I had McCarthy as a professor in the 1980s, just about when it was clear that logic-based AI had hit a wall.
I had McCarthy as a professor in the 1980s, just about when it was clear that logic-based AI had hit a wall. McCarthy was determined to put mathematical logic as a formalism under the real world, and it just wasn't working. He once described the "missionary and cannibals problem" in class. Then he wrote it up in his notation for "circumscription", cranked the notation, and the answer came out. I thought at the time, as he converted the problem to his notation, "here's where the miracle occurs". That's the problem. Hammering the real world into a formalism is very hard. Cranking the formalism is not difficult to automate. Mathematical logic works fine where the problem space can be formalized, such as in software proofs of correctness. But trying to get to "common sense reasoning" that way, which was McCarthy's goal, just didn't work. It was a great disappointment to him. AI as a field has suffered from a repeated problem - believing that the next big development will lead to strong AI. Graph search, the General Problem Solver, expert systems, and big databases of knowledge have all been touted as the solution. In each case, the ceiling of what could be done rose a bit. Machine learning isn't a panacea, but it, too, has raised the ceiling of what can be done. (It's personally frustrating that much of my career was during the "AI winter", from about 1985 to 2005. Expert systems had failed, and machine learning didn't work yet. I spent a lot of time trying to find AI in control theory. Combining machine learning and control theory is at last working out, which is why autonomous drones are now so agile. But it happened 15 years later than I'd hoped.)
Posted by Animats on May 17, 2015, 2:23 AM
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I had McCarthy as a professor in the 1980s, just about when it was clear that logic-based AI had hit a wall.
I had McCarthy as a professor in the 1980s, just about when it was clear that logic-based AI had hit a wall. McCarthy was determined to put mathematical logic as a formalism under the real world, and it just wasn't working. He once described the "missionary and cannibals problem" in class. Then he wrote it up in his notation for "circumscription", cranked the notation, and the answer came out. I thought at the time, as he converted the problem to his notation, "here's where the miracle occurs". That's the problem. Hammering the real world into a formalism is very hard. Cranking the formalism is not difficult to automate. Mathematical logic works fine where the problem space can be formalized, such as in software proofs of correctness. But trying to get to "common sense reasoning" that way, which was McCarthy's goal, just didn't work. It was a great disappointment to him. AI as a field has suffered from a repeated problem - believing that the next big development will lead to strong AI. Graph search, the General Problem Solver, expert systems, and big databases of knowledge have all been touted as the solution. In each case, the ceiling of what could be done rose a bit. Machine learning isn't a panacea, but it, too, has raised the ceiling of what can be done. (It's personally frustrating that much of my career was during the "AI winter", from about 1985 to 2005. Expert systems had failed, and machine learning didn't work yet. I spent a lot of time trying to find AI in control theory. Combining machine learning and control theory is at last working out, which is why autonomous drones are now so agile. But it happened 15 years later than I'd hoped.)
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