> IF you are concerned with only the product, then what’s the difference?
The difference is substantial. If the machine was actually thinking and it understood the meaning of its training data, it would be able to generate correct output based on logic, deduction, and association. We wouldn't need to feed it endless permutations of tokens so that it doesn't trip up when the input data changes slightly. This is the difference between a system with _actual_ knowledge, and a pattern matching system.
The same can somewhat be applied to humans as well. We can all either memorize the answers to specific questions so that we pass an exam, or we can actually do the hard work, study, build out the complex semantic web of ideas in our mind, and acquire actual knowledge. Passing the exam is simply a test of a particular permutation of that knowledge, but the real test is when we apply our thought process to that knowledge and generate results in the real world.
Modern machine learning optimizes for this memorization-like approach, simply because it's relatively easy to implement, and we now have the technical capability where vast amounts of data and compute can produce remarkable results that can fool us into thinking we're dealing with artificial intelligence. We still don't know how to model semantic knowledge that doesn't require extraordinary amounts of resources. I believe classical AI research in the 20th century leaned more towards this direction (knowledge-based / expert systems, etc.), but I'm not well versed in the history.
The difference is substantial. If the machine was actually thinking and it understood the meaning of its training data, it would be able to generate correct output based on logic, deduction, and association. We wouldn't need to feed it endless permutations of tokens so that it doesn't trip up when the input data changes slightly. This is the difference between a system with _actual_ knowledge, and a pattern matching system.
The same can somewhat be applied to humans as well. We can all either memorize the answers to specific questions so that we pass an exam, or we can actually do the hard work, study, build out the complex semantic web of ideas in our mind, and acquire actual knowledge. Passing the exam is simply a test of a particular permutation of that knowledge, but the real test is when we apply our thought process to that knowledge and generate results in the real world.
Modern machine learning optimizes for this memorization-like approach, simply because it's relatively easy to implement, and we now have the technical capability where vast amounts of data and compute can produce remarkable results that can fool us into thinking we're dealing with artificial intelligence. We still don't know how to model semantic knowledge that doesn't require extraordinary amounts of resources. I believe classical AI research in the 20th century leaned more towards this direction (knowledge-based / expert systems, etc.), but I'm not well versed in the history.