Moving beyond detection of statistical patterns in AI

Gary Marcus and Ernest Davis writing for the NYT:

. . . We recently searched on Google for “Did George Washington own a computer?” — a query whose answer requires relating two basic facts (when Washington lived, when the computer was invented) in a single temporal framework. None of Google’s first 10 search results gave the correct answer. The results didn’t even really address the question. The highest-ranked link was to a news story in The Guardian about a computerized portrait of Martha Washington as she might have looked as a young woman.

Google’s Talk to Books, an A.I. venture that aims to answer your questions by providing relevant passages from a huge database of texts, did no better. It served up 20 passages with a wide array of facts, some about George Washington, others about the invention of computers, but with no meaningful connection between the two.

The situation is even worse when it comes to A.I. and the concepts of space and causality. Even a young child, encountering a cheese grater for the first time, can figure out why it has holes with sharp edges, which parts allow cheese to drop through, which parts you grasp with your fingers and so on. But no existing A.I. can properly understand how the shape of an object is related to its function. Machines can identify what things are, but not how something’s physical features correspond to its potential causal effects.

How to Build Artificial Intelligence We Can Trust

Modern AI’s have no basic understanding of the world, and there’s not much progress.