Chatbots Magazine offers a tidy summary of different aspects of AI, such as machine learning, expert systems (does anyone still call their product an ‘expert system’? That’s so ’90s and Prolog), and Natural Language Processing, etc. But yet, they meet the usual chimera such definition attempts run into. Defining AI by current technologies is like defining an elephant as a trunk, tail, and tusks. More familiar is their parting shot:
Tesler’s Law: “AI is whatever hasn’t been done yet.”
Yes, until someone does it, routing a car to you via a mobile view looks like either black magic or AI, but when they do it, it becomes just another app. If it can’t commiserate with you about a failed romance or discuss the finer points of Van Gogh appreciation, it’s just a stupid computer trick.
The definition of AI is at once both a perpetually elusive target and a bar we’ve already hurdled. Plenty of people are willing to call their products “artificial intelligence” because we’re in one of the “AI summers” and AI is once again a hot term free from its past stigma. So anyone with a big if-then-else chain hidden in their code slaps an AI label on it and doubles the price. Needless to say, it devalues the term if it applies to every shape of business analytics program.
Perhaps the point where hype and hope converge is on Artificial General Intelligence, i.e., where AI can have that midnight philosophy discussion or ask you how your date went. Until then… good luck.