The quest for artificial intelligence has some parallels with the quest to create new fully artificial life-forms. We've already done the latter with the advent of a fully synthetic genome for bacteria but the former still eludes us. Or does it? For all we know that synthetic genome is the biological equivalent of program that takes input and echoes it to output; 'close', but still not there yet.
With biology we've had a couple of centuries experience reverse engineering how things work and there is still LOTS we don't yet know. With computation we've engineered things from the ground up from base principles, and therefore understand rather well how it works. Human-like intelligence is more than just a computational problem, and more than just figuring out how and why synapses signal each other.
With the reverse engineering effort we've been approaching the task from both sides of the problem. From the top-down functional analysis, to the bottom up biochemical analysis. There is still a lot in the middle that is undiscovered country. We know it is there and we know it produces certain functions which in turn constrains what the middle bits are, but we don't know for certain.
With computing we're getting to the complexity levels where systems can mimic non-determinism. A perfect example of this is understood by anyone who has had to maintain Windows desktops being used by minimally clued users, at some point things will break in a way that boggles the mind. And yet, if you dig deep enough you can determine why it broke in just that way. Managing this complexity is one of the challenges of modern computing environments.
We've gotten to the point where we can program life, if we're given an existing cellular structure to work with. The instruction set is mind bogglingly vast, contains instructions that seemingly do the same thing but have different side effects in certain circumstances, the documentation is being written on the fly by the programmers attempting to use it, and was developed through unintelligent evolutionary processes. If you thought your mind broke when using LISP, that's peanuts to DNA/RNA. And we haven't yet determined if there are biological equivalents to instruction-set-architectures.
With AI we're... working on it. Attempts to create neural networks that mimic the brain's structure and throwing inputs at it in an attempt to educate the dynamic neural network didn't do what we expected. We haven't finished the work of reverse engineering how brains work yet, so making a digital version is still based on guesswork. We have expert systems but they're not independent decision makers yet.
Biology and computational theory will converge at some point, probably, but we've got a long way to go.
With biology we've had a couple of centuries experience reverse engineering how things work and there is still LOTS we don't yet know. With computation we've engineered things from the ground up from base principles, and therefore understand rather well how it works. Human-like intelligence is more than just a computational problem, and more than just figuring out how and why synapses signal each other.
With the reverse engineering effort we've been approaching the task from both sides of the problem. From the top-down functional analysis, to the bottom up biochemical analysis. There is still a lot in the middle that is undiscovered country. We know it is there and we know it produces certain functions which in turn constrains what the middle bits are, but we don't know for certain.
With computing we're getting to the complexity levels where systems can mimic non-determinism. A perfect example of this is understood by anyone who has had to maintain Windows desktops being used by minimally clued users, at some point things will break in a way that boggles the mind. And yet, if you dig deep enough you can determine why it broke in just that way. Managing this complexity is one of the challenges of modern computing environments.
We've gotten to the point where we can program life, if we're given an existing cellular structure to work with. The instruction set is mind bogglingly vast, contains instructions that seemingly do the same thing but have different side effects in certain circumstances, the documentation is being written on the fly by the programmers attempting to use it, and was developed through unintelligent evolutionary processes. If you thought your mind broke when using LISP, that's peanuts to DNA/RNA. And we haven't yet determined if there are biological equivalents to instruction-set-architectures.
With AI we're... working on it. Attempts to create neural networks that mimic the brain's structure and throwing inputs at it in an attempt to educate the dynamic neural network didn't do what we expected. We haven't finished the work of reverse engineering how brains work yet, so making a digital version is still based on guesswork. We have expert systems but they're not independent decision makers yet.
Biology and computational theory will converge at some point, probably, but we've got a long way to go.