As a grad student studying artificial intelligence, it might be useful to know what exactly intelligence is.  No one can really define it.  We know that we have it.  We know that tables and chairs do not.  Everything in between is up for grabs.  I’m going to discuss what some historical views on intelligence are and what I think it might be.

The classic definition of intelligence in AI is based on the Turing Test.  This is a test created by Alan Turing to determine if something is intelligent.  You have a person and a computer in separate rooms.  In a third room is a judge.  He chats with both of them over a computer.  If he can’t tell which one is the person, then the computer is intelligent.  Basically the computer is considered to be intelligent if it can imitate a person in conversation.  If it doesn’t have this ability then its not intelligent.  This test can be extended to a more complicated task, say if you can’t tell a person and a computer/robot apart in every day life, then the computer/robot would be intelligent.

Then there are a class of people who believe that having intelligent behavior is not enough.  They say its critical what is going on to make that intelligent behavior.  Usually they say that having computer circuits create such behavior is not intelligent.  Some of them say that only having an exact human brain create the behavior is intelligence.  The classic argument from this side is John Searle’s Chinese Room.  There is a man in a room.  He receives pieces of paper with weird symbols on them.  He has a set of directions of what to do when he receives these pieces of paper, which generally involves him creating new weird symbols and passing them back out.  The symbols are Chinese and he is actually answering someone’s questions with someone in Chinese this way. Searle’s main argument is that the man does not know Chinese even though he can act like he does this way.  The room also does not speak Chinese.  Searle goes on to say that even if the man and directions were replaced by a fully accurate simulation of the human brain it would not be intelligent, you need the magic stuff of the brain to be intelligent.  Another point to make is that this example is utterly impossible, it would not be possible to create a set of directions to converse fluently in Chinese.

Another argument from a more computer science perspective is that of Simon and Newell.  They put forth the ‘Physical Symbol System Hypothesis’, which states that “A physical symbol system has the necessary and sufficient means for general intelligent action.”  They define a physical symbol system as any system which physical patterns (symbols) that are related somehow (in a system).  Basically, anything.  They later go on to say that one of the signs of intelligence is avoiding exponential search (Many problems can be reduced to searching through a space of possible solutions.  If the search space is exponential, then it is impractical or impossible to search it exhaustively.  Avoiding this exponential search requires some intelligence in deciding how to avoid it).  This is a pretty interesting idea.  I often feel that one of the key things to do to be successful is to know how much time to put into something and when its not worthwhile anymore, which seems somewhat related to this idea of avoiding exponential search.

Recently, our reading group read a paper by Ned Block that also discussed this topic.  Block is part of the camp that says that the internal processing that creates the intelligent behavior is critical to whether something is intelligent.  He provides a counter-example to the Turing Test.  A computer is loaded with a database of every conceivable conversation that is less than an hour in length.  Then when taking the Turing Test, it looks up a conversation that matches up to the current sentence, and speaks the next sentence in that conversation.  He says that this machine is clearly not intelligent, and therefore intelligent behavior is not enough.  He does allow that computers could be intelligent, but they would have to be doing something better than this, such as learning or adapting.  Another student in the group brought up a great point that the Turing Test is fine if you say that it has to be a feasible machine that passes for a human.  Both this counterexample and the Chinese room are not feasible agents (the number of hour long conversations is much greater than the number of particles in the universe).  Any feasible agent (reasonable memory and computation power) that passes the Turing Test must be doing something intelligent to be acting that way with limited resources.  This is an interesting definition that lets the Turing Test stand and does put a restriction on the internal processing to be ‘feasible’, which any actually realized agent would be.

So where do I stand?  I’m not sure.  Intelligence may be defined on some scale by behavior.  More complex or more efficient behaviors come from more intelligent beings.  I’m not sure that a definition with a restriction on the internal processing of a being will work.  Intuitively I don’t think an agent that is simply looking up a conversation in its database like above is intelligent.  An agent that is learning or adapting seems to me to be more intelligent.  But depending on what level of abstraction you look at, even we aren’t doing anything that exciting.  Sure, you say that we’re understanding what is said to us, thinking of a response, using our memories, and responding.  Which sounds intelligent. But at its basest level, its just chemicals and ions flowing back and forth in our brain, following the inexorable laws of physics.  Which sounds very unintelligent.  The same thing applies in the case of AI.  You can write some cool learning algorithm that adapts and learns how to behave in some environment over time.  But of course how it learns was written by you and is pre-determined and the entire course of what it would do in that environment could probably be predicted if you knew all the variables.  So I don’t think any definition of intelligence that involves the internal processing going on could ever work, because every agent simply has very boring particles following the laws of physics at the core of their internal processing.

In summary, I think a definition of intelligence has to be about producing intelligent behavior and not about the internal processing that creates it.  The Turing Test seems pretty reasonable if you restrict it to feasible agents.  And I still think algorithms that let machines learn and adapt are more exciting that simply programming in a static solution to a computer (I prefer to think of things at the learning abstraction level than the level of particles following the laws of physics).