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Think about it differently. The hard problems are going to be increasingly easier and easier to solve because they themselves consists of simpler problems. Wha

Think about it differently. The hard problems are going to be increasingly easier and easier to solve because they themselves consists of simpler problems. What technology is really good at is to share it's findings across the entire network. What one learn every other entity now have access to. The mistake in dismissing the thinking is in believing that complex problems are irreducible complex, they are not. They are simply the layer of ever more simple problems. Thats how we came to create the complexity and the problems that arose from it.

daveguy 10y ago
> The mistake in dismissing the thinking is in believing that complex problems are irreducible complex, they are not. This is almost certainly false. Current state of the art in complexity theory is that some complex problems are irreducible. The expectation is that P != NP and that means there are irreducible problems. Assuming that P=NP is almost, but not quite, like assuming perpetual motion.
- ThomPete 10y ago
  Sure but thats not what we are discussing here. We are discussing things in everyday live not mathematical complexity. The claim was that the interesting problems are much more complex and thus can't be solved easily by machine learning.My point is that most of these complex problems aren't really anything but simpler problems gaining complexity by connection of simpler problems not by being irreducible. Any engineer will tell you thats exactly how you solve complex problem, by breaking them into smaller ones.
  - dkarapetyan 10y ago
    The way we model the world is with mathematical models and the complexity bounds on those mathematical models are actually statements about irreducibility of those problems to simpler ones. In fact, the model is lower bound approximation to the real thing and if the approximation has certain complexity then the real thing will be much worse. That is the whole point of having a model. You approximate reality with something simpler and hope the solution to the simpler model problem is close enough to the solution of the real problem. So what real world problems are you talking about? I'd say much of engineering and science is coming up with these solvable models for real world phenomena and so far no one has cracked the nut on coming up with a simple solution to problems that have complicated models. You might argue AGI is not one of those problems but that's a different argument.
    - ThomPete 10y ago
      I was responding to this. "In the real world, the interesting problems are much much harder than that." In one of the parents.
leepowers 10y ago
We can conceptualize problems as discrete units to be solved and enumerated. But reality is dynamic and relational, and all problems are ultimately interrelated somehow. It's that true discrete problem units are smaller and simpler than the whole. But this increases the overall work needed. An example is sequencing the genome. This problem was solved much more quickly than anticipated due to advances in computer processing power. But it also revealed a huge new problem space of epigenetics. Complexity is emergent, not static and hierarchical. Progress is a tree, not a ladder.
- ThomPete 10y ago
  It is emergents of simpler problems. My point (claim) is that a lot of the simpler problems are the same. It's only how and to what extent they are combined that they are of various complexity. So once you solve some of those simple problems in some problem spaces you can solve the same level of problems other places. In other words, you don't have to start over with every single problem. I am not saying it's always like that but I believe enough to make the singularity a realistic thing to plan for.

daveguy 10y ago

> The mistake in dismissing the thinking is in believing that complex problems are irreducible complex, they are not. This is almost certainly false. Current state of the art in complexity theory is that some complex problems are irreducible. The expectation is that P != NP and that means there are irreducible problems. Assuming that P=NP is almost, but not quite, like assuming perpetual motion.

ThomPete 10y ago
Sure but thats not what we are discussing here. We are discussing things in everyday live not mathematical complexity. The claim was that the interesting problems are much more complex and thus can't be solved easily by machine learning.My point is that most of these complex problems aren't really anything but simpler problems gaining complexity by connection of simpler problems not by being irreducible. Any engineer will tell you thats exactly how you solve complex problem, by breaking them into smaller ones.
- dkarapetyan 10y ago
  The way we model the world is with mathematical models and the complexity bounds on those mathematical models are actually statements about irreducibility of those problems to simpler ones. In fact, the model is lower bound approximation to the real thing and if the approximation has certain complexity then the real thing will be much worse. That is the whole point of having a model. You approximate reality with something simpler and hope the solution to the simpler model problem is close enough to the solution of the real problem. So what real world problems are you talking about? I'd say much of engineering and science is coming up with these solvable models for real world phenomena and so far no one has cracked the nut on coming up with a simple solution to problems that have complicated models. You might argue AGI is not one of those problems but that's a different argument.
  - ThomPete 10y ago
    I was responding to this. "In the real world, the interesting problems are much much harder than that." In one of the parents.

leepowers 10y ago

We can conceptualize problems as discrete units to be solved and enumerated. But reality is dynamic and relational, and all problems are ultimately interrelated somehow. It's that true discrete problem units are smaller and simpler than the whole. But this increases the overall work needed. An example is sequencing the genome. This problem was solved much more quickly than anticipated due to advances in computer processing power. But it also revealed a huge new problem space of epigenetics. Complexity is emergent, not static and hierarchical. Progress is a tree, not a ladder.

ThomPete 10y ago
It is emergents of simpler problems. My point (claim) is that a lot of the simpler problems are the same. It's only how and to what extent they are combined that they are of various complexity. So once you solve some of those simple problems in some problem spaces you can solve the same level of problems other places. In other words, you don't have to start over with every single problem. I am not saying it's always like that but I believe enough to make the singularity a realistic thing to plan for.