7 ms·

> Artificial Intelligences are almost certainly going to run into the same limitations that prevent natural intelligences from becoming godlike. Right, limitat

> Artificial Intelligences are almost certainly going to run into the same limitations that prevent natural intelligences from becoming godlike. Right, limitations like: width of human mothers' hips limiting head size, inability to continue growing the brain after adulthood, the decades-long education process, the impossibility of doing repeated experiments of different teaching techniques on the same people and hence iterating quickly and accurately, the impossibility of giving one person's intelligence and knowledge directly to anyone else, old age and death limiting how much a single person can learn or accomplish... Once you have a computer Einstein, you can immediately have 100 computer Einsteins (given the hardware), and let them work on different projects (or even let some of them collaborate). That fact alone, while not a singularity, is at least a game-changer in terms of innovation. I have gotten annoyed at science fiction scenarios that allow for duplication of adults and don't answer the question "Ok, so, why hasn't this society been duplicating their best scientists and out-inventing the rest of the galaxy?" I think that, if there is room for skepticism, that has to be at the "Can we get to a computer Einstein?" stage.

CJefferson 7y ago
Except for problems known as "embarassingly parallel", we often have trouble running even quite boring algorithms on 32 or 64 cores. In general setting 64 people in a job won't lead to a better or faster result than 2 or 3. Given that, I don't see an immediate reason to believe throwing multiple AIs at a problem is going to exponentially scale either, given neither people nor bad AIs scale that way.
- a_t48 7y ago
  Parent comment was suggesting solving multiple problems at once.
- mantoto 7y ago
  When is this a real innovation stoper? Don't think we have those kind of problems in that space
- pdimitar 7y ago
  Who said the AI Einsteins will work on boring algorithms, though? Wrong premise IMO.
AkshatM 7y ago
> Right, limitations like: width of human mothers' hips limiting head size, inability to continue growing the brain after adulthood, the decades-long education process, the impossibility of doing repeated experiments of different teaching techniques on the same people and hence iterating quickly and accurately, the impossibility of giving one person's intelligence and knowledge directly to anyone else, old age and death limiting how much a single person can learn or accomplish... Those aren't actually limitations preventing us from becoming godlike. Those are limitations on us becoming more populous and collectively more experienced, which I argue isn't the same thing at all. Apotheosis in the context of AI safety as well as for humankind is really about the accumulation of power, rather than the accumulation of knowledge. The extent of our apotheosis is limited only by what we can control, not by what we know. Knowledge and intelligence only allow you to reason more effectively about paths to power given enough information about the environment, but it doesn't give you a strategic guarantee that you will acquire power - we know there are games that cannot be solved, and games that take too long to solve efficiently, and games that, even with sufficient computing power, rely on factors that cannot be controlled (such as luck), and games for which there are no Nash equilibria or stable winning strategies. The real world is full of such games. Computers have only managed to beat humans at a small fraction of them, and even then only ones where brute-force and smart pruning (optimal game space search) are winning strategies - bots for games that require probabilistic reasoning, such as poker, typically don't fare any better against expert humans, largely because efficiently solving probabilistic games in general is (per a hazy seminar of AI many years ago, so I could be misremembering) an NP-hard problem. There are plenty of both natural and artificial limitations around the accumulation of power besides game solvability and probabilistic reasoning's difficulty: 1) System designs that require a minimum set of resources that intelligences can't acquire. For example, individual humans usually can't build their own thermonuclear devices because they don't have access to weapons-grade uranium. Much of our information security relies on making it computationally expensive and infeasible for an attacker without state-level resources to break in, and getting state-level resources isn't a straightforward task at all. We humans haven't even gotten around to building our Dyson spheres yet, because the resources needed to do it far outstrip our ability to acquire them. Intelligence does not improve resource acquisition by itself. 2) Impossibility theorems and optimization difficulty. These place fundamental limits on power - as an example, we know Moore's law has to stop at some point because increasing chips increases power generation, and the amount of effort needed to generate such chips increases with every nanometer shaved off. Most of humanity's most difficult and important problems are optimization problems, and these optimization problems can be tricky to solve in polynomial time. In many cases, we have to rely on fast crude heuristics that give us less than optimal results. The above limitations partly become easier if you try to parallelize your efforts by duplicating AIs (you can collude to acquire resources and solve problems), but this means you get all the fun parts of distributed systems theory for free: coordination, replication consistency, etc. If only all problems were embarassingly parallel! So it's not necessarily easier to add nodes to a knowledge graph just by adding more processors.
29athrowaway 7y ago
And also, Einstein was only a human. He could only communicate using spoken or written words, illustrations, and of course, mathematical expressions and what not. A computer can potentially communicate much faster, and can transmit "thoughts" much more accurately than a human can. Imagine you could serialize a neural ensemble in your brain and send it to another person, so that person is then immediately able to use it, instantly acquiring a skill. That alone is a huge advantage over a human being.
- deleted 7y ago
  [deleted]
- waterhouse 7y ago
  And even if "me learning something" means different neural rewirings than "you learning something" and isn't transferable, you could instead have 100 instances of yourself go and learn 100 different things, then combine all the rewirings at the end. There might be "merge conflicts", but I suspect it would still mostly work—and you could parallelize the work of finding ways to fix the conflicts! Even if none of that worked, at the very least you could have your branch-instances discover by experiment the fastest way for you personally to learn each thing, then have your main instance follow an optimal educational program, which I will arbitrarily proclaim to be 4x faster than normal learning.
- marcus_holmes 7y ago
  books. You just said books. Only faster.
  - 29athrowaway 7y ago
    Books contain thoughts encoded in words. And words can be ambiguous at times. Writing is a skill, and not everyone is good at it. It is a lossy process.
    - marcus_holmes 7y ago
      Good point. But I imagine that thought transference must be the same - human brains are messes of neurons connected in patterns formed by birth and experience, and a thought process that works fine in one brain may not map correctly to another. In any machine capable of learning, the same kind of semi-random process must occur, and mapping a pattern from one will not fit correctly with another. You can't take half the nodes from a neural network that recognises dogs and add them to a neural network that knows about laundry to get a network that knows how to wash dogs ;)
cabalamat 7y ago
> you can immediately have 100 computer Einsteins (given the hardware), and let them work on different projects (or even let some of them collaborate) You might even get >100 times speedup, since there are probably problems where 10 copies of of the same mind can collaborate better than 10 different minds, e.g. I bet 10 copies of me could collaborate writing a program better than 10 random programmers. (I base this on I can read my own code from long ago a lot easier than most other people's code).
- imtringued 7y ago
  I don't think this is true. If you had 10 instances of yourself then they would diverge as each of them specializes on different things. The gains come from the fact that you can quickly share the knowledge between the AI instances.
  - cabalamat 7y ago
    > If you had 10 instances of yourself then they would diverge as each of them specializes on different things. Yes but only slightly. I can read code I wrote 10 years ago, that I have totally forgotten about since the way-of-thinking behind the code comes easily to my mind.
richardhod 7y ago
I think your sticking point here is "(given the hardware)". There are always limiting factors. AI robots are unlikely to be building and repairing themselves anytime soon, and can always be deprived of electrons.

CJefferson 7y ago

Except for problems known as "embarassingly parallel", we often have trouble running even quite boring algorithms on 32 or 64 cores. In general setting 64 people in a job won't lead to a better or faster result than 2 or 3. Given that, I don't see an immediate reason to believe throwing multiple AIs at a problem is going to exponentially scale either, given neither people nor bad AIs scale that way.

a_t48 7y ago
Parent comment was suggesting solving multiple problems at once.
mantoto 7y ago
When is this a real innovation stoper? Don't think we have those kind of problems in that space
pdimitar 7y ago
Who said the AI Einsteins will work on boring algorithms, though? Wrong premise IMO.

AkshatM 7y ago

> Right, limitations like: width of human mothers' hips limiting head size, inability to continue growing the brain after adulthood, the decades-long education process, the impossibility of doing repeated experiments of different teaching techniques on the same people and hence iterating quickly and accurately, the impossibility of giving one person's intelligence and knowledge directly to anyone else, old age and death limiting how much a single person can learn or accomplish... Those aren't actually limitations preventing us from becoming godlike. Those are limitations on us becoming more populous and collectively more experienced, which I argue isn't the same thing at all. Apotheosis in the context of AI safety as well as for humankind is really about the accumulation of power, rather than the accumulation of knowledge. The extent of our apotheosis is limited only by what we can control, not by what we know. Knowledge and intelligence only allow you to reason more effectively about paths to power given enough information about the environment, but it doesn't give you a strategic guarantee that you will acquire power - we know there are games that cannot be solved, and games that take too long to solve efficiently, and games that, even with sufficient computing power, rely on factors that cannot be controlled (such as luck), and games for which there are no Nash equilibria or stable winning strategies. The real world is full of such games. Computers have only managed to beat humans at a small fraction of them, and even then only ones where brute-force and smart pruning (optimal game space search) are winning strategies - bots for games that require probabilistic reasoning, such as poker, typically don't fare any better against expert humans, largely because efficiently solving probabilistic games in general is (per a hazy seminar of AI many years ago, so I could be misremembering) an NP-hard problem. There are plenty of both natural and artificial limitations around the accumulation of power besides game solvability and probabilistic reasoning's difficulty: 1) System designs that require a minimum set of resources that intelligences can't acquire. For example, individual humans usually can't build their own thermonuclear devices because they don't have access to weapons-grade uranium. Much of our information security relies on making it computationally expensive and infeasible for an attacker without state-level resources to break in, and getting state-level resources isn't a straightforward task at all. We humans haven't even gotten around to building our Dyson spheres yet, because the resources needed to do it far outstrip our ability to acquire them. Intelligence does not improve resource acquisition by itself. 2) Impossibility theorems and optimization difficulty. These place fundamental limits on power - as an example, we know Moore's law has to stop at some point because increasing chips increases power generation, and the amount of effort needed to generate such chips increases with every nanometer shaved off. Most of humanity's most difficult and important problems are optimization problems, and these optimization problems can be tricky to solve in polynomial time. In many cases, we have to rely on fast crude heuristics that give us less than optimal results. The above limitations partly become easier if you try to parallelize your efforts by duplicating AIs (you can collude to acquire resources and solve problems), but this means you get all the fun parts of distributed systems theory for free: coordination, replication consistency, etc. If only all problems were embarassingly parallel! So it's not necessarily easier to add nodes to a knowledge graph just by adding more processors.

29athrowaway 7y ago

And also, Einstein was only a human. He could only communicate using spoken or written words, illustrations, and of course, mathematical expressions and what not. A computer can potentially communicate much faster, and can transmit "thoughts" much more accurately than a human can. Imagine you could serialize a neural ensemble in your brain and send it to another person, so that person is then immediately able to use it, instantly acquiring a skill. That alone is a huge advantage over a human being.

deleted 7y ago
[deleted]
waterhouse 7y ago
And even if "me learning something" means different neural rewirings than "you learning something" and isn't transferable, you could instead have 100 instances of yourself go and learn 100 different things, then combine all the rewirings at the end. There might be "merge conflicts", but I suspect it would still mostly work—and you could parallelize the work of finding ways to fix the conflicts! Even if none of that worked, at the very least you could have your branch-instances discover by experiment the fastest way for you personally to learn each thing, then have your main instance follow an optimal educational program, which I will arbitrarily proclaim to be 4x faster than normal learning.
marcus_holmes 7y ago
books. You just said books. Only faster.
- 29athrowaway 7y ago
  Books contain thoughts encoded in words. And words can be ambiguous at times. Writing is a skill, and not everyone is good at it. It is a lossy process.
  - marcus_holmes 7y ago
    Good point. But I imagine that thought transference must be the same - human brains are messes of neurons connected in patterns formed by birth and experience, and a thought process that works fine in one brain may not map correctly to another. In any machine capable of learning, the same kind of semi-random process must occur, and mapping a pattern from one will not fit correctly with another. You can't take half the nodes from a neural network that recognises dogs and add them to a neural network that knows about laundry to get a network that knows how to wash dogs ;)

cabalamat 7y ago

> you can immediately have 100 computer Einsteins (given the hardware), and let them work on different projects (or even let some of them collaborate) You might even get >100 times speedup, since there are probably problems where 10 copies of of the same mind can collaborate better than 10 different minds, e.g. I bet 10 copies of me could collaborate writing a program better than 10 random programmers. (I base this on I can read my own code from long ago a lot easier than most other people's code).

imtringued 7y ago
I don't think this is true. If you had 10 instances of yourself then they would diverge as each of them specializes on different things. The gains come from the fact that you can quickly share the knowledge between the AI instances.
- cabalamat 7y ago
  > If you had 10 instances of yourself then they would diverge as each of them specializes on different things. Yes but only slightly. I can read code I wrote 10 years ago, that I have totally forgotten about since the way-of-thinking behind the code comes easily to my mind.

richardhod 7y ago

I think your sticking point here is "(given the hardware)". There are always limiting factors. AI robots are unlikely to be building and repairing themselves anytime soon, and can always be deprived of electrons.