[AI@50]
Filene Auditorium, Moore Hall
Dartmouth College
Hanover, NH
Conference notes by Meg Houston Maker
John McCarthy
What Was Expected, What We Did, and AI Today
McCarthy corrects Heckman: "Artificial intelligence is not, by definition, simulation of human intelligence."
The symbolic role of the Dartmouth summer project of 1956 was more important than its specific results.
McCarthy's interest in AI started at a 1948 symposium at CalTech, wherein the brain and computers were compared. However there were no computers yet, so it was all somewhat -- theoretical. Turing had proposed them at that point, but they hadn't been realized yet.
"Artificial intelligence" as a term was chosen "to nail the flag to the mast," because McCarthy was disappointed in how few research papers dealt with making machines behave intelligently. The original idea of the summer project was that participants would work together, but in the end, each had his own research agenda, and they came to campus at various times and for varying lengths of time. "But the real reason we didn't live up to grand hopes was that AI was harder than we thought."
When will we have human-level AI? This is the wrong question. The right question should be focused on the idea that we will reach human level AI when someone solves basic problems.
Three classical problems of AI:
1. The frame problem -- how to avoid specifying what doesn't happen when a action occurs
2. The qualifacation problem -- how to avoid specifying every qualification for an action to be successful
3. The ramification problem -- how to avoid specifying all the side effects of an action
[paraphrasing] "All three have been solved in important contexts and for important applications, but none have been solved at the human level of intelligence. These ideas require extensions to logic," primarily non-monotonic logic. There are also probably several important problems nobody knows about yet.
To have human-level intelligence, we need the property of self-awareness.
Question from audience: Where are we in simulating human-year reasoning? Like a 1-year-old? A 2-year-old?
Answer: In some respects machines are ahead, in others they're not even up to a year. One of my complaints is that people in AI, psychology, and philosophy are too apt to focus on appearance rather than the reality that appears behind the appearance. Just drawing patterns of appearance (of things) is not enough. We are middle-sized objects, and we have the ability to recognize other middle-sized objects that existed before us.
Marvin Minsky
The Emotion Machine
Minsky uses a Mac.
An anecdote: Minsky tells a story about driving his daughter around in the car when she was 18 months old. She would periodically yell "Care!" Dad and mom couldn't figure this out. Turns out that she had seen TV adds for the organization CARE, whose logo is rendered as a stencil. And driving around, she had been seeing telephone poles with stenciled numbers on them, and had associated the stencil concept with the word Care. Logic and reasoning are complex!
Minsky is not a fan of ontologies, because reality isn't rigid. A plane is like a bird in many respects, but not in many others.
Much of the early AI work (60s and 70s) solved high-school and college-level logic and math problems. But today, there is still no vision program than can recognize the objects in a typical room, and there is no language program can answer simple questions about a children's story.
AI researchers have "physics envy" -- they wish for very general formal principles that can be taken as theory of thinking. "AI-ers user Occam's Razor too much!" The brain has myriad architectures. Biology is messy, brains use many different procedures and representations. If you want to understand a cognitive phenomenon, you should make at least 3 theories because it's likely the brain does each of those things in several ways. Look for multiple theories rather than elegant, complex ones. You have to think of the mind as a big jigsaw puzzle, and you can maybe cover each part with an elegant mathematical theory, but you still have to link them together.
We have different ways to think about different problems (and that's what makes us so smart): Analogy, Planning, Simplify, Reformulate, Simulate to Anticipate, Contradition. (See his new book, The Emotion Machine.) We need to formulate a machine that has reflective systems.
In conclusion, there are too many specialists, and we need more self-criticism about which methods are good for what problems.
Thanks for sharing, I found this interesting and intelligent.
Posted by: Andrew Zimmerman | 04 December 2008 at 05:14 AM