Checkers


Understanding the Future of Humans, AI and Quantum Computers – NextBigFuture.com

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I believe it is likely that we will have 10,000 qubit quantum computers within 5 to 10 years. There is rapidly advancing work by IonQ with trapped ion quantum computers and a range of superconducting quantum computer systems by Google, IBM, Intel, Rigetti and 2000-5000 qubit quantum annealing computers by D-Wave Systems. They will be beyond not just any regular computer today but any non-quantum computer ever for those kinds of problems. Those quantum computers will help improve artificial intelligence systems. How certain is this development? What will it mean for humans and our world?


Applying Machine Learning

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All of these words are changing every industry at a very rapid clip. If you read this column a few weeks ago, you learned that machine learning is an application of artificial intelligence, in which computer systems "learn" by making data-driven decisions. The term "machine learning" was coined in the 1950s by Arthur Lee Samuel, who gave the world a successful early demonstration of self-learning and AI through his Samuel checkers-playing program. In healthcare, for instance, researchers have developed a machine learning tool that will determine people who are at risk for an opioid use disorder in the next 12 months. We all know the opioid epidemic is at such a critical crisis that if it's not addressed soon, who knows what will happen.


How Machines are Learning for Modern Agriculture

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Arthur Samuel, an eccentric computer engineer at Stanford University, took part in what could be considered the most important game of checkers ever played. Arthur challenged the then reigning Connecticut state champion to match wits with a computer he programmed to play checkers.a Surprisingly enough, this event is not an artifact of recent history; the fateful game took place in 1961. Decades prior to the personal computer revolution, Professor Samuel built a working prototype capable of what we now call, "machine learning." Rather than programming the 500 quintillion b potential scenarios on a checkerboard, Arthur instructed the computer to react based on games it had played in the past.


ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

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Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.


ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

#artificialintelligence

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.


Databases that learn

@machinelearnbot

In 1952, IBMer Arthur Samuel created the first implementation of a machine learning system in America -- to play checkers. At first, the system was beatable. Samuel continued to improve the learning capabilities of his checkers program, and in part trained the program by having it play thousands of games against itself. By 1961, Samuel's programs played the fourth-ranked checkers player in America and won. This demonstrated a level of play not yet achieved by a computer.


Databases that learn 7wData

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In 1952, IBMer Arthur Samuel created the first implementation of a machine learning system in America -- to play checkers. At first, the system was beatable. Samuel continued to improve the learning capabilities of his checkers program, and in part trained the program by having it play thousands of games against itself. By 1961, Samuel's programs played the fourth-ranked checkers player ... Read More


Languages evolve based on the unique requirements of AI applications

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The evolution of artificial intelligence (AI) grew with the complexity of the languages available for development. In 1959, Arthur Samuel developed a self-learning checkers program at IBM on an IBM 701 computer using the native instructions of the machine (quite a feat given search trees and alpha-beta pruning). But today, AI is developed using various languages, from Lisp to Python to R. This article explores the languages that evolved for AI and machine learning.


Robots in Depth with Peter Corke

Robohub

In this episode of Robots in Depth, Per Sjöborg speaks with Peter Corke, distinguished professor of robotic vision from Queensland University of Technology, and Director of the ARC Centre of Excellence for Robotic Vision. Peter is well known for his work in computer vision and has written one of the books that defines the area. He talks about how serendipity made him build a checkers playing robot and then move on to robotics and machine vision. We get to hear about how early experiments with "Blob Vision" got him interested in analyzing images and especially moving images, and his long and interesting journey giving robots eyes to see the world.


Review of One Jump Ahead: Challenging Human Supremacy in Checkers

AI Magazine

CHINOOK that I also highly recommend. AI Magazine Volume 20 Number 1 (1999) ( AAAI) vided more than a glimpse of the intense process it described. One Jump Ahead was written by the person most involved in the process. Thus, it provides us with a direct view of Schaeffer's maturation--a maturation that we should all hope to have. Schaeffer does not pull any punches in his book; we see many of his elations, his disappointments, and his flaws.