So many ... words... big words ...
Can't compute.
Help.
A Eureka machine that thinks like nature and explores what AI cannot
11–20 of 49 posts
Re: A Eureka machine that thinks like nature and explores what AI cannot
#12Higher-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.
Re: A Eureka machine that thinks like nature and explores what AI cannot
#13This reads like the paper from the Sokal affair.
Re: A Eureka machine that thinks like nature and explores what AI cannot
#14> [...] 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…
This is not especially related to quantum computing. Neuromorphic computing uses an algorithm that tries to replicate how the brain works and then in this case implements it and runs it on an FPGA. There are quite a range of papers on this concept and multiple companies are doing just this to show their work. It is often used as it should theoretically avoid such a brute force approach.
Re: A Eureka machine that thinks like nature and explores what AI cannot
#15They 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.
Re: A Eureka machine that thinks like nature and explores what AI cannot
#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, 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.
Re: A Eureka machine that thinks like nature and explores what AI cannot
#17> [...] 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…
So this isn't quantum computing (in the qubit sense), but instead a different computer architecture (demonstrated on an FPGA) that's based on Fowler–Nordheim (FN) quantum tunneling (a real physical effect, used in flash memory, but simulated here).
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.
Re: A Eureka machine that thinks like nature and explores what AI cannot
#18> 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 don't think they are even referring to gradient descent here. I think they are referring to systems like AlphaEvolve where they use LLMs to give an informed/heuristical guess to try to tackle an otherwise insurmountable search space.
Re: A Eureka machine that thinks like nature and explores what AI cannot
#19Yes, 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
Re: A Eureka machine that thinks like nature and explores what AI cannot
#20> 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 computer that combines quantum-tunnelling physics with a brain-inspired architecture to find solutions to hard mathematical problems”
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? :)