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Imagine every program that fits within 10TB. It’s a finite number of programs. Inside there is GTP-3, 2 and 1. Every GAN. Tesla FSD. It’s everything. What this

Imagine every program that fits within 10TB. It’s a finite number of programs. Inside there is GTP-3, 2 and 1. Every GAN. Tesla FSD. It’s everything. What this entire industry is really about at its core is finding programs in that set that behave in intelligent ways. That’s all. Recently we’ve had success in letting the computer dig through programs automatically until it finds a good one. So overnight we went from testing maybe hundreds per year to billions per month or something like that. So obviously things have been getting weird. Imagine how many we’ve dug up so far out of the total set. It’s an infinitesimal percentage. We haven’t even scratched the surface. Imagine the set. What’s inside? Is there something inside that we will regret?

metalliqaz 5y ago
>Is there something inside that we will regret? Unambiguously yes. 10TB is a lot of space. That's large enough for a program that does nothing but show 8 hours of HD footage of your kids being tortured and eaten. It's also more than enough to define a program that would reliably precipitate a global thermonuclear war if connected to the Internet.
jeremysalwen 5y ago
That's a very interesting way of thinking about things, but I think unfortunately the search techniques we are using (gradient descent with certain datasets/self-supervision tasks) limit us to exploring a very small subset of that 10TB of possible programs. A search strategy that could actually find the optimal 10TB program to accomplish some task would actually be a superintelligence beyond anything we have ever thought of creating using present day AI techniques.
- shawnz 5y ago
  Maybe one of the lesser search strategies will find a better search strategy? Isn't that how humans came around after all?
  - visarga 5y ago
    Evolution used an entire planet and billions of years to come up with us.
- bobthechef 5y ago
  > find the optimal 10TB program to accomplish some task would actually be a superintelligence I am having a difficult time understanding what the operative meaning of "intelligence" here is. "AI" doesn't transcend the Turing machine. Intelligence doesn't mean more computer cycles per unit time either and compute cycles don't transcend the TM. What makes intelligence intelligence is what it can in principle do; speed is irrelevant. There is no essential difference between AI and non-AI.
- FredPret 5y ago
  But we want our search strategy to limit us to the infinitesimal fraction of programs that does useful work. 99.99% of possible bit arrangements in that 10TB will not do anything good, or useful, or even anything at all.
  - margalabargala 5y ago
    The number possible bit arrangements in 10TB is 2 raised to the power of 80 trillion. This comes out to 3.1 × 10^24082399653118 To emphasize the size of that search space- let us measure the diameter of the observable universe in planck lengths, the shortest possible length. We would need over 24 trillion digits to write the number of universes we would need for the quantity of planck lengths to equal the number of possible programs. That 99.99% number is missing several billion additional 9s.
    - supperburg 5y ago
      Isn’t it incredible then that we have already found so many surprising programs in that set?
      - margalabargala 5y ago
        We are fortunately not searching that space with brute force.
phyalow 5y ago
Sorry I dont follow at all. Are you stating that you have a corpus of 10TB of software source and you are frankenstiening it with interesting results? I find that hard to believe.. Surely its like Monkeys and typewriters, 10TB in that context wouldnt be nearly enough.
- mrbungie 5y ago
  Parent is not talking about a 10TB corpus of software but the corpus/set of all possible software weighing 10TB (or less I guess, specially if you take the lottery ticket hypothesis into consideration). A sibling post shows this, it presents the concept very well: https://en.wikipedia.org/wiki/The_Library_of_Babel https://en.wikipedia.org/wiki/The_Library_of_Babel
undershirt 5y ago
https://en.wikipedia.org/wiki/The_Library_of_Babel https://en.wikipedia.org/wiki/The_Library_of_Babel
hyperion2010 5y ago
In order to search that space you have to have a specification language for the behavior of the target program. Things like copilot/GPT-3 currently take natural language as input, but that cannot be used for anything that needs to be verifiable or have correctness. Maybe start by having the nets generate implementations of the IETF RFCs in a variety of languages given the text of the RFC as input? Not easy.
bmc7505 5y ago
While there are those in the AI community who believe scaling laws will unearth such programs, they are deluded. There are problems which scale faster than our computational resources allow, even if hardware scaling continues unabated for millennia to come. The space of all programs in 10TB is far too large to count, even if we could harness galactic computation. Even within a much smaller search space, there are valid programs which cannot be found by gradient descent. Let BT(n) be the number of distinct binary trees less than or equal to height n. This number scales according to the following recurrence relation: BT(n+1)=(BT(n)+2)²−1 Consider the space of all binary trees of height 20 - there fewer atoms in the visible universe. And this is just laying out bits on a hard drive. There are other functions (e.g. Busy beaver and friends) which scale even faster. The space of valid programs in 10TB is too large to enumerate, never mind evaluate. In case anyone here is interested in learning more about program synthesis, there is a new workshop at NeurIPS 2021 which explores some of these topics. You can check it out here: https://aiplans.github.io/ https://aiplans.github.io/
- supperburg 5y ago
  Yeah
- twofornone 5y ago
  You're not wrong, but the numbers are quite different if you have a strong search heuristic. And that is where neural nets excel. See AlphaGo and derivatives. So this isn't just some random gradient descent exploring the full space; a properly designed and trained neural net will effectively cordon off vast swaths of the search space, and suddenly the problem is probably many orders of magnitude more tractable. That's really why deep learning shines in technical applications. Solution search heuristics were until recently solely within the domain of biological neural networks; now we have created technology which is capable of extracting superior heuristics over the course of learning. And it's already paying off in industrial science, despite the cries of naysayers, though the applications are still in infancy.
  - bmc7505 5y ago
    There is a vast chasm of computational complexity between Chess, Go, and protein folding, to program induction. Unlike problems where the configuration space grows exponentially, the space of valid programs is at least super-exponential and depending on the language family, (e.g. context free, context sensitive, recursively enumerable), can often be undecidable. Furthermore, many language induction problems do not have optimal substructure or overlapping subproblems, two important prerequisites for reinforcement learning to work. In these settings, gradient-based heuristics will only get you so far. If you are interested in learning more about the limits of gradient descent, you should look into the TerpreT problem [1]. There are surprisingly tiny Boolean circuits which can be found using constraint solving, but we have not yet been able to learn despite the success of reinforcement learning in other domains. I'm not saying that program induction is impossible, but it is extremely hard even for relative "simple" languages like source code. [1]: https://arxiv.org/pdf/1608.04428.pdf https://arxiv.org/pdf/1608.04428.pdf
deleted 5y ago
[deleted]
soVeryTired 5y ago
Meh. Imagine the set of 10k x 10k pixel RGB images. The mona lisa is in there, as are images from your worst nightmares. But almost all of the images in that set look like grey fuzz. On average there's nothing interesting there.

metalliqaz 5y ago

>Is there something inside that we will regret? Unambiguously yes. 10TB is a lot of space. That's large enough for a program that does nothing but show 8 hours of HD footage of your kids being tortured and eaten. It's also more than enough to define a program that would reliably precipitate a global thermonuclear war if connected to the Internet.

jeremysalwen 5y ago

That's a very interesting way of thinking about things, but I think unfortunately the search techniques we are using (gradient descent with certain datasets/self-supervision tasks) limit us to exploring a very small subset of that 10TB of possible programs. A search strategy that could actually find the optimal 10TB program to accomplish some task would actually be a superintelligence beyond anything we have ever thought of creating using present day AI techniques.

shawnz 5y ago
Maybe one of the lesser search strategies will find a better search strategy? Isn't that how humans came around after all?
- visarga 5y ago
  Evolution used an entire planet and billions of years to come up with us.
bobthechef 5y ago
> find the optimal 10TB program to accomplish some task would actually be a superintelligence I am having a difficult time understanding what the operative meaning of "intelligence" here is. "AI" doesn't transcend the Turing machine. Intelligence doesn't mean more computer cycles per unit time either and compute cycles don't transcend the TM. What makes intelligence intelligence is what it can in principle do; speed is irrelevant. There is no essential difference between AI and non-AI.
FredPret 5y ago
But we want our search strategy to limit us to the infinitesimal fraction of programs that does useful work. 99.99% of possible bit arrangements in that 10TB will not do anything good, or useful, or even anything at all.
- margalabargala 5y ago
  The number possible bit arrangements in 10TB is 2 raised to the power of 80 trillion. This comes out to 3.1 × 10^24082399653118 To emphasize the size of that search space- let us measure the diameter of the observable universe in planck lengths, the shortest possible length. We would need over 24 trillion digits to write the number of universes we would need for the quantity of planck lengths to equal the number of possible programs. That 99.99% number is missing several billion additional 9s.
  - supperburg 5y ago
    Isn’t it incredible then that we have already found so many surprising programs in that set?
    - margalabargala 5y ago
      We are fortunately not searching that space with brute force.

phyalow 5y ago

Sorry I dont follow at all. Are you stating that you have a corpus of 10TB of software source and you are frankenstiening it with interesting results? I find that hard to believe.. Surely its like Monkeys and typewriters, 10TB in that context wouldnt be nearly enough.

mrbungie 5y ago
Parent is not talking about a 10TB corpus of software but the corpus/set of all possible software weighing 10TB (or less I guess, specially if you take the lottery ticket hypothesis into consideration). A sibling post shows this, it presents the concept very well: https://en.wikipedia.org/wiki/The_Library_of_Babel https://en.wikipedia.org/wiki/The_Library_of_Babel

undershirt 5y ago

https://en.wikipedia.org/wiki/The_Library_of_Babel https://en.wikipedia.org/wiki/The_Library_of_Babel

hyperion2010 5y ago

In order to search that space you have to have a specification language for the behavior of the target program. Things like copilot/GPT-3 currently take natural language as input, but that cannot be used for anything that needs to be verifiable or have correctness. Maybe start by having the nets generate implementations of the IETF RFCs in a variety of languages given the text of the RFC as input? Not easy.

bmc7505 5y ago

While there are those in the AI community who believe scaling laws will unearth such programs, they are deluded. There are problems which scale faster than our computational resources allow, even if hardware scaling continues unabated for millennia to come. The space of all programs in 10TB is far too large to count, even if we could harness galactic computation. Even within a much smaller search space, there are valid programs which cannot be found by gradient descent. Let BT(n) be the number of distinct binary trees less than or equal to height n. This number scales according to the following recurrence relation: BT(n+1)=(BT(n)+2)²−1 Consider the space of all binary trees of height 20 - there fewer atoms in the visible universe. And this is just laying out bits on a hard drive. There are other functions (e.g. Busy beaver and friends) which scale even faster. The space of valid programs in 10TB is too large to enumerate, never mind evaluate. In case anyone here is interested in learning more about program synthesis, there is a new workshop at NeurIPS 2021 which explores some of these topics. You can check it out here: https://aiplans.github.io/ https://aiplans.github.io/

supperburg 5y ago
Yeah
twofornone 5y ago
You're not wrong, but the numbers are quite different if you have a strong search heuristic. And that is where neural nets excel. See AlphaGo and derivatives. So this isn't just some random gradient descent exploring the full space; a properly designed and trained neural net will effectively cordon off vast swaths of the search space, and suddenly the problem is probably many orders of magnitude more tractable. That's really why deep learning shines in technical applications. Solution search heuristics were until recently solely within the domain of biological neural networks; now we have created technology which is capable of extracting superior heuristics over the course of learning. And it's already paying off in industrial science, despite the cries of naysayers, though the applications are still in infancy.
- bmc7505 5y ago
  There is a vast chasm of computational complexity between Chess, Go, and protein folding, to program induction. Unlike problems where the configuration space grows exponentially, the space of valid programs is at least super-exponential and depending on the language family, (e.g. context free, context sensitive, recursively enumerable), can often be undecidable. Furthermore, many language induction problems do not have optimal substructure or overlapping subproblems, two important prerequisites for reinforcement learning to work. In these settings, gradient-based heuristics will only get you so far. If you are interested in learning more about the limits of gradient descent, you should look into the TerpreT problem [1]. There are surprisingly tiny Boolean circuits which can be found using constraint solving, but we have not yet been able to learn despite the success of reinforcement learning in other domains. I'm not saying that program induction is impossible, but it is extremely hard even for relative "simple" languages like source code. [1]: https://arxiv.org/pdf/1608.04428.pdf https://arxiv.org/pdf/1608.04428.pdf

deleted 5y ago

[deleted]

soVeryTired 5y ago

Meh. Imagine the set of 10k x 10k pixel RGB images. The mona lisa is in there, as are images from your worst nightmares. But almost all of the images in that set look like grey fuzz. On average there's nothing interesting there.