Analogs and Parallels

I’ve been reading a book titled Dreaming in Code recently, which is a rather extensive case study of the development of an open-source project named Chandler. Many of the lessons and references to programming in general have hit home with my experience. Things like the fact that many programmers would rather program than eat or sleep. In fact it is 3:13 AM right now and I’ve been reading a bit about Knuth’s Literate Programming effort – something that I am sad to say that I don’t know nearly enough about. Evidently the faculty at UNL doesn’t put much stock in his CWEB language. Or at least not at the under-graduate level. Either way, I’ve never written a program using it.

I often wonder what the distinction between mere random behavior and intelligence is. Good ideas are usually born from the ashes of old ideas (formulas, conjectures, and theorems in the parlance of mathematics). Two un-related ideas can be combined in a moment of ingenuity into something awesome. I do not believe in pre-determination or true chance. But of course, you would expect me to say that.

I started a post a while back (which I haven’t finished yet) named “Semantic Comments.” When I happened to come across Knuth’s Literate Programming effort earlier this morning, I was struck by a sense of deja-vu but with a twist. Knuth strives to define meaning in English – a language accessible only to humans. I would prefer to define meaning in terms accessible to both humans and machines. It bothers me that comments are seen as an after-thought – something that the computer shouldn’t have to deal with. It just seems that we lack the precision to express our thoughts accurately.

I started this post with the desire to discuss the Turing Test and online chat bots. Somehow in the last 50 years, despite rapid improvements in memory and processor speeds, a truly intelligent conversation with a machine still eludes us. Some people, like Ray Kurzweil, would argue that we just haven’t reached a sufficient point of maturity in hardware to emulate human brains. Why must we resort to emulation, however? Are we not clever enough to figure out a solution without reverse-engineering ourselves?

Anyone who has spent more than 30 seconds with a modern chat bot will clearly notice a complete lack of depth. Projects like A.L.I.C.E. can seem to be responsive and mildly entertaining, but there is a definite sense that you are talking to a brick wall. My gut feeling is that people who write chat bots focus too much on the details. The bot has to be able to remember your name, or parse a sentence like “I hate you” and respond with a quip. Where do these requirements come from? In their effort to write a “convincing” chat bot, these authors miss the point: creating an intelligent system that can think and respond to your actions.

If I write 2+2=4, most anyone with a basic education will understand what I mean. Language is built upon successive levels of knowledge – all starting with interpretations of the real world. We know what the word “Apple” means depending upon the context. But constructing a chat bot is inherently out of context. It would be as though someone stuck you in a dark room and translated Chinese to you one word at a time and gave you no access to the external world.

So rather than focus on the arcane methods of sentence parsing and learning models, perhaps what chat bots need is a good dose of reality. Nothing less than a whole solution will suffice. You can’t eat Chinese food with a toaster.

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