alt.hn

6/24/2026 at 2:02:16 PM

AI learns the “dark art” of RFIC design

 https://spectrum.ieee.org/ai-radio-chip-design

by Brajeshwar

6/27/2026 at 8:35:22 PM

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..

by bsaul

6/27/2026 at 9:01:33 PM

The "common expectation" I think, misses the point. The idea isn't that fundamental theories are simple or elegant (quantum physics equations are pretty darn ugly), it's that, given the choice between a more complicated and a more simple theory, generally the simplest one is the most accurate choice.

by ambicapter

6/27/2026 at 8:43:18 PM

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.

by iamflimflam1

6/27/2026 at 8:48:03 PM

This has been on my mind lately! Especially in light of the many incomprehensible but machine-checkable proofs we've been hearing about.

Occam's Razor is a useful heuristic, but it biases us towards simpler explanations.

by dnmc

6/27/2026 at 6:06:56 PM

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.

https://en.wikipedia.org/wiki/Evolved_antenna

by robviren

6/27/2026 at 6:20:06 PM

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.

by ben_w

6/27/2026 at 6:22:21 PM

That is my favorite part of GA. Gradient free optimization but it turns out making a good fitness function is hard and like 70% of the time it just exploits some assumptions or gap you have in your theories. Really reveals the problem in different ways that traditional ML.

by robviren

6/27/2026 at 8:11:20 PM

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.)

by pfdietz

6/27/2026 at 6:48:54 PM

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.

by t-writescode

6/27/2026 at 6:54:08 PM

It is a problem because people will talk about what AI can do implying that an LLM can do that thing, making it seem like a pure LLM can do almost anything. On the other hand people will say AI will never be able to do X because an LLM can’t do that thing well natively. AI has become too vague of a term to be useful.

by georgeecollins

6/27/2026 at 7:25:08 PM

We're relearning that intelligence is spikey, and that different things that we consider 'intelligent' can have vastly different capabilities.

by pixl97

6/27/2026 at 8:11:10 PM

We're learning that people are way too lax with where they apply the term "intelligent". LLMs aren't remotely intelligent, but people are trying to ride the hype train and call them intelligence.

by bigstrat2003

6/27/2026 at 8:50:43 PM

Very much indeed. The term itself is not properly defined, strictly speaking.

by quantum_state

6/27/2026 at 8:26:33 PM

> LLMs aren't remotely intelligent

Maybe I'm just significantly and unrepresentatively unlucky, but Claude is significantly more intelligent than the average human around me on most any metric I can think of.

by falcor84

6/27/2026 at 8:25:43 PM

this is just false.

by any meaningful measure of intelligence. the latest models are much smarter than the bulk of the population.

how would you define intelligence?

by subygan

6/27/2026 at 7:03:13 PM

I wish I could wave a magic wand and just make the word "AI" go away. It has no actual meaning. It could mean anything from your opponent in Mario Kart to Stable Diffusion.

by Wowfunhappy

6/27/2026 at 7:25:24 PM

"AI" == "what (through tech) can replace a professional"

It may seem similarly vague, but it does in fact open interesting, productive, and necessary questions. A "computer" was a professional crunching numbers - "replaced", "easily" because of the deterministic procedural nature of said work, but what about the technical effort to arrive there, and what about the less "mechanical" jobs? When do "processes" become "intelligence"?

Some of us had studied AI originally to study the mind - "how do we formalize thought". It's the interdisciplinary, transversal nature of the area.

Also maybe compare that with that large and important intersection between CS and Economics - the "science of optimization" and its implementation in efficient IT systems. The effort in terms of that different discipline may not be evident, yet lots of engineering is "optimizing" and the generalization of those solutions we call Economics (see the book Algorithms to live by).

So: the term "Artificial Intelligence" may not be important as CS solutions to practical problems are built (you just focus on the better solution), but there is relevance to the "side disciplince" of AI, and from that perspective that is the cone, the scope anyway. "How would an intelligent solver approach the problem".

by mdp2021

6/27/2026 at 8:08:20 PM

> "AI" == "what (through tech) can replace a professional"

But as you point out, we used to have human calculators. So is a simple desk calculator a form of "AI"? If so, what type of software isn't AI?

by Wowfunhappy

6/27/2026 at 7:21:52 PM

I disagree. AI is doing exactly what it was predicted it would in science fiction.

The computer can now literally talk to you in natural language and then perfectly produce sophisticated actions in response to completely arbitrary and unstructured input. It trivially passes the Turing test. By any definition prior to the year 2023 we are living with Artificial General Intelligence and it’s here now.

by oceanplexian

6/27/2026 at 7:43:58 PM

Yeah. We'll be arguing "is it really AGI" for many more years. Meanwhile, everyone interesting is going to have moved on from that question, choosing to spend time on "who cares if it's AGI, can it do $foo", for whatever value of $foo is interesting to them. Whether the machine is folding clothes or folding proteins, AGI isn't well defined other than "I'll know it when I see it", so whether or not it's AGI, the question is what job is the machine capable of and is it cheaper than a human? A humanoid robot that can work a warehouse is not putting anyone out of a job if it costs a billion dollars, and neither is a digital AI employee that costs the company a billion dollars either.

by fragmede

6/27/2026 at 7:39:18 PM

So where are the androids? If it's AGI, why is it used as a tool, waiting to be prompted or executed by humans? Where is Skynet? Military applications still rely on human operators.

by goatlover

6/27/2026 at 9:01:20 PM

You realize llms as a field is barely 5 years? Give it at least another 5.

by p1esk

6/27/2026 at 8:05:51 PM

Robotics is advancing a bit slower, but is making progress as well.

by nextaccountic

6/27/2026 at 8:08:06 PM

Yes, but unlike a lot of science fiction, robots, LLMs and other AI remain tools for human use. Augmented Intelligence would have been the more accurate word for real world AI.

by goatlover

6/27/2026 at 7:35:05 PM

Game AI uses behaviour trees, usually coded by hand. Decision trees are used for classification and are normally learned from data. The latter are a traditional AI technique from the early days of the modern machine learning era, in the 1990's.

by YeGoblynQueenne

6/27/2026 at 7:10:22 PM

"AI" is a term cursed by cool sci-fi implications. It makes it a kick ass marketing term because most people are going to have some familiarity with sci-fi AI and "X media predicted Y technology" is a pretty widespread belief for a lot of values of X (star trek, Hitchhikers Guide to the Galaxy, Arthur C. Clarke) and Y (internet, cell phones, VR). If you want to tell someone we're making big strides in something, linking it into some popsci understanding of sci-fi being the great predictor of human achievement is low effort and high impact for quite a few people.

People aren't trying to communicate accurately if their first priority is getting you excited about the thing!

by graypegg

6/27/2026 at 7:14:18 PM

I miss "predictive analytics". Too boring and honest for marketers though.

by z3c0

6/27/2026 at 7:02:18 PM

I have been practicing saying ML for traditional machine learning and LLMs for LLMs for just this reason. Trying not to say AI anymore. Too ambiguous. Sometimes I'm talking about game AI even, I'll try to use shorthand for whatever algorithm I think the AI is using (often I'll talk about its flowchart, though not always sure it's literally using that under the hood).

by silentkat

6/27/2026 at 7:46:41 PM

What is ChatGPT then? Sure it's an LLM, but I can give the app pictures and audio, and it can generate pictures for me. Do we distinguish between the bits of the architecture to accomplish those features separately from the LLM part of the product?

by fragmede

6/27/2026 at 8:27:32 PM

Yes? Or just call it a chatbot if you don't care about the implementation details.

by Marsymars

6/27/2026 at 7:20:58 PM

Just as more successful machine learning fields distanced themselves from the term during the AI winter, I suppose we will (and perhaps are?) be seeing them adopt it again, now that we are in an "AI summer".

by twnettytwo

6/27/2026 at 7:38:08 PM

As always, it's a matter of funding. Both inside academia and outside of it. I remember when nanotechnology was all the rage. Everyone flocked to writing grant proposals about their "nano" technology that was thousands or millons of nanometers, aka micrometers or even millimeters. Stupid but if it works it's not stupid. The old joke is what do you call AI that works? Machine Learning.

The real question is how much compute do you need. With LLMs getting popular, so is compute. That's the real win for non-LLM technologies. The sheer availability of GPU capacity. Yes, it's expensive, but time in a GB300 supercomputer isn't even possible if they don't exist.

Alexnet succeeded for many reasons but a big reason is that computers got good enough to apply those algorithms and techniques in practice. Outside of LLMs, what new AI/ML systems await us in the future? The LLM bubble popping, if it ever does, is going to leave us with supercomputer capacity going unused and available for cheap, meaning experiments that were once infeasibly expensive become practical. I can't afford $10 million to run a weather simulation, but at $1,000 for the same amount of compute, a lot more experimentation becomes practical.

by fragmede

6/27/2026 at 8:11:29 PM

Reinforcement learning can solve a Rubik’s Cube. A LLM that hasn’t been trained to solve a Rubik’s Cube can not.

by dataviz1000

6/27/2026 at 7:06:17 PM

Recently I heard some people conflate procedural generation and generative AI and I had to explain why there isn't some legal or ethical issue with what breaks down to essentially scattering some points.

It's really getting annoying having to have these conversations.

by PacificSpecific

6/27/2026 at 7:32:11 PM

> AI can do a looot of things

AI is not a real thing or a natural kind but a perspective. Whether something qualifies as "AI" or not cannot be decided by the objective features of the thing. Ergo, it can be defined at the author's pleasure.

> conflating established and morally neutral activities in ML

LLMs are no more or less morally neutral than other ML techniques.

by lo_zamoyski

6/27/2026 at 7:08:45 PM

[flagged]

by vlian2088

6/27/2026 at 7:15:27 PM

This is such a strange attitude to have. How can you look at the history of the world and be so caustic to concern?

by krainboltgreene

6/27/2026 at 7:28:43 PM

strange how? in the history of the world, had there been a technology that's been wished out of existence by the concerned?

by vlian2088

6/27/2026 at 8:04:23 PM

Leaded gasoline.

by krainboltgreene

6/27/2026 at 8:11:03 PM

you know that's not what I meant. also, avgas.

by vlian2088

6/27/2026 at 9:00:33 PM

What did you mean, then?

Also, CFCs.

by t-writescode

6/27/2026 at 7:52:02 PM

Phrenology

by kortilla

6/27/2026 at 6:26:47 PM

"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.

by autoexec

6/27/2026 at 7:40:09 PM

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...

by fooqux

6/27/2026 at 6:30:34 PM

Machine learning layer cake with some brute force crumbs.

by ece

6/27/2026 at 6:37:07 PM

Reminds me of this old article - https://www.damninteresting.com/on-the-origin-of-circuits/

One of my favorite little morsels of internet goodness.

by jcims

6/27/2026 at 7:22:12 PM

Yes. An example of a species so specialized and optimized that it can no longer adapt. Also, an example of POSIWID.

by zephen

6/25/2026 at 11:38:51 AM

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"

by flossEveryday

6/27/2026 at 6:21:32 PM

> but i didn't find them addressing it explicitly in the text

Yes, this is exactly what bothers me about this article and about a few similar articles published in the past, that they do not contain any evidence that their claims about the usefulness of AI in design are true.

In TFA it says that the role of AI is replacing the electromagnetic simulator in the optimization process, by guessing the behavior of the structure, which is many orders of magnitude faster than a simulation.

This sounds plausible, but in order to believe this I would want to see the differences between AI guesses and real measurements, in the case of structures with geometries that are very different from those used in the training of the AI.

Also I would want to see exactly with which simulators they have compared the speed of the AI model.

There are various simulation approaches for electromagnetic fields and electronic circuits, that can trade-off accuracy for speed, so I am not convinced that AI inference takes necessarily much less time than some faster low-accuracy methods of simulation, which would still be more accurate and more reliable than AI guesses.

by adrian_b

6/27/2026 at 6:08:31 PM

I came to mention genetic antennae as well!

Since you beat me to it, I'll add something that relates relates you were saying on "realizing some pretty simple... ideas".

I think a big plus of computer aided design like this is "innovization"[1]. Somewhat awkward term. But, a system like this leading one to deeper understanding of a particular process is the general idea. It's a fun feeling in practice.

[1]: https://dl.acm.org/doi/10.1145/1143997.1144266

by iwhalen

6/27/2026 at 8:16:58 PM

Yeah it's a hype slop piece

by vatsachak

6/27/2026 at 8:22:37 PM

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.

by Schlagbohrer

6/25/2026 at 4:32:03 AM

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".

by pseudohadamard

6/27/2026 at 6:37:09 PM

Does it need to be magical to be interesting or useful?

by fred_is_fred

6/27/2026 at 7:33:23 PM

> 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

by taffydavid

6/27/2026 at 7:35:54 PM

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

by georgeburdell

6/27/2026 at 6:46:21 PM

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.

by pshirshov

6/27/2026 at 6:56:34 PM

The AI in the article isn't an LLM.

by SamBam

6/27/2026 at 6:51:09 PM

Read the article.

by dempedempe

6/27/2026 at 7:00:09 PM

Hopefully one day AI will design away the need for popups and other-things-that-prevent-you-from-reading-the-damn-article.

by johnnyApplePRNG

6/27/2026 at 7:18:25 PM

Yep, can't wait until everything is free and costs nothing to generate content. Free hosting and electricity will be super sweet too. Won't need admins or even the Internet. Everything I want will just be free for me because I don't think anything has value.

by esikich

6/27/2026 at 7:04:26 PM

[dead]

by inquirerGeneral

6/27/2026 at 7:23:08 PM

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.

by activexray

6/27/2026 at 8:56:48 PM

>To arrive at the “sweet spot” where all these different parameters are balanced into optimal harmony, designers will typically lay out several different versions of the circuit, using intuitions and methods they have picked up in their years of training.

I kind of thought the real success is when the designer comes up with key things that are well beyond their training or any training that could have been done up until that time. Based on their years of experience living in an environment where training is table stakes but that's not the thing that's relied upon the most in the end.

With LLMs it seems like odds are that a concept which is statistically insignificant in the training set may surface in place of a truly novel solution, effectively displacing the real breakthroughs that actually go beyond trainable performance.

In a way that decision-makers can not tell the difference, and that could be the worst part.

by fuzzfactor

6/27/2026 at 6:46:04 PM

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.

by phendrenad2

6/27/2026 at 7:03:50 PM

I don’t know. I can imagine quite a bit.

by skywhopper

6/27/2026 at 8:12:06 PM

Unexpected Star Wars! A surprise to be sure, but a welcome one :)

by Henchman21

6/27/2026 at 6:50:54 PM

If you don't know how it works, then you don't know that it works.

by Brian_K_White

6/27/2026 at 7:26:16 PM

How does your consciousness work?

by xp84

6/27/2026 at 8:12:46 PM

Pretty well, between all the hallucinogens

by Henchman21

6/27/2026 at 6:47:14 PM

The comments here are trending towards "There's nothing new here, I could design 5g radio chips with a cheap linux box running FTP".

by fred_is_fred

6/27/2026 at 6:17:18 PM

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.

by deadbabe

6/27/2026 at 6:54:52 PM

Then why can't these constraints be encoded into the selection/scoring function ?

by sim04ful

6/27/2026 at 7:14:21 PM

But is this AGI?

by scoopdeddywoop

6/27/2026 at 7:17:42 PM

[flagged]

by yashthakker

6/27/2026 at 6:02:23 PM

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.

by dist-epoch

6/27/2026 at 8:16:00 PM

> I read on HN that AI's can just interpolate the data they have seen in training

No. That can be said about LLMs, but not about all forms of AI. The technique used is not a LLM.

Sadly we've bastardized the term AI that, if it ever meant anything, it's meaningless now. The currently most voted thread in this post discuses the topic.

by Fargren

6/27/2026 at 6:37:17 PM

This is analogies to finding a new prime number by brute force using existing maths, rather than inventing new maths to get there.

The AI in this case didn't create a novel technology- it merely used the existing technology without basing the new design on a previous one. The whole "human couldn't come up with it" is because the possible design space is so large, there's no reason a human would start where the AI did.

The thing the AI did better than humans was brute forcing a solution faster. Still a very handy thing to have, but it isn't "creating" in the sense that it invented new materials or fabrication processes or anything novel.

by zdragnar

6/27/2026 at 6:20:08 PM

Have you read the article? The creative element came from the researchers:

> In our new approach, the architecture begins essentially from nothing and is progressively assembled through successive iterations. The system explores the design space by generating myriad candidate circuit combinations and mapping the resulting performance trade-offs as it navigates this landscape. Because the process is not biased by prior human design choices, it can produce completely novel circuit topologies that look markedly different from those created by human designers.

by dgellow

6/27/2026 at 6:08:42 PM

In my experience, if you tell them to research the web to see if their idea has been pursued before, you can get them to keep proposing new things until something is sufficiently new, even if it's a new interpolation between existing concepts, that it's effectively an original idea.

by LogicFailsMe

6/27/2026 at 8:28:02 PM

This is wrong - the training data is necessary but insufficient. There are a lot of other parts of the architectures used that add a lot of value - otherwise Markov chains would be all you need. There are layers upon layers with non linear activation functions, learned residuals, etc. They still absolutely must interpolate but the space they interpolate through is much more complex than the training data, and they can definitely create things not in their training data. What they can not do is wander outside their non linear parameter space’s convex hull. But this is a really permissive constraint on what they can do “creatively.” People generally under estimate the advantage the architectures confer on that constraint. This is why there was a step function change in expressive power as the architectures (attention, self attention, transformers, diffusions, others) evolved given the same training data. Generally though I challenge you to define “creative” in a way that is precise enough to measure and isn’t self referential or refer to concepts ill defined.

The key tho is can they solve problems not easily solved before with prior techniques. Further can they identify problems not readily presented. Then identify novel solutions. Etc. The answer is emphatically yes they can. These features don’t have to literally exist in their training data, but the supporting highly convoluted network of associations of all their training data does have to in some complex space allow for it to produce these answers. It’s not the same as they’re stochastic parrots at all.

Are they creative? No, because they don’t have awareness. My personal imprecise definition of creative requires both self and awareness as well as free will. There is no driving awareness in all AI architectures, it all derives from extrinsic impetus. Creativity is derived, IMO, from a layer of our minds that is not readily assessed or measured and is only indirectly expressed through language, art, and music. Hence it is not directly trainable and therefore a learning model can’t learn it by reinforcement. It can learn the proxies, but the proxies are not, as we all deeply know, the same as our experienced awareness. We are not our words, our art, our music. We try hard to bridge it, but it’s impossible and you and I know this to be true from experience. In fact we can not even examine our own awareness because it’s not directly observable or possible for us to directly reason about. This is core to a lot of philosophy, especially mid and far eastern philosophy of the mind, the self, the five aggregates of Buddhism, etc. Psychology points at it, and modern psychology avoids it because it’s practically difficult for outcome oriented treatments.

by fnordpiglet