Answers.Body: <p>I am currently reading <em>Superintelligence: Paths, Dangers, Strategies</em> by Nick Bostrom. When he discusses whole brain emulation, although computing power (storage, bandwidth, CPU, body simulation, &amp; environment simulation) is one of the three general key things we are lacking toward its success, he also seems to agree that computing power is the most feasible and attainable of the three general issues we have for attaining it as of now. However he also goes on to to say </p>
  
  <blockquote>
    <p>Just how much technology is required for whole brain emulation
    depends on the level of abstraction at which the brain is simulated.<sup><a href="https://books.google.co.uk/books?id=1mMJBAAAQBAJ&amp;pg=PT41&amp;lpg=PT41&amp;dq=%22Just%20how%20much%20technology%20is%20required%20for%20whole%20brain%20emulation%20depends%20on%20the%20level%20of%22&amp;source=bl&amp;ots=Pmw4PUDele&amp;sig=PfmDRgcJuKBf3TwyCoVtCmp9XmE&amp;hl=en&amp;sa=X&amp;ved=0ahUKEwj4gZy6tbnPAhVRF8AKHUoQDD8Q6AEIHjAA#v=onepage&amp;q=%22Just%20how%20much%20technology%20is%20required%20for%20whole%20brain%20emulation%20depends%20on%20the%20level%20of%22&amp;f=false" rel="nofollow">ref</a></sup></p>
  </blockquote>
  
  <p>Which is an interesting thought, but a whole different discussion.</p>
  
  <p>Anyways, so I think you are correct in thinking that we aren't far from having the computing power and maybe you are on to something, but rather are biggest hurdles are the other two key prerequisites that we need to attain before we can even begin trying, which are scanning, and translation. </p>
  
  <p>Of the three, it would seem translation is the one we need to advance in the most, as of now. A modest prediction of attaining whole brain emulation is at least 15 years or mid century. Theres much more information in this book of all of the different paths that can be taken to achieve super intelligence, and it is well researched, I highly recommend it if you haven't read it already.</p>
  
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Answers.UserDisplayName: Raevan
Answers.Body: <p>I see two main issues with this suggestion.</p>
  
  <p>One: digital circuits take up a lot less space, and they're easier to design, so you can put together a bigger system this way. (not to mention connecting separate chips within a system) This is mainly because in digital circuits your tolerances can be a lot loose.</p>
  
  <p>The bigger one is: we still don't know how neurons work. Artificial neural networks somewhat resemble the natural one, but they behave differently. There are various ion channels, there are electric signals, and with these neurons stimulate each other, and if one's threshold is reached, it fires a spike. When it's reached again soon, you can see a burst in the signal. As far as I know, researchers don't yet know what function you need to implement to simulate it. The closest ANN is the spiking neural network, but it's not very useful in practice.</p>
  
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Answers.UserDisplayName: Nyos
Answers.Body: <p>If the universe is discrete, then analog phenomena (fluidity, curvature) are built on primitively discrete phenomena (bits and pieces).</p>
  
  <p>If the universe is continuous, then discrete phenomena (bits and pieces) are built on primitively continuous phenomena (fluidity, curvature).</p>
  
  <p>If the universe is discrete, the speed of seemingly analog phenomena will be bounded by the number of discrete phenomena that can occur in time and space.</p>
  
  <p>If the universe is continuous, then time, space or matter <em>may</em> be infinitely divisible, which <em>may</em> allow for the execution of some phenomena faster than those phenomena <em>appear</em> to execute in natural environments (like protein folding or electric circuits) - so called "super Turing" computation.</p>
  
  <p>The continuous universe idea begs the question, though: From whence came all this discreteness? A discrete universe can allow for apparent continuous behavior via approximation and randomness (or pseudorandomness), whereas a universe that is infinitely divisible affords no obvious definition of where things should start and end. This is one of the reasons many thinkers eschew considering infinities - they may be illusory.</p>
  
  <p>So, can analog "circuits" execute faster than digital? As of right now, we know of some <em>seemingly</em> analog phenomena that <em>appear</em> execute faster than some digital phenomena (like electron spin vs a silicon logic gate). Whether analog phenomena are <em>intrinsically</em> more efficient than digital depends on the actual nature of the universe, which we have not yet determined.</p>
  
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Answers.UserDisplayName: Doxosophoi
Answers.Body: <p>I'm not sure about "emulating the brain" per-se, but in a more general sense there has been some thought given to using analog computing for AI/ML.  It seems clear that analog computers do have certain advantages over digital computers.  For one, they can (depending on the application) be faster, albeit at the cost of some loss of precision.  But that's OK, because I don't think anybody believes the human brain is calculating floating point math using digital computing techniques either.  The human brain appears, at least superficially, to be largely probabilistic and able to tolerate some "slop" numerically.  </p>
  
  <p>The downside to analog computers, as I understand it, is that they're not as flexible... you basically hardwire a circuit to do one specific "thing" and that's really all it can do.  To change the "programming" you have to literally solder in a new component! Or, I suppose, adjust a potentiometer or adjustable capacitor, etc.  Anyway, the point is that digital computers are supremely flexible, which is one big reason they came to dominate the world.  But I can see where there could be room for going analog for discrete functions that make up some or all of an intelligent system.</p>
  
  <p>As for research in the area, you might look into whatever DARPA was / is doing. There was <a href="http://www.wired.com/2012/08/upside/" rel="nofollow">an article in Wired</a> a while back, talking about some DARPA initiatives related to analog computing.  </p>
  
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Answers.UserDisplayName: mindcrime
Body: <p>Considering the answers of <a href="https://ai.stackexchange.com/questions/1314/how-powerful-a-computer-is-required-to-simulate-the-human-brain">this</a> question, emulating a human brain with the current computing capacity is currently impossible, but we aren't very far from it.</p>
  
  <p>Note, 1 or 2 decades ago, similar calculations had similar results.</p>
  
  <p>The clock frequency of the modern CPUs seem to be stopped, currently the miniaturization (-> mobile use), the RAM/cache improvement and the multi-core paralellization are the main lines of the development.</p>
  
  <p>Ok, but what is the case with the analogous chips? In case of a NN, it is not a very big problem, if it is not very accurate, the NN would adapt to the minor manufacturing differences in its learning phase. And a single analogous wire can substitute a complex integer multiplication-division unit, while the whole surface of the analogous printed circuit could work parallel.</p>
  
  <p>According to <a href="https://engineering.stackexchange.com/questions/3993/do-analog-fpgas-exist">this</a> post, "software rewirable" analogous circuits, essentially "analogous FPGAs" already exist. Although the capacity of the FPGAs is highly below the capacity of the <a href="https://en.wikipedia.org/wiki/Application-specific_integrated_circuit" rel="nofollow noreferrer">ASIC</a>s with the same size, maybe analogous chips for neural networks could also exist.</p>
  
  <p>I suspect, if it is correct, maybe even the real human brain model wouldn't be too far. It would still require a massively parallel system of costly analogous NN chips, but it seems to me not impossible.</p>
  
  <p>Could this idea work? Maybe there is even active research/development into this direction?</p>
  
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Comments.Text: I appreciate that you found my answer useful, but you might consider un-accepting it at least briefly... It may help encourage others to answer and add other useful info.
Comments.UserDisplayName: mindcrime
Tags: <neural-networks><applications>
Title: Emulating human brain - with analogous NN chips
UserDisplayName: peterh
ThreadText: # Emulating human brain - with analogous NN chips
  
  (Asked by: username_0 on 2016-09-07)
  
  Considering the answers of [this](https://ai.stackexchange.com/questions/1314/how-powerful-a-computer-is-required-to-simulate-the-human-brain) question, emulating a human brain with the current computing capacity is currently impossible, but we aren't very far from it.
  
  Note, 1 or 2 decades ago, similar calculations had similar results.
  
  The clock frequency of the modern CPUs seem to be stopped, currently the miniaturization (-> mobile use), the RAM/cache improvement and the multi-core paralellization are the main lines of the development.
  
  Ok, but what is the case with the analogous chips? In case of a NN, it is not a very big problem, if it is not very accurate, the NN would adapt to the minor manufacturing differences in its learning phase. And a single analogous wire can substitute a complex integer multiplication-division unit, while the whole surface of the analogous printed circuit could work parallel.
  
  According to [this](https://engineering.stackexchange.com/questions/3993/do-analog-fpgas-exist) post, "software rewirable" analogous circuits, essentially "analogous FPGAs" already exist. Although the capacity of the FPGAs is highly below the capacity of the [ASIC](https://en.wikipedia.org/wiki/Application-specific_integrated_circuit)s with the same size, maybe analogous chips for neural networks could also exist.
  
  I suspect, if it is correct, maybe even the real human brain model wouldn't be too far. It would still require a massively parallel system of costly analogous NN chips, but it seems to me not impossible.
  
  Could this idea work? Maybe there is even active research/development into this direction?
  
  **Tags:** neural-networks, applications
  > **Comments:**
  > **username_1**: I appreciate that you found my answer useful, but you might consider un-accepting it at least briefly... It may help encourage others to answer and add other useful info.  
  
  ---
  
  ## 4 answers
  
  ### Answer by username_1 on 2016-09-07. Score: 4 | Accepted answer
  
  I'm not sure about "emulating the brain" per-se, but in a more general sense there has been some thought given to using analog computing for AI/ML. It seems clear that analog computers do have certain advantages over digital computers. For one, they can (depending on the application) be faster, albeit at the cost of some loss of precision. But that's OK, because I don't think anybody believes the human brain is calculating floating point math using digital computing techniques either. The human brain appears, at least superficially, to be largely probabilistic and able to tolerate some "slop" numerically.
  
  The downside to analog computers, as I understand it, is that they're not as flexible... you basically hardwire a circuit to do one specific "thing" and that's really all it can do. To change the "programming" you have to literally solder in a new component! Or, I suppose, adjust a potentiometer or adjustable capacitor, etc. Anyway, the point is that digital computers are supremely flexible, which is one big reason they came to dominate the world. But I can see where there could be room for going analog for discrete functions that make up some or all of an intelligent system.
  
  As for research in the area, you might look into whatever DARPA was / is doing. There was [an article in Wired](http://www.wired.com/2012/08/upside/) a while back, talking about some DARPA initiatives related to analog computing.
  
  
  ---
  
  ### Answer by username_2 on 2016-09-25. Score: 3
  
  I am currently reading *Superintelligence: Paths, Dangers, Strategies* by Nick Bostrom. When he discusses whole brain emulation, although computing power (storage, bandwidth, CPU, body simulation, & environment simulation) is one of the three general key things we are lacking toward its success, he also seems to agree that computing power is the most feasible and attainable of the three general issues we have for attaining it as of now. However he also goes on to to say
  
  > Just how much technology is required for whole brain emulation
  > depends on the level of abstraction at which the brain is simulated.[ref](https://books.google.co.uk/books?id=1mMJBAAAQBAJ&pg=PT41&lpg=PT41&dq=%22Just%20how%20much%20technology%20is%20required%20for%20whole%20brain%20emulation%20depends%20on%20the%20level%20of%22&source=bl&ots=Pmw4PUDele&sig=PfmDRgcJuKBf3TwyCoVtCmp9XmE&hl=en&sa=X&ved=0ahUKEwj4gZy6tbnPAhVRF8AKHUoQDD8Q6AEIHjAA#v=onepage&q=%22Just%20how%20much%20technology%20is%20required%20for%20whole%20brain%20emulation%20depends%20on%20the%20level%20of%22&f=false)
  
  Which is an interesting thought, but a whole different discussion.
  
  Anyways, so I think you are correct in thinking that we aren't far from having the computing power and maybe you are on to something, but rather are biggest hurdles are the other two key prerequisites that we need to attain before we can even begin trying, which are scanning, and translation.
  
  Of the three, it would seem translation is the one we need to advance in the most, as of now. A modest prediction of attaining whole brain emulation is at least 15 years or mid century. Theres much more information in this book of all of the different paths that can be taken to achieve super intelligence, and it is well researched, I highly recommend it if you haven't read it already.
  
  
  ---
  
  ### Answer by username_3 on 2019-02-19. Score: 3
  
  I see two main issues with this suggestion.
  
  One: digital circuits take up a lot less space, and they're easier to design, so you can put together a bigger system this way. (not to mention connecting separate chips within a system) This is mainly because in digital circuits your tolerances can be a lot loose.
  
  The bigger one is: we still don't know how neurons work. Artificial neural networks somewhat resemble the natural one, but they behave differently. There are various ion channels, there are electric signals, and with these neurons stimulate each other, and if one's threshold is reached, it fires a spike. When it's reached again soon, you can see a burst in the signal. As far as I know, researchers don't yet know what function you need to implement to simulate it. The closest ANN is the spiking neural network, but it's not very useful in practice.
  
  
  ---
  
  ### Answer by username_4 on 2016-09-30. Score: 2
  
  If the universe is discrete, then analog phenomena (fluidity, curvature) are built on primitively discrete phenomena (bits and pieces).
  
  If the universe is continuous, then discrete phenomena (bits and pieces) are built on primitively continuous phenomena (fluidity, curvature).
  
  If the universe is discrete, the speed of seemingly analog phenomena will be bounded by the number of discrete phenomena that can occur in time and space.
  
  If the universe is continuous, then time, space or matter *may* be infinitely divisible, which *may* allow for the execution of some phenomena faster than those phenomena *appear* to execute in natural environments (like protein folding or electric circuits) - so called "super Turing" computation.
  
  The continuous universe idea begs the question, though: From whence came all this discreteness? A discrete universe can allow for apparent continuous behavior via approximation and randomness (or pseudorandomness), whereas a universe that is infinitely divisible affords no obvious definition of where things should start and end. This is one of the reasons many thinkers eschew considering infinities - they may be illusory.
  
  So, can analog "circuits" execute faster than digital? As of right now, we know of some *seemingly* analog phenomena that *appear* execute faster than some digital phenomena (like electron spin vs a silicon logic gate). Whether analog phenomena are *intrinsically* more efficient than digital depends on the actual nature of the universe, which we have not yet determined.
  
  
  ---
  
  