We can do the same thing with computers real soon now.
Haven't people been saying this for decades? AI has a long history of impressive results, but somehow none of them have actually produced "thought".
Nobody even understands how the brain "thinks" at a neural level, let alone how to model that. All we can do at this point is try different models (which way or may not actually match reality) and hope we find one that works. But there's no evidence that we'll find a working model "real soon now". Impressive results that we can kinda-sorta imagine being the product of an intelligent system haven't historically been enough.
A handful of years ago I put together a computer fully loaded that gave me 1 teraflop of commuting power.
Today I can put together a computer the same size that will give me 32 to 50 teraflops of programmable computing power.
Many of the "AI" advances since 2007 are just running old 1970s-1990s AI algorithms on faster and faster and more parallel hardware. If you have to train a model for a few hundred trillion instructions, but your CPU only does 20 operations per second (and you have to share it with 1,000 other people), you can't iterate your science fast enough to make progress. Now we can iterate our science almost too quickly.
> how the brain "thinks" at a neural level,
Planes don't fly like birds. Birds don't fly like bees. True AI doesn't have to replicate mammalian (or avian or reptilian) neural topology.
Upvote for "Birds don't fly like bees", I like the freshness of not merely pointing out that "humans can do better", but that in effect there are several paths that avoid different constraints to get to the same point!
I do not think that we have the capacity to create a brain capable of being conscious with our current technology. Storing zeroes and ones deterministically on pieces of silicon with crammed together transistors and doing computations by what are basically logical gates is kind of limiting and inefficient. And lets say that the teraflops we're talking about are meaningful. How many teraflops do we need anyway? Shouldn't Google's data-centers suffice already for reaching the potential of a piece of gray matter that fits in under 60 cm of circumference?
I also agree that AI will never be "human" (i.e. it will be different), however without understanding how the human brain works, what chances do we have to create AI?
And we have yet to crack that nut. We have yet to understand even high-level stuff in detail, like how information is flowing from short-term memory to long-term and how we forget and why we do that (i.e. forgetfulness is surely an evolutionary trait). A brain is also fascinating in how it recovers from serious strokes by re-purposing brain structures. We have yet to produce software that is that sophisticated. And we don't even understand the brain from a biological perspective yet.
Surely huge progresses have been made, but on the other hand we may still be hundreds of years away and there's a very real possibility that we lack the intellectual capability, or maybe the resources to do it (we have a history of settling for lesser solutions if we stop seeing financial benefits, like with space exploration).
Turing-complete platforms are universal simulators. There's nothing they can't represent.
> like how information is flowing from short-term memory to long-term
Sure, we know that. The little seahorse helps out.
> re-purposing brain structures
rudimentary artificial neural nets do the same thing. they also self-specialize automatically with no innate programming (line detectors, edge detectors, eye detectors, cat detectors, all the way up—automatically).
> we may still be hundreds of years away
lol. nope. gotta think exponentially.
> lesser solutions if we stop seeing financial benefits, like with space exploration
can't do space exploration without the approval of a nation-state. can do AI tomfoolery in your own basement with nobody else finding out until it's too late.
> Storing zeroes and ones deterministically on pieces of silicon with crammed together transistors and doing computations by what are basically logical gates is kind of limiting and inefficient.
And yet it is less limiting and more efficient than pretty much all analog computing devices we have built. I don't think the hardware is the issue anymore, I suspect that with the right models and training we can have thinking machines.
Jeff Hawkins' team of researchers and the people behind NuPIC and Numenta.org, at least, given how it was explained to me, believe that the human brain does compute digitally (ie the analog values don't matter, the presence or absence of the signals do). Geoff Hinton also appears to believe that the biological neural signals are interpreted in a binary way.
I could have misinterpreted their work, though, as I'm far from an expert, but that's what it sounded like to me.
I think what the parent is trying to say is not that it's easy (it's not) but that there is nothing, in principle, to stop us from writing a program that acts like a brain.
Haven't people been saying this for decades? AI has a long history of impressive results, but somehow none of them have actually produced "thought".
Nobody even understands how the brain "thinks" at a neural level, let alone how to model that. All we can do at this point is try different models (which way or may not actually match reality) and hope we find one that works. But there's no evidence that we'll find a working model "real soon now". Impressive results that we can kinda-sorta imagine being the product of an intelligent system haven't historically been enough.