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AI Is Now The Undisputed Champion Of Computer Chess - AI Summary

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On the other side was a new program called AlphaZero (the "zero" meaning no human knowledge in the loop), a chess engine in some ways very much weaker than Stockfish--powering through just 1/100th as many moves per second as its opponent. The AI engine won the match (winning 28 games and drawing the rest) with dazzling sacrifices, risky moves, and a beautiful style that was completely new to the world of computer chess. British chess grandmaster Matthew Sadler and mathematician and chessmaster Natasha Regan are still piecing together how AlphaZero's strategy works in their new book, Game Changer. We're breaking open two moves in just one of the games to show the aggressive style, what it does, and what humans can learn from our new chess champion. By move 42, AlphaZero has sacrificed even more pawns, and is marching another poor, disposable sucker toward oblivion.


Discourse on the Philosophy of Artificial Intelligence and the Future Role of Humanity

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Artificial intelligence can be defined as "the ability of an artifact to imitate intelligent human behavior" or, more simply, the intelligence exhibited by a computer or machine that enables it to perform tasks that appear intelligent to human observers (Russell & Norvig 2010). AI can be broken down into two different categories: Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI), which are defined as follows: ANI refers to the ability of a machine or computer program to perform one particular task at an extremely high level or learn how to perform this task faster than any other machine. The most famous example of ANI is Deep Blue, which played chess against Garry Kasparov in 1997. AGI refers to the idea that a computer or machine would one day have the ability to exhibit intelligent behavior equal to that of humans across any given field such as language, motor skills, and social interaction; this would be similar in scope and complexity as natural intelligence. A typical example given for AGI is an educated seven-year-old child.


Is Cost-Effective Deep Reinforcement Learning Possible?

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"Is there scientific value in conducting empirical research in reinforcement learning when restricting oneself to small- to mid-scale environments?" Can a research done on a smaller computational budget can provide valuable scientific insights? Given the insane training times and budgets, it is natural to wonder if anything worthwhile in AI comes at a small price. So far, the researchers have focused on the training costs of language models which have become too large. But, what about the deep reinforcement learning(RL) algorithms -the brains behind autonomous cars, warehouse robots and even the AI that beat chess grandmasters?


CausalCity: Simulated city aims to teach AI "counterfactual reasoning"

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AI algorithms struggle to recognise events or objects in contexts that are different from the training set. A situation in the world is something that has no boundaries at all, you don't know what's in the situation, what's out of the situation.") If you're trying to train an AI to deal with this "unframed" world, you run into a lot of challenges. Humans learn about causal relationships by making interventions/actions in a given environment, observing the result, then refining the mental model they've "built" by making similar actions in subtly different environments in the great, fluid thing that is The World. It's hard to build AI training sets that can help algorithms "understand" the myriad causal relationships taking place at any given time in a similar way; rather than train them to understand more fixed patterns of behaviour: e.g. the hard numbers that need to be crunched to beat a human in a game of tightly circumscribed mathematical probabilities like chess.


How AI Revolutionised the Ancient Game of Chess

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I have come to the personal conclusion that while all artists are not chess players, all chess players are artists. Originally called Chaturanga, the game was set on an 8x8 Ashtāpada board and shared two key fundamental features that still distinguish the game today. Different pieces subject to different rules of movement and the presence of a single king piece whose fate determines the outcome. But it was not until the 15th century, with the introduction of the queen piece and the popularization of various other rules, that we saw the game develop into the form we know today. The emergence of international chess competition in the late 19th century meant that the game took on a new geopolitical importance.


Preparing a Dataset for Machine Learning With PHP

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Machine learning is the science of teaching a computer to solve problems by example rather than writing sequential algorithms which instructions run one by one. Data preparation for machine learning is the prior step towards training a model, and usually involves two substeps: creating a dataset and transforming the data. In this post I'll be focusing on the former in the context of building a human-like AI to play chess in PHP. Because contrary to popular belief, Python is not the only programming language for data science in this world. I am preparing the data on this GitHub repo with MySQL, PHP and Rubix ML, a machine learning and deep learning library for the PHP language.


The Future of Business and Chatbots

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The future of marketing is here, and it's not just the robots that will be writing content for you. It's artificial intelligence, or ai. You may think that this isn't possible because robots can't do things like emotional resonance and creativity but ai has been around for a while now. I'm talking about chatbots- computer programs designed to simulate conversation with human beings through text or speech interfaces to solve problems, answer questions, or fulfill customer requests via various digital channels like social media platforms. This article will explore how Chatbots and Ai are the future of marketing and why they're crucial for your business.


Does Artificial Intelligence Aid Cybersecurity?

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Artificial intelligence (AI) still has a long way to go to reach a sci-fi-level of advancement. For businesses in the digital age, however, it's proving to be highly effective in its current state. Artificial intelligence and machine learning, its subset, have reduced costs, streamlined operations, fueled innovations, fast-tracked growth, and managed risks for many businesses. Still, AI isn't yet capable of making apps operate with unlimited autonomy--apps that can constantly create different algorithms that learn and adjust to evolving IT situations. Alan Turing predicted in 1945 that AI will someday play'very good chess.' A computer program did play chess good enough to beat a reigning world chess champion--Garry Kasparov, in 1997.


Why AI Chess Champs Are Not Taking Over The World - AI Summary

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At one time, the AI that beat humans at chess calculated strategies by studying the outcomes of human moves. Joshua Sokol, "Why Artificial Intelligence Like AlphaZero Has Trouble With the Real World" at Quanta Magazine (February 21, 2018) "I feel like that's been neglected by the majority of the AI community." Joshua Sokol, "Why Artificial Intelligence Like AlphaZero Has Trouble With the Real World" at Quanta Magazine (February 21, 2018) Or, as George Gilder says in Gaming AI, in games like chess and Go, the map is the territory. And, as Jeffrey Funk and Gary Smith remind us, failed prophecies of an AI takeover come at a cost: We don't improve what we could improve in human-based services like health care if we are waiting for the phantom AI takeover. At one time, the AI that beat humans at chess calculated strategies by studying the outcomes of human moves.


Melanie Mitchell Takes AI Research Back to Its Roots

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Melanie Mitchell, a professor of complexity at the Santa Fe Institute and a professor of computer science at Portland State University, acknowledges the powerful accomplishments of "black box" deep learning neural networks. But she also thinks that artificial intelligence research would benefit most from getting back to its roots and exchanging more ideas with research into cognition in living brains. This week, she speaks with host Steven Strogatz about the challenges of building a general intelligence, why we should think about the road rage of self-driving cars, and why AIs might need good parents. Listen on Apple Podcasts, Spotify, Android, TuneIn, Stitcher, Google Podcasts, or your favorite podcasting app, or you can stream it from Quanta. Melanie Mitchell: You know, you give it a new face, say, and it gives you an answer: "Oh, this is Melanie." And you say, "Why did you think that?" "Well, because of these billions of numbers that I just computed." Steve Strogatz [narration]: From Quanta Magazine, this is The Joy of x. Mitchell: And I'm like, "Well, I can't under-- Can you say more?" And they were like, "No, we can't say more." Steve Strogatz: Isn't that unnerving, that it's this great virtuoso at these narrow tasks, but it has no ability to explain itself? Strogatz: Melanie Mitchell is a computer scientist who is particularly interested in artificial intelligence. Her take on the subject, though, is quite a bit different from a lot of her colleagues' nowadays. She actually thinks that the subject may be adrift and asking the wrong questions. And in particular, she thinks that it would be better if artificial intelligence could get back to its roots in making stronger ties with fields like cognitive science and psychology, because these artificially intelligent computers, while they're smart, they are smart in a way that is so different from human intelligence. Melanie's been intrigued by these questions for really quite a long time, but her journey got started in earnest when she stumbled across a really big and really important book that was published in 1979.