Be careful when using AI for electronics - Page 1
Forum section: electronics / general

author: eTobey
author_rank: Super Contributor
timestamp: May 11, 2025, 04:39:15 pm
content: I just asked chatgpt, what the voltage drop on a 20cm cable with a 5A fuse (car) would be when a current of 3A flows. It calculated it first with two times the length, but that was the smallest issue. It did not calculate the voltage drop on the fuse, and when i asked about the fuse, it told me, that it is irrelevant for calculating the voltage drop.Just wow...Are there simliar issues? How do other AIs perform in electronics?

author: madires
author_rank: Super Contributor
timestamp: May 11, 2025, 04:56:03 pm
content: It's actually just a pseudo AI, and it gives you the prevalent opinion of the web. Don't expect too much and don't believe the marketing nonsense!

author: Ian.M
author_rank: Super Contributor
timestamp: May 11, 2025, 05:03:29 pm
content: Asking a non-application-specific LLM AI about electronics is like asking the shadiest used car dealer in your state to give you a medical diagnosis over the phone!

author: themadhippy
author_rank: Super Contributor
timestamp: May 11, 2025, 05:39:20 pm
content: Quotemakes sense to me, 20 cm there and 20 cm back

author: golden_labels
author_rank: Super Contributor
timestamp: May 12, 2025, 05:04:03 am
content: 4 years after LLMs hit the public, I’ll limit the reply to just two lines.This is a random text generator. A set of very expensive dice.The dice are shaped and weighted to yield text similar to what humans did reply or would have replied in a similar situation. But the dice don’t have a mental model of the underlying problem. Not in the sense a human would.

author: Berni
author_rank: Super Contributor
timestamp: May 12, 2025, 05:48:16 am
content: Don't ask LLMs questions about calculating things, They can't actually do math like a computer, they just approximate patterns of numbers in calculations (and do get it right for a lot of simple cases).Prompting LLMs to get good answers out of them is also a skill, the way the question is worded has a huge effect on what you get back. If you don't provide enough information the AI will make stuff up. If you provide inaccurate information the AI will blindly believe you and work with it (rather than correcting you). Most of these AIs are trained to be overly nice and agreeing to whatever the user says, likely to not offend all those special snowflakes of users. But this is not how engineering works, feelings don't come above getting things right.If your question is easy to google then just google it.That being said AIs are far from useless. As long as you are aware of their limitations they can be amazingly useful tools. For very specific questions about how to do something they get you an answer much faster than google, as long as you manually check the answer in some way (but often checking it is much much faster than finding it). Also it is very good at programming small snippets of code and putting it into a form that fits into the rest of your code (just don't try to have it write all of your code, it will eventually fail spectacularly)

author: John B
author_rank: Frequent Contributor
timestamp: May 12, 2025, 05:55:40 am
content: Could be worse. It could have told you that eating the fuse is part of a balanced diet will give you the voltage drop.

author: tggzzz
author_rank: Super Contributor
timestamp: May 12, 2025, 11:40:33 am
content: From comp.risks, which every engineer should read:https://catless.ncl.ac.uk/Risks/34/62/#subj46.1CEO of Anthropic's articlehttps://www.darioamodei.com/post/the-urgency-of-interpretabilitycontains snippets (myemphasis):QuoteSummary of that long thoughtful article:https://futurism.com/anthropic-ceo-admits-ai-ignorance

author: madires
author_rank: Super Contributor
timestamp: May 12, 2025, 01:26:54 pm
content: A big black box. And nothing is new about that. Long time ago I learned the basics of neural networks in the industrial image processing course at university. What has changed is just the sheer amount of processing power and the ease of accessing data for training.

author: SiliconWizard
author_rank: Super Contributor
timestamp: May 12, 2025, 01:55:08 pm
content: The fascinating point is that none of the companies developing and hosting those LLMs claim that they are fit for any particular purpose; they are pumped full of disclaimers (for good reasons, legal mostly).Yet, many, many people ("users") are convinced otherwise.Of course, the manipulation is sophisticated: while "AI" companies have lots of disclaimers and their CEOs go as far as warning the world about how "not ready" and dangerous AI is, people are being sold everywhere how AI is going to take their jobs and be used to solve climate change and cure cancer (not kidding, that's what we can hear), so that must mean that AI works very well indeed.If LLMs are going to take engineering jobs, that must mean they are good at engineering, right?Some key "issues" with current LLMs (and probably LLMs in general):- They are black boxes; we don't know how they work internally (at least not to a significant extent; when we raise this issue, there always are some smart ones that will point you to a new "paper" claiming to have solved that problem, although the TLDR is that this isn't useful for any practical purpose). So yes, this looks a bit like sorcery; playing with ingredients until we find something interesting.- We assume that our modeling of neural networks is close to the real ones. In reality, there are a lot of interactions that happen between neurons that we don't model.- By making LLMs general-purpose AI tools, we are assuming that languages are enough to properly model the world. Many language specialists see this as a fallacy.- THere's the problem of liability. Who's responsible for the output of a LLM? There's currently no clear answer to that. Historically, the person responsible for misuse of a tool is the person using the tool, unless they can prove that the tool has a build quality problem or design problem, and that can be a pretty tough legal path, even with very simple tools. Also, for the more complex tools, formal training is usually required (think of driving a car, or flying a plane). With "AI", pretty much nothing of the sort is possible: while LLMs are very complex, no training at all is required to use them, and as to legal recourse, the first listed issue makes it almost impossible. And so, there will always be very wide disclaimers (that nobody will read, as we can see now), making AI companies basically liable for nothing at all.I would strongly advise against using LLMs for anything serious. They can be used as glorified search engines, although even that is not robust. Always check what they spit out. They have a tendency to generate made-up stuff from existing content, sometimes making it hard to spot unless you have good knowledge of the topic at hand.Interestingly, current LLMs tend to be better at analyzing than they are at generating (although again, not to be trusted blindly). You'll notice something funny: give back to some LLM what they just generated, and they may indeed tell you that what you wrote is incorrect. Priceless. After experimenting with that, you may come up with a simple idea: multi-pass LLMs. If a LLM was analyzing its own output in 2 or more passes, the final output may improve significantly. Or not. First consequence of doing that would be a large increase in power consumption, which is already pretty bad for LLMs. Second consequence is that it may lead to worse output on average. I'm sure this is being investigated though. Using different models to check one another would probably work better, but again at the expense of power consumption, which would dramatically increase.

author: tggzzz
author_rank: Super Contributor
timestamp: May 12, 2025, 03:16:15 pm
content: Quote from: madires on May 12, 2025, 01:26:54 pmI learned of the problems with precursors to LLMs in the early 80s, specifically Igor Aleksander's WISARD. Apparently there was a joke that people would only recognise him if the sun was shining, since that mirrored a problem found during an early trial.The fundamentals haven't changed.

author: tooki
author_rank: Super Contributor
timestamp: May 12, 2025, 03:43:44 pm
content: Quote from: eTobey on May 11, 2025, 04:39:15 pmNo offense, but how are you just figuring this out now?!?It is widely known that LLMs don’t understandanythingand routinely fail spectacularly at technical calculations, because they arenatural language models, nothing more, nothing less. They not only don’t know electronics/physics calculations, they don’t know any math whatsoever. They get “1+2=3” correct because statistically, they’ve seen that sequence many times. Same with descriptions (and examples of) ohm’s law. But there is zero understanding of the subject matter.

author: tszaboo
author_rank: Super Contributor
timestamp: May 12, 2025, 04:06:55 pm
content: Quote from: tooki on May 12, 2025, 03:43:44 pmThat's true for yesterday's LLMs. They integrate some sort of agents that can do math.It takes your input, turns it into code. Something that they can already reliably do. And then run the code and give you the result.I've even saw some advanced LLMs that had a closed loop for programming. They wrote unit tests and changed the code until it passed.We have to stop underestimating them.

author: ZGoode
author_rank: Frequent Contributor
timestamp: May 12, 2025, 04:09:18 pm
content: I think the problem here with AI is so many people blindly trust it because they are either too lazy or just don't care (I'd say usually both). AI is horrible at giving any kind of technical insight unless you know enough to call it out when it hallucinates some answer that sounds reasonable but totally isn't.I responded to a posting looking for contract help on a project the other day and the brief I was provided was very obviously written by AI. It had all kinds of arbitrary capacitance and resistance values but nothing that corresponded to actual equipment. It was full of mentions to things that should have not even existed in the document (such as not using a 12V rail when the "BOM" only had things for a 5V rail and single 18650 anyways, also while all the components were only 5V tolerant). I called out a bunch of the inconsistencies in the document and got some BS about how the author had physical limitations preventing them from typing up/researching properly, but at the bare minimum they could have proofread it. I even mentioned some inconsistencies on what the document said the scope of the project would be and they said they didn't know how it got in there, but he claimed he reviewed everything to verify it (if that was the case it shouldn't have been in there then). I drew a hard line about not using AI on this project if he wanted my help and he said it wouldn't work out between us (which tbh bullet dodged cause it seemed like it was going to be a mess).AI's and LLM's have their uses, but I've seen too many instances where non-technical people especially take the word of AI as law.Sorry for the rant, I just absolutely hate when non-technical people use AI for technical stuff and then get all defensive about the use of it even when it is giving totally useless or counterproductive information.

author: temperance
author_rank: Super Contributor
timestamp: May 12, 2025, 05:13:12 pm
content: QuoteTo get a proper answer you must ask a proper question.-What is the cable made of? Aluminium perhaps.-If the cable is 20 cm, it implies that the total length of the path is 40 cm.-The question explicitly states: what is the voltage drop on a 20 cm cable.The question is:what is the voltage drop across awiremeasuring 20 cm with 1mm diameter made of copper + a fuse with a resistance of 100 mOhm for a current of 5A.The answer:So, the total voltage drop across the 20 cm copper wire and the fuse at 5A is approximately 521.4 mV.Edit: 𝑅total= 𝑅wire + 𝑅fuse = 0.00428 + 0.1 = 0.10428 Ω

author: tooki
author_rank: Super Contributor
timestamp: May 12, 2025, 10:33:00 pm
content: Quote from: tszaboo on May 12, 2025, 04:06:55 pmThat might be true for purpose-built programming LLMs. It certainly isn’t true of general-purpose ones like ChatGPT, which is the one used here. It routinely hallucinates calls to APIs and functions that do not exist; those would never compile.I think we very much need to stop underestimatingand stop overestimatingAI. Because it can be good, but often isn’t. The problem is the extraordinary confidence with which they output their results, regardless of whether the output is correct or not.Me, I fall on the side of “we should hold back on rolling this stuff out, because we don’t properly understand its failure modes, emergent behaviors, and other unpredictable effects that could cause direct or indirect harm”. And yes, it absolutely means that someone using or promoting most AI toolsnowdoes lose credibility in my eyes.

author: temperance
author_rank: Super Contributor
timestamp: May 13, 2025, 02:32:23 am
content: Come on, the question wasn't phrased well in this case. If you phrase the question well, you'll get the answer the OP is after. But instead of pointing out where the problem really is you'll get people bashing LLM's in infinitum.To the OP: if something doesn't work you might instead question the input and the output. But in my in my experience this is something you seldom consider a valuable exercise. Instead you question absolutely nothing and come up short-sighted conclusions and meaningless results.

author: tggzzz
author_rank: Super Contributor
timestamp: May 13, 2025, 06:55:19 am
content: Quote from: temperance on May 13, 2025, 02:32:23 amIn general, it is usually easy to determine the right answer.It is much more difficult -and important- to find the right question. LLMs excepted, of course!

author: bitwelder
author_rank: Super Contributor
timestamp: May 13, 2025, 07:20:36 am
content: Yep, similarly, few days ago I asked ChatGPT to calculate the minimum netmask that could include 4 given IP addresses, and not only it gave a wrong answer, but it seemed impossible to make it recognize its mistake and correct the behaviour.

author: tggzzz
author_rank: Super Contributor
timestamp: May 13, 2025, 07:53:07 am
content: Quote from: bitwelder on May 13, 2025, 07:20:36 amDid it apologise profusely and obsequiously when you told it the answer was wrong?

author: tszaboo
author_rank: Super Contributor
timestamp: May 13, 2025, 08:20:16 am
content: Quote from: tooki on May 12, 2025, 10:33:00 pmTrue, but what you seem to point out is that not all these models are created equally.Some of them are good, some of them are tailor made for specific applications. ChatGPT seems to have a large user base because it was early to release models to the public, creating a lot of "buzz". otherwise I find the response from it quite dumb. And LLM benchmarks agree.It's probably also clear that the LLMs are manipulated. When you enter a prompt, it has other parameters passed along it. Like how much compute power they can use for the answer. How much woke bullshit they are supposed to give you for an ethical question. What they can and cannot answer. They are not created equal at all.

author: tggzzz
author_rank: Super Contributor
timestamp: May 13, 2025, 08:34:44 am
content: Quote from: tszaboo on May 13, 2025, 08:20:16 amIrrelevant, but I'm not surprised you descended into that pit.Shame; some of your other non-ranting points were useful.

author: temperance
author_rank: Super Contributor
timestamp: May 13, 2025, 09:46:42 am
content: Bla bla bla bla,... The smug intellectuals of this world have jumped on their bandwagon again.Most of what has been written here by some is in this topic 100 % irrelevant. What is relevant to point out is that if you put shit into it you get shit out of it.AI can perfectly solve this trivial question. LLM's aren't that "stupid". Who is "stupid" in this case if you can't compose a proper question? You or an LLM trying to understand what you're saying? Given the input, the answer provided by the LLM is pretty accurate?Here is the original question again:"I just asked chatgpt, what the voltage drop on a 20cm cable with a 5A fuse (car) would be when a current of 3A flows. "Let's put this same question into the beginners section and see what comes out of it?

author: madires
author_rank: Super Contributor
timestamp: May 13, 2025, 10:09:26 am
content: Quote from: bitwelder on May 13, 2025, 07:20:36 amThat's why I call them pseudo AIs. There's no intelligence, just complex pattern matching which makes many humans feel that it could be possibly 'smart'. Add the hype and the marketing nonsense, and some people start to believe AI is smart. My prediction is that companies using AI will face many liability lawsuits because of their AI's hallucinations. And it's going to be expensive.

author: tggzzz
author_rank: Super Contributor
timestamp: May 13, 2025, 10:21:30 am
content: Quote from: madires on May 13, 2025, 10:09:26 amThere are already examples, e.g.https://catless.ncl.ac.uk/Risks/34/08/#subj4Failed legal defence: “the chatbot is a separate legal entity that is responsible for its own actions.”

author: bitwelder
author_rank: Super Contributor
timestamp: May 14, 2025, 11:52:21 am
content: Quote from: tggzzz on May 13, 2025, 07:53:07 amIt did, but modestly, probably because some time ago I instructed it to cut short of all those BS interjections like "Great question!", "Good finding!", etc.

author: tggzzz
author_rank: Super Contributor
timestamp: May 14, 2025, 12:00:34 pm
content: Quote from: bitwelder on May 14, 2025, 11:52:21 amClearlynothuman

author: temperance
author_rank: Super Contributor
timestamp: May 17, 2025, 03:33:58 am
content: QuoteDamn, my ego just plummeted to GND.

author: 5U4GB
author_rank: Super Contributor
timestamp: May 19, 2025, 12:56:21 am
content: Quote from: themadhippy on May 11, 2025, 05:39:20 pmUphill both ways.

author: radiolistener
author_rank: Super Contributor
timestamp: May 19, 2025, 03:07:23 am
content: Quote from: tooki on May 12, 2025, 03:43:44 pmjust tried wolfram-gpt (chat-gpt with wolfram math engine):Quotepreviously chat-gpt did random mistakes in calculations and used random numbers as result, but now it looks pretty good

author: golden_labels
author_rank: Super Contributor
timestamp: May 19, 2025, 07:37:45 am
content: Quote from: tooki on May 12, 2025, 03:43:44 pmThis is an exaggeration in the opposite direction.It’s accurate to describe LLMs as “glorifiedMarkov chains,” to succinctly draw the general image. But LLMs are notsimpleMarkov chains, that would reproduce sequences of tokens. Even earlier LMs are much more than that. They arestochastic parrots, which underlines their inability to have mental faculties ascribed to them. But it can’t be used for handwaveing and comparing them to/dev/urandom. They’re dice, as I myself often say. But these are very well designed as carefully weighted dice, which do depend in a meaningful way on the prompt. They’re glorified statistics, but that implies we can’t attach consciousness to them and we have a vast body of knowledge and experience applicable to them. Says nothing about their processing abilities.LLMs don’t just complete a sequence “1+2=” with a number 3, because the training set contained enough of “1+2=3” sequences. That would be a plain, old Markov chain. Instead, they actually do have conceptual understanding of quantities 1, 2, and 3, as well as the summing operation. For all we know, their basic numerical skills excel that of any known living species. Including humans. Humans fail above 3, 4 or 5. After that they switch toANS. This one degenerates quickly too, unless additional mental tricks are used. LLMs on the other hand do hold notions of much bigger numbers, both exact and approximate. And they work on them very well.The limitation is of a different kind. First: LLMs’ understanding of numbers is based purely on relationships found in the body of language. This includes both linguistic patterns and any actual knowledge reflected in those patterns. They don’t have all the exposure humans take from other senses and, most notably, interaction with the environment and other beings. Second: they can’t rely on anything but language processing. They can’t reinterpret a problem (e.g. thinking of “23000 + 57714” as “23 + 57, and something”), they can’t apply procedural knowledge, they can’t engage critical thinking.I’d compare the situation to how “look how dumb those people are” shows work. Approaching people on the street and asking them “simple” questions or some TV trivia competitions. Aside from a lot of cherry-picking and bias, they’re designed to prevent the victims from using higher mental processes. A person attacked this way represents what an LLM has at its disposition.So don’t equate solving equations by LLMs to chosing an already seen solution. It’s far more complicated than that. And I skipped the entire possibility of including additional tools in the loop, which smortnet companies are experimenting with all the time.

author: Georgy.Moshkin
author_rank: Frequent Contributor
timestamp: May 29, 2025, 03:30:44 am
content: Quote from: golden_labels on May 19, 2025, 07:37:45 amIndeed! Before arguing on the matter, it is important to try the LLMs at tasks they work well. I love how ChatGPT and DeepSeek create lightweight routines in .NET/JavaScript in seconds. I am a fan of Jeff Hawkins theory on cortical column processing which basically says that abstract knowledge manipulated as geometrical shapes using principles similar to how brain grid cells encode body movement (google for grid cells mice experiments).Why LLMs often fail at electronics? I think the main problem is that EE knowledge is poorly standardized. Schematics, datasheets, calculations... For example, try to copy-and-paste typical schematic from datasheet to LTSpice. Usually, it's impossible because all schematics are provided in image format that requires OCR and/or manual. Even if we find all required spice models after manual schematic capture, we often need to perform several manual manipulations to make it work. No standardized calculations either: we can't copy numerical example from datasheet to Matlab. We can't copy LTSpice schematic to the PCB editor either. And we can't generate schematic back from Gerber files. Therefore, most of EE-related datasets are not machine-learning friendly.

author: MisterHeadache
author_rank: Regular Contributor
timestamp: June 27, 2025, 10:01:48 pm
content: A little bit off topic, but I've been experimenting with the various 'chat' AI's with a simple mechanical engineering question: "I have three cubic meters of helium in a container at a pressure of 25 psi and a temperature of 47 degrees fahrenheit. I compressed the helium adiabatically to 50psi. What temperature will the helium now be?"The results consistently follow this pattern:1. It generates a verbose response showing detailed step-by-step application of the (usually) correct engineering equations.2. Somewhere along the way it makes an enormous mistake. Either in the algebra or in converting units.3. It gets to an incorrect answer, but asserts it as correct with confidence. ChatGPT was certain the gas was at -85F after compression (the correct answer is 209F)4. If I reply pointing out where the mistake is, it will agree and fix it.So like said previously, not useful and very dangerous to use if you don't already have the answer. Pity the lazy fools who are already blindly trusting it.

author: tggzzz
author_rank: Super Contributor
timestamp: June 27, 2025, 10:13:34 pm
content: Quote from: MisterHeadache on June 27, 2025, 10:01:48 pmThat's a good little testI imagine that if itdoesget the correct answer and you tell it is wrong, then the LLM will happily "correct" that tooThat would be very useful to malefactors who want to push propaganda for their own purposes.

author: SiliconWizard
author_rank: Super Contributor
timestamp: June 28, 2025, 03:41:11 pm
content: It can be of course very dangerous.Another test you can try is feed AI's answer back to it, asking if it is correct. Do this in a new chat session so it's not "biased" by the current chat and the answers it gave you.It will often spot the mistake. Which can seem not to make sense at all. But it does once you start to understand roughly how LLMs operate.Don't expect this to be foolproof either. But it's interesting.This is something I already mentioned earlier. LLMs are "better" at analyzing than they are at generating, and if anything, should be used for the former case rather than the latter. And always with caution, of course.

author: Siwastaja
author_rank: Super Contributor
timestamp: June 28, 2025, 05:11:01 pm
content: Quote from: SiliconWizard on June 28, 2025, 03:41:11 pmThis is why I find the usual advice, "Use AI to generate but check and edit manually", very bad. Doing the opposite: write youself, and ask the AI for opinions and checking, seems much better.

author: tggzzz
author_rank: Super Contributor
timestamp: June 28, 2025, 05:39:26 pm
content: Quote from: Siwastaja on June 28, 2025, 05:11:01 pmThat implies you have a clue about the subject. Most people don'tLLMs are being pushed as being useful for people who don't know the subject, or even to replace people, i.e. not checked by a knowledgeable personI also suspect that if you tell the LLM that your text is correct, then it will obsequiously agree with you or will pronounce it isn't sure. After all, Grok was not sure the holocaust happened.

author: Maria
author_rank: Newbie
timestamp: June 28, 2025, 06:22:30 pm
content: I don't usually think much of AI and I rarely use it, however it has come in handy recently when generating small code snippets, for example just now when I was writing software for using SCPI to take a waveform from my oscilloscope and send it to my AWG.It's very much not an instant solution type thing tho, you definitely need to double check its outputs and there's a good chance you will need to correct some of its code as well.In the past I have used GitHub Copilot to auto-complete single lines of code and that worked reasonably well as well as long as you are attentive to what it does.

author: tggzzz
author_rank: Super Contributor
timestamp: June 28, 2025, 06:58:46 pm
content: Quote from: Maria on June 28, 2025, 06:22:30 pmThat's one place where a good IDE with a suitable language is absolutely wonderful. Where you have that, ctrl-space invokes autocomplete and "writes" an awful lot of your code for you!A quick way to test for that is to see whether the IDE can tell youeveryplace the current function/method is invoked, andeveryfunction/method that it invokes. Then check whether a function/method/variable can be renamed, and the IDE guarantees to simultaneously changeallreferences to that function/method/variable. If it can't, you are missing outFundamental requirements are that the IDE is able to build an abstract syntax tree of the code on the fly, and that the language is defined in such a way that, given the current cursor point, possible completions can be enumerated.Not all languages enable that; many modern ones do.

author: tggzzz
author_rank: Super Contributor
timestamp: June 28, 2025, 09:38:43 pm
content: Quote from: Siwastaja on June 28, 2025, 05:11:01 pmJust arrived relevant comp.risks post:https://catless.ncl.ac.uk/Risks/34/69/#subj7.1my emphasisQuotePresumably file asanotherexample of "it compiles and passes inadequate unit tests so deploy it".

author: Siwastaja
author_rank: Super Contributor
timestamp: June 29, 2025, 06:58:14 am
content: Quote from: tggzzz on June 28, 2025, 06:58:46 pmBut then you need to know what you are doing. LLM coding allows you to solve relatively simple problems outside of your expertise (and therefore not simpleto you) - it's just a new, more flexible version ofstack overflow copy-paste coding.

author: tggzzz
author_rank: Super Contributor
timestamp: June 29, 2025, 08:07:36 am
content: Quote from: Siwastaja on June 29, 2025, 06:58:14 amMore or less, but see my post immediately before yoursArguably ctrl-space autocompletion requires a little brainwork, since you have to think how to start each element and explicitly select which completion.I'm not a fan of stack overflow because it is trivial and boring "which button do I press to turn the splurgle blue" stuff. But I do use it occasionally.