Put another way there is nothing magical about a child learning about the world. A child's brain is just a large neural network being fed patterned data over the course of many years by a variety of extremely high resolution analog sensors. Eventually the child begins to respond to the patterns.
Second, the 3D topology of a neuron is IMO more complex than reducing it to an FP32 activation threshold (all IMO of course).
Finally, I have to admit as a former biologist, I'm intrigued by microtubule activity and it seems like Dileep George and even Geoffrey Hinton are heading towards smarter but fewer neurons as opposed to just increasing the neuron count. Not surprisingly, the deep learning digerati are resisting this notion mightily just like the SVM peeps harped on neural networks until they kicked them in the keester.
TLDR: It's still early, and I'm biased that there are some interesting twists and turns yet to unfold here.
If you can computationally define how different common neurotransmitters affect the function of neurons at a broad, high level, then you can create your "psychoactive drug" by just writing a routine that excessively applies the function that those neurotransmitters represent.
An artificial serotonin reuptake inhibitor would just allow the serotonin-like activity to more active in the model.
Seems a bit early to jump to the conclusion that we understand cognition. We don't. I agree that there is nothing exotic or metaphysical about brain meat, but really we're still feeling around in the dark with respect to how thinking occurs.
I'm confident that we'll get there eventually though.
mimicking the brain's power-consumption-to-compute-power ratio is difficult, if not impossible, with today's technology.
an aside: since reading an article about the potential role of quantum mechanics in photosynthesis, i've wondered, as a lay person, whether quantum mechanics play a role in human cognition.
[1] http://www.scottaaronson.com/democritus/lec10.5.html [2] http://www.csicop.org/sb/show/is_the_brain_a_quantum_device
Why not? Your brain isn't magic, just highly associative. 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.
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).
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.
I could have misinterpreted their work, though, as I'm far from an expert, but that's what it sounded like to me.
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.
No existing computer is a universal turing machine. The infinite ram requirement is pretty hard to implement in practice.
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.
[1]http://www.catb.org/jargon/html/R/Real-Soon-Now.html [2]http://c2.com/cgi/wiki?RealSoonNow
It's also not pure algorithm, it's a physical entity, tangible and with real world properties and interactions.
Who said (or proved) it's just an information processing device?
So are computers.
Whether that's the case in human cognition remains to be shown (else we're taking for granted what we're trying to prove).
That's not correct.
> The physical properties of the computer don't matter in this regard.
That's not correct.
> You could do exactly what a computer does with pen and paper (it would just take a much longer time).
Yes, and that time matters greatly as it's the difference between practical and hypothetical. Beyond that, programs that can evolve their hardware have been shown to come up with optimizations no human could have created and thus the physical properties of the computer do matter.
If we can bridge the simulated world to our world then we can interact with it.
Being in different worlds does not imply that it can never reach conciousness (among other properties). To imply that is invoking magic.
Anything from our world can be simulated.
To be literally "as real in its world (as we in ours)" several things need to happen:
1) its world should be an 1-1 simulated mapping of our world. Perhaps not to its whole extend (e.g. not the whole universe), but to ANY extend that affects the final result.
2) its world should have randomness equivalent to the quality of randomness (not sure if it's perfect) that our world has.
As for "Anything from our world can be simulated" -- that's a bold claim, provided that we haven't simulated ANYTHING at all yet, to the degree of interactions and complexity that exist in our world.
When we simulate the behavior of water in a fluids physics simulation, or the behavior of planets etc, it's amazing how much stuff we leave out. Our simulations are to a full-blown simulation what South Park cut-outs are to a photograph.
Besides, this notion reminds me of the naive 19th century ideas, that they could predict the course of the universe if only they had the details (motion, momentum, weight, etc) of all objects and the capacity to calculate their interactions. QM put a hole in that.
As for 2) likewise, randomness isn't a requirement, you're arbitrarily picking one quality and saying that quality has to be identical for it to be real. why? I don't believe that for a second.
I'm fully aware of the simplifications of simulation... being simplified compared to an external universe does not change the premise of it being real to its inhabitants. Quantum Mechanics does not say that the universe is not mechanistic, just that there is a random element (that in itself may ultimately be modelled).
It has, if it has to be "moisture" and also to be "just as real".
Else, you can define as "moisture" any parameter in the simulation (since it can be "whatever it likes").
E.g. the property of being "alive" in Conways Game of Life.
In what sense will that be a simulation of "moisture" and "just as real" inside the simulation as moisture is to us?
A child knows that if it says "Mama food," it is likely to get attention, and if it gets attention, it is likely to minimize its hunger. Right now, a neural network can be trained to know that "Mama" occurs often in human dialogue, what words occur around it, even its dictionary definition and images of mothers. But it's not making the deeper connection to a strategy that minimizes hunger.
When I think about this, I wonder if insights from the world of gaming "AI" would be useful in developing the training datasets for real AI. Because you can't be a mother to a billion virtual babies, but you might be able to program a set of heuristics to be a mother to a billion virtual babies. Then you have some system that trains on their life experiences...? All speculation, but very interesting stuff.
See any of the recent papers from Google DeepMind, such as [1] or their most recent work which is startlingly good [2]
[1] http://www.nature.com/nature/journal/v518/n7540/full/nature1... [2] http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-i...
The problem is simulations of the brain are not "machines", they are algorithms, e.g. they assume everything is happening at the information processing level.
To use your own example, we can design an algorithm to simulate making coffee. But the algorithm can never make coffee -- unless it's fitted and connected to a coffee making apparatus.
Or take something being "wet" for example. We can emulate the motions and powers in play in liquids, but not "wetness" in the sense of the physical property (moisture etc). If something depends on it, e.g. the emulation actually watering some actual flowers, then it will fail. An emulation can only water emulated flowers.
Simulations are executed on concrete machines that exist in the real world. Algorithms are abstract concepts.
> e.g. they assume everything is happening at the information processing level.
Everything does happen at the information processing level. Any kind of physical process can be seen as a type of information processing. Information processing is not an abstract concept like an algorithm, for it to occur requires the time-evolution of concrete physical processes.
> We can emulate the motions and powers in play in liquids, but not "wetness" in the sense of the physical property (moisture etc).
The physical property is experienced as sensory input. Machines can have sensory input.
> An emulation can only water emulated flowers.
You are asserting that virtual reality is different from reality, which is true. That's not the GP's question. The question is whether there is a fundamental difference between machines in the real world (with sensors and arms and so on) and the human body and brain.
This is pure philosophy, as no one yet knows the answers, but what if brain-like intelligence is an emergent property of non-deterministic processes? Wouldn't it then follow that a classical computer could not be able to compute the "think function" before the heat death of the universe?
personally my intuition says that strong AI cannot be encoded in silicon, or that it is a victim of the halting problem. I think we need a different substrate on which to model cognition. Or maybe not. Who knows?
That's an irrational and indefensible position.