5 ms·

This whole hoopla about AI using memristors seems misguided to me. Memristors are interesting because they can more efficiently do things that currently require

Fargren 16y ago
I believe the common arguments is that memristors are more similar two neurons in that they cna be used for processing as well as storage. Not that it's a good argument, the fact that we need more kinds of pieces was never an obstacle.
joe_the_user 16y ago
This whole hoopla about AI using memristors seems misguided to me. Memristors are interesting because they can more efficiently do things that currently require collections of transisters and capacitors, but they don't have any particular advantage in AI. Uh, the argument that is being made is that moving memory and processing power much closer together allows a much more direct simulation of brain-like functions. Do you any counter-argument to this? Yes, of course it's a "consequence of performance improvements", that's always the reason for advances due to new hardware. It seems a memristor could act directly as a cellular automaton or conventional AI neural network so these would not be the awkward simulations that they are today. It plausible that this could result in notable advances. If you don't agree, perhaps you could say why.
- diiq 16y ago
  (I'll assume that, here, performance means speed of memory access and calculation.)If all that memristors offer is performance, and you propose memristors will lead to AI, you are suggesting that what is currently preventing an artificial intelligence from succeeding is speed of calculation, and further that the particular kind of calculation needed is the same kind potentially improved by memristors.I can't completely demonstrate that to be untrue without referencing every attempt at AI ever made, because you're asking for a negative proof --- that NO current AI research fulfills those two requirements. I haven't read all the AI papers ever, let alone talked to researchers that haven't published. But I've read what I can, and I... I guess I just don't feel like the evidence suggests that current research is hindered solely by speed, NOR that "moving memory and processing power closer together" has much to do with any somewhat-successful attempts machine intelligence.
  - Benjo 16y ago
    So what DO you think is holding us back? My understanding is the memristor is a much closer model of a synapse. So if the problem isn't raw computing power and it's not the synapse model then what do you think it is? What's holding us back from simulating a brain? Do we just not know enough about the design of a brain? Or do we need to understand something about consciousness itself? For what it's worth, this project seems to think we are only 10-20 years away from simulating it with conventional computing: http://en.wikipedia.org/wiki/Blue_Brain_Project http://en.wikipedia.org/wiki/Blue_Brain_Project
    - Fargren 16y ago
      What's holding us back in our attempts to simulate a brain is that we don't know how the brain works. That's a much more important hurdle to overcome than performance, I believe.
      - Benjo 16y ago
        Do we understand the building blocks? What's the smallest system we can't model?
        possibilistic 16y ago
        Helium.
        gnosis 16y ago
        I'm not even sure that a single helium atom can be accurately modeled, if you take in to account all of the subatomic particles and processes involved (which are still in the process of being discovered and understood).
    - jerf 16y ago
      If we knew precisely what was holding us back, it wouldn't be holding us back. You're asking a question you can always just move the goalposts for. (I'm not saying you will, but you can.)
- kevinpet 16y ago
  There are two issues: 1. Will memristors give a performance improvement? Yes, they probably will. Will this be helpful to AI? Yes, it will, because at least some aspects of AI are limited by performance right now. 2. Are memristors particularly well suited to modeling the brain? Though there are small scale similarities, I think this is akin to saying a mechanical computer based on gears turning is uniquely suited to doing CAD for automobiles, or a wooden keyboard helps with furniture design. The brain consists of a huge number of slow processing elements. Electronic computers consist of orders of magnitude fewer processing elements running orders of magnitude faster. Even if we approach AI from neuron-level simulations, we're better off using the ~1000 floating point op per second per transistor to simulate a set of neurons serially than to waste 5 or 10 memristors per neuron and enormous interconnect bandwidth to try to build an electronic analogue of the brain. Edit: You quoted one thing I said, but you cut the crucial word "pure". If memristors are uniquely suited to AI, then this is not a "pure consequence of performance improvements". When I say that I mean that performance is fungible. Memristors or transistors doesn't matter for brains any more than it matters for high frequency trading or rendering graphics.
Benjo 16y ago
IanaEE, but I am a CE: The human brain has 10e15 synapses [1] and 2.5 petabytes of storage[2]. A modern quad core processor has about 7e8[3] transistors. Now the brains switches and storage probably can't be directly equated to a processor, but presumably it is within a few orders of magnitude. A conservate estimate would require about 10e6 times the density to get the same density as the human brain on a chip. 10e6 ~ 2^20, so that's about 20 years assuming Moore's Law holds. So in terms of raw performance, we have a ways to go. Memristors are about 10 times as dense as transistors currently [4]. They also offer non-volatile storage in addition to processing power and the possibility of fitting in a three dimensional structure. I won't guess what this imlies, but I assume it's going to get us much closer to a circuit similar to a human brain. Am I wrong? Probably somewhere. Does any of this matter? I have no idea. We won't know until we try. Personally, I'm VERY excited about the possibilities behind memristors, and wish I understood them better. I can't think of a development in the past 20 years that has more potential to advance computers. [1] http://news.ycombinator.com/item?id=1920485 http://news.ycombinator.com/item?id=1920485 and http://en.wikipedia.org/wiki/Human_brain http://en.wikipedia.org/wiki/Human_brain [2] http://www.scientificamerican.com/article.cfm?id=what-is-the-memory-capacity http://www.scientificamerican.com/article.cfm?id=what-is-the... [3] http://en.wikipedia.org/wiki/Nehalem_(microarchitecture) http://en.wikipedia.org/wiki/Nehalem_(microarchitecture) [4] http://www.physorg.com/news156526733.html http://www.physorg.com/news156526733.html
- Someone 16y ago
  Following your reasoning: on the one hand adjusting for volume easily gains three orders of magnitude. On the other hand, there is the issue of power. The human brain takes about the same amount of power as a top of the line quad core processor. For now, I do not think either of these matter, though. We simply do not know enough of how the human brain works to warrant attempting to build one. Physicists didn't start by building the LHC, either.
- gnosis 16y ago
  "Now the brains switches and storage probably can't be directly equated to a processor, but presumably it is within a few orders of magnitude." What makes you say that? Also, what makes you think the difference is one of quantity and not of quality?
ippisl 16y ago
Memristors(both in their neuromorphic forms and in their fpga forms) will offer many more people access to very high performance hardware, and will greatly decrease the time and cost of experimenting with AI algorithms. So while it isn't definite that they'll solve the AI problem , they'll probably do alot to advance the field.

Fargren 16y ago

I believe the common arguments is that memristors are more similar two neurons in that they cna be used for processing as well as storage. Not that it's a good argument, the fact that we need more kinds of pieces was never an obstacle.

joe_the_user 16y ago

This whole hoopla about AI using memristors seems misguided to me. Memristors are interesting because they can more efficiently do things that currently require collections of transisters and capacitors, but they don't have any particular advantage in AI. Uh, the argument that is being made is that moving memory and processing power much closer together allows a much more direct simulation of brain-like functions. Do you any counter-argument to this? Yes, of course it's a "consequence of performance improvements", that's always the reason for advances due to new hardware. It seems a memristor could act directly as a cellular automaton or conventional AI neural network so these would not be the awkward simulations that they are today. It plausible that this could result in notable advances. If you don't agree, perhaps you could say why.

diiq 16y ago
(I'll assume that, here, performance means speed of memory access and calculation.)If all that memristors offer is performance, and you propose memristors will lead to AI, you are suggesting that what is currently preventing an artificial intelligence from succeeding is speed of calculation, and further that the particular kind of calculation needed is the same kind potentially improved by memristors.I can't completely demonstrate that to be untrue without referencing every attempt at AI ever made, because you're asking for a negative proof --- that NO current AI research fulfills those two requirements. I haven't read all the AI papers ever, let alone talked to researchers that haven't published. But I've read what I can, and I... I guess I just don't feel like the evidence suggests that current research is hindered solely by speed, NOR that "moving memory and processing power closer together" has much to do with any somewhat-successful attempts machine intelligence.
- Benjo 16y ago
  So what DO you think is holding us back? My understanding is the memristor is a much closer model of a synapse. So if the problem isn't raw computing power and it's not the synapse model then what do you think it is? What's holding us back from simulating a brain? Do we just not know enough about the design of a brain? Or do we need to understand something about consciousness itself? For what it's worth, this project seems to think we are only 10-20 years away from simulating it with conventional computing: http://en.wikipedia.org/wiki/Blue_Brain_Project http://en.wikipedia.org/wiki/Blue_Brain_Project
  - Fargren 16y ago
    What's holding us back in our attempts to simulate a brain is that we don't know how the brain works. That's a much more important hurdle to overcome than performance, I believe.
    - Benjo 16y ago
      Do we understand the building blocks? What's the smallest system we can't model?
      - possibilistic 16y ago
        Helium.
        gnosis 16y ago
        I'm not even sure that a single helium atom can be accurately modeled, if you take in to account all of the subatomic particles and processes involved (which are still in the process of being discovered and understood).
  - jerf 16y ago
    If we knew precisely what was holding us back, it wouldn't be holding us back. You're asking a question you can always just move the goalposts for. (I'm not saying you will, but you can.)
kevinpet 16y ago
There are two issues: 1. Will memristors give a performance improvement? Yes, they probably will. Will this be helpful to AI? Yes, it will, because at least some aspects of AI are limited by performance right now. 2. Are memristors particularly well suited to modeling the brain? Though there are small scale similarities, I think this is akin to saying a mechanical computer based on gears turning is uniquely suited to doing CAD for automobiles, or a wooden keyboard helps with furniture design. The brain consists of a huge number of slow processing elements. Electronic computers consist of orders of magnitude fewer processing elements running orders of magnitude faster. Even if we approach AI from neuron-level simulations, we're better off using the ~1000 floating point op per second per transistor to simulate a set of neurons serially than to waste 5 or 10 memristors per neuron and enormous interconnect bandwidth to try to build an electronic analogue of the brain. Edit: You quoted one thing I said, but you cut the crucial word "pure". If memristors are uniquely suited to AI, then this is not a "pure consequence of performance improvements". When I say that I mean that performance is fungible. Memristors or transistors doesn't matter for brains any more than it matters for high frequency trading or rendering graphics.

Benjo 16y ago

IanaEE, but I am a CE: The human brain has 10e15 synapses [1] and 2.5 petabytes of storage[2]. A modern quad core processor has about 7e8[3] transistors. Now the brains switches and storage probably can't be directly equated to a processor, but presumably it is within a few orders of magnitude. A conservate estimate would require about 10e6 times the density to get the same density as the human brain on a chip. 10e6 ~ 2^20, so that's about 20 years assuming Moore's Law holds. So in terms of raw performance, we have a ways to go. Memristors are about 10 times as dense as transistors currently [4]. They also offer non-volatile storage in addition to processing power and the possibility of fitting in a three dimensional structure. I won't guess what this imlies, but I assume it's going to get us much closer to a circuit similar to a human brain. Am I wrong? Probably somewhere. Does any of this matter? I have no idea. We won't know until we try. Personally, I'm VERY excited about the possibilities behind memristors, and wish I understood them better. I can't think of a development in the past 20 years that has more potential to advance computers. [1] http://news.ycombinator.com/item?id=1920485 http://news.ycombinator.com/item?id=1920485 and http://en.wikipedia.org/wiki/Human_brain http://en.wikipedia.org/wiki/Human_brain [2] http://www.scientificamerican.com/article.cfm?id=what-is-the-memory-capacity http://www.scientificamerican.com/article.cfm?id=what-is-the... [3] http://en.wikipedia.org/wiki/Nehalem_(microarchitecture) http://en.wikipedia.org/wiki/Nehalem_(microarchitecture) [4] http://www.physorg.com/news156526733.html http://www.physorg.com/news156526733.html

Someone 16y ago
Following your reasoning: on the one hand adjusting for volume easily gains three orders of magnitude. On the other hand, there is the issue of power. The human brain takes about the same amount of power as a top of the line quad core processor. For now, I do not think either of these matter, though. We simply do not know enough of how the human brain works to warrant attempting to build one. Physicists didn't start by building the LHC, either.
gnosis 16y ago
"Now the brains switches and storage probably can't be directly equated to a processor, but presumably it is within a few orders of magnitude." What makes you say that? Also, what makes you think the difference is one of quantity and not of quality?

ippisl 16y ago

Memristors(both in their neuromorphic forms and in their fpga forms) will offer many more people access to very high performance hardware, and will greatly decrease the time and cost of experimenting with AI algorithms. So while it isn't definite that they'll solve the AI problem , they'll probably do alot to advance the field.