8 ms·

The brain exceeds the most powerful computers in efficiency

71 points by yters 8y ago

bloak 8y ago
The headline makes an amazingly weak claim. Since people still seem to be discussing what features of nerve impulses are significant (their frequency? precise relative timing?) it would appear that a reasonable estimate of the information processing capacity of a single neuron is currently lacking, let alone an estimate for an entire brain taking account of redundancy. Does the comparison even make sense?
yetihehe 8y ago
It's like saying "A motorboat exceeds the most powerful cars in swimming". Yep, there are amphibia, but most motorboats are still better at swimming. Now, try to calculate 325*528. Did you made it faster than a millisecond? If not, your pocket calculator is more efficient than your brain.
- slx26 8y ago
  Yes. While I agree with the idea that brains are extremely efficient, especially at certain tasks like image recognition or any kind of pattern identification, I also thought about what you said, and I was like: when doing mathematical operations, I'd be more efficient riding a bicycle to charge a battery that powers a computer which then does the operations, than doing them by myself with my brain. That's... really weird. And there are so many tasks at which computers seem way more efficient than I could possibly be. EDIT: I guess someone downvoted you for apparently conflating speed and efficiency, but I still think the point stands.
  - trashtester 8y ago
    It doesn't matter if you look at speed (seconds/operation) of efficiency (joules/operation). For most mathematical operations, a computer cpu will outperform a human brain by many orders of magnitudes. This is both true and a somewhat irrelevant. It is a bit like saying that a cpu can run recursive, non-parallelizable algorithms faster and more efficiently than a GPU, even if the GPU has more flops. The GPU optimized to run a few types of computation very quickly, and is very slow on other tasks. This applies to the human brain as well, and to a much greater degree.
  - raxxorrax 8y ago
    I have implemented a stereoscopic vision once (1 angled camera and projector, not 2 cameras, but details...). It worked and the quality of the results could reasonably be scaled with additional computing power (and general hardware). While I wouldn't claim that the setup was very sophisticated, it worked reliable at least. But it doesn't even come close to the little nub in the back of your skull. Matching minuscule differences and quantifying these in images probably uses a lot of processing power, indifferent of the algorithms involved. It doesn't only eat you CPU, it would do the same with your memory. So yes, the brain is pretty good with images. But I expect it to be good at other tasks too that doesn't involve the visual cortex. You might not remember what you have eaten today morning, but there must be at least some form of reliable memory in your brain. And you do complex, high-speed mathematical calculations that include basic algebraic calculus. Just maybe not those you want.
0xBA5ED 8y ago
The brain is specialized hardware. I wouldn't expect a general purpose computer to be as efficient at performing the same tasks.
- willbw 8y ago
  It's actually not hardware at all. I say this not to be annoying but to make the point that perhaps we shouldn't be computer-morphizing the human brain. (I know that's not a word but was the best I could do!)
  - 0xBA5ED 8y ago
    I agree with not "computer-morphizing" the brain, but I think hardware still applies well enough. Is there a specific reason you think it doesn't apply out of curiosity? I mean, are you just trying to avoid the "duality of hardware and software"?
  - jcims 8y ago
    technomorphizing maybe? it's definitely something we do.
  - jacquesm 8y ago
    It is a substrate. Not quite the same as hardware but close enough for government work.
- Symmetry 8y ago
  The difference between the deterministic substrate in an ASIC and non-deterministic substrate in a brain means we should resist simple comparisons even with any sort of computer hardware, specialized or not. Synapses sit, from an electronics perspective, in a weird in-between space between linear analog computation and digital computation. There are lab prototype computational devices that might make good comparisons but it's just too different from, say, the sort of specialized decoders you'd find on your phone SoC for that to be a good analogy.
roenxi 8y ago
The core of this essay is indisputable that the comparison is Apples and Oranges. A computer puts in more effort and gets a better result. However when choosing between more efficient or better results, I'd prefer better results. The human brain's bias towards efficiency is an evolutionary handicap on intelligence. I can provide my body with as much energy as it wants to consume - I'd rather be able to burn energy learning things than have to put up with a brain that keeps trying to take shortcuts at every turn to conserve energy, and I would love to be able to think deterministicly! It is a stretch to say humans are out thinking the machine with that frame, machine intelligence might simply have firmer foundations. I do have challenges to the method of calculating how much power is put into the human training process. 10^8 might be too many orders of magnitude to breach, but humans: 1) Use data picked up visually to provide basic concepts used in playing go (like "connected", "lines", "territory", "me", "opponent"). Many of these concepts are potentially developed from visual data, and humans process a lot more than 50 bits of visual data in a second. This observation is very hard to quantity respecting how much visual data can be employed to learn about Go - how much of an advantage does the concept of territory give humans in terms of learning? It is probably huge. EDIT Thinking about it a bit more, it is very plausible given AlphaGo's endgame play that it never developed a concept of 'territory' as we would understand it. Given that humans judge the state of the game by estimating territory for both players then assuming the winner is the one with more territory, that is a huge deal. It might explain why the learning seems so inefficient vs a human. A human would never make AlphaGo's endgame moves because they are bad by the territory heuristic. 2) Learn Go in a very communal environment. Game records date back a few centuries and all the patterns that are detected get passed on through the generations of professional players. The practical amount of cached processing power available to humans is higher than first inspection might suggest. Humans don't learn mainly through self play, it is closer to supervised learning. A 1900s player would simply lose to modern technique because of this accretion. 3) High level Go players are going to be strongly biased to people who 'got lucky' in how their brains map concepts. An average human probably isn't as good at learning Go as a peak professional.
- gpderetta 8y ago
  Efficiency, or better, the ability to dissipate waste heat, is a constraint on computational power though.
entity345 8y ago
"CPU cycles" is not a very good metric, in my view. However, the brain is indeed much more efficient when comparing energy consumption considering that it uses only about 20W or power.
- trashtester 8y ago
  Actually, training AZ (5000 TPUS for 8 hours) requires 1.4e10 joules, assuming that each TPU needs 100watts. For comparison, the human brain operating training 4 hours per day every day for 14 years also uses 1.4e10 joules. So the energy efficiency is quite similar.
  - entity345 8y ago
    That's comparing apples and oranges. The brain works and learns 24/7. It needs 20W to function.
    - trashtester 8y ago
      So how do you see that changing my point, that the brain seems to need about the same amount of energy as AZ does?
      - entity345 8y ago
        The point is that it obviously does not.
        trashtester 8y ago
        I'm not sure what your claim is. Have you switched to the view that AZ is more efficient (by a factor of 6, if you assume 24/7 consumption), or do you still think that the brain is more efficient at learning GO? Edit: In case this is the cause for your objection: In the calculation for the energy needed for the human brain to master go (where I ended up with 14GJ=1.4*10^6 J), I only counted the consumption during the average 4 hours per day that one can expect someone to train. If you count all 24 hours, the 14GJ budget would be spent in 2-3 years. (Obviously, nobody can master GO in 2-3 years, regardless of effort.)
deleted 8y ago
[deleted]
- deleted 8y ago
  [deleted]
finnjohnsen2 8y ago
TLDR: "it is comparing apples and oranges"
- mattnewport 8y ago
  Worse than that, it's comparing apples and the color orange.
ben_w 8y ago
My disagreement is something this article half-acknowledges: > The brain has the potential for a petaflop per second of processing power. However, the measured performance of conscious processing is only about 50 bits per second. Why assume that either training or playing Go involves only conscious decision making? Anything that we call “common sense”, “gut feeling”, “aesthetically pleasing” etc. is something we can’t (or cannot easily) generate a conscious rationale for. Of course, that’s doesn’t mean humans don’t beat computers at all; last I checked, humans can make acceptable quality inferences from far fewer examples than an A.I.
- gasg0d 8y ago
  I once played in two poker tournaments on back to back weekends. Tournament 1 on adderall. Tournament 2 without adderall. Tournament 1 my consciousness and subconscious seemed to be working in tandom. I knew what action to take, and why I was taking that action. Tournament 2 my subconscious seemed to play a bigger role being off the adderall. I was making the right decisions for the most part same as the week before while on adderall, but I was not consciously completely sure of my reasoning for most decisions until several seconds, minutes, or even hours after I had reflected on my decision making. A little food for thought
  - kowdermeister 8y ago
    So which version worked for you better financially?
  - skohan 8y ago
    I wonder if that's part of what's going on with autism-spectrum-disorders: i.e. an autistic mind doesn't set the threshold of what's worthy of conscious thought at as high a level as neurotypical people, so they get more caught-up in the low-level details.
- trashtester 8y ago
  Exactly this. Conscious information processing constitutes only a tiny part of the processing within the brain. Real computational power of the brain is in the order of petaflops to exaflops 1). On the other hand, AZ used a lot more than 1E18 calculations of training to beat human level play, so that part is also wrong. In fact, the total processing power of the 5000 TPU's used are in the order of 1E17 operations per second. Still, since 1E17 flops is comparable (within 1-2 orders of magnitude) to the human brain's raw capacity, and AZ could reach superhuman level in GO in less than 10 hours, that means that AZ can be seen as more efficient than human brains at GO (in terms of learning rate/calculation, not necessarily per watt) This should also not be surprising. After all, GO (like chess) is a very abstract game, far removed from the tasks that the biological brain has evolved to do well. For tasks that the brain does well, such as pattern recognition in the environment that humans live, the human brain is still much more efficient. A human can learn to drive a car in 10's of hours, while AI's still require millions of hours of training, and they still end up inferior. So, in the end, the title is correct (and also common knowledge for anyone following this development), even if almost all of the content in the article is way off. Was this a high school essay? 1) http://hplusmagazine.com/2009/04/07/brain-chip/ http://hplusmagazine.com/2009/04/07/brain-chip/
js8 8y ago
I think there are two important factors to this. One is that the operating frequency of the human brain is much lower. That results in much smaller energy consumption. The other is that current neural networks are probably very inefficient algorithms. They are fundamentally based on linear algebra but there is some evidence that the non-linearity is the key. My feeling is also that brain extensively uses probabilistic algorithms with hash encoding, conceptually similar to bloom filters, count-min, etc.
- zaarn 8y ago
  The brain has other differences that are probably a major influences on it's efficiency; 1) It's incredibly parallel, few operations happen linearly. Even modern GPUs don't really match that. 2) It doesn't run everything all the time. Parts of the brain not in use are shut down. Modern computers do this to some extend but CPU cores don't switch off parts of the ALU not in use. The brain has much better energy saving options, only 10-30% of the brain are active at a given time.
  - rodiger 8y ago
    Building on 2- this is likely not a result of saving energy but rather hyper-specialization in specific regions of the brain. Like if modern CPUs had 10-20 cores, each tuned to handle a specific task. The result is energy efficiency, but in contrast to current energy saving options, the brain can't simply "speed up" and use more power and activate more areas of the brain.
  - skohan 8y ago
    > It's incredibly parallel, few operations happen linearly. Even modern GPUs don't really match that. Modern GPUs aren't even close: neither in terms of degree or in kind. A GPU does a lot of linear operations in parallel, but in the brain all the "compute units" are interacting with each-other in real time.
- marcyb5st 8y ago
  My theory is that, instead, the brain is a super-duper complex neural network with some differences about how the update of the weights happen :) Let me explain: Albeit true that our brain is much slower than a modern CPU in terms of ops/s, every neuron is connected to up to 10000 other neurons[0]. And we have something around 100 billion neurons in our brains. This means that the number of synapses is between 100 and 1000 trillions. If we assume that every synapse can be considered like a "weight" in a neural network, we have a model that is much much bigger than anything developed to date. Moreover, the topology of said neural network is not a simple stack of layers, but much more complex like a super intricated residual network. Finally, the brain is not using backpropagation to update the mentioned weights/synapses' strength, but instead is based on how often said synapses fire. [0]: http://www.human-memory.net/brain_neurons.html http://www.human-memory.net/brain_neurons.html
feanaro 8y ago
The article is comparing the number of raw machine operations (which we can objectively quantify with a great deal of certainty) with a figure of 50 operations per second, which is the supposed number of operations a human (mind? brain? consciousness?) can execute in a second. Furthermore, it seems to conflate the bit rate (bits per second) with operations per second. It seems wrong to suggest the brain itself, examined as a biological machine, executes only 50 discrete operations per second. In fact, judging by the footnote, it doesn't seem to be suggesting that: it's just talking about the conscious information processing rate. But consciousness is a can of worms itself. It's not at all clear that information has to travel through consciousness in order for the mind to compute or decide something. In fact, the opposite seems clear from many experiments, such as https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3052770/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3052770/ I'm not sure that the article's comparison is very illuminating.
- darkerside 8y ago
  I don't disagree with the conclusion of the study you linked, but I don't think the study itself deserves the conclusion that it reaches. Electrocuting someone, albeit imperceptibly from a conscious standpoint, means that we are "predicting volition" because they react?
rdruxn 8y ago
Yeah, you can’t power a super computer with a sandwich
axaxs 8y ago
I'm -not- a hardware guy, so this may be a dumb question/thought, but here goes - The human brain is pretty amazing, often thought of a 'petaflops' or beyond, and I imagine pretty energy efficient as well, seeing as that our heads don't reach boiling temperatures. Has there been any work into 'rethinking' chips to work more like a human brain (i dont want to go as far to to say 'organic chips'), or is it just not well enough understood? Or unfeasible for some other reason?
- slondr 8y ago
  The answer to that question is 50% "We don't know enough about the human brani" and 50% "Yes." There's been a lot of research into organic computing and how it could relate to neural networks, but it's an insanely difficult task and will be decades until anything viable is produced.

71 points by yters 8y ago

The headline makes an amazingly weak claim. Since people still seem to be discussing what features of nerve impulses are significant (their frequency? precise relative timing?) it would appear that a reasonable estimate of the information processing capacity of a single neuron is currently lacking, let alone an estimate for an entire brain taking account of redundancy. Does the comparison even make sense?

yetihehe 8y ago

It's like saying "A motorboat exceeds the most powerful cars in swimming". Yep, there are amphibia, but most motorboats are still better at swimming. Now, try to calculate 325*528. Did you made it faster than a millisecond? If not, your pocket calculator is more efficient than your brain.

slx26 8y ago
Yes. While I agree with the idea that brains are extremely efficient, especially at certain tasks like image recognition or any kind of pattern identification, I also thought about what you said, and I was like: when doing mathematical operations, I'd be more efficient riding a bicycle to charge a battery that powers a computer which then does the operations, than doing them by myself with my brain. That's... really weird. And there are so many tasks at which computers seem way more efficient than I could possibly be. EDIT: I guess someone downvoted you for apparently conflating speed and efficiency, but I still think the point stands.
- trashtester 8y ago
  It doesn't matter if you look at speed (seconds/operation) of efficiency (joules/operation). For most mathematical operations, a computer cpu will outperform a human brain by many orders of magnitudes. This is both true and a somewhat irrelevant. It is a bit like saying that a cpu can run recursive, non-parallelizable algorithms faster and more efficiently than a GPU, even if the GPU has more flops. The GPU optimized to run a few types of computation very quickly, and is very slow on other tasks. This applies to the human brain as well, and to a much greater degree.
- raxxorrax 8y ago
  I have implemented a stereoscopic vision once (1 angled camera and projector, not 2 cameras, but details...). It worked and the quality of the results could reasonably be scaled with additional computing power (and general hardware). While I wouldn't claim that the setup was very sophisticated, it worked reliable at least. But it doesn't even come close to the little nub in the back of your skull. Matching minuscule differences and quantifying these in images probably uses a lot of processing power, indifferent of the algorithms involved. It doesn't only eat you CPU, it would do the same with your memory. So yes, the brain is pretty good with images. But I expect it to be good at other tasks too that doesn't involve the visual cortex. You might not remember what you have eaten today morning, but there must be at least some form of reliable memory in your brain. And you do complex, high-speed mathematical calculations that include basic algebraic calculus. Just maybe not those you want.

0xBA5ED 8y ago

The brain is specialized hardware. I wouldn't expect a general purpose computer to be as efficient at performing the same tasks.

willbw 8y ago
It's actually not hardware at all. I say this not to be annoying but to make the point that perhaps we shouldn't be computer-morphizing the human brain. (I know that's not a word but was the best I could do!)
- 0xBA5ED 8y ago
  I agree with not "computer-morphizing" the brain, but I think hardware still applies well enough. Is there a specific reason you think it doesn't apply out of curiosity? I mean, are you just trying to avoid the "duality of hardware and software"?
- jcims 8y ago
  technomorphizing maybe? it's definitely something we do.
- jacquesm 8y ago
  It is a substrate. Not quite the same as hardware but close enough for government work.
Symmetry 8y ago
The difference between the deterministic substrate in an ASIC and non-deterministic substrate in a brain means we should resist simple comparisons even with any sort of computer hardware, specialized or not. Synapses sit, from an electronics perspective, in a weird in-between space between linear analog computation and digital computation. There are lab prototype computational devices that might make good comparisons but it's just too different from, say, the sort of specialized decoders you'd find on your phone SoC for that to be a good analogy.

roenxi 8y ago

The core of this essay is indisputable that the comparison is Apples and Oranges. A computer puts in more effort and gets a better result. However when choosing between more efficient or better results, I'd prefer better results. The human brain's bias towards efficiency is an evolutionary handicap on intelligence. I can provide my body with as much energy as it wants to consume - I'd rather be able to burn energy learning things than have to put up with a brain that keeps trying to take shortcuts at every turn to conserve energy, and I would love to be able to think deterministicly! It is a stretch to say humans are out thinking the machine with that frame, machine intelligence might simply have firmer foundations. I do have challenges to the method of calculating how much power is put into the human training process. 10^8 might be too many orders of magnitude to breach, but humans: 1) Use data picked up visually to provide basic concepts used in playing go (like "connected", "lines", "territory", "me", "opponent"). Many of these concepts are potentially developed from visual data, and humans process a lot more than 50 bits of visual data in a second. This observation is very hard to quantity respecting how much visual data can be employed to learn about Go - how much of an advantage does the concept of territory give humans in terms of learning? It is probably huge. EDIT Thinking about it a bit more, it is very plausible given AlphaGo's endgame play that it never developed a concept of 'territory' as we would understand it. Given that humans judge the state of the game by estimating territory for both players then assuming the winner is the one with more territory, that is a huge deal. It might explain why the learning seems so inefficient vs a human. A human would never make AlphaGo's endgame moves because they are bad by the territory heuristic. 2) Learn Go in a very communal environment. Game records date back a few centuries and all the patterns that are detected get passed on through the generations of professional players. The practical amount of cached processing power available to humans is higher than first inspection might suggest. Humans don't learn mainly through self play, it is closer to supervised learning. A 1900s player would simply lose to modern technique because of this accretion. 3) High level Go players are going to be strongly biased to people who 'got lucky' in how their brains map concepts. An average human probably isn't as good at learning Go as a peak professional.

gpderetta 8y ago
Efficiency, or better, the ability to dissipate waste heat, is a constraint on computational power though.

entity345 8y ago

"CPU cycles" is not a very good metric, in my view. However, the brain is indeed much more efficient when comparing energy consumption considering that it uses only about 20W or power.

trashtester 8y ago
Actually, training AZ (5000 TPUS for 8 hours) requires 1.4e10 joules, assuming that each TPU needs 100watts. For comparison, the human brain operating training 4 hours per day every day for 14 years also uses 1.4e10 joules. So the energy efficiency is quite similar.
- entity345 8y ago
  That's comparing apples and oranges. The brain works and learns 24/7. It needs 20W to function.
  - trashtester 8y ago
    So how do you see that changing my point, that the brain seems to need about the same amount of energy as AZ does?
    - entity345 8y ago
      The point is that it obviously does not.
      - trashtester 8y ago
        I'm not sure what your claim is. Have you switched to the view that AZ is more efficient (by a factor of 6, if you assume 24/7 consumption), or do you still think that the brain is more efficient at learning GO? Edit: In case this is the cause for your objection: In the calculation for the energy needed for the human brain to master go (where I ended up with 14GJ=1.4*10^6 J), I only counted the consumption during the average 4 hours per day that one can expect someone to train. If you count all 24 hours, the 14GJ budget would be spent in 2-3 years. (Obviously, nobody can master GO in 2-3 years, regardless of effort.)

deleted 8y ago

[deleted]

deleted 8y ago
[deleted]

finnjohnsen2 8y ago

TLDR: "it is comparing apples and oranges"

mattnewport 8y ago
Worse than that, it's comparing apples and the color orange.

ben_w 8y ago

My disagreement is something this article half-acknowledges: > The brain has the potential for a petaflop per second of processing power. However, the measured performance of conscious processing is only about 50 bits per second. Why assume that either training or playing Go involves only conscious decision making? Anything that we call “common sense”, “gut feeling”, “aesthetically pleasing” etc. is something we can’t (or cannot easily) generate a conscious rationale for. Of course, that’s doesn’t mean humans don’t beat computers at all; last I checked, humans can make acceptable quality inferences from far fewer examples than an A.I.

gasg0d 8y ago
I once played in two poker tournaments on back to back weekends. Tournament 1 on adderall. Tournament 2 without adderall. Tournament 1 my consciousness and subconscious seemed to be working in tandom. I knew what action to take, and why I was taking that action. Tournament 2 my subconscious seemed to play a bigger role being off the adderall. I was making the right decisions for the most part same as the week before while on adderall, but I was not consciously completely sure of my reasoning for most decisions until several seconds, minutes, or even hours after I had reflected on my decision making. A little food for thought
- kowdermeister 8y ago
  So which version worked for you better financially?
- skohan 8y ago
  I wonder if that's part of what's going on with autism-spectrum-disorders: i.e. an autistic mind doesn't set the threshold of what's worthy of conscious thought at as high a level as neurotypical people, so they get more caught-up in the low-level details.
trashtester 8y ago
Exactly this. Conscious information processing constitutes only a tiny part of the processing within the brain. Real computational power of the brain is in the order of petaflops to exaflops 1). On the other hand, AZ used a lot more than 1E18 calculations of training to beat human level play, so that part is also wrong. In fact, the total processing power of the 5000 TPU's used are in the order of 1E17 operations per second. Still, since 1E17 flops is comparable (within 1-2 orders of magnitude) to the human brain's raw capacity, and AZ could reach superhuman level in GO in less than 10 hours, that means that AZ can be seen as more efficient than human brains at GO (in terms of learning rate/calculation, not necessarily per watt) This should also not be surprising. After all, GO (like chess) is a very abstract game, far removed from the tasks that the biological brain has evolved to do well. For tasks that the brain does well, such as pattern recognition in the environment that humans live, the human brain is still much more efficient. A human can learn to drive a car in 10's of hours, while AI's still require millions of hours of training, and they still end up inferior. So, in the end, the title is correct (and also common knowledge for anyone following this development), even if almost all of the content in the article is way off. Was this a high school essay? 1) http://hplusmagazine.com/2009/04/07/brain-chip/ http://hplusmagazine.com/2009/04/07/brain-chip/

js8 8y ago

I think there are two important factors to this. One is that the operating frequency of the human brain is much lower. That results in much smaller energy consumption. The other is that current neural networks are probably very inefficient algorithms. They are fundamentally based on linear algebra but there is some evidence that the non-linearity is the key. My feeling is also that brain extensively uses probabilistic algorithms with hash encoding, conceptually similar to bloom filters, count-min, etc.

zaarn 8y ago
The brain has other differences that are probably a major influences on it's efficiency; 1) It's incredibly parallel, few operations happen linearly. Even modern GPUs don't really match that. 2) It doesn't run everything all the time. Parts of the brain not in use are shut down. Modern computers do this to some extend but CPU cores don't switch off parts of the ALU not in use. The brain has much better energy saving options, only 10-30% of the brain are active at a given time.
- rodiger 8y ago
  Building on 2- this is likely not a result of saving energy but rather hyper-specialization in specific regions of the brain. Like if modern CPUs had 10-20 cores, each tuned to handle a specific task. The result is energy efficiency, but in contrast to current energy saving options, the brain can't simply "speed up" and use more power and activate more areas of the brain.
- skohan 8y ago
  > It's incredibly parallel, few operations happen linearly. Even modern GPUs don't really match that. Modern GPUs aren't even close: neither in terms of degree or in kind. A GPU does a lot of linear operations in parallel, but in the brain all the "compute units" are interacting with each-other in real time.
marcyb5st 8y ago
My theory is that, instead, the brain is a super-duper complex neural network with some differences about how the update of the weights happen :) Let me explain: Albeit true that our brain is much slower than a modern CPU in terms of ops/s, every neuron is connected to up to 10000 other neurons[0]. And we have something around 100 billion neurons in our brains. This means that the number of synapses is between 100 and 1000 trillions. If we assume that every synapse can be considered like a "weight" in a neural network, we have a model that is much much bigger than anything developed to date. Moreover, the topology of said neural network is not a simple stack of layers, but much more complex like a super intricated residual network. Finally, the brain is not using backpropagation to update the mentioned weights/synapses' strength, but instead is based on how often said synapses fire. [0]: http://www.human-memory.net/brain_neurons.html http://www.human-memory.net/brain_neurons.html

feanaro 8y ago

The article is comparing the number of raw machine operations (which we can objectively quantify with a great deal of certainty) with a figure of 50 operations per second, which is the supposed number of operations a human (mind? brain? consciousness?) can execute in a second. Furthermore, it seems to conflate the bit rate (bits per second) with operations per second. It seems wrong to suggest the brain itself, examined as a biological machine, executes only 50 discrete operations per second. In fact, judging by the footnote, it doesn't seem to be suggesting that: it's just talking about the conscious information processing rate. But consciousness is a can of worms itself. It's not at all clear that information has to travel through consciousness in order for the mind to compute or decide something. In fact, the opposite seems clear from many experiments, such as https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3052770/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3052770/ I'm not sure that the article's comparison is very illuminating.

darkerside 8y ago
I don't disagree with the conclusion of the study you linked, but I don't think the study itself deserves the conclusion that it reaches. Electrocuting someone, albeit imperceptibly from a conscious standpoint, means that we are "predicting volition" because they react?

rdruxn 8y ago

Yeah, you can’t power a super computer with a sandwich

axaxs 8y ago

I'm -not- a hardware guy, so this may be a dumb question/thought, but here goes - The human brain is pretty amazing, often thought of a 'petaflops' or beyond, and I imagine pretty energy efficient as well, seeing as that our heads don't reach boiling temperatures. Has there been any work into 'rethinking' chips to work more like a human brain (i dont want to go as far to to say 'organic chips'), or is it just not well enough understood? Or unfeasible for some other reason?

slondr 8y ago
The answer to that question is 50% "We don't know enough about the human brani" and 50% "Yes." There's been a lot of research into organic computing and how it could relate to neural networks, but it's an insanely difficult task and will be decades until anything viable is produced.