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AI learns the “dark art” of RFIC design

spectrum.ieee.org

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Re: AI learns the “dark art” of RFIC design

#171

Post by Planktonne »

dmix wrote: post #155
pitched wrote: post #147

Earlier quoted context omitted.

Math is a language to explain systems. Teaching someone that force varies linearly to mass is a helpful first pass. It isn’t exactly linear but is not exponential at all. Gaining expertise is always the hard part and our new LLM overlords are making that much harder. So the simple “pure” functions as a teaching aid have never been more important. End users have never cared about how the sausage is made though.

> LLM overlords are making that much harder. LLMs can explain complex things to humans with tons of specific context that you don’t find in textbooks or even a google search. It’s probably never been easier to grasp a large codebase than it is today for example. You can probe and ask specific questions without going through a maze of imports and relationships and config files yourself. Learning things will always be…

> LLMs can explain complex things to humans

I keep meeting people who think this and have enormous understanding gaps in the topics they've had an LLM teach them.

The absolute worst judge of how well someone understands a complex topic is the novice themselves.

Re: AI learns the “dark art” of RFIC design

#172

Post by avian »

> 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.

Re: AI learns the “dark art” of RFIC design

#173

Post by mdp2021 »

Wowfunhappy wrote: post #67
mdp2021 wrote: post #49

Earlier quoted context omitted.

"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 "i…

> "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?

> is a simple desk calculator a form of "AI"

If what it does is "taking care of the carry", it represents a pretty minimal requirement for intelligence - it does replace a professional that could do it, but that professional does not have to apply too much proficiency and cleverness to do its job. It is improper AI.

> what type of software isn't AI

That which would not correspond to the job of an intelligent entity. Maybe blitting bitmaps around a screen?

As I tried to convey, it is more of a matter of perspective: the area of "implementing ways to solve problems as an intelligent entity would". It is a discipline that intersects others - engineering, logic, brain science, philosophy, epistemology, maybe again economics (as "the science of optimality and efficiency" - as an intelligent solver would do)... Consider it a special discipline that spans many other realms.

Re: AI learns the “dark art” of RFIC design

#174

Post by brookst »

pfdietz wrote: post #70

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.)

If patents can’t be granted to an AI inventor, I don’t see how such AI “inventions” could be used as prior art.

Re: AI learns the “dark art” of RFIC design

#175

Post by jingpostmedia »

AaronFriel wrote: post #158

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 Eve…

[flagged]

Re: AI learns the “dark art” of RFIC design

#176

Post by pfdietz »

brookst wrote: post #174
pfdietz wrote: post #70

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.)

If patents can’t be granted to an AI inventor, I don’t see how such AI “inventions” could be used as prior art.

Prior art doesn't have to be a patent. A frickin' Soviet movie was prior art in the Blue Origin/SpaceX lawsuit about landing rockets on barges.

Re: AI learns the “dark art” of RFIC design

#177

Post by pitched »

dmix wrote: post #155
pitched wrote: post #147

Earlier quoted context omitted.

Math is a language to explain systems. Teaching someone that force varies linearly to mass is a helpful first pass. It isn’t exactly linear but is not exponential at all. Gaining expertise is always the hard part and our new LLM overlords are making that much harder. So the simple “pure” functions as a teaching aid have never been more important. End users have never cared about how the sausage is made though.

> LLM overlords are making that much harder. LLMs can explain complex things to humans with tons of specific context that you don’t find in textbooks or even a google search. It’s probably never been easier to grasp a large codebase than it is today for example. You can probe and ask specific questions without going through a maze of imports and relationships and config files yourself. Learning things will always be…

The difference now is that the learning is optional (more often but not always) to getting the task done.

When gaining mastery is not a requirement to doing novice-level work, many fewer people will get there. It takes more dedication than it did before.

Re: AI learns the “dark art” of RFIC design

#178

Post by Wowfunhappy »

mdp2021 wrote: post #173
Wowfunhappy wrote: post #67

Earlier quoted context omitted.

> "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?

> is a simple desk calculator a form of "AI" If what it does is "taking care of the carry", it represents a pretty minimal requirement for intelligence - it does replace a professional that could do it, but that professional does not have to apply too much proficiency and cleverness to do its job. It is improper AI. > what type of software isn't AI That which would not correspond to the job of an intelligent entity.…

> Maybe blitting bitmaps around a screen?

Okay, that makes sense. Even so:

> If what it does is "taking care of the carry", it represents a pretty minimal requirement for intelligence - it does replace a professional that could do it, but that professional does not have to apply too much proficiency and cleverness to do its job. It is improper AI.

I think you're underselling how much mental work is required to solve complex arithmetic. Yes, it's simple for a computer, but (1) even basic computers are extremely complex in absolute terms, and (2) even the most complex computing tasks could be considered simple once you break them down far enough—for example, a large language model is "just" fancy matrix multiplication.

So I feel like there's a "sufficiently advanced technology is indistinguishable from magic" element here. Something becomes AI once it seems sufficiently advanced. But then time passes and it doesn't feel that advanced anymore.

I understand that human language doesn't always have a super precise definition, and I'm not trying to be pedantic. I think the term "artificial intelligence" is under-specified to the point of having virtually no meaning. To the extent that it is useful—obviously, a lot of people are using it conversation, so something is getting communicated—it's because it's possible to infer from context what someone is referring to (ie "the student used AI to write her essay" is clearly referring to an LLM, not Eliza).

We'd all be better off if we used words that describe what we're actually talking about.

Re: AI learns the “dark art” of RFIC design

#179

Post by brookst »

pfdietz wrote: post #176
brookst wrote: post #174

Earlier quoted context omitted.

If patents can’t be granted to an AI inventor, I don’t see how such AI “inventions” could be used as prior art.

Prior art doesn't have to be a patent. A frickin' Soviet movie was prior art in the Blue Origin/SpaceX lawsuit about landing rockets on barges.

Yes, but that movie was made by a human.

My point was that it’s hard to imagine citing something that could not be patented as prior art. It would be like citing a phone book as proof that a software program can’t be copyrighted (“the exact bytes appear in the 1973 Albany NY white pages, therefore it wasn’t original”)

Re: AI learns the “dark art” of RFIC design

#180

Post by abdullahkhalids »

flossEveryday wrote: post #3

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…

They address this in the conclusion

> How generalizable are these methods? Can they consistently deliver truly high performance? Can we get to a place where AI produces designs that maximize every conceivable trade-off, holistically optimizing every parameter to its most ideal physical state? .... AI can hallucinate a design that creates bad circuits that don’t work. This means verification methods need to remain under human oversight.

And they are essentially correct. We need better validation and verification methods, both software and hardware to keep in check the mistakes of automated random processes.

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