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It seems they are on the right direction. But, the number of parameters hit the physical barrier of silicon-based device. To make the neuron network be a stron

It seems they are on the right direction. But, the number of parameters hit the physical barrier of silicon-based device. To make the neuron network be a strong AI, we may need the parameters in scales of 10^19~10^44 (uncertainty limit and Planck time) estimated from how many frames per second in real world. I personally got a feeling that this is bound to how nature calculate the physical phenomenon. For simulation, there are lots of "for loop" in our code/machine. But, in real world, we could not observe any "for loop". It is fast and continuous in the macro scale. In order to reach strong AI, it is time to focus on quantum computing.

sudosysgen 6y ago
For loops in AI are actually only there to implement continuous mathematical operations such as matrix multiplications in a single core. Modern AI chips and implementations use very little to no for loops.
- LdSGSgvupDV 6y ago
  Sorry, maybe it is a wrong analogy. What I want to emphasize is that there is no calculating time in nature. At least, we could not feel "lag" in our "Nature Server". For the part of chips, did you mean that it runs the "for loop" as parallel in each micro-chip (or gate or whatever tiny structure it is)?
  - sudosysgen 6y ago
    >Sorry, maybe it is a wrong analogy. What I want to emphasize is that there is no calculating time in nature. At least, we could not feel "lag" in our "Nature Server". Yes, but the reality is that there is lag in our projection of what nature is and that we just ignore it. Different parts of our perception actually don't exist at the same instant. >For the part of chips, did you mean that it runs the "for loop" as parallel in each micro-chip (or gate or whatever tiny structure it is)? Pretty much, the for-loop is unfolded and ran in parallel.
i-am-curious 6y ago
> To make the neuron network be a strong AI, we may need the parameters in scales of 10^19~10^44 (uncertainty limit and Planck time) estimated from how many frames per second in real world. There is absolutely no basis for claiming this. It might turn out that there are better algorithms for AI than neural nets. Do you even realize just how many parameters even 10^12 is???

sudosysgen 6y ago

For loops in AI are actually only there to implement continuous mathematical operations such as matrix multiplications in a single core. Modern AI chips and implementations use very little to no for loops.

LdSGSgvupDV 6y ago
Sorry, maybe it is a wrong analogy. What I want to emphasize is that there is no calculating time in nature. At least, we could not feel "lag" in our "Nature Server". For the part of chips, did you mean that it runs the "for loop" as parallel in each micro-chip (or gate or whatever tiny structure it is)?
- sudosysgen 6y ago
  >Sorry, maybe it is a wrong analogy. What I want to emphasize is that there is no calculating time in nature. At least, we could not feel "lag" in our "Nature Server". Yes, but the reality is that there is lag in our projection of what nature is and that we just ignore it. Different parts of our perception actually don't exist at the same instant. >For the part of chips, did you mean that it runs the "for loop" as parallel in each micro-chip (or gate or whatever tiny structure it is)? Pretty much, the for-loop is unfolded and ran in parallel.

i-am-curious 6y ago

> To make the neuron network be a strong AI, we may need the parameters in scales of 10^19~10^44 (uncertainty limit and Planck time) estimated from how many frames per second in real world. There is absolutely no basis for claiming this. It might turn out that there are better algorithms for AI than neural nets. Do you even realize just how many parameters even 10^12 is???