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A Eureka machine that thinks like nature and explores what AI cannot

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Re: A Eureka machine that thinks like nature and explores what AI cannot

#21

Post by wmertens »

realo wrote: post #11

So many ... words... big words ... Can't compute. Help.

I had a long ELI16 session with Claude about it, and the way I understand it is that they

- use Ising machines to describe a certain problem into clauses, storing system state (e.g. spin of something) in variables

- then use a neural network layer where each neuron determines the value of one clause

- then for each state item, use the neuron output to determine if flipping that state would improve the overall system score

- and then use FN-like "noise" to determine whether to flip or no

If the energy landscape of the problem is pretty local, this is guaranteed to find a good solution to the system, using way less compute than brute-forcing it.

Re: A Eureka machine that thinks like nature and explores what AI cannot

#23

Post by anthk »

dave1010uk wrote: post #16

> Explores what AI cannot In other words, gradient descent isn't good at combinatorial optimisation. I'm sure the research is better but the hype in the blog post leaves a bad taste. There must be a version of Rich Sutton’s Bitter Lesson that applies to alternative computing like this, along with all the other exciting specialised hardware we've seen come and go over the years, like expert systems, optical computing,…

In hardware Prolog/Kanren/expert systems? That would be possible with libre microcode for Intel, and not this spyware corporate shithole we are living it.

We would be able to switch microcode at boot and set one for security, another one for C performance, others for Lisp performance and so on.

Re: A Eureka machine that thinks like nature and explores what AI cannot

#26

Post by GistNoesis »

I think this is about Ising Computers. I can't judge whether or not the worth of this paper.

But here are some good video introduction for what Ising computers are and how they work by Aaron Danner : https://www.youtube.com/watch?v=mD-0VpNSJA0&list=PLXb3r5ny8_... Ising Computers #1: Introduction Ising Computers #2: The Number Partitioning Problem Ising Computers #3: The Max-Cut Problem

It's an alternative way of computing, by setting up physical system, letting them evolve, and looking what state they evolve to.

You are setting problem by defining a system of coupled harmonic oscillators. Statistically (Boltzmann) after a long time it should settle in a configuration of low energy state, where the energy function is defined by the values of the coupling constant you set up.

It has a lot of similarity with quantum computing but none of the weirdness and you can simulate them numerically on standard computer instead of using real hardware to study them.

Re: A Eureka machine that thinks like nature and explores what AI cannot

#27

Post by fc417fc802 »

gobdovan wrote: post #25

> a neuromorphic computer that combines quantum-tunnelling physics with a brain-inspired architecture This ought to be the most rhetorically compressed, stacked-legitimacy-seeking hype phrase I've ever seen in a tech description.

Amusingly the nature paper is also an incredibly dense wall of hype terms but actually appears to have substance. It's like a weird alternate reality where a scam artist attempting to fleece gullible investors took things too far and performed rigorous science.

Re: A Eureka machine that thinks like nature and explores what AI cannot

#29

Post by bitwize »

dave1010uk wrote: post #16

> Explores what AI cannot In other words, gradient descent isn't good at combinatorial optimisation. I'm sure the research is better but the hype in the blog post leaves a bad taste. There must be a version of Rich Sutton’s Bitter Lesson that applies to alternative computing like this, along with all the other exciting specialised hardware we've seen come and go over the years, like expert systems, optical computing,…

> General purpose commodity silicon with rapidly evolving software generally beats specialised hardware.

All of the Amiga people are sighing right now, as they recall how their beautiful, elegant system synergistically designed with custom chips was outpaced by CPU/memory brute force in the early 90s.

Re: A Eureka machine that thinks like nature and explores what AI cannot

#30

Post by dv_dt »

repelsteeltje wrote: post #3

> [...] quantum-inspired computing built on CMOS technology [...] So at the heart of the solution is some FPGA that does something (close to?) quantum computing and that helps exploring exponential search space in somewhat feasible way? Is the gist that we might have stumbled upon a practical application of QC? And if so, what's the secret sauce if not lots of qbits? A new algorithm? Is it just hype? Can someone that…

I'll have to see if I can find references to an older effort on previous learning algorithm optimizations with FPGAs in the loop - it must be 20+ years old by now. The algorithm did indeed optimize the toy problems that it was setup to optimize, but it exploited non-digital, analog electronic characteristics of the individual FPGA to do it, so the solutions were not portable to any other FPGA - even of the same model.

Edit: There it is, Adrian Thompson evolution of tone generators, 1997.

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

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