In a college course on neural networks, a professor once described to the class how the reputation of artificial intelligence had taken a nose dive in the 1980’s. A divided community and its pundits had built up a perception that C-3PO-like robots and talking, thinking computers were not far off. AI’s visionaries over promised and under delivered.
To this very day, entrepreneurs hesitate to utter the words “artificial intelligence” for fear of losing credibility. Various systems are often called by more specific names whether it be “Bayesian classifier”, “prediction system”, “search engine”, “knowledge base”, etc. These terms all have various meanings known well to the AI community, but we dare not lump them together and utter the words “artificial intelligence.”
There are plenty who would say I am bastardizing terminology. Artificial intelligence’s very definition is gray. Is a car engine that employs a neural network to manage a fuel air mixture actually intelligent? Is Google intelligent? At what point is information retrieval AI? Is a spell checker AI? As many others have said before me, I take the viewpoint that AI (or oftentimes things that apply AI) is a continuum without clearly defined boundaries.
Rather than trying to carefully classify certain algorithms, I devise solutions that make use of various methods that might be borrowed from an AI textbook, might arise from mathematics or that simply come from my own ideas. If the approach is particularly probabilistic without adhering to well defined mathematics or relies on certain kinds of innovations employing non-deterministic or difficult to predict behavior, I tend to call it AI.
During the course of applying or developing AI, I rarely use such words as “artificial” or “intelligent”. Afterall, to me I’m just building a program in a way that makes sense to me.
The most difficult to solve problems in practical applications tend to be those with many possible answers or no exact answers. We run into cases where we cannot build a computer program to solve the problem with a reasonable amount of time and computing resources. Other times, given infinite time and resources the problem is still unsolvable. In computer science, these are often problems said to have “non-polynomial” solutions. For such problems, we can not solve them at all or we must devise a solution that provides an approximate answer.
Approximate answers to hard problems very often involve smart solutions — artificially intelligent solutions. Much of AI is about reducing a problem to what matters most and then pumping out a best guess … just like real human beings semi-solving real problems.
As we approach more human or more intelligent approaches, I’m unafraid to call these solutions “artificial intelligence”.
With vast amounts of computing power and more creative approaches to problems, I believe our constraints to building pretty good solutions are more and more just the limitations of our own minds. And even there, plenty of AI algorithms do things their own creators (including myself) don’t fully comprehend.
I don’t think about “am I solving a problem logically and intelligently” so much as I try to approach all problems logically and intelligently. But if you ask whether I’m building AI … most of the time in these situations my answer will be “Yes, in which shade of gray?”
Eric Tucker 