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Exploring the Applications of Machine Learning - JAXenter

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The term itself originated in the 1950s. Arthur Samuel from IBM coined the term based on his research on computer checkers. In a game between a computer and a Connecticut checkers master, the computer won. This outcome opened up a world of possibilities. Today, machine learning has expanded far beyond simple games of checkers.


Machine Learning Explained.

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Machine learning is a branch of artificial intelligence that focuses on the use of algorithms to make decisions. These algorithms are trained with historical data and based on what they infer from that data, are able to make predictions, classifications, and numerous other decisions, all without being explicitly programmed to do so. While the term machine learning has only recently become a buzzword, it has been around as far back as 1959, when it was coined by Arthur Samuel, a pioneer in the field. One of the earliest applications was in a game of checkers, in which self-proclaimed checkers master, Robert Nealey, lost against a computer on an IBM 7094. From those humble beginnings, technological developments around storage and processing power have enabled more powerful and widespread applications of machine learning, such as Amazon's recommendation engine and Google's self-driving cars.


What is Machine Learning?

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Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to simulate the way humans learn, gradually increasing its accuracy. IBM has a rich history of machine learning. One of them, Arthur Samuel, is famous for coining the term "machine learning" in his research on the game of checkers. Robert Neely, a self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962 and lost to the computer. This feat appears almost trivial in comparison to what can be done today, but it is regarded as a significant milestone in the field of artificial intelligence. Over the next two decades, data storage and processing technology will create some of the innovative products we know and love today, like the Netflix recommendation engine or self-driving cars.


On Thinking Machines, Machine Learning, And How AI Took Over Statistics

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Sixty-five years ago, Arthur Samuel went on TV to show the world how the IBM 701 plays checkers. He was interviewed on a live morning news program, sitting remotely at the 701, with Will Rogers Jr. at the TV studio, together with a checkers expert who played with the computer for about an hour. Three years later, in 1959, Samuel published "Some Studies in Machine Learning Using the Game of Checkers," in the IBM Journal of Research and Development, coining the term "machine learning." He defined it as the "programming of a digital computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning." A few months after Samuel's TV appearance, ten computer scientists convened in Dartmouth, NH, for the first-ever workshop on artificial intelligence, defined a year earlier by John McCarthy in the proposal for the workshop as "making a machine behave in ways that would be called intelligent if a human were so behaving."


On Thinking Machines, Machine Learning, And How AI Took Over Statistics

#artificialintelligence

Sixty-five years ago, Arthur Samuel went on TV to show the world how the IBM 701 plays checkers. He was interviewed on a live morning news program, sitting remotely at the 701, with Will Rogers Jr. at the TV studio, together with a checkers expert who played with the computer for about an hour. Three years later, in 1959, Samuel published "Some Studies in Machine Learning Using the Game of Checkers," in the IBM Journal of Research and Development, coining the term "machine learning." He defined it as the "programming of a digital computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning." On February 24, 1956, Arthur Samuel's Checkers program, which was developed for play on the IBM 701, ... [ ] was demonstrated to the public on television A few months after Samuel's TV appearance, ten computer scientists convened in Dartmouth, NH, for the first-ever workshop on artificial intelligence, defined a year earlier by John McCarthy in the proposal for the workshop as "making a machine behave in ways that would be called intelligent if a human were so behaving."


Machine learning includes deep learning and neural nets

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Human intelligence reflects our brain's ability to learn. Computer systems that act like humans use artificial intelligence. That means these systems are under the control of computer programs that can learn. Just as people do, computers can learn to use data and then make decisions or assessments from what they've learned. Called machine learning, it's part of the larger field of artificial intelligence.


How to Train a Deep Learning TensorFlow Analytic to Play Checkers

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Bot Libre now allows you to create generic deep learning analytics and train them through our web API. Deep learning analytics can be used for a wide array of purposes to analyze and make predications on data. This example shows how to train a deep learning analytic to play checkers. You can use either the Bot Libre deep learning library, or the TensorFlow deep learning library. You can choose the inputs, outputs, and layers.


Bayer Crop Science posted on LinkedIn

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Knowledge is power, but our capacity to learn is even more important. The same is true for machine learning. See how #artificialintelligence is constantly...


What do we mean by intelligence?

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Providing a formal definition of Intelligence can be a quite intimidating task. In fact, no common agreement about this topic has been reached so far. Since the beginning of the human history, different definitions of intelligence have been proposed and these varied depending on the historical time and culture. For example, in a society in which language and communication skills play an important role, an individual donated of these kinds of skills might be recognised as to be "more intelligent" than others. In the meantime, in a society in which numerical skills are valued most other individuals might be regarded as to be "more intelligent".


Is The Goal-Driven Systems Pattern The Key To Artificial General Intelligence (AGI)?

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Since the beginnings of artificial intelligence, researchers have long sought to test the intelligence of machine systems by having them play games against humans. It is often thought that one of the hallmarks of human intelligence is the ability to think creatively, consider various possibilities, and keep a long-term goal in mind while making short-term decisions. If computers can play difficult games just as well as humans then surely they can handle even more complicated tasks. From early checkers-playing bots developed in the 1950s to today's deep learning-powered bots that can beat even the best players in the world at games like chess, Go and DOTA, the idea of machines that can find solutions to puzzles is as old as AI itself, if not older. As such, it makes sense that one of the core patterns of AI that organizations develop is the goal-driven systems pattern.