andrew Platt, Stuart Platt 12/14: Connect 6, from Professor I-Chen Wu 12/15: Spades, public domain 12/16: Saint Petersburg, from Bernd Brunnhofer, 2004 Spiel des Jahres Nominee 12/17: Cribbage, from Sir John Suckling 12/18: Yokai, from Julien Griffon 12/19: Quantik, from Nouri Khalifa, from Gigamic’s Classic game line 12/20: Nanga Parbat, from Steve Finn 12/21: Oh-Seven, from Alex Weldon 12/22: Bandido, from Martin Nedergaard Andersen 12/23: Concept, from Gaëtan Beaujannot & Alain Rivollet, 2014 SdJ Nominee 12/24: Haiclue, from Will Leaf 12/25: Via Magica, from Paolo Mori, reimplementation of Rise of Augustus 12/26: Martian Dice, from Scott Almes 12/27: Dungeon Roll, from Chris Darden 12/28: Con Sonar!, from Christian Boutin 12/29: Big Time Soccer, from Andrew S. Fischer 12/30: Forbidden Island, from Matt Leacock, 2011 Spiel des Jahres Nominee 12/31: Klaverjassen, public domain @BoardGameArena Patrick Hillier @overthehillier Eric Buscemi @EricBuscemi Charles Hasegwa @charleshasegawa
Chess-playing computers have been better than human World Chess Champions for more than 20 years now. But when DeepMind published a preprint in December 2017 about AlphaZero, a computer program based on neural networks and mastering the games of chess, Shogi and Go, the entire world paid attention.Today’s episode, moderated by Kenneth Cukier of The Economist, explores recent AI developments in chess and how these developments have impacted chess played by humans. Join Peter Heine Nielsen, Chess Grandmaster and coach of World Chess Champion Magnus Carlsen, and Sebastian J. Kuhnert, CEO of chess24, as they share their experiences using AI in chess and illustrate various innovations in sports during COVID-19 and beyond. Kenneth Cukier is a Senior Editor at The Economist, and host of its weekly podcast on technology. He is also an associate fellow at Said Business School at Oxford, researching artificial intelligence. Kenn is the co-author of “Big Data: A Revolution That Transforms How We Live, Work, and Think” with Viktor Mayer-Schönberger. It was a NYT Bestseller translated in over 20 languages, and sold over 1 million copies worldwide. It won the National Library of China’s Wenjin Book Award and was a finalist for the FT Business Book of the Year. Kenn also co-authored a follow-on book, “Learning with Big Data: The Future of Education.” Previously, Kenn was a foreign correspondent for two decades in Europe, Asia and America. From 2002-2004, he was a research fellow at Harvard’s Kennedy School of Government. Kenn’s writings have appeared in the New York Times, Wall Street Journal, Washington Post, Financial Times and Foreign Affairs, among others. He has been a frequent commentator on CBS, CNN, NPR, the BBC and was a member of the World Economic Forum’s global council on data-driven development.Peter Heine Nielsen is a Danish chess trainer. He was awarded the title of Grandmaster by FIDE in 1994. Nielsen became an International Grandmaster in 1994. He won the Danish Chess Championship five times: in 1996, 1999, 2001, 2003, and 2008. He played for Denmark in seven Chess Olympiads, three times on top board. Nielsen coached World Champion Viswanathan Anand from 2002 until 2012. Anand won the World Championship title in Mexico 2007, and defended it in Bonn 2008, Sofia 2010 and Moscow 2012. Nielsen has been coaching world number one, Magnus Carlsen, since 2013. Carlsen won the Candidates Tournament 2013, which gave him the right to challenge Anand for the world championship. He defeated Anand, and has since defended the title three times. Nielsen has previously coached Carlsen in Khanty-Mansiysk 2005, where Carlsen became the youngest player ever to qualify for the candidate matches.Sebastian J. Kuhnert is an internet executive with over 12 years of experience, including general management of startups and small to midsize organizations, corporate development, product development, multinational business operations, strategy, sales, partnerships, fundraising, investor relations, communication, and M&A. Currently, Sebastian serves as the COO at the Play Magnus Group, the web’s most powerful chess ecosystem and CEO of several of its subsidiaries, including chess24, the number one chess broadcasting site and digital home to World Champion Magnus Carlsen. Prior to Play Magnus, he served as CEO at Tradimo Interactive, one of the web’s highest quality education sites for active financial trading, which he co-founded originally on behalf of Etruvian and later moved to Denmark following an investment by the CFH Group. Sebastian learned from a number of leaders in diverse industries such as online education, marketing, price management, management consulting and HR consulting prior to his recent ventures. See acast.com/privacy for privacy and opt-out information.
Download for Mobile | Podcast Preview | Full Timestamps Woolie's childhood, Mandalorian season 2, League of Legends, Reggie vs Demon's Souls, Shogi. You can watch us record the podcast live on twitch.tv/castlesuperbeast Outro: The Mandalorian - Theme (16-bit SEGA Genesis Cover) - Noah N Copeland Go to http://expressvpn.com/superbeast to get an extra 3 months of ExpressVPN for free! Go to https://bit.ly/castlesuperbeast… to get cozy at home this holiday season! Go to http://greenchef.com/superbeast90 and use code SUPERBEAST90 to get $90 off including free shipping! New fighting game from ArcSys, Neople and Eighting: "DNF Duel" (Dungeon & Fighter Duel) Hollywood studio MGM puts itself up for sale at $5bn SPAWN: MICHAEL JAI WHITE REVEALS BIZARRE REBOOT IDEA FROM TODD MCFARLANE XBAND ONLINE IN 2020 ARN-01D is now available in Punch Planet NieR Re[in]carnation launches February 18, 2021 in Japan; NieR: Automata collaboration event announced Bloodstained: Ritual of the Night's Classic Mode Your PS5 will now tell you if you're playing the PS4 edition of a cross-gen game
Was habe ich gespielt?– Patchwork– Res Arcana– Sprawlopolis (Solo und Co-op)– Der Marsch des Fortschritts Mit den Kinder– Similo– Biberbande– Onitama – Memory– Harry Potter Strike– Paku Paku– Nochmals Similo Apps und Onlinespiele– Yucata.de: Terra Mystica mit Tobi und ode.– Yucata.de: Thurn und Taxis mit dem Brettagogen und dem Pöppelschieber– Yucata.de: Die Burgen von Burgund … Folge 81 – Shogi weiterlesen
In which our heroes discuss the end of the world, the resurgence of 2000's fashion and how that's bad, Deca-Dence, Japan Sinks, Japanese foreign relations, the end of the world again, March Comes in Like a Lion, Shogi, Dragon Ball, Tyler and his legit Jewness, WTF is Yiddish, LeafyIsHere banned from YouTube, where'd all the quarters go? LA sucks, youtube is broken, fan questions from twitter!
For those of you who do not like the idea that Weiqi is a war, perhaps it could be better fathomed as a dialogue between two antagonistic players, who are ready to back their words up with force. But the skill lies not in direct confrontation. Rather, the aim is to outmaneuver one's opponent and influence as much territory as possible before it comes to blows. It was this constant strategic maneuvering that makes The Cold War the real third world war. So for those of you who are still waiting for World War Three to start, I've got news for you: It ended three decades ago. After the collapse of the Soviet Union, there was a short inter-war period of relative peace. And then on September 11th, 2001 it all started up again... When we look at the global big picture we've been 'strummin'it through World War Four and nobody has noticed. But then, that's the nature of most humans: Dreaming of a past that never existed while wishing for a future that will never come. While in the meantime, those that are aware of the nature of the game, are seizing territory all over the place. Striking deals over here while living in gangsta paradise over there. And sadly, there are certain countries around the world that have or are becoming Weiqi boards in which players can agree to be the worst of enemies while still in other places be the best of friends. I guess I could say that about all chess games but I am a Weiqi (Go/Baduk) player. And each chess game is based on different focuses with different scales. Western chess is strongly focused on the politics within castle walls. Chinese chess focuses on unifying a country already divided under one banner. And Japanese chess or Shogi is interesting as it allows for mercenaries – a reflection of the time when the samurai were in ascendancy. Whatever the chess game, Weiqi is the most abstract and the most simple: A stone is just a stone and once played, it stays played. The only way a stone can leave the board or move is if it is captured. Every move is final. If you happen to be a stone played upon your own board, it must take a lot of nerve and a lot of inner reconciliation to realise that the only way you are going to exit the game is through death? Acceptance of death. The way of the samurai is found in death. The samurai takes each day as if it is the last. A zen precept along the Tao ⚔️圍棋戰聖
This Week In Japan, Japan’s “Go To Travel” Campaign to Begin from July 22nd, A New Shogi Master is Born, The Biggest Host Club in Tokyo Is Shutting Down, The Former Emperor of Japan Discovers a New Species, and Tokyo Schools Ban Undercut Hairstyle for Boys. Word of the Week: "Yankee and Sukeban (ヤンキー, スケバン)” [Japanese Explanation] This Week In Japanでは毎週、日本で話題になっているニュースについて、イギリス人アーティストのJulianと日本人起業家のYasuが英語で意見を交わしています。他にも日本文化や、面白い日本語のフレーズ等も紹介しています。 (Recorded on July 17th, 2020, in Roppongi, Tokyo) Support the show (https://www.buymeacoffee.com/thisweekinjapan)
将棋の藤井聡太さんが「棋聖」になる、今までで一番若い、の記事を取り上げます。 https://www3.nhk.or.jp/news/easy/k10012519331000/k10012519331000.html
This Week on The Casual Hour… After seeing credits roll on The Last of Us Part 2, there are some OPINIONS around The Casual Hour offices. But we also manage to squeeze in some talk on the most interesting games coming out in July. Plus hear someone butcher the rules of Hanafuda. All that and more on this edition of The Casual Hour. Let's Talk! We love fan mail, send us some! thecasualhour@gmail.com You can follow our show on Twitter at: @thecasualhour Enjoy what we have to say? Please leave us a review! The Casual Hour is: Bobby Pease - Host - Twitter: @bobbypease | Website: www.Lumberjacksmack.com Chase Koeneke - Co-host - Twitter: @chase_koeneke | Website: www.gamersonthego.com Johnny Amizich - Co-host - Twitter @jamizich Love our theme music? It was created by Patric Brown. You can follow his antics on twitter @insaneanalog or check out more of his music and download our theme at www.insaneanalog.com If you are enjoying what we do and would like to further support us you can do so by making a monthly contribution through Anchor! --- Support this podcast: https://anchor.fm/thecasualhour/support
We're ready to ante up, tee off, checkmate, and much more with our first foray into the wide world of Clubhouse Games on Nintendo Switch. We also hit up some news, including a deeper look at the Pokemon Sword & Shield Crown Tundra update! 0:24 – Intro 12:21 – Clubhouse Games Review 55:22 - News Rundown FIND YOUR CHARITY OF CHOICE https://www.charitynavigator.org JOIN OUR NINTENDO SWITCH COMMUNITY Facebook - https://www.facebook.com/groups/TheSw... Twitter - @TheSwitchCast Discord - https://discord.gg/Ty6bwnt Email - KC@TheSwitchCast.com Apple Podcasts - https://itunes.apple.com/us/podcast/t... The SwitchCast Friend Code List - http://bit.ly/scfriendcodelist MUSIC Special thanks to the HeatleyBros for bringing real Nintendo power to our Nintendo Switch podcast with great chiptunes! "8 Bit Joy" By HeatleyBros Free Music For Your YouTube Video https://www.youtube.com/user/HeatleyBros
最近僕たちは、日本のあるboard gameにはまっています。 それは「将棋（しょうぎ）」です。今回はその将棋についてHiroとMasayoで紹介します。 In this episode, we(Hiro and Masayo) talk about a Japanese board game "Shogi". Recently we are really into this game. Enjoy learning Japanese! --- Send in a voice message: https://anchor.fm/hiro-japanese/message
Nice haircut, nerd!
Written By: Annie VanderMeer MitsodaRead By: Max WilliamsEdited By: Robert CroyWherein Yasuki Oguri has a business meeting with the Mantis but keeps thinking about Shogi.Follow at:Check out the site hereFacebook PageTwitterFind it on Spotify, iTunes, GooglePlayEmail at ttiycast@gmail.comJoin us on the l5R DiscordShosuro Croy: Robert CroyBecome a Patron and support the show!Find out more at https://the-table-is-yours.pinecast.coThis podcast is powered by Pinecast.
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. SAHSS+19. https://arxiv.org/abs/1911.08265 Find out more on the Robustly Beneficial Wiki: https://robustlybeneficial.org/wiki/index.php?title=Reinforcement_learning Next week's paper is: A Roadmap for Robust End-to-End Alignment. LN Hoang 18. https://arxiv.org/abs/1809.01036
Intelligent Autonomous Things on the Battlefield. AI for the Internet of Everything. A Kott and E Stump 19. https://arxiv.org/ftp/arxiv/papers/1902/1902.10086.pdf Slaughterbots. Future of life Institute 17. https://www.youtube.com/watch?v=HipTO_7mUOw The Future of War, and How It Affects YOU (Multi-Domain Operations). Smarter Every Day 211. https://www.youtube.com/watch?v=qOTYgcdNrXE Find out more on the Robustly Beneficial Wiki: https://robustlybeneficial.org/wiki/index.php?title=Robustly_beneficial https://robustlybeneficial.org/wiki/index.php?title=Robust_statistics Next week's paper is about MuZero. Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. SAHSS+20. https://arxiv.org/abs/1911.08265
Geoff discusses Alpha Zero, a neural net that can play Go, Chess, and Shogi better than anyone in the world. It defeated the best AI in those respective games (each of whom had previously defeated the best humans in the world) with only a few hours of training. What does this spell for the future of AI, and the future of game design?
Dans ce mois de l'amour foufou, le canapé revient en grande forme pour vous faire part de ses dernières découvertes afin de propager avec son équipe la passion dévorante et fruitée qui la dévore. Z'avez vu comment c'est beau quand on pense à vous 21:00) La saga Warioland par Xéfir (21:00 => 36:21) La Halo Masterchief Collection par Otak (36:21 => 51:12) Le jeu du canapé, Catastronauts (51:26 => 58:26) La pause musicale choisie par Bebert, Les (vieux) mangas de Nemo avec Yu Gi Oh, GTO, Love Hina, Tokyo Toybox et sa suite Giga Tokyo Toybox et Kings of Shogi (01:01:56 => 01:22:05) Abyss, un jeu de cartes présenté par Bebert (01:22:16 => 01:36:37) Conclusion (01:36:37 => 01:47:25) Nos conseils : "Jouez à Abyss" (Bebert) / "Parlez mieux à vos ainés" (Axel) / "Si vous avez une vielle console qui traine, rallumez la et retrouvez votre vous d'avant" (Xéfir) / "Faites vous dépister pour l'apnée du sommeil" (Nemo) / "Laissez reposer votre estomac" (Otak). Musique de fin : Générique de Casper, le petit fantôme (qui a sacrément envie de jouer quand même !)
This week brings a more condensed episode, as Jon has a bit of a cold. Anyway, in this episode, Jon will be telling us all about Shogi, a traditional Japanese board game that is definitely not just chess on drugs. Meanwhile, Tom shares his joint excitement and trepidation at the news that Log Horizon will be returning for a third season!Told you it would be condensed, didn't I? Would you like to hear us talk about Japan some more? Find our podcast here:Spotify: https://tinyurl.com/G-GSpot Itunes: https://tinyurl.com/Ge-Gaitune Twitter: https://twitter.com/GGaijins Facebook: https://tinyurl.com/y5kk5key
Alpha Zero is a computer program that trained itself to play chess, Go and Shogi at superhuman levels in 24 hours. The post https://drpepermd.com/episode/what-is-alphazero/ (What is AlphaZero?) appeared first on https://drpepermd.com (Dr Peper MD).
Alpha Zero is a computer program that trained itself to play chess, Go and Shogi at superhuman levels in 24 hours. The post What is AlphaZero? appeared first on Dr Peper MD.
First publication of many conversations I wish I had with person of interest. Garang aka Shogi my 2nd youngest brother born in 1999 Khartoum, raised in Cairo & Perth. We speak about mental health and what it means to him and his generation (gen Z).
Among his many chess distinctions, GM Larry Kaufman was the 2008 World Senior Chess Champion. He is also quite accomplished as a chess author, and a developer of chess engines. He helped to develop many renowned chess engines over the years, and currently works on Komodo with its co-creator Mark Lefler. When he sits down to write a chess book, GM Kaufman puts his vast knowledge of chess engines to good use. His new work, Kaufman’s New Repertoire for Black and White is filled with fresh opening ideas and its moves have been vetted by multiple 3500+ elo engines. In addition to discussing chess engines generally, and GM Kaufman’s new book in particular, we had some other fascinating topics to discuss. For one thing, GM Kaufman was the best non-Japanese born Shogi player in the world for many years. For another, his son, Raymond Kaufman has also ascended to the level of International Master. Please sit back and enjoy GM Kaufman talk about his illustrious career and share some great stories. As always, more details and timestamps can be found below. (If you are not seeing embedded hyperlinks on your podcast app, you can find them at the podcast website here:) https://www.perpetualchesspod.com/new-blog/2019/12/3/episode-154-gm-larry-kaufman 0:00- We begin by discussing GM Larry Kaufman’s new/forthcoming book, New Repertoire for Black and White. Our conversation starts with GM Kaufman’s providing some details about which engines and computers he used for his research of openings for this book. Mentioned: LcZero, Komodo MCTS, NM Jeremy Kane, FM Larry Gilden, Chessbase’s Fat Fritz, GM Fabiano Caruana, Keep it Simple 1. e4 16:30- We transition to discussing some of the broader conclusions from GM Kaufman’s engine work. Is White getting an advantage? If so, with which openings, and how big is the edge? Mentioned: The Grunfeld, The Nimzo-Indian, The Spanish, The Ruy Lopez, The Tarrasch French 27:30- Larry fields a few questions from a Patreon supporter of the podcast, Han Schut, relating to which modern engine is the strongest, and whether his new book will be published on Chessable? Mentioned: Stockfish, New in Chess Publishing, Sabotage the Grunfeld by GM Larry Kaufman 33:30- Larry explains the nature of the partnership between Komodo and Chess.com 36:00- To check out the World Championship chess mugs, go to Chessmugs.com. If you decide to buy a set of mugs, use the code “perpetualchess” at checkout to save 15%. 37:30- As a 72 year old chess player who has remained active for decades, what advice does Larry have for other adult chess players? How should we be utilizing chess engines? How is Larry’s chess game aging? 47:30- Larry tells some fun stories about some well-known chess players of the past and present. . Mentioned: Harold Phillips, Wilhelm Steinitz, GM John Fedorwicz, GM Bobby Fischer 57:30- We discuss GM Kaufman’s groundbreaking research on how to assign point values to chess pieces. Mentioned: IM Erik Kislik, NM Dan Heisman, The Evolution of Material Imbalances (by GM Larry Kaufman). Here are Larry’s more accurate point values for chess pieces: Pawn= 1, Bishop = 3.5 Knight = 3.5, Bishop pair = 7.5, Rook = 5.25, Queen = 10 1:05- Supporter of the podcast Peter Newhall writes in to ask GM Kaufman some salient questions about the differences between shogi and chess. Mentioned: Toshiyuki Moriuchi, IM Jack Peters, GM Jonathan Rowson’s The Seven Deadly Chess Sins, GM Magnus Carlsen, GM Peter Heine Nielsen, The Art of Shogi by Tony Hosking 1:19- Larry shares his experiences as a chess parent. His son is IM Raymond Kaufman. Mentioned: GM Arthur Bisguier 1:24- Thanks to Larry for sharing his wisdom and stories. Keep up with him via the Facebook Komodo group or his Chess.com account. Pre-order his book from Amazon here: https://www.amazon.com/Kaufmans-Repertoire-Black-White-User-Friendly-ebook/dp/B07ZQT4NVH/ref=sr_1_1?keywords=kaufman+chess&qid=1575310518&s=books&sr=1-1 or get it from Forward Chess (available now) here: https://forwardchess.com/product/kaufman's-new-repertoire-for-black-and-white?sscid=91k3_fazxa If you would like to help support Perpetual Chess, you can do so here: ttps://www.patreon.com/perpetualchess
Welcome to another special edition of „Mediocrity and Madness“! Usually this Podcast is dedicated to the ever-widening gap between talk and reality in our big organizations, most notably in our global corporates. Well, I might have to admit that in some cases the undertone is a tiny bit angry and another bit tongue-in-cheek. The title might indicate that. Today’s episode is not like this. Well, it is but in a different way. Upon reflection, it still addresses a mighty chasm between talk and reality but the reason for this chasm appears more forgivable to me than those many dysfunctions we appear to have accepted against better judgement. Today’s podcast is about artificial intelligence and our struggles to put it to use in businesses. This podcast is to some measure inspired by what I learned in and around two programs of Allianz, “IT Literacy for top executives” and “AI for the business”, which I had the privilege and the pleasure to help developing and facilitating. I am tempted to begin this episode with the same claim I used in the last (German) one: With artificial intelligence it is like with teenage sex. Everybody talks about it, but nobody really knows how it works. Everybody thinks that everyone else does it. Thus, everybody claims he does it. And again, Dan Ariely gets all the credits for coining that phrase with “Big Data” instead of “artificial intelligence” which is actually a bit related anyway. Or not. As we will see later. To begin with, the big question is: What is “artificial intelligence” after all? The straightforward way to answering that question is to first define what intelligence is in general and then apply the notion that “artificial” is just when the same is done by machines. Yet here begins the problem. There simply is no proper definition of intelligence. Some might say, intelligence is what discerns man from animal but that’s not very helpful, too. Where’s the boarder. When I was a boy, I read that a commonplace definition was that humans use tools while animals don’t. Besides the question whether that little detail would be one that made us truly proud of our human intelligence, multiple examples of animals using tools have been found since. To make a long story short, there is no proper and general definition of intelligence. Thus, we end up with some self-referentiality: “It’s intelligent if it behaves like a human”. In a way, that’s quite a dissatisfying definition, most of all because it leaves no room for types of intelligences that behave – or “are” – significantly non-human. “Black swan” is greeting. But we’re detouring into philosophy. Back to our problem at hand: What is artificial intelligence after all? Well, if it’s intelligent, if it behaves like a human, then the logical answer to this question is: “artificial intelligence is when a computer/machine behaves like a human”. For practical purposes this is something we can work with. Yet even then another question looms: How do we evaluate whether it behaves like a human? Being used to some self-referentiality already, the answer is quite straight forward: “It behaves like a human if other humans can’t tell the difference from human behavior.” This is actually the essence of what is called the “Turing test”, devised by the famous British mathematician Alan Turing who next to basically inventing what we today call computer sciences helped solving the Enigma encryption during World War II. Turing’s biography is as inspiring as it is tragic and I wouldn’t mind if you stopped listening to this humble podcast and explored Turing in a bit more depth, for example by watching “The imitation game” starring Benedict Cumberbatch. If you decide to stay with me instead of Cumberbatch, that’s where we finally are: “Artificial intelligence is when a machine/robot behaves in a way that humans can’t discern that behavior from human behavior.” As you might imagine, the respective tests have to be designed properly so that biases are avoided. And, of course, also the questions or problems designed to ascertain human or less human behavior have to be designed carefully. These are subjects of more advanced versions of the Turing test but in the end, the ultimate condition remains the same: A machine is regarded intelligent if it behaves like a human. (Deliberately) stupid? It has taken us some time to establish this somewhat flawed, extremely human-centric but workable definition of machine intelligence. It poses some questions and it helps answering some others. One question that is discussed around the Turing test is indeed whether would-be artificial intelligences should deliberately put a few mistakes into their behavior even despite better knowledge, just in order to appear more human. I think that question comes more from would-be philosophers than it is a serious one to consider. Yet, you could argue that if taking the Turing test seriously, in order to convince a human of being a fellow human the occasional mistake is appropriate. After all, “to err is human”. Again, the question appears a bit stupid to me. Would you really argue that it is intelligent only if it occasionally errs? The other side of that coin though is quite relevant. In many discussions about machine intelligence, the implicit or explicit requirement appears to be: If it’s done by a machine, it needs to be 100%. I reason that’s because when dealing with computer algorithms, like calculating for example the trajectory of a moon rocket, we’re used to zero errors; given that the programming is right, that there are no strange glitches in the hardware and that the input data isn’t faulty as such. Writing that, a puzzling thought enters my mind: We trustin machine perfection and expect human imperfection. Not a good outlook in regard to human supremacy. Sorry, I’m on another detour. Time to get back to the question of intelligence. If we define intelligence as behavior being indiscernible from human one, why then do we wonder if machine intelligence doesn’t yield 100% perfect results. Well, for the really complex problems it would actually be impossible to define what “100% perfect” even is, neither ex ante nor ex post but let’s stick to the simpler problems for now: pattern recognition, predictive analysis, autonomous driving … . Intelligent beings make mistakes. Even those whose intelligence is focused onto a specific task. Human radiologists identify some spots on their pictures falsely as positive signs of cancer whilst they overlook others that actually would be malicious. So do machines trained to the same purpose. Competition I am rather sure that the kind listener’s intuitive reaction at this point is: “Who cares? – If the machine makes less errors than her human counterpart, let her take the lead!” And of course, this is the only logical conclusion. Yet quite often, here’s one major barrier to embracing artificial intelligence. Our reaction to machines threatening to become better than us but not totally perfect is poking for the outliers and inflating them until the use of machine intelligence feels somewhat disconcerting. Well, they are competitors after all, aren’t they? The radiologist case is especially illuminating. In fact, the problem is that amongst human radiologists there is a huge, huge spread in competency. Whilst a few radiologists are just brilliant in analyzing their pictures, others are comparatively poor. The gap not only results from experience or attitude, there are also significant differences from county to country for example. Thus, even if the machine would not beat the very best of radiologists, it would be a huge step ahead and saving many, many lives if one could just provide a better average across the board; – which is what commonly available machines geared to the task do. Guess what your average radiologist thinks about that. – Ah, and don’t mind, if the machine would not yet be better than her best human colleagues, it is but a matter of weeks or months or maybe a year or two until she is as we will see in a minute. You still don’t believe that this impedes the adaption of artificial intelligence? – Look this example that made it into the feuilletons not long ago. Autonomous driving. Suppose you’re sitting in a car that is driven autonomously by some kind of artificial intelligence. All of a sudden, another car – probably driven by a human intelligence – comes towards you on the rather narrow street you’re driven through. Within microseconds, your car recognizes its choices: divert to the right and kill a group of kids playing there, divert to the left and kill some adults in their sixties one of which it recognizes as an important advisor to an even more important politician or keep the track and kill both, the occupants of the oncoming car … and unfortunately you yourself. The dilemma has been stylized to a kind of fundamental question by some would-be philosophers with the underlying notion of “if we can’t solve that dilemma rationally, we might better give up the whole idea of autonomous driving for good.” Well, I am exaggerating again but there is some truth in that. Now, as the dilemma is inextricable as such: bye, bye autonomous driving! Of course, the real answer is all but philosophical. Actually, it doesn’t matter what choice the intelligence driving our car makes. It might actually just throw a dice in its random access memory. We have thousands of traffic victims every year anyway. Humankind has decided to live with that sad fact as the advantages of mobility outweigh these bereavements. We have invented motor liability insurance exactly for that reason. Thus, the only and very pragmatic question has to be: Do the advantages of autonomous driving outweigh some sad accidents? – And fortunately, probability is that autonomous driving will massively reduce the number of traffic accidents so the question is actually a very simple one to deal with. Except probably for motor insurance companies … and some would-be philosophers. Irreversible Here’s another intriguing thing with artificial intelligence: irreversibility. As soon as machine intelligence has become better than man in a specific area, the competition is won forever by the machines. Or lost for humankind. Simple: as soon as your artificial radiologist beats her human colleague, the latter one will never catch up again. On the contrary. The machine will improve further, in some cases very fast. Man might improve a little, over time but by far not at the same speed as his silicon colleague … or competitor … or potential replacement. In some cases, the world splits into two parallel ones: the machine world and the human world. This is what happened in 1997 with the game of Chess when Deep Blue beat the then world champion Gary Kasparow. Deep Blue wasn’t even an intelligence. It was just a brute force with input from some chess savvy programmers but then humans have lost the game to the machines, forever. In today’s chess tournaments not the best players on earth compete but the best human players. They might use computers to improve their game but none of them would stand the slightest chance against a halfway decent artificial chess intelligence … or even a brute force algorithm. The loss of chess for humankind is a rather ancient story compared to the game of Go. Go being multitudes more complex than chess resisted the machines about twenty years more. Brute force doesn’t work for Go and thus it took until 2016 until AlphaGo, an artificial intelligence designed to play Go by Google’s DeepMind finally conquered that stronghold of humanity. That year, AlphaGo defeated Lee Sedol, one of the best players in the world. A few months later, the program also defeated Ke Jie, the then top-ranking player in the world. Most impressive though it is that again only a few months later DeepMind published another version of its Go-genius: AlphaGo Zero. Whilst AlphaGo had been trained with huge numbers of Go matches played by human players, AlphaGo Zero had to be taught only the rules of the game and developed its skills purely by playing against versions of itself. After three days, this version beat her predecessor that had won against Lee Sedol 100:0. And again only three months later, another version was deployed. AlphaZero learnt the games of Chess and Go and Shogi, another highly complex strategy game, in only a few hours and defeated all previous versions in a sweep. By then, man was out of the picture for what can be considered an eternity by measures of AI development cycles. AlphaZero not only plays a better Go – or Chess – than any human does, it develops totally new strategies and tactics to play the game, it plays moves never considered reasonable before by its carbon-based predecessors. It has transcended its creators in the game and never again will humanity regain that domain. This, you see, is the nature of artificial intelligence: as soon as it has gained superiority in a certain domain, this domain is forever lost for humankind. If anything, another technology will surpass its predecessor. We and our human brains won’t. We might comfort ourselves that it’s only rather mundane tasks that we cede to machines of specialized intelligence, that it’s a long way still towards a more universal artificial intelligence and that after all, we’re the creators of these intelligences … . But the games of Chess and Go are actually not quite so mundane and the development is somewhat exponential. Finally, a look into ancient mythology is all but comforting. Take Greece as an example: the progenitor of gods, Uranos, was emasculated by his offspring, the Titans and these again were defeated and punished by their offspring, the Olympians, who then ruled the world, most notably Zeus, Uranos’ grandson. Well, Greek mythology is probably not what the kind listener expects from a podcast about artificial intelligence. Hence, back to business. AI is not necessarily BIG Data Here’s a not so uncommon misconception: AI or advanced analytics is always Big Data or – more exactly: Big Data is a necessary prerequisite for advanced analytics. We could make use of the AlphaZero example again. There could hardly be less data necessary. Just a few rules of the game and off we go! “Wait”, some will argue, “our business problems aren’t like this. What we want is predictive analysis and that’s Big Data for sure!”. I personally and vehemently believe this is a misconception. I actually assume, it is a misconception with a purpose but before sinking deeper into speculation, let’s look at an example, a real business problem. I have spent quite some years in the insurance business. Hence please apologize for me using an insurance example. It is very simple. The idea is using artificial intelligence for calculating insurance premiums, specifically motor insurance third party liability (TPL). Usually, this is a mandatory insurance. The risk it covers is that you in your capacity of driving a car – or parking it – damage an object that belongs to someone else or that you injure someone else. Usually, your insurance premium should reflect the risk you want to cover. Thus, in the case of TPL the essential question from an actuary’s point of view is the following one: Is the person under inspection a good driver or a not so good one? “Good” in the insurer’s sense: less prone to cause an accident and if so, one that usually doesn’t come with a big damage. There are zillions of ways to approach that problem. The best would probably be to get an individual psychological profile of the respective person, add a decently detailed analysis of her driving patterns (where, when, …) and calculate the premium based on that analysis, maybe using some sort of artificial intelligence in order to cope with the complex set of data. The traditional way is comparatively simplistic and indirect. We use a mere handful of data, some of them related to the car like type and registration code, some personal data like age or homeownership and some about driving patterns, mostly yearly mileage and calculate a premium out of these few by some rather simple statistical analysis. If we were looking for more Big Data-ish solutions we could consider basing our calculation on social media timelines. Young males posting photos that show them Friday and Saturday nights in distant clubs with fancy drinks in their hands should emerge with way higher premiums than their geeky contemporaries who spend their weekends in front of some computers using their cars only to drive to the next fast food restaurant or once a week to the comic book shop. The shades in between might be subtle and an artificial intelligence might come up with some rather delicate distinctions. And you might not even need a whole timeline. Just one picture might suffice. The forms of our faces, our haircut, the glasses we fancy, the jewelry we wear, the way we twinkle our noses … might well be very good indicators of our driving behavior. Definitely a job for an artificial intelligence. I’m sure, you can imagine other avenues. Some are truly Big Data, others are rather small in terms of data … and fancy learning machines. The point is, these very different approaches may well yield very similar results ie, a few data related to your car might reveal quite as much about the question at hand as an analysis of your Instagram story. The fundamental reason is that data as such are worthless. Valuable is only what we extract from that data. This is the so-called DIKW hierarchy. Data, Information, Knowledge, Wisdom. The true challenge is extracting wisdom from data. And the rule is not: more data – more wisdom. On the contrary. Too much data might in fact clutter the way to wisdom. And in any case, very different data might represent the same information, knowledge or wisdom. As what concerns our example, I have first of all to admit that I have nor analytical proof – or wisdom – about specifics I am going to discuss but I feel confident that the examples illustrate the point. Here we go. The type of car – put into in the right correlation with a few other data -- might already contain most of the knowledge you could gain from a full-blown psychological analysis or a comprehensive inspection of a person’s social media profile. Data representing a 19 year old male, living in a certain area of town, owning a used but rather high powered car, driving a certain mileage per year might very well contain the same information with respect to our question about “good” driving as all the pictures we find in his Facebook timeline. And the other way around. The same holds true for the information we might get out of a single static photo. Yet the Facebook timeline or the photo are welling over with information that is irrelevant for our specific problem. Or irrelevant at all. And it is utterly difficult to get a) the necessary data in a proper breadth and quality at all and b) to distill relevant information, knowledge and wisdom from this cornucopia of data. Again: more data does not necessarily mean more wisdom! It might. But one kind of data might – no: will – contain the same information as other kinds. Even the absence of data might contain information or knowledge. Assume for instance, you have someone explicitly denying her consent to using her data for marketing purposes. That might mean she is anxious about her data privacy which in turn might indicate that she is also concerned about other burning social and environmental issues which then might indicate she doesn’t use her car a lot and if so in a rather responsible way … . You get the point. Most probably that whole chain of reasoning won’t work having that single piece of data in isolation but put into the context of other data there might actually be wisdom. Actually, looking at the whole picture, this might not even be a chain of reasoning but more a description of the certain state of things that denies decomposition into human logic. Which leads us to another issue with artificial intelligence. The unboxing problem Artificial intelligences, very much like their human contemporaries, can’t always be understood easily. That is, the logic, the chain of reasoning, the parameters that causally determine certain outcomes, decisions or predictions are in many cases less than transparent. At the same time, we humans demand from artificial intelligence what we can’t deliver for our own reasoning: this very transparency. Quite like us demanding 100% machine perfection, some control-instinct of ours claims: If it’s not transparent to us (humans), it isn’t worth much. Hence, a line of research in the field of artificial intelligence has developed: “Unboxing the AI”. Except for some specific cases yet, the outlook for this discipline isn’t too bright. The reason is the very way artificial intelligence works. Made in the image of the human brain, artificial intelligences consist of so-called “neural networks”. A neural network is more or less a – layered – mesh of nodes. The strength of the connections between these nodes determines how the input to the network determines the output. Training the AI means varying the strengths of these connections in a way that the network finally translates the input into a desired output in a decent manner. There are different topologies for these networks, tailored to certain classes of problems but the thing as such is rather universal. Hence AI projects can be rather simple by IT standards: define the right target function, collect proper training data, plug that data to your neural network, train it … . It takes but a couple of weeks and voila, you have an artificial intelligence thatyou can throw on new data for solving your problem. In short, what we can call “intelligence” is the state of strengths of all the connections in your network. The number of these connections can be huge and the nature of the neural network is actually agnostic to the problem you want it to solve. “Unboxing” would thus mean to backwardly extract specific criteria from such a huge and agnostic network. In our radiologist case for example, we would have to find something like “serrated fringes” or “solid core” in nothing but this set of connection strengths in our network. Have fun! Well, you might approach the problem differently by simply probing your AI in order to learn that and how it actually reacts to serrated fringes. But that approach has its limits, too. If you don’t know what to look for or if the results are determined not by a single criterion but by the entirety of some data, looking for specifics becomes utterly difficult. Think of AlphaZero again. It develops strategies and moves that have been unknown to man before. Can we really claim we must understand the logic behind, neglecting the fact that Go as such has been quite resistant to straightforward tactics and logic patterns for the centuries humans have played it. The question is: why “unboxing” after all? – Have you ever asked for unboxing a fellow human’s brain? OK, being able to do that for your adolescent kids’ brains would be a real blessing! But normally we don’t unbox brains. Why are we attracted by one person and not by another? Is it the colour of her eyes, her laughter lines, her voice, her choice of words …? Why do we find one person trustworthy and another one not? Is it the way she stands, her dress, her sincerity, her sense of humour? How do we solve a mathematical problem? Or a business one? When and how do the pieces fall into place? Where does the crucial idea emerge from? Even when we strive to rationalize our decision making, there always remain components we cannot properly “unbox”. If the problem at hand is complex – and thus relevant – enough. We “factor in” strategic considerations, assumptions about the future, others’ expectations … . Parts of our reasoning are shaped by our personal experiences, our individual preferences, like our risk-appetite, values, aspirations, … . Unbox this! Humankind has learnt to cope with the impossibility of “unboxing” brains or lives. We probe others and if we’re happy with the results, we start trusting. We cede responsibilities and continue probing. We cede more responsibilities … and sometimes we are surpassed by the very persons we promoted. Ah, I am entering philosophical grounds again. Apologies! To make it short. I admit, there are some cases in which you might need full transparency, complete “unboxing”. And in case you don’t get it, abolish the idea of using AI for the problem you had in mind. But there are more cases in which the desire for unboxing is just another pretense for not chartering new territory. If it’s intelligent if it behaves like a human why do we ask for so much more from the machines than we would ask from man? Again, I am drifting off into questions of dangerously fundamental nature. Let’s assume for once that we have overcome all our concerns, prejudices and excuses, that despite all of them, we have a business problem we full-heartedly want to throw artificial intelligence at. Then comes the biggest challenge of all. The biggest challenge of all: how to operationalize it Pretty much like in our discussion at the beginning of this post, on the face of it, it looks simple: unplug the human intelligence occupied with the work at hand and plug in the artificial one. If it is significant – quite some AI projects are still more in the toy category – this comes along with all the challenges we are used to in what we call change management. Automating tasks comes with adapting to new processes, jobs becoming redundant, layoffs, re-training and rallying the remaining workforce behind the new ways of working. Yet changes related to artificial intelligence might have a very different quality. They are about “intelligence” after all, aren’t they? They are not about replacing repetitive, sometimes strenuous or boring work like welding metal or consolidating accounting records, they dig to the heart of our pride. Plus, the results are by default neither perfect nor “unboxable”. That makes it very hard to actually operationalize artificial intelligence. Here’s an example. It is more than fifteen years old, taking place at a time when a terabyte was an still an incredible amount of storage, when data was still desired to be stored in warehouses and not floating around in lakes or oceans and when true machine learning was still a purely academic discipline. In short: the good old times. This gives us the privilege to strip the example bare of complexity and buzz. At that time, I was together with a few others responsible for developing Business Intelligence solutions in the area of insurance sales. We had our dispositive data stored in the proverbial warehouse, some smart actuaries had applied multivariate statistics to that data and hurrah, we got propensities to buy and rescind for our customers. Even with the simple means we had by then, these propensities were quite accurate. As an ex-post analysis showed, they hit the mark at 80% applying the relevant metrics. Cutting the ranking at rather ambitious levels, we pushed the information to our agents: customers who with a likelihood of more than 80% were to close a new contract or to cancel one … or both. The latter one sounds a bit odd, but a deeper look showed that these were indeed customers who were intensely looking for a new insurance without a strong loyalty. – If we won them, they would stay with us and loyalty would improve, if a competitor won them, they would gradually transfer their portfolio to him. You would think that would be a treasure trove for any salesforce in the world, wouldn’t you? Far from it! Most agents either ignored the information or – worse – they discredited it. To the latter purpose, they used anecdotal evidence: “My mother in law was on the list”, they broadcast, “she would never cancel her contract”. Well, some analysis showed that she was on the list for a reason but how would you fight a good story with the intricacies of multivariate statistics? Actually, the mother-in-law issue was more of a proxy for a deeper concern. Client relationship is supposed to be the core competency of any salesforce. And now, there comes some algorithm or artificial intelligence that claims to understand at least a (major) part of that core competency as good as that very salesforce … . Definitely a reason to fight back, isn’t it? Besides this, agents did not use the information because they regarded it not too helpful. Many of the customers on the high-propensity-to-buy-list were their “good” customers anyway, those with who they were in regular contact already. They were likely indeed to make another buy but agents reasoned they would have contacted them anyway. So, don’t bother with that list. Regarding the list of customers on the verge of rescinding, the problem was a different one. Agents had only very little (monetary) incentive to prevent these from doing so. There was a recurring commission but asked whether to invest valuable time into just keeping a customer or going for new business, most were inclined to choose the latter option. I could continue on end with stories around that work, but I’d like to share only one more tidbit here before entering a brief review of what went wrong: What was the reaction of management higher up the food-chain when all these facts trickled in? Well, they questioned the quality of the analysis and demanded to include more – today we would say “bigger” – data in order to improve that quality, like buying sociodemographic data which was the fad at that time. Well, that might have increased the quality from 80% to 80+% but remember the discussion we had around redundancy of data. The type of car you drive or the sum covered by your home insurance might say much more than sociodemographic data based on the area you live in. … Not to speak of that eternal management talk that 80% would be good enough. What went wrong? First, the purpose of the action wasn’t thought through well enough from the start. We more or less just choose the easiest way. Certainly, the purpose couldn’t have been to provide agents with a list of leads they already knew were their best customers. From a business perspective the group of “second best customers” might have been much more attractive. Approaching that group and closing new contracts there would have not only created new business but also broadened the base of loyal customers and thus paved the way for longer term success. The price would of course have been that these customers would have been more difficult to win over than the “already good” ones so that agents would have needed an incentive to invest effort into this group. Admittedly going for the second-best group would have come with more difficulties. We might have faced for example many more mother-in-law anecdotes. Second, there was no mechanism in place to foster the use of the information. Whether the agents worked on the leads or not didn’t matter so why should they? Worse even with the churn-list. From a long-term business perspective, it makes all the sense in the world to prevent customer churn as winning new customers is way more expensive. It also makes perfect sense to try making your second-best customers more loyal but from a short-term salesman’s or -woman’s perspective boiling the soup of already good customers makes more short-term sense. Thus, in order to operationalize AI target systems might need a thorough overhaul. If you are serious, that is. The same holds true if you would for example want to establish machine assisted sentiment analysis in your customer care center. Third, there was no good understanding of data and data analytics neither on the supposed-to-be users’ side nor on the management side. This led to the “usual” reflexes on both sides: resistance on the one side and an overly simplified call for “better” on the other one. Whatever “better” was supposed to mean. Of course, neither the example nor the conclusions are exhaustive, but I hope they help illustrate the point: more often than not it is not the analytics part of artificial intelligence that is the tricky one. It is tricky indeed but there are smart and experienced people around to deal with that type of tricky business. More often than not, the truly tricky part is to put AI into operations, to ask the right questions in the first place, to integrate the amazing opportunities in a consistent way into your organization, processes and systems, to manage a change that is more fundamental than simple automation and to resist the reflex that bigger is always better! So much for today from “Mediocrity and Madness”, the podcast that usually deals with the ever-growing gap between corporate rhetoric and action. I dearly thank all the people who provided inspiration and input to these musings especially in and around the programs I mentioned in the intro, most notably Gemma Garriga, Marcela Schrank Fialova, Christiane Konzelmann, Stephanie Schneider, Arnaud Michelet and the revered Prof. Jürgen Schmidhuber! Thank You for listening … and I hope to have you back soon!
On this episode of Anime Roulette we watch some random anime and pitch the idea of what the show can be off the name. We contemplate a mature Majin Buu spinoff on HBO, and a retelling of the Lion King if he was Batman from the Dark Knight Rises. Then we eventually talk about the anime we watched.This is a KnaveryInk podcast.
In this new Side Quest episode, Joe and Mike delve into the history of Shogi or Japanese Chess. The evolution of what we might call grid-based board games has had many twists and turns along the way, from the Gupta Empire of classical India to chess, checkers, reversi and other games we now find familiar. […]
(9:10) Vyhlášení soutěže (16:50) Marquis (19:40) Mechanica (20:46) Z pohádky do pohádky (23:00) Kubrt (24:45) Na křídlech (39:08) Star Wars: Vnější okraj (1:02:00) Tiny Epic Mechs (1:08:57) Sherlock (1:20:45) Tapestry (1:43:11) Escape Room pro 2 (1:49:30) Milostný dopis (1:54:55) Pax Pamir (2:03:12) Monstrum (2:22:29) Nemesis (2:34:00) Mezi dvěma zámky šíleného krále Ludvíka (2:43:50) Opčíkovy rozhovory (2:57:32) Tříhodinový nášup informací o abstraktkách (3:08:20) Isis (3:11:58) Čínské šachy (3:14:20) Shogi (3:30:40) Mankala (3:39:20) Hnefatafl (4:04:24) Othello (4:14:36) Havannah (4:17:00) Othello (4:19:00) Gigamic: Quixo, Quoridor (4:27:40) Nestor Games (4:40:40) GIPF projekt: Dvonn, Zértz (4:51:20) Hive (4:54:31) Blokus (4:57:30) Axiom (5:02:40) Steffen Spiele: Soluna, Ringo, Kulami (5:17:00) Tao Long (5:19:17) Hermetica (5:20:32) Onitama (5:26:18) Octi (5:28:38) Siam (5:29:25) Hypergrid (5:31:25) Pyramid Arcade (5:40:23) 3D tisk (5:42:35) AI, PC (5:56:09) Dotazy
This series' Nintendo 64 launch retrospective wraps up with a look back at the, uh, second N64 launch title: Pilotwings 64. As with Super Mario 64, it builds on a Super NES launch title by expanding its design into proper 3D space while also making its overall design and progression a bit more bite-sized and approachable. Although it's quite dated all these years later, you can definitely see it as a leap forward from what had come before. Also this episode: The Japan-exclusive third launch title, Saikyo Habu Shogi.
Virtual Boy Works ended on a slightly downbeat note with Virtual Bowling, a fine take on the 10-pin pastime by Athena that remains inaccessible to normal humans thanks to its alarming rarity and terrifyingly high price. Well, here's the happy twist coda: Pocket Bowling is a direct sequel to Virtual Bowling that carries forward many of the other game's core mechanics and design elements, but costs a LOT less and doesn't require a piece of fragile, hard-to-come-by equipment to enjoy. Sure, Game Boy Color can't support the immersive viewpoint and design of Virtual Bowling, but this captures the enjoyable core of the other game quite neatly. Let's hear it for small victories, eh? PLUS: Japan-only release for this episode: Warashi's Honkaku Shougi: Shougi Ou.
Nippon MoshiMoshi - over eten met eetstokjes - Shogi initiatie op de ambassade van Japan te Brussel (inschrijven via e-mail: info@bx.mofa.go.jp NipponMoshiMoshi is bereikbaar via Anchor: https://anchor.fm/jan-moens E-mail: nipponmoshimoshi@gmail.com Facebook: https://www.facebook.com/moshimoshipodcast Je kan abonneren via Anchor, Google Podcasts, Spotify, Breaker en Pocket Casts Moshimoshi is een podcast waarin ik mijn interesse voor Japan met iedereen deel. In iedere aflevering bespreek ik één of twee topics in verband met de dagdagelijkse cultuur, de geschiedenis en het leven in Japan. Dat bekijk ik door de ogen van een Europeaan en ik probeer toe te lichten, of de aandacht te trekken op verschillen en overeenkomsten die het land zo interessant maken. Heb je een vraag of een opmerking, laat het dan gerust weten. Indien mogelijk gaan ik er dan ook op in. --- Send in a voice message: https://anchor.fm/jan-moens/message
Shogi is the single most complicated board game in existence, prove us wrong!
En este tour recorremos la historia y orígenes del superhéroe Kalimán, uno de los personajes más famosos y queridos de México y latinoamérica que recién volvió a ser publicado en formato cómic. En nuestra sección de juegos de mesa entrevistamos a Gerardo del grupo Jaguares Shogi y nos explica en qué consiste este fascinante juego
Karolina Styczyńska jest profesjonalną zawodniczką Shogi w Japonii. Strony Karoliny: http://www.shogi.pl YouTube Shogi Harbour: https://www.youtube.com/channel/UCRnXG7CkKfEN6IINKcO_uBg Wesprzyj podcast na Patronite: www.patronite.pl/emiltruszkowski Instagram: @truszkowskiemil
It's been called the 'gymnasium of the mind', both mental exercise and a way to build self-esteem. Born some 1,500 years ago, the game of chess was one of the world's first strategy board games, though little is still known about its origins. Was it first conceived to teach Indian army generals? Or devised to turn a tyrannical King into a virtuous ruler? Or was it a meditative diversion for Japanese monks? It's easy to forget that the modern game of chess is only 500 years old – and that other ancient forms of Chess, like Xiangqi in China and Shogi in Japan, are much older, still evolving and still played today. Joining Bridget Kendall to explore the history of chess, are the chess historians Jean-Louis Cazaux and Rick Knowlton, the novelist Andrei Kurkov who's followed the dramas of Russian chess through the ages, and the Grandmaster Jovanka Houska who'll be challenging Bridget to a game of chess in the studio. Photo: Rick Knowlton's sculpted reproductions of the first confirmed chessmen ever discovered. The original pieces were found in Afrasiab, the ancient city of Samarkand (in present-day Uzbekistan) in 1977. They are dated at approximately AD 700. (Rick Knowlton)
Kennt ihr noch die tollen Fußballstars von RTL II? In der 91. Ausgabe unseres Shortcuts-Podcasts wird nämlich die Redaktionsehre beim Thema Sport-Anime auf die Probe gestellt. Torschützenkönig Shin hat dafür seine besten Jungs und Mädels – namentlich Dimbula, Giira und Fuma – zusammengetrommelt. Das Problem ist nur: Sie haben ihr Wissen über Sport ausschließlich aus Anime. Dann geht die Schale wohl ans Gegnerteam, was? Vielleicht schlagen sie sich aber auch in Brett- und Kartenspielen wie Shogi, Karuta oder dem »Yu-Gi-Oh!«-Sammelkartenspiel besser? Aber ist das überhaupt Sport? Darum geht's in der heutigen Folge.
Ce dernier épisode de Tech Café de l'année 2018 vous donne le pouls de l'actualité tech de cette semaine avec de l'intelligence artificielle, de la mobilité et les derniers fails Facebook. tous les liens et réactions à l’épisode sur techcafe.fr Discutez avec nous sur Telegram Soutenez l’émission sur Patreon Face the truth Rubrique Jugeote 2.0 Lassé du Go, Alpha Zero devient grand maître d’échecs et de Shogi. Yes we GAN : les IA lisent les Captcha. Certainement mieux que moi. Faces in the Crowd : les IA créent des visages à la demande. Rubrique Faciale Les tests de reconnaissance faciales se poursuivent à Londres. Et seront bientôt à votre porte avec Amazon et Nest. ...Ready For it ? Taylor Swift l’utilise dans ses concerts. Rubrique Autonome Phoenix become human : des voitures robots Waymo attaquées. Das Auto(nomous) : Audi se lance dans les voitures autonomes. Hyundai lance des voitures à verrouillage biométrique. Bientôt fesse ID ? Networkaholic Weekly Facebook Fails Facebook aurait partagé vos données avec 150 “partenaires”. Un gros rapport du sénat sur l’ingérence Russe de 2016. Jeux de mains, jeux de vilains : l’IRA soutien nofap ! Interagissez avec votre trolls favoris avec une story Instagram. La NAACP lance une campagne #logoutfacebook. Et Cher s’en va. Matter of fact checker : Facebook s’en balance. Et aussi, le bug de la semaine : des photos persos en fuite… Les cerveaux des enfants modifiés par le temps d’écran ? Apple Weekly Le réseau social d’Apple Music ferme. C’est triste. Ou pas. MAJ iOS pour cause de procès en Chine. Qualcomm part maintenant à la chasse des iPhones XR et XS. Plus de vente sur Cydia, qui se rapproche de la sortie ? En bref VR Zenimax et Facebook enterrent le touch controller de guerre. IMAX jette l’éponge avec ses salles d’arcade VR. Pendant que le VR parc llucity ouvre à Paris. Hola qué Xtal ? Un casque VR complètement Varjo. Gaming Arrêt de production de NES mini pour de bon jusqu’à la prochaine fois. Pour pas cannibaliser la Switch et ses nouveaux pad NES ? La prochaine Xbox sera Anaconda. On lui souhaite plus de succès qu’au film. Chaîne de l’espoir : le nombre de possesseurs de crypto augmente. Payer avec son numéro de téléphone : le virement bancaire instantané arrive. Google Photos vous demande de compresser vos vidéos et photos s’il vous plaît. Aliens : Verizon dévoré de l’intérieur par … AOL et Yahoo. Bonus GPP : Le livre “Du sang, des larmes et des pixels” de Jason Schreier. Guillaume : Niptech et Mug Life sur iOS Participants : Guillaume Poggiaspalla Présenté par Guillaume Vendé
The second anime the IBeatItFirst.com crew decided to tackle for Cutscene is Asobi Asobase -Workshop of Fun-. The series centers on Hanako, Olivia, and Kasumi, second-year students at an all-girl middle school and the only three members of the Pastimers Club, a not-officially-recognized group. The club has very ambiguous goals, usually consisting of whatever so-called […]
Studio Lunches was an autonomous podcast project by Chris MacInnes, supported by CCA. Its goal was to open up artists’ practices within Glasgow through conversation, exploring the diverse, obscure and intriguing interests that reside at the heart of this. A Crypt of Living Timbre (idiom) 1. A phrase used to describe something as having a confusing or contradictory nature, due to it’s state as living or non-living. 2. A presence of life or liveliness in something that otherwise appears dead. 3. A presence of death or decay in something that otherwise appears living. 4. A trope used to allude to or acknowledge the presence of something of a mysterious, obstructed or unclear nature. It is used either literally as the name of a location, in conversation, or by form of symbolism. This episode is a chat with Brandon Cramm around his video installation, A Crypt of Living Timbre. The work explores the ways in which desktop and user environments influence interaction and expression, creating uncanny spaces where the living and the apparently ‘dead’ become almost indistinguishable. Taking net artist Olia Lialina’s essay, Rich User Experience, UX and Desktopization of War as a starting point we weave our way through the scripting of our online lives. Discussing online Shogi tournaments, Tony Hawk’s Pro Skater, UX design and the uncanniness of the digital world, we try to understand the crypt of living timbre we inhabit as users and netizens. Audio featured: A Crypt of Living Timbre by Brandon Cramm
Originally recorded June 6, 2018 Playing board games for a living sounds like a dream come true, but for Rei it's all he has left in his broken shamble of a life. Driven into depression by the onslaught of circumstance & personal tragedy, the young shogi master struggles from day to day trying to cope with his barren husk of a teenage life. Punctuated only by the clicking of tiles & compassion of the other broken people around him. Sounds like the perfect upbeat adventure for the Dub Talk Podcast to tackle! Shogi master Andrew assembles fellow players Jet, NoahClue & Megan for a deep dive into the English dub of Studio SHAFT's March Comes In Like a Lion. Looking for the happiness in this sadness fiesta while getting unnaturally personal on this very special episode of the podcast. PANELISTS: @MangaMan9000 @Divinenega @NoahClue @Queenira2 EDITOR: @NoahClue MUSIC: OST from March Comes In Like a Lion Like what we do? Support us on Patreon! https://www.patreon.com/dubtalkpodcast Or consider buying us a Ko-fi! https://ko-fi.com/dubtalk DUB TALK SOCIAL MEDIA Twitter: @DubTalkPodcast Instagram: @dubtalkpodcast Twitch: dubtalkpodcast Tumblr: dubtalkpodcast
Matt Harvey was DFA’d?? Not to be confused with the Woken one, Matt Hardy. This is the Mets pitcher, and he’s been kind of bad since 2016 after being good in the World Series, at least as far as I remember. That Royals team was good though, baseball life is unfair. Anyway. The Taiiku Podcast is finally back! I’ve been packing or moving and Chris and Camellia were gonna pinch hit for me, but life got in the way. It’s good. We recorded this back in April, but I just haven’t slotted the time to edit this until just now, so here it is! Star Wars day, and releasing a podcast. Chris (@gokuffy) and I return to the Akira Kurosawa series at long last, talking about Rashomon, the first of a string of classics from Kurosawa. Then Jared (@savevsjared) joins us to talk about the second season of March Comes in Like a Lion. Listen Show notes: 0:31 – Chris and I talk about Rashomon! 29:49 – Jared joins us to talk about March Comes in Like a Lion! 1:06:25 – We answer question! Next time is…something…
Ghost In The Cloud: An Anime Podcast Episode 41: March Comes in Like a Lion Guest: Blake! Join us this week with John, Bridgette and guest Blake as they discuss the ancient game Shogi and the prodigy shogi player from March Comes in Like a Lion. They even play a shogi match of their own! After listening to this week's episode make sure you check out our first bonus video on YouTube covering our shogi experience!: https://www.youtube.com/watch?v=GBNwpCbA0ww&fmt=18 *If you loved the video and want to see the full match with commentary become a member of our Patreon team to get access!* *As always, spoiler warning!* Like iTunes? No problem: https://itunes.apple.com/us/podcast/ghost-in-the-cloud-an-anime-podcast/id1247332819?mt=2 Android fan? Check us out on Google Play (or look us up on your favorite podcast app): https://play.google.com/music/listen?u=1&pli=1#/ps/Ijbvn34qrpqa3jimwtagnbiwery Listen on Youtube: www.youtube.com/channel/UCQYRnBMdtJszDdSl3DYcQyA Questions, comments, concerns? Contact us by email at gitcanime@gmail.com Join our Patreon and become a supporter. You can also BECOME AN ANIME CHARACTER. Click here to learn how: www.patreon.com/GITCAnime Check out our awesome merch on Redbubble!: www.redbubble.com/people/gitcanime Find us on other social media outlets: Facebook: www.facebook.com/gitcanimepodcast/ Twitter: www.twitter.com/GITCAnime Instagram: www.instagram.com/ghostinthecloud A big shout-out to Anthony for making our catchy theme song. Check out his podcast at @whatsyourfortune
It’s Friday and you be listening to this podcast, son! ALSO, GO SEE STAR WARS: THE LAST JEDI!!! Join your hosts Robert, The David, and The Girlfriend as the talk about Star Wars (Spoiler Free), video games, awkward teenage years, … Continue reading →(Read more...)Categories: "The" David Drinking Food Gaming Holidays Movies Music Podcast Pop-Culture Useless Drivel Video GamesTags: Christmas GiftsClone WarsGold BaseHemetHorrible Christmas GiftsJapanKeaton JonesPersona 5Phil LamarrRevenge of the SithScientologySex ToysShogiStar Wars: Rogue OneStar Wars: The Last JediThe El CapitanThe Hero's HammerThe RoomThor: RagnarokYakuza 0
Bundesverband Digitale Wirtschaft will Virtual Reality fördern Der Bundesverband Digitale Wirtschaft eröffnet eine Plattform für Virtual- und Mixed-Reality-Systeme. Die Initiative richtet sich vor allem an Unternehmen und will Möglichkeiten für den professionellen Einsatz der neuen Techniken aufzeigen. Noch vor gar nicht langer Zeit galt 2017 als das Jahr des Durchbruchs für Virtuelle Realität, aber dazu kam es bisher nicht. „Alle warten noch immer auf den ‚iPhone-Moment‘, doch die überschwängliche Begeisterung ist bislang ausgeblieben“, sagte Jonas Larbalette, Virtual Reality-Experte des Partners MediaCom. YouTube will eigenen Musikdienst starten YouTube arbeitet offenbar an einem Musikdienst, der im März online gehen soll. Das zumindest verrieten Insider der Nachrichtenseite Bloomberg. Es wäre ein erneuter Versuch der Google-Mutter Alphabet, mit Spotify und Apple Music zu konkurrieren. Wie die anonymen Informanten weiter berichten, soll die Warner Music Group bereits Verträge unterzeichnet haben. YouTubes neuer Musikdienst soll außer Songs auf Abruf auch Videoclips anbieten. Immer mehr Phishing-Webseiten setzen auf HTTPS Sicherheitsforschern von Phislabs zufolge nutzen 2017 ein Viertel aller Phishing-Webseiten die Transportverschlüsselung HTTPS, um Opfer effektiver zu ködern. Verglichen mit 2016 ist das eine Steigerung um das Achtfache. Ruft man eine Webseite via HTTPS auf, wird ein Schloss neben der Adresse im Webbrowser angezeigt. Mit diesem Symbol verbinden viele Menschen offenbar mehr Sicherheit, als dieses Schloss zu leisten imstande ist. In Wirklichkeit sorgt HTTPS aber "nur" dafür, dass die Übertragung von Nutzerdaten an die Webseite verschlüsselt stattfindet. Damit ist HTTPS ein attraktives Werkzeug für Betrüger geworden. Künstliche Intelligenz: AlphaZero meistert Schach, Shogi und Go Die Google-Tochter DeepMind hat mit "AlphaZero" einen Algorithmus entwickelt, der selbstständig die Strategiespiele Schach, Shogi und Go lernt. Und nicht nur das: Die daraus resultierende "Künstliche Intelligenz" spielt das jeweilige Spiel besser als die stärksten bisherigen Programme. Bemerkenswert ist vor allem, dass für diese drei unterschiedlichen Strategiespiele im Prinzip ein und dasselbe Verfahren angewendet wird. Grundlage ist ein neuronales Netz. Diese und alle weiteren aktuellen Nachrichten finden Sie auf heise.de
Why hello there lovelies, Sit back and take a seat as the hosts of ACB Discuss some funky fresh anime music~ Stay tuned for our review of the SHAFT anime March Comes in Like a Lion (3-gatsu no Lion) Good Ol Time Stamps: Main Topic: Music in Anime - 4:34 Spoiler Free Review: March Comes in Like a Lion (3-gatsu no Lion) - 32:36 Ratings - 1:09:34 Come into the Spoiler Room ( ͡º ͜ʖ ͡º) - 1:13:15 Time for all the disclaimers! Are ya ready, kids? ***All animes mentioned belong to their rightful owners. ***All art used belongs to their rightful owners. ***Podcast Image created by Z Whelan. All rights reserved. ***The Music (reunion- opening section and rainy day- closing section) used in this video is property of Sakagami Souichi, all rights reserved. ***We own nothing besides our own voices and opinions. ****** ***Support us on Patreon at: bit.ly/2H38II9 ***Follow us on twitter at: bit.ly/2RPLnla ***Write in an E-mail at: anichatmail@gmail.com ***Watch us on YT at: bit.ly/2H7TVA4 ***Listen to us on SC at: bit.ly/2HuxnZI ***Watch us live dub video games on Twitch at: bit.ly/2ESz1yH ***Find Cami's Twitter here: bit.ly/2WmmKwL ***Find Cassie's Twitter here: bit.ly/2Uf4tPO ***Find Craig's Twitter here: bit.ly/2RQWnjO ***Find Jon's Twitter here: bit.ly/2DxUwaB Like gaming? Then give this a listen: bit.ly/2JQfyT9 Once again, thank you so much! See you next week!
We are back once again, and it is finally baseball season! My White Sox are a surprising 6-5 only nine games into their season, but there’s still time to #TankForBeer for the rebuilding Sox. More importantly, we’re here with Chris (@gokuffy) and Jared (@savevsjared) to talk about March Comes in Like a Lion. Chris and I also continue our trip through the Kurosawa catalogue, this time checking out his second ever film, The Most Beautiful. Listen Show notes: 0:35 – We start the show with Chris, talking The Most Beautiful! 20:59 – Jared joins us to discuss the recently ended March Comes in Like a Lion! Next time will be Big Windup! I should also consider getting back to Slam Dunk.
北米同窓会10周年記念講演会より、糸谷哲郎さんの講演「棋士と将棋ソフトの闘いに AI がもたらした変化」の様子をお届けします。 Show Notes 竹田潔教授（医学系研究科）、牛尾知雄准教授（工学研究科）が大阪科学賞を受賞！ 大阪大学北米同窓会１０周年記念イベントが開催されました 大阪大学大学院文学研究科・文学部 Deep Blue (chess computer) - Wikipedia コンピュータ将棋 - Wikipedia AlphaGo 2016年10月3日(月) 大阪大学秋季入学式のご案内（全て英語での開催です） 2016年9月23日(金)から25日(日) Oktoberfest By The Bay 2016 2016年9月30日(金)から10月1日(土) Questival Adventure Race
In this classic GameTek, Geoff discusses brain imaging studies on shogi players. For images relating to this story, check out Geoff's blog entry on BoardgameGeek. Duration: 06:58
In this episode, GameTek talks about Shogi, Dexter and the Chief take a look at Yomi, and we introduce the Voice of Ameritrash 2.0! Mary helps us with the news, Moritz discusses Grey Giant Games, and Fathergeek wonders if you should let you child win games. We take a look at some new games, including Thunderstone: Dragonspire and Cargo Noir, and talk about our most looked forward to game of 2011.
In this episode, GameTek talks about Shogi, Dexter and the Chief take a look at Yomi, and we introduce the Voice of Ameritrash 2.0! Mary helps us with the news, Moritz discusses Grey Giant Games, and Fathergeek wonders if you should let you child win games. We take a look at some new games, including Thunderstone: Dragonspire and Cargo Noir, and talk about our most looked forward to game of 2011.
Anime-Bliss Episode 3 opening theme: I KO YA KO {pre-mix} by Shen Zhen, Guangdong Province, China Website: http://www.reverbnation.com/celsi Topic: Anime Clubs An anime club is an organization that meets to discuss, show, and promote anime in a local community setting and can also focus on broadening Japanese cultural understanding.[1] Anime clubs are increasingly found at universities and high schools. Organizers may also utilize public meeting spaces such as a library or a government center. Any anime club attendees identify themselves as otaku. Although the core of anime club attendees are in their twenties, there are generally no age requirements. Adults in their fifties and sixties and teenagers also attend. Source: http://en.wikipedia.org/wiki/Anime_club Anime club meetings can occur on a weekly or monthly basis. In addition to viewing anime, clubs engage in other activities such as viewing anime music videos, reading manga, karaoke and cosplaying.Many clubs host online forums to further foster community interaction, and feature library to lend books and manga to members. Participants of an anime club often are also involved in volunteering and organization of local anime conventions. Dependent on the scope of the club, activities can also have a broader range, to include playing of table top games such as Shogi, Go, and Mahjong. Outside activities include Saké tasting and visits to cultural events such as National Cherry Blossom Festival or a Kendo demonstration. Example of anime clubs within anime: Genshiken - a manga and anime featuring a college anime club. Otaku no Video - comedy anime spoofing the life and culture of otaku Community center - a common place for anime clubs to meet Mu Epsilon Kappa - a national society of anime clubs in the United States Prominent anime clubs Anime O-Tekku (Georgia Tech since 1995) RIT Anime Club (Since 1990s) Anime Sushi, Orlando's Premiere Anime Club MIT anime club Anime club tips http://www.ehow.com/how_156053_start-anime-club.html http://www.wikihow.com/Run-a-Successful-Anime-Club Anime cons and events Commerical break “Travel” by Tigarah Website: http://www.myspace.com/tigarah First Night Boston December 31, 2009 Hynes Convention Center Boston, MA M.E.W. Convention December 31, 2009 - January 2, 2010 Red Lion Hotel Vancouver at the Quay Vancouver, WA Ikkicon January 1-3, 2010 Hilton Austin Austin, TX Animé Los Angeles January 8-10, 2010 Los Angeles Airport Marriott Los Angeles, CA Ichibancon January 8-10, 2010 Great Wolf Lodge Concord, NC Sac-Anime January 8-10, 2010 Radisson Hotel Sacramento The Center for Hmong Studies Presents "The Hmong Global Identities in the 21st Century" The Third International Conference on Hmong Studies To be held on the campus of Concordia University, St. Paul April 9th - 11th, 2010 For more information or sponsorship opportunity please contact Lee Pao Xiong 651-641-8870 or xiong@csp.edu Tzy Lee Ya 651-603-6337 or ya@csp.edu Chef Eric's Culinary Classroom Sushi Making Class $90 Per Person Sat, 1/30/10 7pm-10pm OR Fri, 2/26/10 7pm-10pm (COUPLES ONLY) OR Sat, 3/27/10 7pm-10pm OR Fri, 4/23/10 7pm-10pm OR Sat, 5/22/10 7pm-10pm OR Fri, 6/25/10 7pm-10pm You will learn how to buy the freshest fish, and how to cut and prepare it properly for all of the dishes we will be making. Learn the creative art of Sushi Making as we design an Asian meal for you and your friends. We will show you how to make traditional Sushi Rice, a variety of Sushi Rolls (Nori), Vegetable Rolls, Varieties of Sushi on Rice (Nigiri), Spicy Sushi Rolls, Sea Eel Rolls, and how to display and serve them decoratively with the Garnishes and Sauces that we will be making. Add some Home Made Pickled Cucumbers, Pickled Ginger, Teriyaki Sauce and Sanbiauzu Sauce to the mix for a flavorful array to dip your delicious Sushi in. We’ll also make authentic Vegetable Tempura with Daishi and Ginger Dipping Sauce and Hot and Sour Soup to balance out your meal. CHEF ERIC will show you how to cut Oranges to finish off your fabulous Sushi experience. Call, e-Mail or Register On-Line 24 Hours a Day Sign up for our Newsletter – we’ll send you Class Listings and Seasonal Recipes! 1 Block East of Overland, Just North of Pico - Convenient Parking Just North on Overland 2366 PELHAM AVENUE - LOS ANGELES, CA 90064 PHONE: 310-470-2640 - FAX: 310-470-2642 CHEFERIC@CULINARYCLASSROOM.COM WWW.CULINARYCLASSROOM.COM e-mail: podcast@anime-bliss.com Facebook: irafuse(look for indiefaith logo) Myspace: myspace.com/urbanplusgeek
Joe Peterson discusses the Shogi Family of games
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