Deep Blue AI : In 3 Minutes

So what exactly was Deep Blue ?

Deep Blue was a chess-playing computer developed by IBM. It is known for being the first computer chess-playing system to win both a chess game and a chess match against a reigning world champion under regular time controls.

Deep Blue won its first game against a world champion on 10 February 1996, when it defeated Garry Kasparov in game one of a six-game match. However, Kasparov won three and drew two of the following five games, defeating Deep Blue by a score of 4–2. Deep Blue was then heavily upgraded, and played Kasparov again in May 1997.Deep Blue won game six, therefore winning the six-game rematch 3½–2½ and becoming the first computer system to defeat a reigning world champion in a match under standard chess tournament time controls !

Kasparov accused IBM of cheating and demanded a rematch. But IBM refused and retired Deep Blue.

Interesting, So How did it work ?

A bit technical, but bear with me :)

As put forth in Shannon’s paper, the primary ingredient in a chess-playing program is the evaluation function. Since we can’t look forward all the way to the end of the game and see if a particular move will win (especially since we don’t know what the other player will do during their turns!), we must create a function which takes in a state of the game (in our case, a board arrangement) and boils it down to a real-number evaluation of the state.

For example, imagine the function to be a judge in reality show which gives points to every contestant. the function could give higher scores to board states in which the player of interest has more of their pieces on the board than the opponent. In particular, we would probably want the function to assign an extremely high score (perhaps even infinity !) to the board arrangement in which the opponent’s king is in checkmate, meaning that the player of interest is guaranteed to win the game.

Something which would visually look like this

Conclusion :

As Steve Jobs said, ““You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future.”

Deep Blue had an impact on computing in many different industries. It was programmed to solve the complex, strategic game of chess, so it enabled researchers to explore and understand the limits of massively parallel processing. This research gave developers insight into ways they could design a computer to tackle complex problems in other fields, using deep knowledge to analyze a higher number of possible solutions.

The architecture used in Deep Blue was applied to financial modeling, including marketplace trends and risk analysis; data mining — uncovering hidden relationships and patterns in large databases; and molecular dynamics, a valuable tool for helping to discover and develop new drugs.

Ultimately, Deep Blue was retired to the Smithsonian Museum in Washington, DC, but IBM went on to build new kinds of massively parallel computers such as IBM Blue Gene and Watson.

One thing in particular we learned is that there’s more than one way to look at a complex problem. For example, in chess there’s the human way, which is very pattern recognition–based and intuition-based, and then there’s the machine way, which is very search intensive and looks through millions or billions of possibilities. Often these approaches are complementary.

That’s definitely true in chess but also in many real-world problems — that computers and humans together are better than either one alone.