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That was really interesting. I'm interested why he's so pessimistic about the simulating a brain approach. Yes it's the boring and obvious approach but it also

That was really interesting. I'm interested why he's so pessimistic about the simulating a brain approach. Yes it's the boring and obvious approach but it also seems the most direct. Also found this quote interesting > Might have to make it illegal to evolve AI strains or an upper bound of computation per person and closely track all computational resources on earth.

tw1010 9y ago
I think the main problem with the brain simulation approach is that we don't yet have a really good model of how the brain actually works.
- ethbro 9y ago
  We know how components of the brain work. Is it inconceivable that we luck into the proper arrangements and interrelationships?
visarga 9y ago
The brain is slow and redundant. It has to be like that because it is not produced in a factory - it is created by self replication. Self replication imposes strict limits and requirements on the type of brain that can be created. AI neurons, on the other hand, are perfect - they never get old or tired and always remember. A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision). We can achieve superhuman results in vision with much less neurons, and faster. This is the kind of logic that makes brain emulation a far flung possibility compared to the current day deep neural nets. That and the fact that the brain simulation guys don't have anything to show for. There are no human-level tasks that could be replicated by this approach yet.
- CuriouslyC 9y ago
  The brain is slow in "cycles"/second, but the amount of computation done by each cycle isn't directly comparable to that done by a computer. Forgetting isn't a bug, it's a feature. Forgetting is basically like dimensionality reduction on input data - we extract the principle components/exemplars, remember a weighting, and trash the redundancy. Training a ML model is a lot faster on smaller data, and the same is true for us. Don't compare ANNs with the brain strictly on a time/time basis. Time isn't the only factor, power consumption and heat production are also factors, and if you include them the brain comes out way ahead. People with an engineering background almost universally underestimate how freaking awesome biology is. Our brains are self-constructing, self-replicating, self repairing (mostly) hyper-efficient pattern recognition systems. The more we learn about them the more awesome we realize they are. Don't be so arrogant as to assume a few hundred years of engineering will universally eclipse hundreds of millions of years of evolution.
  - mythrwy 9y ago
    Evolution creeps along. Engineering ability however appears to be growing exponentially. It took millions of years for the brain to reach the place it is now but it only took a few thousand years to get to the moon and create the Internet. I wouldn't count out the power of engineering because biology is complex. Particularly given ever more powerful tools of computation and communication that have only recently (historically speaking, not in lifespans of javascript frameworks) come on line.
    - CuriouslyC 9y ago
      Two points: - The first part of a sigmoidal curve looks exponential. - Evolution is massively parallel.
- eggie 9y ago
  > The brain is slow and redundant. It has to be like that because it is not produced in a factory - it is created by self replication. Self replication imposes strict limits and requirements on the type of brain that can be created. It is also limited by the efficiency that it must attain in order to operate under the energy conditions of our environment. The energy efficiency of biological systems might be a hint that we should more-directly employ them in the "artificial" minds we build. You're right that the human brain is limited because of its context, but we'll only get superhuman hard artificial intelligence when we're able to build/grow a big one in a vat. > A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision). Is it? There is probably a lot more going on in that 1/3 of our brain than mapping images to words.
- danmaz74 9y ago
  > A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision) Oh come on.
- 21 9y ago
  > A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision) I'd say ResNet-150 is far from solving human vision, in the edge cases. Can it distinguish between a baseball texture and an actual baseball? How about between an actual cat versus a toy cat? Sometimes you want the two to be in the same category, other times you don't. The human can put the same two images in the same or in different categories, based on the higher task at hand. Neural networks are far from that, because they don't (yet) have a model of the world.
- deleted 9y ago
  [deleted]
tim333 9y ago
The problem with the brain simulation approach seems not that it's boring and obvious but that it's hard to do. They struggle to simulate C. elegans which has 302 neurons in a fixed layout. Humans are harder. >[OpenWorm] project coordinator Stephen Larson estimates it as "only 20 to 30 percent of the way towards where we need to get. (wikipedia) That said, figuring what we can about how the human brain works and trying to make a computer equivalent seems quite promising. For example modern artificial vision seems to process data in a way similar to human vision even if it doesn't simulate human neurons.
- ghthor 9y ago
  Numenta is doing much better than the worm brain simulation.
  - yorwba 9y ago
    Why do you think so? The Wikipedia page on their method of hierarchical temporal memory[1] says that: The goal of current HTMs is to capture as much of the functions of neurons and the network (as they are currently understood) within the capability of typical computers and in areas that can be made readily useful such as image processing. For example, feedback from higher levels and motor control are not attempted because it is not yet understood how to incorporate them and binary instead of variable synapses are used because they were determined to be sufficient in the current HTM capabilities. It doesn't seem like they are even close to a simulation that could accurately model something like C. elegans. [1] https://en.wikipedia.org/wiki/Hierarchical_temporal_memory https://en.wikipedia.org/wiki/Hierarchical_temporal_memory
wyager 9y ago
Oh boy. Legislating government control of all computational resources is not a path we want to go down. Read Vernor Vinge's "Rainbows End" for some fun ideas on how this screws everyone over. Watch Cory Doctorow's talk about the war on general-purpose computing for some more immediate concerns. I wonder if nebulous fears about AI soon will be added to the ranks of famous justifications for horrendously overbearing laws, like stopping terrorism, the war on drugs, or thinking of the children.
- visarga 9y ago
  I think you might be right. AI fear will probably be made into a justification for government power grabs.
- acgourley 9y ago
  As bad as governmental overreach is, destroying ourselves with AI might be worse.
kowdermeister 9y ago
> I'm interested why he's so pessimistic about the simulating a brain approach. While theoretically possible, I think going with that approach would be admitting we don't understand the origin of general intelligence, so let's just copy the wetvare.
jandrewrogers 9y ago
The pessimism over simulating a human brain is two-fold. First, the human brain is built on a computational substrate that is completely and utterly unlike silicon. It is extremely inefficient to effect computation by simulating a computing model on silicon that is almost pathological for silicon to express. The abstract computational model of the human brain necessarily has an equivalent direct expression in computing hardware we actually have thanks to Turing equivalence. It just may look nothing like a human brain once you build it with algorithms optimized for silicon. Second, and related, the abstract mathematical nature of intelligence is well-understood and the human brain must be an expression of that. However, there is currently a huge gap between that abstract theory and reduction to practice i.e. our computer science for building intelligence from first principles is severely lacking. There are many things that are easy to express in mathematics that go for decades before some reduces it to practical computable algorithms and data structures. Given the fundamental limitations and inherent complexity of simulating (poorly) the human brain, many people feel that applying a similar amount of effort to this direct approach is much more likely to produce a viable result. And in any case, a simulated human brain would be completely eclipsed eventually by a more pure design by virtue of being several orders of magnitude more efficient computationally. Simulating a human brain is not a particularly productive detour on the long-term path given this.
- skummetmaelk 9y ago
  Silicon is actually closer to the brain than you might think. Neurons transfer charge by diffusion, so do transistors operating in sub-threshold. The problem is that we almost exclusively use transistors operating above threshold because it is required for digital logic. Analog CMOS circuits can approach the energy efficiency of real neurons.
  - hatsunearu 9y ago
    Uh oh, not this again. There is so much woo about using subthreshold FETs to simulate neurons when we don't even know how neurons work. I've seen the work of J. Hasler in school and she seemed to be fond about simulating a type of neuron (winner-take-all) that is hard to train with backpropagation (vanishing gradients just by inspection) and has limited grounds on physical simulation of neurons. Do you have any other resources about serious attempts at using subthreshold FETs to simulate neurons?
abecedarius 9y ago
For much more discussion, see http://www.fhi.ox.ac.uk/brain-emulation-roadmap-report.pdf http://www.fhi.ox.ac.uk/brain-emulation-roadmap-report.pdf My take is that it's a lot of R&D work, and it's not clear which approach will get to a human level first nor which is safer. The emulation approach seems lower variance to me than an intelligence-from-scratch approach, even though there's tons of variance there too. We're just looking at a very uncertain future.

tw1010 9y ago

I think the main problem with the brain simulation approach is that we don't yet have a really good model of how the brain actually works.

ethbro 9y ago
We know how components of the brain work. Is it inconceivable that we luck into the proper arrangements and interrelationships?

visarga 9y ago

The brain is slow and redundant. It has to be like that because it is not produced in a factory - it is created by self replication. Self replication imposes strict limits and requirements on the type of brain that can be created. AI neurons, on the other hand, are perfect - they never get old or tired and always remember. A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision). We can achieve superhuman results in vision with much less neurons, and faster. This is the kind of logic that makes brain emulation a far flung possibility compared to the current day deep neural nets. That and the fact that the brain simulation guys don't have anything to show for. There are no human-level tasks that could be replicated by this approach yet.

CuriouslyC 9y ago
The brain is slow in "cycles"/second, but the amount of computation done by each cycle isn't directly comparable to that done by a computer. Forgetting isn't a bug, it's a feature. Forgetting is basically like dimensionality reduction on input data - we extract the principle components/exemplars, remember a weighting, and trash the redundancy. Training a ML model is a lot faster on smaller data, and the same is true for us. Don't compare ANNs with the brain strictly on a time/time basis. Time isn't the only factor, power consumption and heat production are also factors, and if you include them the brain comes out way ahead. People with an engineering background almost universally underestimate how freaking awesome biology is. Our brains are self-constructing, self-replicating, self repairing (mostly) hyper-efficient pattern recognition systems. The more we learn about them the more awesome we realize they are. Don't be so arrogant as to assume a few hundred years of engineering will universally eclipse hundreds of millions of years of evolution.
- mythrwy 9y ago
  Evolution creeps along. Engineering ability however appears to be growing exponentially. It took millions of years for the brain to reach the place it is now but it only took a few thousand years to get to the moon and create the Internet. I wouldn't count out the power of engineering because biology is complex. Particularly given ever more powerful tools of computation and communication that have only recently (historically speaking, not in lifespans of javascript frameworks) come on line.
  - CuriouslyC 9y ago
    Two points: - The first part of a sigmoidal curve looks exponential. - Evolution is massively parallel.
eggie 9y ago
> The brain is slow and redundant. It has to be like that because it is not produced in a factory - it is created by self replication. Self replication imposes strict limits and requirements on the type of brain that can be created. It is also limited by the efficiency that it must attain in order to operate under the energy conditions of our environment. The energy efficiency of biological systems might be a hint that we should more-directly employ them in the "artificial" minds we build. You're right that the human brain is limited because of its context, but we'll only get superhuman hard artificial intelligence when we're able to build/grow a big one in a vat. > A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision). Is it? There is probably a lot more going on in that 1/3 of our brain than mapping images to words.
danmaz74 9y ago
> A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision) Oh come on.
21 9y ago
> A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision) I'd say ResNet-150 is far from solving human vision, in the edge cases. Can it distinguish between a baseball texture and an actual baseball? How about between an actual cat versus a toy cat? Sometimes you want the two to be in the same category, other times you don't. The human can put the same two images in the same or in different categories, based on the higher task at hand. Neural networks are far from that, because they don't (yet) have a model of the world.
deleted 9y ago
[deleted]

tim333 9y ago

The problem with the brain simulation approach seems not that it's boring and obvious but that it's hard to do. They struggle to simulate C. elegans which has 302 neurons in a fixed layout. Humans are harder. >[OpenWorm] project coordinator Stephen Larson estimates it as "only 20 to 30 percent of the way towards where we need to get. (wikipedia) That said, figuring what we can about how the human brain works and trying to make a computer equivalent seems quite promising. For example modern artificial vision seems to process data in a way similar to human vision even if it doesn't simulate human neurons.

ghthor 9y ago
Numenta is doing much better than the worm brain simulation.
- yorwba 9y ago
  Why do you think so? The Wikipedia page on their method of hierarchical temporal memory[1] says that: The goal of current HTMs is to capture as much of the functions of neurons and the network (as they are currently understood) within the capability of typical computers and in areas that can be made readily useful such as image processing. For example, feedback from higher levels and motor control are not attempted because it is not yet understood how to incorporate them and binary instead of variable synapses are used because they were determined to be sufficient in the current HTM capabilities. It doesn't seem like they are even close to a simulation that could accurately model something like C. elegans. [1] https://en.wikipedia.org/wiki/Hierarchical_temporal_memory https://en.wikipedia.org/wiki/Hierarchical_temporal_memory

wyager 9y ago

Oh boy. Legislating government control of all computational resources is not a path we want to go down. Read Vernor Vinge's "Rainbows End" for some fun ideas on how this screws everyone over. Watch Cory Doctorow's talk about the war on general-purpose computing for some more immediate concerns. I wonder if nebulous fears about AI soon will be added to the ranks of famous justifications for horrendously overbearing laws, like stopping terrorism, the war on drugs, or thinking of the children.

visarga 9y ago
I think you might be right. AI fear will probably be made into a justification for government power grabs.
acgourley 9y ago
As bad as governmental overreach is, destroying ourselves with AI might be worse.

kowdermeister 9y ago

> I'm interested why he's so pessimistic about the simulating a brain approach. While theoretically possible, I think going with that approach would be admitting we don't understand the origin of general intelligence, so let's just copy the wetvare.

jandrewrogers 9y ago

The pessimism over simulating a human brain is two-fold. First, the human brain is built on a computational substrate that is completely and utterly unlike silicon. It is extremely inefficient to effect computation by simulating a computing model on silicon that is almost pathological for silicon to express. The abstract computational model of the human brain necessarily has an equivalent direct expression in computing hardware we actually have thanks to Turing equivalence. It just may look nothing like a human brain once you build it with algorithms optimized for silicon. Second, and related, the abstract mathematical nature of intelligence is well-understood and the human brain must be an expression of that. However, there is currently a huge gap between that abstract theory and reduction to practice i.e. our computer science for building intelligence from first principles is severely lacking. There are many things that are easy to express in mathematics that go for decades before some reduces it to practical computable algorithms and data structures. Given the fundamental limitations and inherent complexity of simulating (poorly) the human brain, many people feel that applying a similar amount of effort to this direct approach is much more likely to produce a viable result. And in any case, a simulated human brain would be completely eclipsed eventually by a more pure design by virtue of being several orders of magnitude more efficient computationally. Simulating a human brain is not a particularly productive detour on the long-term path given this.

skummetmaelk 9y ago
Silicon is actually closer to the brain than you might think. Neurons transfer charge by diffusion, so do transistors operating in sub-threshold. The problem is that we almost exclusively use transistors operating above threshold because it is required for digital logic. Analog CMOS circuits can approach the energy efficiency of real neurons.
- hatsunearu 9y ago
  Uh oh, not this again. There is so much woo about using subthreshold FETs to simulate neurons when we don't even know how neurons work. I've seen the work of J. Hasler in school and she seemed to be fond about simulating a type of neuron (winner-take-all) that is hard to train with backpropagation (vanishing gradients just by inspection) and has limited grounds on physical simulation of neurons. Do you have any other resources about serious attempts at using subthreshold FETs to simulate neurons?

abecedarius 9y ago

For much more discussion, see http://www.fhi.ox.ac.uk/brain-emulation-roadmap-report.pdf http://www.fhi.ox.ac.uk/brain-emulation-roadmap-report.pdf My take is that it's a lot of R&D work, and it's not clear which approach will get to a human level first nor which is safer. The emulation approach seems lower variance to me than an intelligence-from-scratch approach, even though there's tons of variance there too. We're just looking at a very uncertain future.