Checkers


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.


The 10 Coolest Machine-Learning And AI Startups Of 2018 (So Far)

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An artificial intelligence revolution has been eagerly awaited since the late 1950s, when pioneering IBM researcher Arthur Samuel trained the world's first self-learning computer to play a mean game of checkers. But only in the past few years has the long-promised technology become mature, effective and -- thanks to a variety of new offerings -- readily accessible to the channel. AI and machine learning are now taking the industry by storm, with the cloud fast-tracking adoption of solutions that make decisions, automate business processes, deliver predictions and insights and learn from their own experience. Next-gen startups are at the forefront of the revolution, delivering infrastructure, development frameworks, and intelligent applications that allow enterprises to take advantage of their data in ways never before possible.


What's the Difference Between AI, Machine Learning, and Deep Learning?

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AI, machine learning, and deep learning - these terms overlap and are easily confused, so let's start with some short definitions. AI means getting a computer to mimic human behavior in some way. Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems. Those descriptions are correct, but they are a little concise.


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.


A Very Short History Of Artificial Intelligence (AI)

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In an expanded edition published in 1988, they responded to claims that their 1969 conclusions significantly reduced funding for neural network research: "Our version is that progress had already come to a virtual halt because of the lack of adequate basic theories… by the mid-1960s there had been a great many experiments with perceptrons, but no one had been able to explain why they were able to recognize certain kinds of patterns and not others."


How poker and other games help artificial intelligence evolve

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Michael Bowling has always loved games. When he was growing up in Ohio, his parents were avid card players, dealing out hands of everything from euchre to gin rummy. Meanwhile, he and his friends would tear up board games lying around the family home and combine the pieces to make their own games, with new challenges and new markers for victory. Bowling has come far from his days of playing with colourful cards and plastic dice. He has three degrees in computing science and is now a professor at the University of Alberta.


Databases that learn

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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 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