51–60 of 76 posts
Earlier quoted context omitted.
This quote also stumped me >“You can’t predict whether the stock market will go up or down because you can’t compute it,” If you had an accurate model of the agents, you could easily compute the stock market.
But as soon as you want to use that knowledge to actually make money, your agent is participating in the market, so it has to account for itself...
Of course the brain is computable. The brain is a computer.
[citation needed]
I too believe this. Here is my argument: 1) If we have to simulate it at a low level, the human brain is far too complex for any computer in any timeframe of our current lives to have enough power to simulate properly 2) Working backwards and simulating the high level processes (AI, etc) have been a dismal failure at actually replicating human thought processes, and will continue to be. While NN or the like can theor…
> I too believe this. You don't. The article argues that there is a theoretical barrier that prevents a brain emulation in principle , you argue that technology isn't ready yet and won't be in our lifetimes. Opponents of your viewpoint argue that you simply can't imagine the state of technology in 50 years.
> That’s because its most important features are the result of unpredictable, non-linear interactions amongst billions of cells, Nicolelis says.
If he thought it was theoretically impossible, then the "billions of cells" would be redundant. It would only take 1 un-computable cell. Without a longer interview, we can't be sure what exactly he means.
My interpretation was "unpredictable, non-linear" (i.e. not computable by a simple algorithm, would have to be very complex, because of non linear interactions between inputs) amongst "billions of cells" = an obscene amount of computational data. I don't think he means unpredictable to mean strictly uncomputable.
> Opponents of your viewpoint argue that you simply can't imagine the state of technology in 50 years.
Yes, but at the same time there are things we thought we would be able to do 50 years ago that there is no way we can do now. There are physical constraints to the universe, and we can't just assume "technology" will overcome all of them. Nobody can imagine the state of technology in 50 years accurately, but I am still willing (and have done) to take bets on this 50 years into the future.
>But Nicolelis is in a camp that thinks that human consciousness (and if you believe in it, the soul) simply can’t be replicated in silicon. That’s because its most important features are the result of unpredictable, non-linear interactions amongst billions of cells, Nicolelis says. This is a fringe opinion, and I really wish the title reflected that. Ignoring the absurd 'linear' part of the article, I don't believe…
According to Max Tegmark's calculations, the importance of quantum mechanical effects on brain processes is negligible: http://arxiv.org/abs/quant-ph/9907009 This paper posits the brain can be modeled as a classical (e.g. Newtonian, "billiard ball"-style) physical system
The conventional neural network model neglects the interior of the neuron. Gene regulatory networks for complex eukaryotes are on the order of neural networks in complexity and involve quantum-scale interactions, which opens the possibility of quantum effects being significant. Gene regulation within the neuron affects neural firing behavior and, more importantly, profoundly affects neural growth patterns and thus learning and longer term forms of cognition.
This also neglects the possibility (now considered probable) that more cell types than just neurons are involved in brain activity:
http://en.wikipedia.org/wiki/Gliotransmitter
In short: the brain is not a neural network. Rather, those mathematical connectionist models are just that: models of aspects of the brain. We do not yet know to what extent these other mechanisms play a role, and what their role is. Given their nature it seems in both cases that their role might be more long-term, affecting long duration learning, planning, etc.
It really seems to me as if the most ardent and enthusiastic adherents of the Kurzweilian vision are computer scientists who don't really respect the domain of biology and like to hand-wave away its complexity as "background noise." You can't do that. I say this as a lifelong computer programmer who has studied biology. Studying biology really blew away any notions I had of simple, classical computer programs becoming movie-style AI.
The author is not making an anti-scientific "magic" argument. He is simply pointing out that biological systems are analog, embodied, electrochemical (and thus physical and possibly quantum), nonlinear complex systems, and he is being skeptical about the idea that such a system is going to yield readily to digital computer simulation. I agree with his skepticism.
Prediction: brain simulations will simulate superficial brain behavior but they will not become sentient. More specific prediction: they will get stuck in closed cycle loops. They will not exhibit the higher order motivation, creativity, or learning behavior seen in brains, which is probably because these behaviors emerge from all the real embodied biophysical stuff the CS people are ignoring.
Earlier quoted context omitted.
According to Max Tegmark's calculations, the importance of quantum mechanical effects on brain processes is negligible: http://arxiv.org/abs/quant-ph/9907009 This paper posits the brain can be modeled as a classical (e.g. Newtonian, "billiard ball"-style) physical system
This is based on the idea that the sum total of what the brain does can be explained by and represented by neural network type models. The conventional neural network model neglects the interior of the neuron. Gene regulatory networks for complex eukaryotes are on the order of neural networks in complexity and involve quantum-scale interactions, which opens the possibility of quantum effects being significant. Gene r…
>But Nicolelis is in a camp that thinks that human consciousness (and if you believe in it, the soul) simply can’t be replicated in silicon. That’s because its most important features are the result of unpredictable, non-linear interactions amongst billions of cells, Nicolelis says. This is a fringe opinion, and I really wish the title reflected that. Ignoring the absurd 'linear' part of the article, I don't believe…
I didn't realize anyone had worked out the level at which quantum mechanics decoheres to Newtonian mechanics.
Not with our current technology, I agree But saying the problem can't be solved ever seems dead wrong to me. We humans excel at understanding and replicating what nature did - then at improving it. Once we clearly understand how memories are stored, if they can be read and written that'll be half of the problem : accessing the data. If the theory about memory being encoded in the microtubules is right, imagine some n…
" We humans excel at understanding and replicating what nature did - then at improving it. " Do we?
We have food optimized for tastiness. (I must say I'm waiting for food optimized for health benefits, but still we did improve it)
So yes we do.
Earlier quoted context omitted.
This is based on the idea that the sum total of what the brain does can be explained by and represented by neural network type models. The conventional neural network model neglects the interior of the neuron. Gene regulatory networks for complex eukaryotes are on the order of neural networks in complexity and involve quantum-scale interactions, which opens the possibility of quantum effects being significant. Gene r…
This paper specifically addresses microtubules and is definitely not neglecting the interior of neurons. I suggest you read the paper instead of just the abstract before you give feedback.
Here's a pretty decent article:
http://www.nature.com/news/2011/110615/pdf/474272a.pdf
My point is that a neuron is not an equation in a connection model. It's a cell. You can't hand-wave away its identity as such or all the things that happen in cells.
Even if nothing quantum is involved (and I didn't say it was... we don't know), including significant aspects of intra-cellular activity and including other cell types (glia, etc.) and other types of interactions adds exponentially to the classical computation requirements.
I didn't mean that I dismissed the idea of classical computers simulating the brain or the implications. I just meant that I'm skeptical. Even if it is possible I'm very skeptical of it happening soon due to the absolutely insane computational requirements. My intuition is that it would require a leap on the order of vacuum tube ENIAC -> Intel Core i7. Meanwhile consumer demand for faster and faster chips is giving way to demand for slower but more energy efficient chips for mobile devices, which is subtracting from the economic incentive to continue Moore's Law. (AMD just bowed out of the x86 race for instance, leaving us with an Intel monopoly at the high end. Monopolies get lazy and stagnate.)
Personally I'm much more optimistic about "wet" transhumanism-- life extension, augmentation, brain hacking, etc., than I am about "real" AI in the near term or mind uploading. I also see computers stagnating a little for a while the way aerospace did from the Apollo era until about now... entirely for economic rather than physical reasons.
>But Nicolelis is in a camp that thinks that human consciousness (and if you believe in it, the soul) simply can’t be replicated in silicon. That’s because its most important features are the result of unpredictable, non-linear interactions amongst billions of cells, Nicolelis says. This is a fringe opinion, and I really wish the title reflected that. Ignoring the absurd 'linear' part of the article, I don't believe…
> the brain operates at a much higher, more macro level than quantum mechanics I didn't realize anyone had worked out the level at which quantum mechanics decoheres to Newtonian mechanics.
Well, yes, of course -- the Heisenberg Uncertainty principle is the border between them:
http://en.wikipedia.org/wiki/Uncertainty_principle
Equation: 𝝈x 𝝈p >= ℏ/2
I emphasize the "border" isn't a like a solid barrier, it's more a statement about probability.
Also, everyone should realize that the relationship between human brain function and QM is very speculative. It might be true, it might just be someone's pet idea. There's no real evidence for or against a role for QM in brain function, and a number of persuasive suggestions that it's not involved.
Not with our current technology, I agree But saying the problem can't be solved ever seems dead wrong to me. We humans excel at understanding and replicating what nature did - then at improving it. Once we clearly understand how memories are stored, if they can be read and written that'll be half of the problem : accessing the data. If the theory about memory being encoded in the microtubules is right, imagine some n…
Computability in this case is a theoretical term and doesn't really relate to levels of technology. Whether or not all physical processes are computable in the sense that they can be simulated by a turing machine is an open question, although my impression is that most folk who care to express an opinion think that they can. There's a little bit more on the Wikipedia article on the Church-Turing-Deutsch principle. ht…
Also, it would help creating silicon equivalent. Imagine we have something just as small (or smaller) than a biological neuron, which can interface and work in the very same way.
Computability or not, if it works in exactly the same way, I'd guess it could work the same on a higher level too - like, in a brain, especially if we have figured how to extract the stored information and if we can feed it back. (unless there are emergent properties we missed in the first place, but then if they can be identified, maybe they can be replicated too?)
That would allow an iterative trial/error (ex: if it doesn't work - why?) which might not resolve the computability question, but bring even more interesting issues about why it might not possible - something we can learn only from an experimental approach.