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Protein folding is still an exponential time algorithm when done inside a computer and biological systems do this in constant time, massively, in parallel. Det

Protein folding is still an exponential time algorithm when done inside a computer and biological systems do this in constant time, massively, in parallel. Determining whether a molecule is an agonist takes a long time to calculate. I've heard the complexity is O(N^3). Biological systems do this in constant time, trillions of times a second in parallel. If you could simulate biological systems easily, you could do drug development completely inside a computer.

alphagrep12345 8y ago
So are you saying that we need not build bigger networks with huge data, but rather have better algorithms with significant parallelization? Sounds like we're back to symbolic AI again!
- narrator 8y ago
  New computing architectures like quantum computing could simulate these systems far more efficiently than classical architectures -- in theory. The engineering still needs more work though. Also, new AI techniques are good at finding good enough equivalents to exactly replicating human cognition for many things, but that might get harder as the problems become even more general.
fwip 8y ago
I'm concerned that you're ascribing unique characteristics to biological systems, when in fact we see the same characteristics in almost any natural system. Biological systems don't solve protein folding. Proteins fold in biological systems. Fluids take an enormous amount of computing power to simulate correctly. But water isn't smart, it just does what water does. Heck, the N-body problem is a classic O(n^2) algorithm, but we don't say that the planets and stars are "solving" it.
- narrator 8y ago
  These techniques have already been used to solve problems using DNA computing [1]. Digital circuits don't solve anything either, they are just electrons flowing around mediated by semiconductors after all and the results of their computation are just measurements of electrical charge values after those physical systems have had time to flow around. As long as you can set the input and get a consistent output and measure that output you can compute with it. To use your logic: Large integrated circuits take an enormous amount of computing power to simulate correctly. But integrated circuits aren't smart, they just do what integrated circuits do. [1]https://en.m.wikipedia.org/wiki/DNA_computing https://en.m.wikipedia.org/wiki/DNA_computing

alphagrep12345 8y ago

So are you saying that we need not build bigger networks with huge data, but rather have better algorithms with significant parallelization? Sounds like we're back to symbolic AI again!

narrator 8y ago
New computing architectures like quantum computing could simulate these systems far more efficiently than classical architectures -- in theory. The engineering still needs more work though. Also, new AI techniques are good at finding good enough equivalents to exactly replicating human cognition for many things, but that might get harder as the problems become even more general.

fwip 8y ago

I'm concerned that you're ascribing unique characteristics to biological systems, when in fact we see the same characteristics in almost any natural system. Biological systems don't solve protein folding. Proteins fold in biological systems. Fluids take an enormous amount of computing power to simulate correctly. But water isn't smart, it just does what water does. Heck, the N-body problem is a classic O(n^2) algorithm, but we don't say that the planets and stars are "solving" it.

narrator 8y ago
These techniques have already been used to solve problems using DNA computing [1]. Digital circuits don't solve anything either, they are just electrons flowing around mediated by semiconductors after all and the results of their computation are just measurements of electrical charge values after those physical systems have had time to flow around. As long as you can set the input and get a consistent output and measure that output you can compute with it. To use your logic: Large integrated circuits take an enormous amount of computing power to simulate correctly. But integrated circuits aren't smart, they just do what integrated circuits do. [1]https://en.m.wikipedia.org/wiki/DNA_computing https://en.m.wikipedia.org/wiki/DNA_computing