alt Hacker NewsA Eureka machine that thinks like nature and explores what AI cannot
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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.
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.
> 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, neuromorphic computing, etc.
Something like:
General purpose commodity silicon with rapidly evolving software generally beats specialised hardware.
Specialized hardware may give a single, significant improvement that grabs headlines but in the long term, compounding small improvements win.
> 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.
> [...] 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 understands quantum computing please comment?
Higher-order neuromorphic Ising machines—autoencoders and Fowler-Nordheim annealers are all you need for scalability[0]
I wonder how this compares to thermal wells?
They seem to work in a similar way, sampling from chaotic datasets to find the lowest energy state.
Is one fundamentally more scalable? More efficient?
This isn’t even a research paper.
Is there some code or results from experiments where we can see the speed up?
There are a lot of buzzwords in there. Does it work?
We should ask Stuart Hameroff for help then.
They have replicated a neuromorphic algorithm (brain like) on a FPGA, but this implementation at this scale is doubtful to have any improvement over a brute force effort. Quite a few companies feel this is the way forward, although the end goal would be potentially better using photonic chips than qubits and obviously better than an fpga.
The title is especially buzzword based with minimal meaning for the actual paper.
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Yes, I actually believe that if we really want to build AI and physical AI, we need this. I'm working on this for a while. vantar.xyz
Seems like the key elements in this are the use of a neuromorphic autoencoder (instead of a 'regular' one), plus Fowler–Nordheim annealing dynamics and Ising energy minimization so that the system is not just passively settling, it's being taken through a controlled search process designed to avoid premature trapping and scale to higher-order combinatorial optimization problems. [1]
A 'regular' autoencoder is a neural network trained to compress data and then reconstruct it.
A neuromorphic autoencoder is instead implemented using brain-inspired computing elements like spiking neurons, event-driven updates, local interactions, sometimes specialized hardware. In this paper, looks like the autoencoder is being used as a structured energy-minimizing circuit for an Ising optimization problem. The architecture manipulates Ising clauses rather than only pairwise spin interactions.
Ordinary artificial neurons compute matrix ops such as y=f(Wx+b), while this uses artificial neurons that accumulate input, which emit a spike when they cross a threshold, like biological neurons (event driven neural dynamics).
[1] https://www.nature.com/articles/s41467-026-71937-4