One of the most intriguing learning methods in machine learning literature is reinforcement learning. Through reward and punishment, an agent learns to reach a given goal by selecting an action from a set of actions in a known or unknown environment. Reinforcement learning, unlike supervised and unsupervised learning techniques, does not require any initial data. In this article, we will demonstrate how to implement a version of the reinforcement learning technique Deep Q-Learning, to create an AI agent capable of playing Checkers at a decent level. Deep reinforcement learning is a branch of machine learning that combines deep learning and reinforcement learning (RL).

This work was created with DALL-E 2. The prompt used for the character creation is thoughtfully constructed. This gives uniqueness and unrepeatability to the work. Once came out from the algorithm, the image resolution is pretty small so is enhanced, then is painted in Adobe Photoshop using an oil brush. The resulted image has a very good print quality.

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 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.

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.

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."

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."

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.

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.

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