I would like to observe that you can divide sciences as follows:
1) physical sciences (Physics and Chemistry are models): investigating the fundamentals using the old idea of the scientific method, ie hypothesis to experiment to accepting or rejecting hypothesis. It is a predict and test method.
2) historic sciences (Biology, Geology, Cosmology are models but not Biochemistry and Biophysics which go it the first group); uses the theories of physical science to create historic theories (like evolution or plate tectonics) about how things became what they are. It is only rarely that a hypothesis can be directly tested by experiment and the method sort of proceeds as observation/taxonomy to hypothesis explaining bodies of observations to simple experiments to prove that proposed processes are possible. It is a collect data and try to organize into a integrated story sort of method and very inductive.
3) inventive Science (Engineering, Medicine are examples): uses the theories of physical and historic sciences to create useful and/or profitable things. Here the method is to identify a problem then look for solutions and test proposed solutions in tests/trials.
Associated with these sciences are theoretical areas (Mathematics, Information Theory are examples): These do not test theories and are not in the business of predicting and testing against reality. Their deductive rather then inductive. They create theoretic structures that are logically robust and can be used by the other sciences as powerful tools.
I have left out the social sciences, history proper, linguistics, anthropology and economics because it is not clear to me that they are even sciences and they do not fit the mold of mathematics either. But they are (like the others) large communal scholarships and I am not putting them down when I say they may not be sciences.
My definition of a science is a communal scholarship that
a) is not secretive but public using peer reviewed publication (or its equivalent) in enough detail that the work could be repeated,
b) is concerned with understanding material physical reality and doing so by testing theories in experiments or their equivalents ie no magical or untestable explanations,
c) accepts the consensus of convinced scientists in the appropriate field rather than an authority as a measure of truth. The method by which the scientists are convinced may be anything in principle, but scientists are not likely to be convinced if the math has mistakes, logic is faulty, experiments are without controls, supernatural reasons are used etc. etc.
AI would definitely fall into the inventive sciences. An appropriate method would be to identify a problem, invent solutions using knowledge of physical and historic science and the tools of math etc., see if the solutions work in systematic tests. None of these is simple. To identify a problem, you need to have a vision of what is the end point success and a proposed path to get there. Vision does not come cheap. Invention of solutions is a creative process. Testing takes as much skill and systematic, clear thinking as any other experimental-ish procedure.
AI would definitely fall into the inventive sciences.
It seems that well-established physical or historical sciences invariably serve as the theoretical underpinning for each of the inventive sciences (electrical/mechanical/chemical engineering has the physical sciences, medicine has biology, etc.). What is the theoretical underpinning of AI? Traditionally it has been computer science, but on the face of it CS says little or nothing about the mechanisms of intelligence. Neuroscience isn’t quite it either, since neuroscience is focused on describing the human brain, and any principles that might apply to intelligence in general need to be abstracted away.
It would seem, then, that AI has no theoretical underpinning, and one can make a good argument that the lack of advanced AI is due to this very fact. Certainly the goal of AI is to (eventually) engineer machine intelligence, but it would seem that a major focus of present-day AI is to acquire theoretical insight that would serve as the foundation of an engineering effort. I think this shows that either AI is not just an inventive science, or that we need to talk separately about intelligence science and intelligence engineering.
“AI has no theoretical underpinning”
Very good—that is the hole in what I said about AI. I have always thought of AI as a part of computer science but of course it is possible to think of it as separate. In that case it is underpinned by computer science and (?). Neuroscience if you are trying to duplicate human intelligence (or even ant intelligence) in a non-biological system. But neuroscience is not sufficiently developed to underpin anything. I don’t know how far Information Theory has progressed but I suspect it is not up to the job at present.
Excellent analysis. This is the kind of discussion I was looking for.
Note that it is necessary to do empirical science before inventive science becomes possible. Engineering depends almost completely on knowledge of physical laws. So a plausible diagnosis of the cause of the limited progress in AI, is that it’s an attempt to do invention before the relevant empirical science has become available.
Historically, invention often, maybe usually, precedes the science that describes it. Thermodynamics grew out of steam engines, not the other way around, and the same for transistors in the fifties, for two examples off the top of my head. I suspect it is because the technology provides a simpler and clearer example of the relevant science than natural examples. And the development of empirical sciences are useful to the further development of the technologies.
I would like to observe that you can divide sciences as follows:
1) physical sciences (Physics and Chemistry are models): investigating the fundamentals using the old idea of the scientific method, ie hypothesis to experiment to accepting or rejecting hypothesis. It is a predict and test method.
2) historic sciences (Biology, Geology, Cosmology are models but not Biochemistry and Biophysics which go it the first group); uses the theories of physical science to create historic theories (like evolution or plate tectonics) about how things became what they are. It is only rarely that a hypothesis can be directly tested by experiment and the method sort of proceeds as observation/taxonomy to hypothesis explaining bodies of observations to simple experiments to prove that proposed processes are possible. It is a collect data and try to organize into a integrated story sort of method and very inductive.
3) inventive Science (Engineering, Medicine are examples): uses the theories of physical and historic sciences to create useful and/or profitable things. Here the method is to identify a problem then look for solutions and test proposed solutions in tests/trials.
Associated with these sciences are theoretical areas (Mathematics, Information Theory are examples): These do not test theories and are not in the business of predicting and testing against reality. Their deductive rather then inductive. They create theoretic structures that are logically robust and can be used by the other sciences as powerful tools.
I have left out the social sciences, history proper, linguistics, anthropology and economics because it is not clear to me that they are even sciences and they do not fit the mold of mathematics either. But they are (like the others) large communal scholarships and I am not putting them down when I say they may not be sciences.
My definition of a science is a communal scholarship that
a) is not secretive but public using peer reviewed publication (or its equivalent) in enough detail that the work could be repeated,
b) is concerned with understanding material physical reality and doing so by testing theories in experiments or their equivalents ie no magical or untestable explanations,
c) accepts the consensus of convinced scientists in the appropriate field rather than an authority as a measure of truth. The method by which the scientists are convinced may be anything in principle, but scientists are not likely to be convinced if the math has mistakes, logic is faulty, experiments are without controls, supernatural reasons are used etc. etc.
AI would definitely fall into the inventive sciences. An appropriate method would be to identify a problem, invent solutions using knowledge of physical and historic science and the tools of math etc., see if the solutions work in systematic tests. None of these is simple. To identify a problem, you need to have a vision of what is the end point success and a proposed path to get there. Vision does not come cheap. Invention of solutions is a creative process. Testing takes as much skill and systematic, clear thinking as any other experimental-ish procedure.
It seems that well-established physical or historical sciences invariably serve as the theoretical underpinning for each of the inventive sciences (electrical/mechanical/chemical engineering has the physical sciences, medicine has biology, etc.). What is the theoretical underpinning of AI? Traditionally it has been computer science, but on the face of it CS says little or nothing about the mechanisms of intelligence. Neuroscience isn’t quite it either, since neuroscience is focused on describing the human brain, and any principles that might apply to intelligence in general need to be abstracted away.
It would seem, then, that AI has no theoretical underpinning, and one can make a good argument that the lack of advanced AI is due to this very fact. Certainly the goal of AI is to (eventually) engineer machine intelligence, but it would seem that a major focus of present-day AI is to acquire theoretical insight that would serve as the foundation of an engineering effort. I think this shows that either AI is not just an inventive science, or that we need to talk separately about intelligence science and intelligence engineering.
“AI has no theoretical underpinning” Very good—that is the hole in what I said about AI. I have always thought of AI as a part of computer science but of course it is possible to think of it as separate. In that case it is underpinned by computer science and (?). Neuroscience if you are trying to duplicate human intelligence (or even ant intelligence) in a non-biological system. But neuroscience is not sufficiently developed to underpin anything. I don’t know how far Information Theory has progressed but I suspect it is not up to the job at present.
Excellent analysis. This is the kind of discussion I was looking for.
Note that it is necessary to do empirical science before inventive science becomes possible. Engineering depends almost completely on knowledge of physical laws. So a plausible diagnosis of the cause of the limited progress in AI, is that it’s an attempt to do invention before the relevant empirical science has become available.
Historically, invention often, maybe usually, precedes the science that describes it. Thermodynamics grew out of steam engines, not the other way around, and the same for transistors in the fifties, for two examples off the top of my head. I suspect it is because the technology provides a simpler and clearer example of the relevant science than natural examples. And the development of empirical sciences are useful to the further development of the technologies.