5 ms·

Not an expert but I'd be surprised if some modern computer viruses don't have some dynamic adaptability of their obfuscation to evade antivirus programs.

jqpabc123 6y ago
They probably do --- within the strict limits and confines of logic provided by the programmer. A computer virus has no ability to change or adapt beyond these rigid bounds without being re-programmed with new logic. But a biological virus can change and adapt to new situations and environments all on it's own --- with new logic of it's own design. You could easily argue that a simple biological virus is more "intelligent".
- phreeza 6y ago
  I don't know... Are the confines of nature, i.e. physical law, and the confines of programming that different in the end? The search space may be larger and thus it makes sense to use a different optimization strategy, but in the end both cases just follow a relatively simple program to achieve their goal.
  - jqpabc123 6y ago
    The obvious difference is that a simple biological virus is arguably much more effective at exploring it's environment and adapting to it in order to survive, grow and expand.
- Isinlor 6y ago
  The difference is not only of quality, but quantity. Computing power that biological platforms provide are quite huge. Each infected person carries 1e9 - 1e11 virions during peak infection [0]. If there are 1 million (1e6) infected people right now then we have around 1e15 - 1e17 virons. With a viron size of 32kb and 8h replication time [1] we get speed of ~10 bits/s or very roughly 1 FLOPS per viron. That gives us computing power used for virus replication between 1 petaFLOPS to 100 petaFLOPS. The number is probably an underestimate because 1 viron probably needs to produce 1000 or more copies to maintain replication rate of 1. The upper bound then would be 100 exaFLOPS. That's in the range of TOP500 supercomputers (1.3 petaFLOPS - 442 petaFLOPS) [2]. The sum of all TOP500 supercomputers is 2.4 exaFLOPS, so some 40 times less than the upper bound on the virus of 100 exaFLOPS. [0] https://www.medrxiv.org/content/10.1101/2020.11.16.20232009v1 https://www.medrxiv.org/content/10.1101/2020.11.16.20232009v... [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224694/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224694/ [2] https://www.top500.org/statistics/perfdevel/ https://www.top500.org/statistics/perfdevel/
  - jqpabc123 6y ago
    So in other words; by your math, all the supercomputers in the world are not enough to replicate the "intelligence" of a simple biological virus --- which is technically not even alive. Replicating "intelligence" that is alive is probably a trillion times more difficult. Correct me if I'm wrong but I think you just made an argument for "Why Computers (as we know them) Won't Make Themselves Smarter".
    - Isinlor 6y ago
      Just yesterday [0] I also did an upper bound estimate on computing power of a human brain based on assumption that brains carry out irreversible computations. E.g. inputs of an AND gate can not be recovered from the gate output. This forces increase of entropy. This is known as Landauer's principle or Landauer's limit [1]. The Landauer's limit suggests that a brain consuming 20W at 36C can carry out no more than ~1e22 irreversible operations per second (20W / (309.2K * Boltzmann constant * ln(2))). That bounds brains at ~1e21 FLOP/s (~10 bits erased per FLOP). The best supercomputer right now does ~4e17 FLOP/s. That's log2(1e21 / 4e17) ~11 doublings of computing power until supercomputers will be faster than thermodynamic maximum of FLOPS power of brains. Assuming we keep up the doubling time below 3 years, we will have supercomputers faster than a human brain in up to 3 * 11 ~ 33 years. And at that rate we will need another log2(1000^3) ~ 30 doublings to reach humanity brains computational power, so up to another 90 years. Overall, I don't see a reason why not to use biological platform for general type computations. If we are able to grow muscle tissue in a lab on an industrial scale, then we should be able to learn how to grow neurons in a lab on an industrial scale. Apparently neurons are a lot more difficult to grow, but it's still perfectly doable [2]. The issue is that we don't know how to grow them so that they do useful computation. But I can easily imagine that AWS and competition will be selling access to teachable biological neurons in the mid term future e.g. in 30 years. [0] https://news.ycombinator.com/item?id=26623730 https://news.ycombinator.com/item?id=26623730 [1] https://en.wikipedia.org/wiki/Landauer%27s_principle https://en.wikipedia.org/wiki/Landauer%27s_principle [2] https://www.youtube.com/watch?v=V2YDApNRK3g https://www.youtube.com/watch?v=V2YDApNRK3g

jqpabc123 6y ago

They probably do --- within the strict limits and confines of logic provided by the programmer. A computer virus has no ability to change or adapt beyond these rigid bounds without being re-programmed with new logic. But a biological virus can change and adapt to new situations and environments all on it's own --- with new logic of it's own design. You could easily argue that a simple biological virus is more "intelligent".

phreeza 6y ago
I don't know... Are the confines of nature, i.e. physical law, and the confines of programming that different in the end? The search space may be larger and thus it makes sense to use a different optimization strategy, but in the end both cases just follow a relatively simple program to achieve their goal.
- jqpabc123 6y ago
  The obvious difference is that a simple biological virus is arguably much more effective at exploring it's environment and adapting to it in order to survive, grow and expand.
Isinlor 6y ago
The difference is not only of quality, but quantity. Computing power that biological platforms provide are quite huge. Each infected person carries 1e9 - 1e11 virions during peak infection [0]. If there are 1 million (1e6) infected people right now then we have around 1e15 - 1e17 virons. With a viron size of 32kb and 8h replication time [1] we get speed of ~10 bits/s or very roughly 1 FLOPS per viron. That gives us computing power used for virus replication between 1 petaFLOPS to 100 petaFLOPS. The number is probably an underestimate because 1 viron probably needs to produce 1000 or more copies to maintain replication rate of 1. The upper bound then would be 100 exaFLOPS. That's in the range of TOP500 supercomputers (1.3 petaFLOPS - 442 petaFLOPS) [2]. The sum of all TOP500 supercomputers is 2.4 exaFLOPS, so some 40 times less than the upper bound on the virus of 100 exaFLOPS. [0] https://www.medrxiv.org/content/10.1101/2020.11.16.20232009v1 https://www.medrxiv.org/content/10.1101/2020.11.16.20232009v... [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224694/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224694/ [2] https://www.top500.org/statistics/perfdevel/ https://www.top500.org/statistics/perfdevel/
- jqpabc123 6y ago
  So in other words; by your math, all the supercomputers in the world are not enough to replicate the "intelligence" of a simple biological virus --- which is technically not even alive. Replicating "intelligence" that is alive is probably a trillion times more difficult. Correct me if I'm wrong but I think you just made an argument for "Why Computers (as we know them) Won't Make Themselves Smarter".
  - Isinlor 6y ago
    Just yesterday [0] I also did an upper bound estimate on computing power of a human brain based on assumption that brains carry out irreversible computations. E.g. inputs of an AND gate can not be recovered from the gate output. This forces increase of entropy. This is known as Landauer's principle or Landauer's limit [1]. The Landauer's limit suggests that a brain consuming 20W at 36C can carry out no more than ~1e22 irreversible operations per second (20W / (309.2K * Boltzmann constant * ln(2))). That bounds brains at ~1e21 FLOP/s (~10 bits erased per FLOP). The best supercomputer right now does ~4e17 FLOP/s. That's log2(1e21 / 4e17) ~11 doublings of computing power until supercomputers will be faster than thermodynamic maximum of FLOPS power of brains. Assuming we keep up the doubling time below 3 years, we will have supercomputers faster than a human brain in up to 3 * 11 ~ 33 years. And at that rate we will need another log2(1000^3) ~ 30 doublings to reach humanity brains computational power, so up to another 90 years. Overall, I don't see a reason why not to use biological platform for general type computations. If we are able to grow muscle tissue in a lab on an industrial scale, then we should be able to learn how to grow neurons in a lab on an industrial scale. Apparently neurons are a lot more difficult to grow, but it's still perfectly doable [2]. The issue is that we don't know how to grow them so that they do useful computation. But I can easily imagine that AWS and competition will be selling access to teachable biological neurons in the mid term future e.g. in 30 years. [0] https://news.ycombinator.com/item?id=26623730 https://news.ycombinator.com/item?id=26623730 [1] https://en.wikipedia.org/wiki/Landauer%27s_principle https://en.wikipedia.org/wiki/Landauer%27s_principle [2] https://www.youtube.com/watch?v=V2YDApNRK3g https://www.youtube.com/watch?v=V2YDApNRK3g