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Comment by dragontamer | Hacker News Reader

Its always funny to realize how "easy" beating human-intelligence is (Chess AI, Go AI, even Mathematical Proofs), but how hard beating human-simple behaviors are.

IE: Stand up on two legs, walk forward, and move these 10 objects from this bin to that bin without breaking any of those objects. Or fold these clothes (surprisingly difficult to get a robot to do that).

We can build super-advanced Chess and Go AIs that beat out the grand summation of theoretical human knowledge (with Chess theory going back hundreds of years, and Go theory going back maybe thousands!!), but we still can't move a variety of objects from one bin to another successfully and repeatably.

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Another funny example: AIs (or various algorithms at least) have solved the object camera tracking problem, but still basically fail at depth perception and figuring out if something is "still" or "moving" based on sight alone.

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A fun way to think about this is by analyzing the 'training-time' for these tasks.

The things we think are easy, we've been trained to do through millions of years of evolution. Running with two feet through a forest is pretty easy for us. Not so much so for computers.

The things that we think are mentally taxing, we've only been doing for a few thousand years or so.

> Running with two feet through a forest is pretty easy for us.

Easy for a twelve year old who has practicing motor skills for their entire life. And who would still be spastic on some assembly lines

>The things that we think are mentally taxing, we've only been doing for a few thousand years or so.

It's kind of fascinatingly serendipitous in the sense that computers/AI are super quick at performing the tasks that we humans find extremely taxing (complex mathematical computation, visual simulation) while struggling with tasks that comes naturally to us humans (gripping objects, running with two feet--as you described) owing to evolution.

I wonder how much of that is because of constituent 'building block' materials that make up the basic structure of homo sapiens and computers -- carbon and silicon, respectively.

This phenomenon has a name: Moravec's Paradox [1]. If a chimp or a four year old can do it, a computer probably has a hard time with it.

An interesting counter-example to the trend is theorem proving. A symbolic task that wasn't present in the ancestral environment, but humans crush computers at it.

1: https://en.wikipedia.org/wiki/Moravec%27s_paradox

> Its always funny to realize how "easy" beating human-intelligence is (Chess AI, Go AI, even Mathematical Proofs), but how hard beating human-simple behaviors are.

This is the baseless myth that won't die. We are nowhere near rivaling humans in mathematical proving. Even after a human has proved a theorem, it can be an astronomic amount of work to even explain the proof to a machine after the fact (i.e. to construct a machine-checkable version of the proof), even when using suites of sophisticated software tools developed solely to facilitate this process. Progress has been glacially slow since the field began in the 1950s. Deep learning has had zero impact.

We will have robots that can grasp competently way before we have machines that can rival humans in mathematical proving.

True: automated mathematical proving is strictly limited to certain domains right now. First order logic (and other higher forms of logic) are outside the scope of machines, although machines can somewhat solve logic puzzled correctly stated in first-order form (its a semi-decidable problem, so "somewhat solve" is basically the best we can ever hope for).

But move down to boolean logic and symbolic computation... the stuff that translates statements in Verilog or VHDL into pure logic statements for small LUTs in FPGAs or logical NAND gates of hardware synthesis??

Oh yeah, machines are way better than humans. People pretty much rely upon automatic synthesis for circuit design and boolean logic these days. Proof is in the pudding.

Perhaps it isn't the same kind of math you were thinking about. But Boolean algebra is still math, logic, and proofs. Of course, in practice, humans use these tools to build larger structures... but I'd argue that the machine handles a lot of the rote proof stuff (optimally figuring out the minimum number of NAND gates to represent a certain truth table).

It is very telling to me that very large animals that we would consider much less intelligent than a human (elephants, sperm whales) still have much larger brains than us. All that grey matter is needed for something! (not that all of it is going towards locomotion).

>> Its always funny to realize how "easy" beating human-intelligence is (Chess AI, Go AI, even Mathematical Proofs), but how hard beating human-simple behaviors are.

Perhaps the lesson to learn is that we are not as good as we think at board games and maths, etc.

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