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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

#31

Post by pathOf_aFineMan »

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…

the use of 'quantum' appears to be tagging onto the potential of quantum annealers (which have repetitively [1] [2] been shown to be classically tractable) while trying to mimic a kind of quantum tunneling, ie the annealing schedule, without any kind of promises about exponential speedups etc. Quantum annealers themselves have few promised advantages for general combinatorial optimization problems without significant changes to extant hardware paradigms [3]

[1] https://arxiv.org/abs/2503.05693 [2] https://arxiv.org/pdf/2507.22117 [3] https://arxiv.org/abs/2008.09913

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

#32

Post by _bypa »

geremiiah wrote: post #12
jumploops wrote: post #5

Higher-order neuromorphic Ising machines—autoencoders and Fowler-Nordheim annealers are all you need for scalability[0] [0] https://www.nature.com/articles/s41467-026-71937-4

OK, this is just ridiculous now. Cut it with all this "all you need" crap. I'm only commenting on the title. I like their work.

The unreasonable effectiveness of regurgitated partial titles.

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

#33

Post by froh »

jumploops wrote: post #5

Higher-order neuromorphic Ising machines—autoencoders and Fowler-Nordheim annealers are all you need for scalability[0] [0] https://www.nature.com/articles/s41467-026-71937-4

ah that's the actual paper the OP is about! took me a bit. thanks for the link.

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

#34

Post by nyeah »

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,…

I'm not sure whether these FPGA codes count as specialized hardware.

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

#35

Post by stared »

The whole title is a buzzword cluster, until proven otherwise.

Which tasks, in particular, does it do better? Not as in "it could do them better", but actually there are benchmarks. If they are, they are buried beneath marketing; if not - well, we have our answer.

What is "thinks like nature"? Spin systems, are no more (or less) nature than transistors.

That said, I am all for exploring various systems for computation and simulation - I think there is a lot to discover.

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

#36

Post by incognito124 »

pipo234 wrote: post #4
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…

> Can someone that understands quantum computing please comment? ... Crickets ...

Very well then, could someone that _got used to quantum computing_ please comment?

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

#37

Post by clownpenis_fart »

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,…

[dead]

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

#39

Post by ngriffiths »

stared wrote: post #35

The whole title is a buzzword cluster, until proven otherwise. Which tasks, in particular, does it do better? Not as in "it could do them better", but actually there are benchmarks. If they are, they are buried beneath marketing; if not - well, we have our answer. What is "thinks like nature"? Spin systems, are no more (or less) nature than transistors. That said, I am all for exploring various systems for computatio…

Yeah, I mean it's obviously meant to be a marketing pitch but it's not a very good one.

> The hardest computational problems are not waiting for faster chips – they are waiting for machines that compute in a fundamentally different way.

Surely they don't actually believe that, right? Like you say the benefits must be limited to specific shapes of problems (not all of "the hardest" ones), and the whole history of computing is about how faster chips is an excellent answer to difficult computational problems.

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

#40

Post by gobdovan »

ngriffiths wrote: post #39
stared wrote: post #35

The whole title is a buzzword cluster, until proven otherwise. Which tasks, in particular, does it do better? Not as in "it could do them better", but actually there are benchmarks. If they are, they are buried beneath marketing; if not - well, we have our answer. What is "thinks like nature"? Spin systems, are no more (or less) nature than transistors. That said, I am all for exploring various systems for computatio…

Yeah, I mean it's obviously meant to be a marketing pitch but it's not a very good one. > The hardest computational problems are not waiting for faster chips – they are waiting for machines that compute in a fundamentally different way. Surely they don't actually believe that, right? Like you say the benefits must be limited to specific shapes of problems (not all of "the hardest" ones), and the whole history of comp…

> and the whole history of computing is about how faster chips is an excellent answer to difficult computational problems.

I don't really disagree, and I am definitely not taking their marketing pitch seriously. Yet, you could look at the same computation history and interpret it as an economically constrained hill-climbing around an idea that was simple enough to work reliably (von Neumann architecture) and that worked and scaled so well that we were rarely forced or desperate enough to move conceptually far away from it.

Sufficiently general digital computers can simulate other computational models, so I think 'faster' is ultimately the end game, but for some classes of computation, as you also noted, we may need to go for analog hardware, (maybe) quantum devices, optical interconnects, and so on.

Bret Victor has a talk about this, more or less: [0]

[0] https://www.youtube.com/watch?v=8pTEmbeENF4

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