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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
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.
> 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.
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
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.
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:
Software is just so much faster to iterate and improve than hardware. AI is also improving it too (eg AlphaEvolve).Specialized hardware may give a single, significant improvement that grabs headlines but in the long term, compounding small improvements win.
I have Bruce Sterling’s Ascendaries: The Best of Bruce Sterling” and… the reality is somewhere here in his stories…
Or take Charles Stross and his Accelerando book.
Do you think that teams behind such projects are avid readers and just fulfill the sci-fi stories? :)
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.
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.
This ought to be the most rhetorically compressed, stacked-legitimacy-seeking hype phrase I've ever seen in a tech description.
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?
From the paper:
> The FN-dynamics may be realized either by a physical FN-tunneling device or via a digital emulation of the FN-tunneling dynamical systems. In this work, we employ the digital emulation to achieve the precision required for simulated annealing in the low-temperature regime.
With a "real" (read: analog) FN device, you potentially get large speed ups and even larger cost/energy savings, because the physics is essentially working for "free" -- that's the quantum part.
What's unclear is how scalable the autoencoder architecture would be with analog FN devices today.
[1] https://arxiv.org/abs/2503.05693 [2] https://arxiv.org/pdf/2507.22117 [3] https://arxiv.org/abs/2008.09913
Edit: There it is, Adrian Thompson evolution of tone generators, 1997.
https://en.wikipedia.org/wiki/Evolvable_hardware
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Crickets
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[0]https://www.nature.com/articles/s41467-026-71937-4
I'm only commenting on the title. I like their work.
https://extropic.ai
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?
Is there some code or results from experiments where we can see the speed up?
[0]https://github.com/aimlab-wustl/NeuroSA-HO
Can't compute.
Help.
- 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.
The title is especially buzzword based with minimal meaning for the actual paper.