I think there is a science of intelligence which (in my opinion) is closely related to computation, biology, and production functions (in the economic sense).
Interesting that you’re taking into account the economic angle. Is it related to Eric Baum’s ideas (e.g. “Manifesto for an evolutionary economics of intelligence”)?
The difficulty is that there is much debate as to what constitutes intelligence: there aren’t any easily definable results in the field of intelligence nor are there clear definitions.
Right, so in Kuhnian terms, AI is in a pre-paradigm phase where there is no consensus on definitions or frameworks, and so normal science cannot occur. That implies to me that people should spend much more time thinking about candidate paradigms and conceptual frameworks, and less time doing technical research that is unattached to any paradigm (or attached to a candidate paradigm that is obviously flawed).
It actually comes from Peter Norvig’s definition that AI is simply good software, a comment that Robin Hanson made: , and the general theme of Shane Legg’s definitions: which are ways of achieving particular goals.
I would also emphasize that the foundations of statistics can (and probably should) be framed in terms of decision theory (See DeGroot, “Optimal Statistical Decisions” for what I think is the best book on the topic, as a further note the decision-theoretic perspective is neither frequentist nor Bayesian: those two approaches can be understood through decision theory). The notion of an AI as being like an automated statistician captures at least the spirit of how I think about what I’m working on and this requires fundamentally economic thinking (in terms of the tradeoffs) as well as notions of utility.
Interesting that you’re taking into account the economic angle. Is it related to Eric Baum’s ideas (e.g. “Manifesto for an evolutionary economics of intelligence”)?
Right, so in Kuhnian terms, AI is in a pre-paradigm phase where there is no consensus on definitions or frameworks, and so normal science cannot occur. That implies to me that people should spend much more time thinking about candidate paradigms and conceptual frameworks, and less time doing technical research that is unattached to any paradigm (or attached to a candidate paradigm that is obviously flawed).
It actually comes from Peter Norvig’s definition that AI is simply good software, a comment that Robin Hanson made: , and the general theme of Shane Legg’s definitions: which are ways of achieving particular goals.
I would also emphasize that the foundations of statistics can (and probably should) be framed in terms of decision theory (See DeGroot, “Optimal Statistical Decisions” for what I think is the best book on the topic, as a further note the decision-theoretic perspective is neither frequentist nor Bayesian: those two approaches can be understood through decision theory). The notion of an AI as being like an automated statistician captures at least the spirit of how I think about what I’m working on and this requires fundamentally economic thinking (in terms of the tradeoffs) as well as notions of utility.
Surely Peter Norvig never said that!
Go to 1:00 minute here
“Building the best possible programs” is what he says.
Ah, what he means is having an agent which will sort through the available programs—and quickly find one that efficiently does the specified task.