Artificial Intelligence—at the Intersection of CS and Data Science

When a baby first opens its eyes, does it “see” its parent’s faces? Does it understand any notion of what a face is—or even what a simple shape is? Babies must “learn” the world around them. That’s what artificial intelligence (AI) is doing today. It’s looking at massive amounts of data and learning from it. AI is being used to play games, implement a wide range of computer-vision applications, enable self-driving cars, enable robots to learn to perform new tasks, diagnose medical conditions, translate speech to other languages in near real time, create chatbots that can respond to arbitrary questions using massive databases of knowledge, and much more. Who’d have guessed just a few years ago that artificially intelligent self-driving cars would be allowed on our roads—or even become common? Yet, this is now a highly competitive area. The ultimate goal of all this learning is artificial general intelligence—an AI that can perform intelligence tasks as well as humans.

Several artificial-intelligence milestones, in particular, captured people’s attention and imagination, made the general public start thinking that AI is real and made businesses think about commercializing AI: 

1. In a 1997 match between IBM’s DeepBlue computer system and chess Grandmaster Gary Kasparov, DeepBlue became the first computer to beat a reigning world chess champion under tournament conditions. IBM loaded DeepBlue with hundreds of thousands of grandmaster chess games. DeepBlue was capable of using brute force to evaluate up to 200 million moves per second! This is big data at work. IBM received the Carnegie Mellon University Fredkin Prize, which in 1980 offered $100,000 to the creators of the first computer to beat a world chess champion.

2. In 2011, IBM’s Watson beat the two best human Jeopardy! players in a $1 million match. Watson simultaneously used hundreds of language-analysis techniques to locate correct answers in 200 million pages of content (including all of Wikipedia) requiring four terabytes of storage. Watson was trained with machine learning and reinforcement-learning techniques.

3. Go—a board game created in China thousands of years ago —is widely considered to be one of the most complex games ever invented with 10 possible board configurations. To give you a sense of how large a number that is, it’s believed that there are (only) between 10 and 10 atoms in the known universe! In 2015, AlphaGo—created by Google’s DeepMind group—used deep learning with two neural networks to beat the European Go champion Fan Hui. Go is considered to be a far more complex game than chess. 

4. More recently, Google generalized its AlphaGo AI to create AlphaZero—a game-playing AI that teaches itself to play other games. In December 2017, AlphaZero learned the rules of and taught itself to play chess in less than four hours using reinforcement learning. It then beat the world champion chess program, Stockfish 8, in a 100-game match—winning or drawing every game. After training itself in Go for just eight hours, AlphaZero was able to play Go vs. its AlphaGo predecessor, winning 60 of 100 games.