[HN Gopher] AI-designed chips are so weird that 'humans cannot u...
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AI-designed chips are so weird that 'humans cannot understand them'
Author : anonymousiam
Score : 77 points
Date : 2025-02-23 19:36 UTC (3 hours ago)
(HTM) web link (www.livescience.com)
(TXT) w3m dump (www.livescience.com)
| andrewfromx wrote:
| now we are talking! next level for sure.
| janice1999 wrote:
| Is this really so novel? Engineers have been using evolutionary
| algorithms to create antennas and other components since the
| early 2000s at least. I remember watching a FOSDEM presentation
| on an 'evolved' DSP for radios in the 2010s.
|
| https://en.wikipedia.org/wiki/Evolved_antenna
| happytoexplain wrote:
| I don't believe it's comparable. Yes, we've used algorithms to
| find "weird shapes that work" for a long time, but they've
| always been very testable. AI is being used for more complex
| constructs that have exponentially-exponentially greater
| testable surface area (like programs and microarch).
| xanderlewis wrote:
| This is really interesting and I'm surprised I've never even
| heard of it before.
|
| Now I'm imagining antennas breeding and producing cute little
| baby antennas that (provided they're healthy enough) survive to
| go on to produce more baby antennas with similar
| characteristics, and so on...
|
| It's a weird feeling to look at that NASA spacecraft antenna,
| knowing that it's the product of an evolutionary process in the
| genuine, usual sense. It's the closest we can get to looking at
| an alien. For now.
| jhot wrote:
| Two antennas get married. The wedding was ok but the
| reception was great!
| NitpickLawyer wrote:
| > AI models have, within hours, created more efficient wireless
| chips through deep learning, but it is unclear how their
| 'randomly shaped' designs were produced.
|
| IIRC this was also tried at NASA, they used some "classic"
| genetic algorithm to create the "perfect" antenna for some
| applications, and it looked unlike anything previously designed
| by engineers, but it outperformed the "normal" shapes. Cool to
| see deep learning applied to chip design as well.
| Frenchgeek wrote:
| Wasn't there an GA FPGA design to distinguish two tones that
| was so weird and specific not only did it use capacitance for
| part on its work but literally couldn't work on another chip of
| the same model?
| robotresearcher wrote:
| Yes. The work of Adrian Thompson at the University of Sussex.
|
| https://scholar.google.com/citations?user=5UOUU7MAAAAJ&hl=en
| isoprophlex wrote:
| Yes, indeed, although the exact reference escapes me for the
| moment.
|
| What I found absolutely amazing when reading about this, is
| that this is exactly how I always imagined things in nature
| evolving.
|
| Biology is mostly just messy physics where everything happens
| at the same time across many levels of time and space, and a
| complex system that has evolved naturally appears to always
| contain these super weird specific cross-functional hacks
| that somehow end up working super well towards some goal
| alexpotato wrote:
| > Yes, indeed, although the exact reference escapes me for
| the moment.
|
| It's mentioned in a sister comment:
| https://www.damninteresting.com/on-the-origin-of-circuits/
| actionfromafar wrote:
| I think it was that or a similar test where it would not even
| run on another part, just the single part it was evolved on.
| valine wrote:
| I strongly dislike when people say AI when they actually mean
| optimizer. Calling the product of an optimizer "AI" is more
| defensible, you optimized an MLP and now it writes poetry. Fine.
| Is the chip itself the AI here? That's the product of the
| optimizer. Or is it the 200 lines of code that defines a reward
| and iterates the traces?
| trollbridge wrote:
| If you want grant and VC dollars, you'll rebrand things as
| "AI".
| scotty79 wrote:
| The chip is not called AI-chip but rather AI-designed chip. At
| least in the title.
| valine wrote:
| My point is that it's equally ridiculous to call either AI.
| If our chip here is not the AI then the AI has to be the
| optimizer. By extension that means AdamW is more of an AI
| than ChatGPT.
| ulonglongman wrote:
| I don't understand. I learnt about optimizers, and genetic
| algorithms in my AI courses. There are lots of different
| things we call AI, from classical AI (algorithms for
| discrete and continuous search, planning, sat, Bayesian
| stuff, decision trees, etc.) to more contemporary deep
| learning, transformers, genAI etc. AI is a very very broad
| category of topics.
| valine wrote:
| Optimization can be a tool used in the creation of AI.
| I'm taking issue with people who say their optimizer _is_
| an AI. We don 't need to personify every technology that
| can be used to automate complex tasks. All that does is
| further dilute an already overloaded term.
| kadoban wrote:
| It's not "an AI", it's AI as in artificial intelligence,
| the study of making machines do things that humans do.
|
| A fairly simple set of if statements is AI (an "expert
| system" specifically).
|
| AI is _not_ just talking movie robots.
| rowanG077 wrote:
| I don't understand what your gripe is. Both are AI. Even
| rudimentary decision trees are AI.
| valine wrote:
| Lets just call every turning complete system AI and be
| done with it.
| catlifeonmars wrote:
| Yesterday I used a novel AI technology known as "llvm" to
| remove dead code paths from my compiled programs.
| LPisGood wrote:
| Optimization is near and dear to my heart (see username), but I
| think it's fine to call optimization processes AI because they
| are in the classical sense.
| satvikpendem wrote:
| _Sigh,_ another day, another post I must copy paste my
| bookmarked Wikipedia entry for:
|
| > "The AI effect" refers to a phenomenon where either the
| definition of AI or the concept of intelligence is adjusted to
| exclude capabilities that AI systems have mastered. This often
| manifests as tasks that AI can now perform successfully no
| longer being considered part of AI, or as the notion of
| intelligence itself being redefined to exclude AI
| achievements.[4][2][1] Edward Geist credits John McCarthy for
| coining the term "AI effect" to describe this phenomenon.[4]
|
| > McCorduck calls it an "odd paradox" that "practical AI
| successes, computational programs that actually achieved
| intelligent behavior were soon assimilated into whatever
| application domain they were found to be useful in, and became
| silent partners alongside other problem-solving approaches,
| which left AI researchers to deal only with the 'failures', the
| tough nuts that couldn't yet be cracked."[5] It is an example
| of moving the goalposts.[6]
|
| > Tesler's Theorem is:
|
| > AI is whatever hasn't been done yet.
|
| > -- Larry Tesler
|
| https://en.wikipedia.org/wiki/AI_effect
| taberiand wrote:
| We'll never build true AI, just reach some point where we
| prove humans aren't really all that intelligent either
| rkagerer wrote:
| In a sense, Adrian Thompson kicked this off in the 90's when he
| applied an evolutionary algorithm to FPGA hardware. Using a
| "survival of the fittest" approach, he taught a board to discern
| the difference between a 1kHz and 10KHz tone.
|
| The final generation of the circuit was more compact than
| anything a human engineer would ever come up with (reducible to a
| mere 37 logic gates), and utilized all kinds of physical nuances
| specific to the chip it evolved on - including feedback loops,
| EMI effects between unconnected logic units, and (if I recall)
| operating transistors outside their saturation region.
|
| Article: https://www.damninteresting.com/on-the-origin-of-
| circuits/
|
| Paper:
| https://www.researchgate.net/publication/2737441_An_Evolved_...
|
| Reddit:
| https://www.reddit.com/r/MachineLearning/comments/2t5ozk/wha...
| hiAndrewQuinn wrote:
| I remember talking about this with my friend and fellow EE grad
| Connor a few years ago. The chip's design really feels like a
| biological approach to electrical engineering, in the way that
| all of the layers we humans like to neatly organize our
| concepts into just get totally upended and messed with.
| pharrington wrote:
| Biology also uses tons of redundancy and error correction
| that the generative algorithm approach lacks.
| quanto wrote:
| Fascinating paper. Thanks for the ref.
|
| Operating transistors outside the linear region (the saturated
| "on") on a billion+ scale is something that we as engineers and
| physicists haven't quite figured out, and I am hoping that this
| changes in future, especially with the advent of _analog_
| neuromorphic computing. The quadratic region (before the "on")
| is far more energy efficient and the non-linearity could
| actually help with computing, not unlike the activation
| function in an NN.
|
| Of course, the modeling the nonlinear behavior is difficult. My
| prof would say for every coefficient in SPICE's transistor
| models, someone dedicated his entire PhD (and there are a lot
| of these coefficients!).
|
| I haven't been in touch with the field since I moved up the
| stack (numerical analysis/ML) I would love to learn more if
| there has been recent progress in this field.
| ImHereToVote wrote:
| I believe neuromorphic spiking hardware will be the step to
| truly revolutionize the field of anthropod contagion issues.
| Terr_ wrote:
| IIRC the flip-side was that it was hideously specific to a
| particular model and batch of hardware, because it relied on
| something that would otherwise be considered a manufacturing
| flaw.
| svilen_dobrev wrote:
| long time ago, maybe in russian journal "Radio" ~198x, there
| was someone there describing that if one gets certain
| transistor from particular batch of particular factory/date,
| and connect it in whatever weird way, will make a full FM
| radio (or similar-complex-thing).. because they've wronged
| the yields. No idea how they had figured that out.
|
| But mistakes aside, what would it be if the chips from the
| factory could _learn_ / fine-tune how to work (better) , on
| the run..
| userbinator wrote:
| That's exactly what I thought of too when I saw the title.
|
| Basically brute force + gradient descent.
| alexpotato wrote:
| I read the damn interesting post back when it came out and
| seeing the title of the post immediately led me to thinking of
| Thompson's post as well.
| breatheoften wrote:
| I remember this paper being discussed in the novel "Science of
| Discworld" -- a super interesting book involving collaboration
| between a fiction author and some real world scientists --
| where the fictional characters in the novel discover our
| universe and its rules ... I always thought there was some deep
| insight to be had about the universe within this paper. Now
| moreso I think the unexpectedness says something instead about
| the nature of engineering and control and human mechanisms for
| understanding these sorts of systems ... -- sort of by
| definition human engineering relies on linearized
| approximations to characterize the effects being manipulated --
| so something which operates in modes far outside those models
| is basically inscrutable. I think that's kind of expected but
| the results still provoke the fascination to ponder the
| solutions super human engineering methods might yet find with
| the modern technical substrates.
| viccis wrote:
| I really wish I still had the link, but there used to be a
| website that listed a bunch of times in which machine learning
| was used (mostly via reinforcement learning) to teach a
| computer how to play a video game and it ended up using
| perverse strategies that no human would do. Like exploiting
| weird glitches (https://www.youtube.com/watch?v=meE5aaRJ0Zs
| shows this with Q*bert)
|
| Closest I've found to the old list I used to go to is this:
| https://heystacks.com/doc/186/specification-gaming-examples-...
| robertjpayne wrote:
| Make no mistake most humans will exploit any glitches and
| bugs they can find for personal advantage in game. It's just
| machines can exploit timing bugs better.
| codr7 wrote:
| And this is the kind of technology we use to decide if someone
| should get a loan, or if something is a human about to be run
| over by a car.
|
| I think I'm going to simply climb up a tree and wait this one
| out.
| dang wrote:
| Related. Others?
|
| _The origin of circuits (2007)_ -
| https://news.ycombinator.com/item?id=18099226 - Sept 2018 (25
| comments)
|
| _On the Origin of Circuits: GA Exploits FPGA Batch to Solve
| Problem_ - https://news.ycombinator.com/item?id=17134600 - May
| 2018 (1 comment)
|
| _On the Origin of Circuits (2007)_ -
| https://news.ycombinator.com/item?id=9885558 - July 2015 (12
| comments)
|
| _An evolved circuit, intrinsic in silicon, entwined with
| physics (1996)_ - https://news.ycombinator.com/item?id=8923902
| - Jan 2015 (1 comment)
|
| _On the Origin of Circuits (2007)_ -
| https://news.ycombinator.com/item?id=8890167 - Jan 2015 (1
| comment)
|
| That's not a lot of discussion--we should have another thread
| about this sometime. If you want to submit it in (say) a week
| or two, email hn@ycombinator.com and we'll put it in the
| second-chance pool (https://news.ycombinator.com/pool,
| explained at https://news.ycombinator.com/item?id=26998308), so
| it will get a random placement on HN's front page.
| zahlman wrote:
| If we can't understand the designs, how rigorously can we really
| test them for correctness?
| 42lux wrote:
| Results?
| evrimoztamur wrote:
| Can you always test the entire input space? Only for a few
| applications.
| 42lux wrote:
| I am really curious about how you test software...
| djmips wrote:
| Especially true if the computer design creates a highly coupled
| device that could be process sensitive.
| molticrystal wrote:
| Our human designs strive to work in many environmental
| conditions. Many early AI designs, if iterated in the real
| world, would incorporate local physical conditions into their
| circuits. For example, that fluorescent lamp or fan I'm picking
| up(from the AI/evolutionary design algorithm's perspective) has
| great EM waves that could serve as a reliable clock source,
| eliminating the need for my own. Thus if you move things it
| would break.
|
| I am sure there are analogous problems in the digital
| simulation domain. Without thorough oversight and testing
| through multiple power cycles, it's difficult to predict how
| well the circuit will function, and how incorporating feedback
| into the program will affect its direction, if not careful,
| causing the aforementioned strange problems.
|
| Although the article mentions corrections to the designs, what
| may be truly needed is more constraints. The better we define
| these constraints, the more likely correctness will emerge on
| its own.
| skissane wrote:
| > Our human designs strive to work in many environmental
| conditions. Many early AI designs, if iterated in the real
| world, would incorporate local physical conditions into their
| circuits. For example, that fluorescent lamp or fan I'm
| picking up(from the AI/evolutionary design algorithm's
| perspective) has great EM waves that could serve as a
| reliable clock source, eliminating the need for my own. Thus
| if you move things it would break.
|
| This problem may have a relatively simple fix: have two FPGAs
| - from different manufacturing lots, maybe even different
| models or brands - each in a different physical location,
| maybe even on different continents. If the AI or evolutionary
| algorithm has to evolve something that works on _both_ FPGAs,
| it will naturally avoid purely local stuff which works on one
| and not the other, and produce a much more general solution.
| PhilipRoman wrote:
| Ask the same "AI" to create a machine readable proof of
| correctness. Or even better - start from an inefficient but
| known to be working system, and only let the "AI" apply
| correctness-preserving transformations.
| awinter-py wrote:
| > The AI also considers each chip as a single artifact, rather
| than a collection of existing elements that need to be combined.
| This means that established chip design templates, the ones that
| no one understands but probably hide inefficiencies, are cast
| aside.
|
| there should be a word for this process of making components
| efficiently work together, like 'optimization' for example
| pmlnr wrote:
| These are highly complicated pieces of equipment almost as
| complicated as living organisms. ln some cases, they've
| been designed by other computers. We don't know exactly
| how they work.
|
| Westworld, 1973
| neltnerb wrote:
| Actual Article:
| https://www.nature.com/articles/s41467-024-54178-1#Fig1
| choxi wrote:
| Maybe we're all just in someone's evolutionary chip designer
| DrNosferatu wrote:
| Its inevitable: software (and other systems) will also become
| like this.
| codr7 wrote:
| And then it's pretty much game over.
| DrNosferatu wrote:
| It's better we [democracies] ride and control the AI change
| of paradigm than just let someone else do it for us.
| satvikpendem wrote:
| I've been using Cursor, it already is. I've found myself
| becoming merely a tester of the software rather than a writer
| of it, the more I use this IDE.
| DrNosferatu wrote:
| It's a bit clunky still IMHO. Or you found a good tutorial to
| leverage it fully?
| mwkaufma wrote:
| "In particular, many of the designs produced by the algorithm did
| not work"
| mikewarot wrote:
| I've only started to look into the complexities involved in chip
| design (for my BitGrid hobby horse project) but I've noticed that
| in the Nature article, all of the discussion is based on
| simulation, not an actual chip.
|
| Let's see how well that chip does if made by the fab. (I doubt
| they'd actually make it, likely there are a thousand design rule
| checks it would fail)
|
| If you paid them to over-ride the rules at make it anyway, I'd
| like to see if it turned out to be anything other than a short-
| circuit from Power to Ground.
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