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Reminds me of good ol genetic algorithm search. Guess and check can be quite powerful, especially if you can toss in agent in the loop guidance.
Was going to say much the same. I recall one story about a genetic algorithm to make an oscillator with the fewest possible components, and it successfully did so by surprising the humans with a single wire, i.e. an antenna picking up nearby stray RF.
The other side is Cognitive Radio [1] which also evolve the OTA protocols for cooperative diversity from IEEE 802.22 onwards. Now I can see AI, via a local SLM/NPU plus agentic GNURadio loops for new radio use cases. This is going to be much more wide spread in the upcoming 3GPP 6G releases in 2030.
GA’s optimize only combinatorial problems though — where you have discrete set of choices (~genes) for each variable, and therefore do not have a gradient
It may come as a surprise, but this phenomenon of "uninterpretable" circuits designed by algorithms is 30 years old now.
Adrian Thompson's research in the 90s evolved FPGAs that did signal analysis with bizarre features:
- A tiny number of cells (far fewer than expected)
- No clock, despite performing signal analysis
- FPGA cells that were logically disconnected, but when removed caused the device to stop working
Even then their approach was taking advantage of the physics in the FPGA. One can only imagine how effective this could be when applied to circuit design with the compute budget of a frontier lab.
https://cacm.acm.org/research/analysis-of-unconventional-evo...
Those "evolved" FPGAs weren't much of a mystery. They just used undefined parameters (inductive coupling, power supply rail glitching, etc) to achieved the trained outputs. They didn't work when the ambient temperature changed, or when moved to another example of the same FPGA.
One great application of AI design is patent poisoning. Use AI to churn out masses of variant designs, make them publicly visible on a web site, and if future patents come out use any collisions to invalidate them or at least restrict their scope (generalization of a patent is limited by prior art.)
I’m reminded of lawyer Damien Riehl’s (performative) reaction to the Sam Smith infringement decision, back in 2019/2020. He and programmer Noah Rubin algorithmically generated every possible melody (within a certain combinatorial space, in MIDI format as I recall), and purported to release them under CC-0 license [0]. He went on to attract some attention and explain his argument at a regional TEDx event [1].
I seem to recall legal commentators reacting with an eyeroll—apparently judges split much finer hairs than these for a living—but it was a cute stunt.
[1] https://m.youtube.com/watch?v=sJtm0MoOgiU and https://www.the-independent.com/tech/music-copyright-algorit...
Wouldn't work. Judges would not treat the AI generated designs as prior art without proof of human involvement (above and beyond entering the prompt).
If patents can’t be granted to an AI inventor, I don’t see how such AI “inventions” could be used as prior art.
"Humans couldn't even imagine" seems like overselling it, but I'm sure that machine learning algorithms can brute force their way to chip designs no one has tried before and that some of those might be useful to us. That seems like a pretty reasonable thing for a computer to do.
Here's a story about a genetic algorithm evolving a circuit that works with logic gates that aren't even connected, seemingly by using magnetic flux: https://www.damninteresting.com/on-the-origin-of-circuits/
It's marketing bullshit. For one, it's like proving a negative; you can't prove to me that humans couldn't have imagined it. Second, humans have already imagined quite a lot of crazy stuff...
Machine learning layer cake with some brute force crumbs.
I wonder if our common expectation that true theories somehow had to be beautiful and elegant is going to survive the coming century. What if "real" nature phenomenon were actually best described by horrible mess of impossible equations, that only machines could actually manipulate and reason about ?
That would be really sad..
> our common expectation
I think you're going too far with this. Most people understand scientific theories to be an approximation. F=ma is approximately true, in the sense that it's only accurate within the newtonian regime and each of those terms includes so many asterisks that you will only ever measure it approximately.
The latter is the jokes about the physicists "assuming a perfectly spherical cow."
In fact that's kinda the whole point of the "unreasonable effectiveness of mathematics" essay. It is unreasonable that mathematical approximations are so good at describing our world.
I often think this about medicine and the human body. We want to believe that our bodies are some miraculous well oiled machine. But it often seems that it’s a barely held together bag of mess.
>I wonder if our common expectation that true theories somehow had to be beautiful and elegant is going to survive the coming century.
That's the layman's idea of physics theories. They are beautiful and elegant only on the surface, that's why they're technically models and approximations of the real world. The standard model renormalization techniques are a mess of patches and ad-hoc heuristics, pretty far from the "this lagrangian literally contains all physics". Generally you just _ignore_ higher order terms and just call it a day. The famous E=mc^2 it's just the first term of a Taylor expansion. The beautiful form of physics it's what you would call "good enough" and often just a pedagogical tool.
It would represent a pretty sharp inversion from all the progress of mathematical physics until the present.
Up until the present it has been a nearly uniform march of revealed symmetries, collapsed privileged frames of reference, and other such (in the deepest sense) simplifications in our model of reality that has improved its fidelity to the measurable.
I hang qualifier about these developments being simplifying because the result isn't simple in the details: quantum chromodynamics is a daunting subject! But it's not just an enumeration of details and contradictions, the particle zoo that preceded the Eightfold Way looked like line noise, now in indexed notation the Lagrangian of the entire Standard Model fits on a page (or so I've been told I've never actually seen the page).
It's almost tautological that the frontier where it's still messy involves an unrevealed symmetry or a persistent privileged frame of reference, that's what frontier means, we don't see past it to the seam where it folds up.
Personally I suspect AI systems will be a great deal more inclined to discard the parochial axioms that have every point placed human ego above simplicity.
It doesn't resolve all of the open problems in physics if you amputate consciousness, free will, agency persistent identity, and an unambiguous arrow of time.
But it starts looking possible to make progress.
I’m a bit frustrated. AI can do a looot of things; but I think as we continue to muddy the waters between LLMs and more traditional machine learning like Monte Carlo, Genetic Algoriths, Expert Systems and other Statistics magic tricks, we’re too aggressively conflating established and morally neutral activities in ML with the concerns that people have about LLMs and Stable Diffusion.
Though I also imagine that that is the point.
the biggest question for me is how robust are these designs.
in the journal articles they did show measurements of real devices which agreed fine with predictions, but i didn't find them addressing it explicitly in the text. also, some systems they presented contained subblocks that were conventionally designed that could be carrying some of the weight.
or maybe i'm just sour that they're coming for my job? or maybe that's what they want us to think?
i think what wins in practice is simple ideas that can work in spite of all manufacturing and environment variations, and model limitations -- think stuff like feedback and symmetry. and what they show here is the opposite of that. i've done blind optimization of circuit parameters some times only to end up realizing some pretty simple such ideas that i'd missed (like "you need symmetry here" or "you just need more bandwidth here") and made complete sense when you thought about them. so i wonder if we can't tweak a few pixels in their structures and reveal something simpler.
also, obligatory mention: "genetic antennas"
Reminds me of this old article - https://www.damninteresting.com/on-the-origin-of-circuits/
One of my favorite little morsels of internet goodness.
In case anyone feels déjà vu, Popular Mechanics wrote about this professor's lab in Jan 2025, with almost the same title: "AI Designed Computer Chips That the Human Mind Can't Understand".
I feel a bit of unease when I read this title, not because of the threat of AI, but because the prevailing aphorism that "RF is black magic" is a slap in the face to the millions of physicists and RF engineers who DO understand every bit of this. It's a fun harmless anti-intellectual saw that I don't believe is harmless at all. We need more RF engineers and telling people it's all "black magic" and "wizardry" (and worst of all, saying "even RF engineers don't understand RF") makes it seem like it's not worth studying.
One takeaway from the article is that they had to get rid of the tried and tested fundamental building blocks of chip design to generate this advancement. I wonder if the same applies for mundane coding. Are the incredible innovations in AI coding actually hampered by rust and python? Should we let AI tools just code in the lowest level possible?
It's not really that magical. As TFA points out, RFIC design, way beyond normal RF engineering, is close to black magic that relies a lot on the knowledge and experience of the designer, assisted by what would have been supercomputer-level-a-few-decades-ago modelling and design tools. What AI can do is a breadth-first exploration of all possible outcomes and then pick the best-performing one rather than the human-level "this seems like a good path to go down, let's explore it further".
The comments here are trending towards "There's nothing new here, I could design 5g radio chips with a cheap linux box running FTP".
Can oscilloscopes go the way of the calculator?
When can one reasonably expect $10 10GHz oscilloscope on a chip, with some pins for video out and user input in?
At some point the economy will realize theres more LLM inference than access to scientific & technologic measurements, its an economic waste not to connect as much scientific instruments as possible to inference which already exists.
If the "dark arts" (which never really were that dark, analog designers for higher frequencies used the same Maxwell equations as the analog designers for lower frequencies, even if the implications change with frequency) end up automated by AI, the high wages will disappear, and oscilloscope mfrs won't be able to charge as much.
I work in a related field and “inverse” design is what this is called. Such designs usually are not manufacturable. I’m not too worried about my iob.
That said we’ve had some success internally having Claude do parameter sweeps
> That’s not even to speak of all the movie plots that would have been ruined.
I clicked on all the links. Pretty much all of those movies could still work with wired technology. Even the one called cellular, in which a woman is trapped in an attic with a broken landline phone and manages to connect wires and dial a random number.
Yes I'm nitpicking. I guess I'm glad we have Wi-Fi and all, but don't try to sell me on it as a crucial plot device
We have always known the old trick of genetic algorithms to produce better radio chips.
The problem isn’t the design: its manufacturing restraints.
This is nothing new or impressive.
> But freed from the constraints of human-designed templates and the need for humans to even understand the rationale of electromagnetic structures, ... ICs ... can take on truly wild-looking yet efficient designs.
I feel like technology is going to become alien at some point. We're all going to be using magical runes instead of chips.
Hopefully one day AI will design away the need for popups and other-things-that-prevent-you-from-reading-the-damn-article.
Very interesting.
I wonder how hardware craft would look like after adapting AI in a massive scale.
Ai can’t even place and route a two layer board with a microcontroller and a few peripherals.
If you don't know how it works, then you don't know that it works.
The methods outlined in this article aren't new. Scientists were using "genetic algorithms" to design antennas that weren't understood by anyone, but worked well, decades ago.
Chips? I've tried to task Opus, Gemini and Codex with a simple PCB. All of them placed holes correctly but can't understand that the traces should not cross physically.
Great, and here I thought my job was safe.
I don’t know. I can imagine quite a bit.
What a well written article
I did my PhD on inverse design of electromagnetic structures. I really hate that we're calling this AI when there isn't any training, really.
> Freed from intelligibility and aesthetics, AI designs faster
I like this headline. In other words, AI will suck out every last bit that makes engineering fun.
I know, I know. The job is to make money for your employer not have fun. AI makes money faster so shut up and do your job.
But fuck, I took this career because I found joy in understanding things and making things that look and work well.
Now let's get them to come up with a valid design including a valid QR code. Maybe one containing Maxwell's equations.
But is this AGI?
I am confused, every day I read on HN that AI's can just interpolate the data they have seen in training, and that they are structurally incapable of coming up with something new, creative and not in the training distribution.