The media is all-abuzz with tales of Artificial Intelligence (AI). The provocative two-letter symbol conjures up images of invading autonomous robot drones and Terminator-like machines wreaking havoc on mankind. Then there\u2019s the pervading presence of deep learning and big data, also referred to as artificial intelligence. This might leave some of us wondering, is artificial intelligence one or all of these things?<\/p>\n
In that sense, AI leaves a bit of an ambiguous trail \u2013 there does not seem to be a clear definition, even amongst scientists and researchers in the field. There are certainly many different branches of AI. I asked Dr. Roger Schank<\/a>, Professor Emeritus at Northwestern University, for a more clear definition; he told me that artificial intelligence is not big data and deep learning algorithms, at least not in the pure sense of the definition.<\/p>\n Roger emphasizes that intelligence has everything to do with the intersection of learning and interaction and memory. \u201cI will tell you the number one thing people do, it\u2019s pretty obvious \u2013 they talk to each other. Guess how hard that is? That is phenomenally hard, that is the subsection of AI called natural language processing, the part that I worked on my whole life, and I understand how far away we are from that.\u201d<\/p>\n Take a \u201csimple\u201d AI concept, such as how to create a computer that plays chess, to better understand the challenge. There are, more or less, two approaches to creating an intelligent machine that can play chess like a champion. The first approach requires programming the computer to predict thousands of moves ahead of time, while the second approach involves building a computer system that tries to imitate a grand master. In the historical pursuit of how to create an artificially intelligent entity, a vast majority of scientists chose the first option of programming based on prediction.<\/p>\n Predicting thousands of moves sounds like a next to impossible task, but scientists have worked over decades to try and do just that, because the second option \u2014 imitation through trial and error \u2014 is that much more complex. Still, if we want to be creating an artificial intelligence that thinks on its own, Schank argues that the second option is the more promising of the two. \u201cSome of us always saw that this could be the field that could tell us more about people by pursuing method number two (i.e. imitating the grand master),\u201d he says.<\/p>\n Learning more about people while simultaneously developing true intelligence is why Schank entered the field in the first place. \u201cWhen we talk about Facebook, we might think about the work of AI and face recognition; this technology has certainly come a long way, but that\u2019s a different part of AI. The part of AI that people imagine \u2013 the talking and teaching and thinking robots \u2013 most people that talk about AI are not really talking about these questions.\u201d<\/p>\n