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Binary computing is never going to emulate wetware on a reasonable scale. A few neurons? Sure. It's going to take a different pardigm, likely biological. Evolv

Binary computing is never going to emulate wetware on a reasonable scale. A few neurons? Sure. It's going to take a different pardigm, likely biological. Evolving FPGA's (by ignoring the digital abstraction) is a window into why.

aeternum 7y ago
Citation needed. Let's look at the scale. A brain neuron is 4 microns to 100 microns in size. Whereas a transistor sizes are now pushing 3nm. That's a lot of free space to work with, why can't we easily simulate many neurons within the space of a biological neuron?
- jakeogh 7y ago
  Start with something easier, like simulating water. Our best ab-initio simulations, last time I checked, don't correctly predict it's phase change locations. Digital abstractions deliberately supress every single other interaction unless explicitely accounted for. Those interactions happen "for free" in real systems. We need to boil quite a bit of water to even approach simulating a small subset of atoms. Consider the 3 body problem. Without a math breakthrough, we can only approximate, and even with a math breakthrough, we can still only approximate (because real systems are not closed). This idea that we can simulate something misses the point. It's always going to be a guess, on bounded memory, because we cant copy real states. Letting go of the digital abstraction provides a window into why this is hard. https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.9691&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.... These circuits evolve to become hyper-specific to their enviroment. We have no hope of repeating those results with software, it's the physical attributes of the hardware that are being exploited.
  - aeternum 7y ago
    Sure we can only approximate, but why wouldn't those approximations be sufficient? We were able to solve the n-body problem with sufficient accuracy for Voyager to successfully encounter and gravity-slingshot around Jupiter twice and every other outer planet, with only the computing power available in 1977. The mechanism of action of a neuron is not very complex. It follows the all-or-none principle along with an activation potential which is easily emulated in a binary circuit. It does have additional chemical inputs which can raise or lower activation potential. The only difficult part is sufficient connectivity, but we can simulate that, no need to use an FPGA to get actual connectivity. Modern transistors are super-fast and super-small compared to neurons so you have quite a bit of room to sum inputs, check activation energy, check chemical levels, and lookup + signal the 'connected' neurons.
    - jakeogh 7y ago
      We will see! Our intuition that we understanding something is often incomplete in critical ways. Exciting times ahead either way.

aeternum 7y ago

Citation needed. Let's look at the scale. A brain neuron is 4 microns to 100 microns in size. Whereas a transistor sizes are now pushing 3nm. That's a lot of free space to work with, why can't we easily simulate many neurons within the space of a biological neuron?

jakeogh 7y ago
Start with something easier, like simulating water. Our best ab-initio simulations, last time I checked, don't correctly predict it's phase change locations. Digital abstractions deliberately supress every single other interaction unless explicitely accounted for. Those interactions happen "for free" in real systems. We need to boil quite a bit of water to even approach simulating a small subset of atoms. Consider the 3 body problem. Without a math breakthrough, we can only approximate, and even with a math breakthrough, we can still only approximate (because real systems are not closed). This idea that we can simulate something misses the point. It's always going to be a guess, on bounded memory, because we cant copy real states. Letting go of the digital abstraction provides a window into why this is hard. https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.9691&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.... These circuits evolve to become hyper-specific to their enviroment. We have no hope of repeating those results with software, it's the physical attributes of the hardware that are being exploited.
- aeternum 7y ago
  Sure we can only approximate, but why wouldn't those approximations be sufficient? We were able to solve the n-body problem with sufficient accuracy for Voyager to successfully encounter and gravity-slingshot around Jupiter twice and every other outer planet, with only the computing power available in 1977. The mechanism of action of a neuron is not very complex. It follows the all-or-none principle along with an activation potential which is easily emulated in a binary circuit. It does have additional chemical inputs which can raise or lower activation potential. The only difficult part is sufficient connectivity, but we can simulate that, no need to use an FPGA to get actual connectivity. Modern transistors are super-fast and super-small compared to neurons so you have quite a bit of room to sum inputs, check activation energy, check chemical levels, and lookup + signal the 'connected' neurons.
  - jakeogh 7y ago
    We will see! Our intuition that we understanding something is often incomplete in critical ways. Exciting times ahead either way.