8 ms·

ANN neurons are Pitts-McCulloch neurons, an extremely stylized model of the 1940's understanding of neurons. Each neuron represents a dot product plus a functio

ANN neurons are Pitts-McCulloch neurons, an extremely stylized model of the 1940's understanding of neurons. Each neuron represents a dot product plus a function application. A biological neuron is 6e17 Daltons[0], so on the order of quadrillions of atoms. A single synapse is a huge landscape studded with receptors of various kinds, and the whole thing is swimming in salt solution where chemicals diffuse stochastically. And then there's the glia. This is why I dismiss any claims about the computational power of the human brain, most of which seem to begin with the assumption that "1 spike = 1 FLOP", and that'll all that goes on in the brain. [0]: https://faculty.washington.edu/chudler/facts.html https://faculty.washington.edu/chudler/facts.html and https://www.wolframalpha.com/input/?i=1E-06+grams+in+dalton https://www.wolframalpha.com/input/?i=1E-06+grams+in+dalton

singularity2001 5y ago
Not to speak of all the 'weights' which are stored internaly in DNA methylation, among other memory forming mechanisms (proven recently). (part of the quadrillion atoms system of course)
- zetalyrae 5y ago
  And if you need ab-initio quantum chemistry (or equivalent) simulations of every atom in a human brain, the timelines for both AGI and whole brain emulation get pushed far, far into the future. Assuming AGI requires about as much hardware as it'd take to emulate a brain, at least.
  - greens 5y ago
    My 2c (apologies for the aggressive tone -- I'm just excited about AGI): That's a very very weak upper bound on how much hardware it takes. I think it's not all that different from emulating a Nintendo64 with a quantum simulation of the hardware. For complex systems to work (not to mention evolve), they need to be robust to small perturbations -- there's no way the computation the brain is doing is sensitive to the details of particular atoms. There has to be redundancy, modularity, etc. These things aren't human inventions so much as they are the only way to meaningfully move in a 2^|giant-number| state-space.
    - zetalyrae 5y ago
      DNA/RNA is near-atomic level storage. There's redundancy in that there are multiple instances of each neuron|receptor|etc.
      - greens 5y ago
        That's a great point. You could argue despite the huge number of physical degrees of freedom, the operations on DNA can be reduced to copy, repair, express, suppress. On the other hand, there's still a ton of intrinsic complexity in storing a huge amount of data, and yeah some nucleotides are totally essential. The other thing a wonder about: sure, maintaining a proteome is hugely complicated, but how much of this complexity goes into maintaining homeostasis (e.g. metabolism, cytoskeloton and membrane maintenance, replication,...) vs. enabling computation. Seems like silicon has the advantage here.
nabla9 5y ago
Your terminology is inaccurate. Threshold neurons like Pitts-McCulloch neurons are rarely used in artificial neural network architectures. They can't learn. ANN's uses a nonlinear activation function that is differentiable (at least in practice). That small change makes huge difference.
- zetalyrae 5y ago
  I stand corrected. But the point is that artificial neurons are computationally simple.
- l33t2328 5y ago
  > They can't learn Is there a good (i.e. theoretical) reason for this?
  - Arech 5y ago
    Non-differentiability, obviously
  - shmageggy 5y ago
    No, because it's wrong. Threshold neurons are still differentiable almost everywhere, no different than Relus which are ubiquitous. They may not be very good activation functions but they don't prevent a network from learning.
    - sdenton4 5y ago
      Sorry, but I think I disagree... If I understand the PM neuron, it's outputs are boolean. This means there's no backprop signal, despite differentiability, since the outputs are constant (thus, gradient zero) in any neighborhood, which in turns zeros out any learning signal you would want to backprop through them. So you need a different learning strategy than backprop to use them. (see also: the 'dead neuron' problem/phenomenon with ReLU activations.)
      - orbifold 5y ago
adrianN 5y ago
Transistors, while dramatically simpler than neurons, also have fairly complicated physics. It's quite hard to determine the computational power of a microprocessor when you only know roughly how transistors work and have no idea how microprocessors work.
- yarcob 5y ago
  But digital chips are designed to use transistors as on/off switches, so the complicated physics don't matter for understanding a microprocessor. (It's different for analog ICs, where the characteristics of the individual transistors do matter)
  - adrianN 5y ago
    Yes, but you wouldn't know that if the best knowledge you had about the inner workings of microchips is essentially looking which parts get hot while they perform different tasks.
  - titzer 5y ago
    Wouldn't it suck if brains are just reservoirs used by the internet that can store roughly 3 memes at a time?
    - taneq 5y ago
      holds up a Duracell battery "You think thermodynamics means using humans as batteries is a dumb idea? Where do you think you learned thermodynamics?" faint nyan cat music in the background
      - cwillu 5y ago
        "May I have a physics textbook?" "No. The real world doesn't run on math."
- zetalyrae 5y ago
  True, but transistors are designed by humans (for now), so you have an upper bound on complexity as well as a certain degree of modularity. Whereas biology has no such limitations because evolution doesn't care about the understandability of its designs. So I tend to err on the side of biology being more complex than not. But it's absolutely possible that the high-level behaviour is simple while the underlying implementation is complex and chaotic, as in the gas laws.
IshKebab 5y ago
Isn't that the point of this research? To find out roughly how many ANN neurons a real neuron corresponds to? The number of atoms is irrelevant.
nootropicat 5y ago
To add to that, there's evidence that may mean that memories are stored in DNA, shuffled between neurons in RNA capsids, evolutionary borrowed from retroviruses. That would make each neuron a complicated computer, functionally equivalent to one pc in capability and data storage. "The neuronal gene Arc is essential for long-lasting information storage in the mammalian brain, mediates various forms of synaptic plasticity, and has been implicated in neurodevelopmental disorders. However, little is known about Arc’s molecular function and evolutionary origins. Here, we show that Arc self-assembles into virus-like capsids that encapsulate RNA. Endogenous Arc protein is released from neurons in extracellular vesicles that mediate the transfer of Arc mRNA into new target cells, where it can undergo activity-dependent translation. Purified Arc capsids are endocytosed and are able to transfer Arc mRNA into the cytoplasm of neurons. These results show that Arc exhibits similar molecular properties to retroviral Gag proteins. Evolutionary analysis indicates that Arc is derived from a vertebrate lineage of Ty3/gypsy retrotransposons, which are also ancestors to retroviruses. These findings suggest that Gag retroelements have been repurposed during evolution to mediate intercellular communication in the nervous system." https://www.cell.com/cell/fulltext/S0092-8674(17)31504-0 https://www.cell.com/cell/fulltext/S0092-8674(17)31504-0
- twic 5y ago
  The passage you quote very much does not amount to "memories are stored in DNA", and as far as i know, memories are not stored in DNA sequence, and would be very surprised if they were. What this mechanism does is take the transcription level of a gene in one cell, and induce a proportional signal in another cell. There are numerous mechanisms which do that in cells. Most of them are nowhere near as weird as Arc, but the net result is similar.
  - caddemon 5y ago
    It's arguable that some memories are stored via epigenetic modifications to DNA, depending on how you define "storing memories". It is very unlikely it is storing memories in the way humans typically think of memories. But I don't think "in DNA" necessarily implies "in DNA sequence".
causi 5y ago
Indeed. Building an AI that matches human intelligence using equal or less mass than a human brain requires one or both of two things to be true: 1. The computational mass efficiency of brain tissue is very far from optimum. Considering the amount of time evolution has been improving upon it, I highly doubt that is true. 2. Most of the brain's computation is not involved in cognition. That may be true. We don't really know. There are hard limits. No matter how you try you can't perfectly simulate three atoms using two atoms. If it turns out we have to, in software, represent fifty percent of neuronal activity to create consciousness we're in real trouble. A dragonfly can take inputs from thousands of ommatidia and use them to track targets in space using only sixteen neurons. How many transistors would it take us to do the same? Take that ratio and apply it to the 86 billion neurons in the human brain and you have a rough idea of what it will take to create strong AI. The numbers aren't promising.
- sdenton4 5y ago
  /2. Most of the brain's computation is not involved in cognition. That may be true. We don't really know./ I thought this at least was fairly well understood: We do in fact use our whole brains, as anything less would be a fantastic waste of resources, which evolution would have taken care of long ago. We have numerous human-specific adaptations to deal with the relatively massive brains we're carrying around.
  - causi 5y ago
    When you're building an AI you may not need the neurons involved with, for example, breathing. That's what I'm talking about. I'm not a neuroscientist so I don't know for sure whether all the neurons we use for muscle and organ control do double-duty to help us cogitate.
    - sdenton4 5y ago
      Depending on what you think about the AGI-ness of other mammals, this could be helpful to meditate upon: https://en.wikipedia.org/wiki/Brain-to-body_mass_ratio https://en.wikipedia.org/wiki/Brain-to-body_mass_ratio "In animals, it is thought that the larger the brain, the more brain weight will be available for more complex cognitive tasks. However, large animals need more neurons to represent their own bodies and control specific muscles;[clarification needed][citation needed] thus, relative rather than absolute brain size makes for a ranking of animals that better coincides with the observed complexity of animal behaviour. The relationship between brain-to-body mass ratio and complexity of behaviour is not perfect as other factors also influence intelligence, like the evolution of the recent cerebral cortex and different degrees of brain folding,[5] which increase the surface of the cortex, which is positively correlated in humans to intelligence."
fnord77 5y ago
what's also interesting is non-neuronal computational abilities. Allosteric regulation on receptors chained together seem like they have computational abilities. And they're on all sorts of cells I'm sure there's others.
gnramires 5y ago
> A biological neuron is 6e17 Daltons[0], so on the order of quadrillions of atoms You may be implying there could be information stored in all those atoms, but I'm not sure that's possible. We live in a thermal bath, this means the behavior of atoms is usually stochastic. Their position cannot reliably hold information without dispersing it rapidly into the thermal environment. One way to get around this is to form chemical bonds, like in the DNA, where some kind of order or position is stable. But it necessitates this chemical structure, and importantly it necessitates as well a chemical reading mechanism and a chemical writing mechanism (or some kind of kinetic activation). Most parts of a cell are not prepared for any of that as far as I can tell. Information really should be carried by discrete elements such as neurotransmitters, as well as continuous but temporary (unstable) elements such as electric impulses (potentially caused by complex responses to electric potential and current inside the neuron). Even in the electric case, the fact about information stands; electric state is also encoded in atoms. In other words, we almost certainly don't need the full fidelity to reproduce the behavior of a neuron. I would need more rigorous examination of the neuron to give a confident estimate, but as a rule of thumb the concentration of relevant information everywhere is much less than DNA's (and mostly negligible everywhere) -- certainly a very interesting research program. From a quick googling, E. Coli DNA has about 4.6 x 10^6 bp, so I would be reasonably confident in an upper bound to neuron information as (volume of neuron/volume of e. coli) x 5 x 10^6 bits (i.e. 1 megabit, 125 kb). The reality is probably much less. DNA is so dense because the reading and replication time is relatively slow. It's not made for rapid, random access at the speed of thought. If I were to guess, I'd say long term information is probably retained within the concentration of compounds that can be read electro-chemically. The question of information then is how sensitive the neural system as a whole is to differences in concentration and differences in timing and amplitude of neural impulses. Again given thermal noise in the brain and limitations of amplitude, you can give strict upper bounds on neural communication (I'd be surprised at sensitivities more than a few ppm). So essentially 1 neuron <= log2(distinguishable chemical state) (memory) And also 1 spike <= log2(electrical degrees of freedom) (bandwidth) Again, I'd require more information on spikes, but they carry maybe 20-40 bits at most -- so not more than a double f.p., although in ANNs again because of low sensitivities due to architecture most of LSB information doesn't contribute significantly to the computation (whereas the brain could multiplex information more effectively). So it's still likely in the order of 1 spike <= 10 flops.
api 5y ago
Older estimates are probably short by multiple orders of magnitude. We can see this in practice by looking at e.g. a self-driving Tesla vs. a mouse. Watch a frightened mouse run across an uneven landscape, climbing and clambering and avoiding obstacles as it controls four independent limbs and countless small muscles in real time. A Tesla's auto-drive is nowhere near that good in spite of having only a few levers to control: accelerate, brake, left, and right. It also has far better sensors than the mouse including better eyesight, a wider field of vision, etc., and last I checked mice do not have access to a cellular network supplying them with a heads-up macro view of the local environment. They don't have "fleet-wide learning" either. The Tesla's AI uses over a hundred watts of power. The mouse's brain uses milliwatts. As I wrote this my brain was consuming between 30 and 60 watts. My laptop peaks out at 80. We are not even close to what biological neurons accomplish in raw compute, and while we are getting pretty good at training giant regression models that we call AI I am not convinced we really understand things at the algorithmic level yet either.
- datameta 5y ago
  Perhaps Spot from Boston Dynamic is a more fair SotA comparison. Though even then, the magnitude of scale is off and still the gulf in ability is wide. I wonder how our artificial insects stack up in comparison to organic ones computationally. I think there is much to learn from the bee brain, for instance. Relevant read: "The thermodynamic efficiency of computations made in cells across the range of life" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686401/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686401/
taneq 5y ago
> A biological neuron is 6e17 Daltons[0] A grain of sand is around 6.02E19 Daltons, imagine how much computational power there is in a sand castle! On the other hand, snails have been shown to use just two neurons to generate their complex (relatively, I mean they're snails after all) feeding behaviour with just two neurons: https://neurosciencenews.com/neurons-decision-making-4370/ https://neurosciencenews.com/neurons-decision-making-4370/ (Veering slightly offtopic here but this is the first I've heard the term 'Dalton', I've always just heard them called AMUs.)

singularity2001 5y ago

Not to speak of all the 'weights' which are stored internaly in DNA methylation, among other memory forming mechanisms (proven recently). (part of the quadrillion atoms system of course)

zetalyrae 5y ago
And if you need ab-initio quantum chemistry (or equivalent) simulations of every atom in a human brain, the timelines for both AGI and whole brain emulation get pushed far, far into the future. Assuming AGI requires about as much hardware as it'd take to emulate a brain, at least.
- greens 5y ago
  My 2c (apologies for the aggressive tone -- I'm just excited about AGI): That's a very very weak upper bound on how much hardware it takes. I think it's not all that different from emulating a Nintendo64 with a quantum simulation of the hardware. For complex systems to work (not to mention evolve), they need to be robust to small perturbations -- there's no way the computation the brain is doing is sensitive to the details of particular atoms. There has to be redundancy, modularity, etc. These things aren't human inventions so much as they are the only way to meaningfully move in a 2^|giant-number| state-space.
  - zetalyrae 5y ago
    DNA/RNA is near-atomic level storage. There's redundancy in that there are multiple instances of each neuron|receptor|etc.
    - greens 5y ago
      That's a great point. You could argue despite the huge number of physical degrees of freedom, the operations on DNA can be reduced to copy, repair, express, suppress. On the other hand, there's still a ton of intrinsic complexity in storing a huge amount of data, and yeah some nucleotides are totally essential. The other thing a wonder about: sure, maintaining a proteome is hugely complicated, but how much of this complexity goes into maintaining homeostasis (e.g. metabolism, cytoskeloton and membrane maintenance, replication,...) vs. enabling computation. Seems like silicon has the advantage here.

nabla9 5y ago

Your terminology is inaccurate. Threshold neurons like Pitts-McCulloch neurons are rarely used in artificial neural network architectures. They can't learn. ANN's uses a nonlinear activation function that is differentiable (at least in practice). That small change makes huge difference.

zetalyrae 5y ago
I stand corrected. But the point is that artificial neurons are computationally simple.
l33t2328 5y ago
> They can't learn Is there a good (i.e. theoretical) reason for this?
- Arech 5y ago
  Non-differentiability, obviously
- shmageggy 5y ago
  No, because it's wrong. Threshold neurons are still differentiable almost everywhere, no different than Relus which are ubiquitous. They may not be very good activation functions but they don't prevent a network from learning.
  - sdenton4 5y ago
    Sorry, but I think I disagree... If I understand the PM neuron, it's outputs are boolean. This means there's no backprop signal, despite differentiability, since the outputs are constant (thus, gradient zero) in any neighborhood, which in turns zeros out any learning signal you would want to backprop through them. So you need a different learning strategy than backprop to use them. (see also: the 'dead neuron' problem/phenomenon with ReLU activations.)
    - orbifold 5y ago

adrianN 5y ago

Transistors, while dramatically simpler than neurons, also have fairly complicated physics. It's quite hard to determine the computational power of a microprocessor when you only know roughly how transistors work and have no idea how microprocessors work.

yarcob 5y ago
But digital chips are designed to use transistors as on/off switches, so the complicated physics don't matter for understanding a microprocessor. (It's different for analog ICs, where the characteristics of the individual transistors do matter)
- adrianN 5y ago
  Yes, but you wouldn't know that if the best knowledge you had about the inner workings of microchips is essentially looking which parts get hot while they perform different tasks.
- titzer 5y ago
  Wouldn't it suck if brains are just reservoirs used by the internet that can store roughly 3 memes at a time?
  - taneq 5y ago
    holds up a Duracell battery "You think thermodynamics means using humans as batteries is a dumb idea? Where do you think you learned thermodynamics?" faint nyan cat music in the background
    - cwillu 5y ago
      "May I have a physics textbook?" "No. The real world doesn't run on math."
zetalyrae 5y ago
True, but transistors are designed by humans (for now), so you have an upper bound on complexity as well as a certain degree of modularity. Whereas biology has no such limitations because evolution doesn't care about the understandability of its designs. So I tend to err on the side of biology being more complex than not. But it's absolutely possible that the high-level behaviour is simple while the underlying implementation is complex and chaotic, as in the gas laws.

IshKebab 5y ago

Isn't that the point of this research? To find out roughly how many ANN neurons a real neuron corresponds to? The number of atoms is irrelevant.

nootropicat 5y ago

To add to that, there's evidence that may mean that memories are stored in DNA, shuffled between neurons in RNA capsids, evolutionary borrowed from retroviruses. That would make each neuron a complicated computer, functionally equivalent to one pc in capability and data storage. "The neuronal gene Arc is essential for long-lasting information storage in the mammalian brain, mediates various forms of synaptic plasticity, and has been implicated in neurodevelopmental disorders. However, little is known about Arc’s molecular function and evolutionary origins. Here, we show that Arc self-assembles into virus-like capsids that encapsulate RNA. Endogenous Arc protein is released from neurons in extracellular vesicles that mediate the transfer of Arc mRNA into new target cells, where it can undergo activity-dependent translation. Purified Arc capsids are endocytosed and are able to transfer Arc mRNA into the cytoplasm of neurons. These results show that Arc exhibits similar molecular properties to retroviral Gag proteins. Evolutionary analysis indicates that Arc is derived from a vertebrate lineage of Ty3/gypsy retrotransposons, which are also ancestors to retroviruses. These findings suggest that Gag retroelements have been repurposed during evolution to mediate intercellular communication in the nervous system." https://www.cell.com/cell/fulltext/S0092-8674(17)31504-0 https://www.cell.com/cell/fulltext/S0092-8674(17)31504-0

twic 5y ago
The passage you quote very much does not amount to "memories are stored in DNA", and as far as i know, memories are not stored in DNA sequence, and would be very surprised if they were. What this mechanism does is take the transcription level of a gene in one cell, and induce a proportional signal in another cell. There are numerous mechanisms which do that in cells. Most of them are nowhere near as weird as Arc, but the net result is similar.
- caddemon 5y ago
  It's arguable that some memories are stored via epigenetic modifications to DNA, depending on how you define "storing memories". It is very unlikely it is storing memories in the way humans typically think of memories. But I don't think "in DNA" necessarily implies "in DNA sequence".

causi 5y ago

Indeed. Building an AI that matches human intelligence using equal or less mass than a human brain requires one or both of two things to be true: 1. The computational mass efficiency of brain tissue is very far from optimum. Considering the amount of time evolution has been improving upon it, I highly doubt that is true. 2. Most of the brain's computation is not involved in cognition. That may be true. We don't really know. There are hard limits. No matter how you try you can't perfectly simulate three atoms using two atoms. If it turns out we have to, in software, represent fifty percent of neuronal activity to create consciousness we're in real trouble. A dragonfly can take inputs from thousands of ommatidia and use them to track targets in space using only sixteen neurons. How many transistors would it take us to do the same? Take that ratio and apply it to the 86 billion neurons in the human brain and you have a rough idea of what it will take to create strong AI. The numbers aren't promising.

sdenton4 5y ago
/2. Most of the brain's computation is not involved in cognition. That may be true. We don't really know./ I thought this at least was fairly well understood: We do in fact use our whole brains, as anything less would be a fantastic waste of resources, which evolution would have taken care of long ago. We have numerous human-specific adaptations to deal with the relatively massive brains we're carrying around.
- causi 5y ago
  When you're building an AI you may not need the neurons involved with, for example, breathing. That's what I'm talking about. I'm not a neuroscientist so I don't know for sure whether all the neurons we use for muscle and organ control do double-duty to help us cogitate.
  - sdenton4 5y ago
    Depending on what you think about the AGI-ness of other mammals, this could be helpful to meditate upon: https://en.wikipedia.org/wiki/Brain-to-body_mass_ratio https://en.wikipedia.org/wiki/Brain-to-body_mass_ratio "In animals, it is thought that the larger the brain, the more brain weight will be available for more complex cognitive tasks. However, large animals need more neurons to represent their own bodies and control specific muscles;[clarification needed][citation needed] thus, relative rather than absolute brain size makes for a ranking of animals that better coincides with the observed complexity of animal behaviour. The relationship between brain-to-body mass ratio and complexity of behaviour is not perfect as other factors also influence intelligence, like the evolution of the recent cerebral cortex and different degrees of brain folding,[5] which increase the surface of the cortex, which is positively correlated in humans to intelligence."

fnord77 5y ago

what's also interesting is non-neuronal computational abilities. Allosteric regulation on receptors chained together seem like they have computational abilities. And they're on all sorts of cells I'm sure there's others.

gnramires 5y ago

> A biological neuron is 6e17 Daltons[0], so on the order of quadrillions of atoms You may be implying there could be information stored in all those atoms, but I'm not sure that's possible. We live in a thermal bath, this means the behavior of atoms is usually stochastic. Their position cannot reliably hold information without dispersing it rapidly into the thermal environment. One way to get around this is to form chemical bonds, like in the DNA, where some kind of order or position is stable. But it necessitates this chemical structure, and importantly it necessitates as well a chemical reading mechanism and a chemical writing mechanism (or some kind of kinetic activation). Most parts of a cell are not prepared for any of that as far as I can tell. Information really should be carried by discrete elements such as neurotransmitters, as well as continuous but temporary (unstable) elements such as electric impulses (potentially caused by complex responses to electric potential and current inside the neuron). Even in the electric case, the fact about information stands; electric state is also encoded in atoms. In other words, we almost certainly don't need the full fidelity to reproduce the behavior of a neuron. I would need more rigorous examination of the neuron to give a confident estimate, but as a rule of thumb the concentration of relevant information everywhere is much less than DNA's (and mostly negligible everywhere) -- certainly a very interesting research program. From a quick googling, E. Coli DNA has about 4.6 x 10^6 bp, so I would be reasonably confident in an upper bound to neuron information as (volume of neuron/volume of e. coli) x 5 x 10^6 bits (i.e. 1 megabit, 125 kb). The reality is probably much less. DNA is so dense because the reading and replication time is relatively slow. It's not made for rapid, random access at the speed of thought. If I were to guess, I'd say long term information is probably retained within the concentration of compounds that can be read electro-chemically. The question of information then is how sensitive the neural system as a whole is to differences in concentration and differences in timing and amplitude of neural impulses. Again given thermal noise in the brain and limitations of amplitude, you can give strict upper bounds on neural communication (I'd be surprised at sensitivities more than a few ppm). So essentially 1 neuron <= log2(distinguishable chemical state) (memory) And also 1 spike <= log2(electrical degrees of freedom) (bandwidth) Again, I'd require more information on spikes, but they carry maybe 20-40 bits at most -- so not more than a double f.p., although in ANNs again because of low sensitivities due to architecture most of LSB information doesn't contribute significantly to the computation (whereas the brain could multiplex information more effectively). So it's still likely in the order of 1 spike <= 10 flops.

api 5y ago

Older estimates are probably short by multiple orders of magnitude. We can see this in practice by looking at e.g. a self-driving Tesla vs. a mouse. Watch a frightened mouse run across an uneven landscape, climbing and clambering and avoiding obstacles as it controls four independent limbs and countless small muscles in real time. A Tesla's auto-drive is nowhere near that good in spite of having only a few levers to control: accelerate, brake, left, and right. It also has far better sensors than the mouse including better eyesight, a wider field of vision, etc., and last I checked mice do not have access to a cellular network supplying them with a heads-up macro view of the local environment. They don't have "fleet-wide learning" either. The Tesla's AI uses over a hundred watts of power. The mouse's brain uses milliwatts. As I wrote this my brain was consuming between 30 and 60 watts. My laptop peaks out at 80. We are not even close to what biological neurons accomplish in raw compute, and while we are getting pretty good at training giant regression models that we call AI I am not convinced we really understand things at the algorithmic level yet either.

datameta 5y ago
Perhaps Spot from Boston Dynamic is a more fair SotA comparison. Though even then, the magnitude of scale is off and still the gulf in ability is wide. I wonder how our artificial insects stack up in comparison to organic ones computationally. I think there is much to learn from the bee brain, for instance. Relevant read: "The thermodynamic efficiency of computations made in cells across the range of life" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686401/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686401/

taneq 5y ago

> A biological neuron is 6e17 Daltons[0] A grain of sand is around 6.02E19 Daltons, imagine how much computational power there is in a sand castle! On the other hand, snails have been shown to use just two neurons to generate their complex (relatively, I mean they're snails after all) feeding behaviour with just two neurons: https://neurosciencenews.com/neurons-decision-making-4370/ https://neurosciencenews.com/neurons-decision-making-4370/ (Veering slightly offtopic here but this is the first I've heard the term 'Dalton', I've always just heard them called AMUs.)