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Discovering algorithms by enumerating terms in Haskell

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Re: Discovering algorithms by enumerating terms in Haskell

#41

Post by [deleted] »

nextos wrote: post #40
YeGoblynQueenne wrote: post #32

Earlier quoted context omitted.

>> There are quite a few publications that explore the concept of generating programs, either using typed or untyped functional languages. That's Inductive Functional Programming (IFP), a kind of Inductive Programming that also includes Inductive Logic Programming (ILP). The canonical example of IFP is Magic Haskeller: https://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.h... As an example of a modern ILP I sug…

Thanks, I know ILP quite well and also your research, Muggleton et al, etc. It is a very interesting field which I hope makes a comeback once systems become neurosymbolic.

[deleted]

Re: Discovering algorithms by enumerating terms in Haskell

#42

Post by YeGoblynQueenne »

nextos wrote: post #40
YeGoblynQueenne wrote: post #32

Earlier quoted context omitted.

>> There are quite a few publications that explore the concept of generating programs, either using typed or untyped functional languages. That's Inductive Functional Programming (IFP), a kind of Inductive Programming that also includes Inductive Logic Programming (ILP). The canonical example of IFP is Magic Haskeller: https://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.h... As an example of a modern ILP I sug…

Thanks, I know ILP quite well and also your research, Muggleton et al, etc. It is a very interesting field which I hope makes a comeback once systems become neurosymbolic.

Knowin ILP at all, let alone well, is so rare that I totally misread your comment.

I'm one of the last purely symbolic hold-outs in ILP I guess. But if you're interested in recent neurosymbolic work using MIL this is some recent work from my colleagues:

Abductive Knowledge Induction from Raw Data

https://www.ijcai.org/proceedings/2021/254

Re: Discovering algorithms by enumerating terms in Haskell

#43

Post by CyberDildonics »

nextos wrote: post #40
YeGoblynQueenne wrote: post #32

Earlier quoted context omitted.

>> There are quite a few publications that explore the concept of generating programs, either using typed or untyped functional languages. That's Inductive Functional Programming (IFP), a kind of Inductive Programming that also includes Inductive Logic Programming (ILP). The canonical example of IFP is Magic Haskeller: https://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.h... As an example of a modern ILP I sug…

Thanks, I know ILP quite well and also your research, Muggleton et al, etc. It is a very interesting field which I hope makes a comeback once systems become neurosymbolic.

once systems become neurosymbolic

What does this mean?

Re: Discovering algorithms by enumerating terms in Haskell

#44

Post by nextos »

CyberDildonics wrote: post #43
nextos wrote: post #40

Earlier quoted context omitted.

Thanks, I know ILP quite well and also your research, Muggleton et al, etc. It is a very interesting field which I hope makes a comeback once systems become neurosymbolic.

once systems become neurosymbolic What does this mean?

There's a whole research area that tries to combine some ideas from symbolic AI with neural architectures.

IMHO, this might eventually become mainstream. For example, see all the work that merges theorem provers with RL and NN.

Re: Discovering algorithms by enumerating terms in Haskell

#45

Post by CyberDildonics »

nextos wrote: post #44
CyberDildonics wrote: post #43

Earlier quoted context omitted.

once systems become neurosymbolic What does this mean?

There's a whole research area that tries to combine some ideas from symbolic AI with neural architectures. IMHO, this might eventually become mainstream. For example, see all the work that merges theorem provers with RL and NN.

What are examples of "symbolic AI", "neural architecture" and a system that is "neurosymbolic" ?

Re: Discovering algorithms by enumerating terms in Haskell

#46

Post by optimalsolver »

CyberDildonics wrote: post #45
nextos wrote: post #44

Earlier quoted context omitted.

There's a whole research area that tries to combine some ideas from symbolic AI with neural architectures. IMHO, this might eventually become mainstream. For example, see all the work that merges theorem provers with RL and NN.

What are examples of "symbolic AI", "neural architecture" and a system that is "neurosymbolic" ?

Almost everything that comes out of Deep Mind.

Re: Discovering algorithms by enumerating terms in Haskell

#47

Post by CyberDildonics »

optimalsolver wrote: post #46
CyberDildonics wrote: post #45

Earlier quoted context omitted.

What are examples of "symbolic AI", "neural architecture" and a system that is "neurosymbolic" ?

Almost everything that comes out of Deep Mind.

What makes it symbolic?

Re: Discovering algorithms by enumerating terms in Haskell

#48

Post by optimalsolver »

CyberDildonics wrote: post #47
optimalsolver wrote: post #46

Earlier quoted context omitted.

Almost everything that comes out of Deep Mind.

What makes it symbolic?

There's usually a DL part (the neural network), welded together with a classical AI algorithm, e.g. Monte Carlo Tree Search, or some kind of theorem prover.

Re: Discovering algorithms by enumerating terms in Haskell

#49

Post by CyberDildonics »

optimalsolver wrote: post #48
CyberDildonics wrote: post #47

Earlier quoted context omitted.

What makes it symbolic?

There's usually a DL part (the neural network), welded together with a classical AI algorithm, e.g. Monte Carlo Tree Search, or some kind of theorem prover.

What's the symbolic part? What does 'DL' mean here?

Re: Discovering algorithms by enumerating terms in Haskell

#50

Post by optimalsolver »

CyberDildonics wrote: post #49
optimalsolver wrote: post #48

Earlier quoted context omitted.

There's usually a DL part (the neural network), welded together with a classical AI algorithm, e.g. Monte Carlo Tree Search, or some kind of theorem prover.

What's the symbolic part? What does 'DL' mean here?

The symbolic part is the MCTS and/or theorem prover. DL is deep learning, i.e. the multilayered neural network. Neurosymbolic approaches form a significant branch of current machine learning. You can read up on all of it if you're actually curious and asking all this in good faith, but I suspect you're not.
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