How deep learning turned sci-fi-esque predictive intelligence into reality

20 years ago, IBM’s supercomputer Deep Blue defeated world chess champion Gary Kasparov in a historical first victory for artificial intelligence. Today, supercomputers are smart enough to easily beat not only chess players, but also succeed in similarly sophisticated games, like the 3,000-year-old Chinese game of Go, and most recently, poker challenges against multiple human pros. These wins against humans were a result of artificial intelligence.

Self-learning takes from human learning, then outperforms it

The goal of artificial intelligence (AI) is to make computers as smart, or even smarter than human beings, by giving them human-like thinking and reasoning abilities. However, there are also many other ways to achieve this.

Several years ago, Deep Blue was taught by using hand-written functions, copying the knowledge and wisdom of top human chess players. By implementing AI, the IBM supercomputer was able to identify things it had seen before, consider all possible moves, predict human responses, and then decide on the best move. This wouldn’t be possible without training it to look at large amounts of data and use algorithms that gave it the ability to perform tasks without any human intervention. This process led to what is now known as “machine learning.”

But it doesn’t end there. It takes even more intelligent systems to defeat human players in more complex games like Chinese Go and especially Poker – there are not only billions of options to foresee, but they require “feeling” or intuition. This is where the process of deep learning comes into play.

Deep learning is a highly innovative branch of machine learning that closely imitates the work of the human brain in processing data and creating patterns of decision-making. In other words, it is how humans learn from practice as we try many different approaches before making a final decision....