You are assuming that the required knowledge can be presented in a modular way, to reduce its complexity.
Knowledge can only be simplified so much... what if the knowledge required for AI, after being made as simple as possible, is still more complex than the most gifted human being can understand?
I like to think that the ultimate nature of reality is simple and beautiful. But all I can be sure of is that the things that I can see are simple enough for me to grasp.
We've already designed, or grown might be a more accurate description, working (most of the time) systems which defy understanding [in full] by the most gifted humans. See the world financial system, our national electricity grid, Windows, Wikipedia, etc. etc. We may not be able to design AI, but we are going to develop it.
Well the thing is, the code analogy works really well for things like algorithms, math, and factual knowledge. Really, the things you are talking about that cannot be modularized are things that require large amounts of training. However, the training algorithm may be able to be modularized and encoded thus enabling the AI to learn these things. This is the goal of machine learning.
Comments
You are assuming that the required knowledge can be presented in a modular way, to reduce its complexity.
Knowledge can only be simplified so much... what if the knowledge required for AI, after being made as simple as possible, is still more complex than the most gifted human being can understand?
I like to think that the ultimate nature of reality is simple and beautiful. But all I can be sure of is that the things that I can see are simple enough for me to grasp.
We've already designed, or grown might be a more accurate description, working (most of the time) systems which defy understanding [in full] by the most gifted humans. See the world financial system, our national electricity grid, Windows, Wikipedia, etc. etc. We may not be able to design AI, but we are going to develop it.
We can design/grow some things that we cannot understand.
Does it follow that we can therefore design/grow all things that we cannot understand?
Well the thing is, the code analogy works really well for things like algorithms, math, and factual knowledge. Really, the things you are talking about that cannot be modularized are things that require large amounts of training. However, the training algorithm may be able to be modularized and encoded thus enabling the AI to learn these things. This is the goal of machine learning.
A training algorithm selects a hypothesis from a hypothesis space.
What happens if the hypothesis space does not include the true hypothesis?
Defining the hypothesis space is tricky - though the training/search part is also tricky :-).