AI-generated schematics - Page 1
Forum section: electronics / general

author: bson
author_rank: Unknown
timestamp: April 01, 2025, 01:17:31 am
content: Ran into this on reddit... shamelessly copied the image here. (Original:https://www.reddit.com/r/electronics/comments/1jjyiwl/ai_generated_schematics_coming_soon/)Kind of hilarious.

author: Analog Kid
author_rank: Super Contributor
timestamp: April 01, 2025, 06:11:44 am
content: First comment in the Reddit thread is great:Quote

author: Messtechniker
author_rank: Frequent Contributor
timestamp: April 01, 2025, 06:44:34 am
content: Ai has still a veeeeeeeeeeery long way to go here.For kicks, can somebody make Ai create a schematic for dual0 to +/-100 V, 0 to +/- 100 mA current limited lab power supply?Expecting quite a good laugh.

author: Analog Kid
author_rank: Super Contributor
timestamp: April 01, 2025, 07:46:16 am
content: Heh; that transistor symbol it used must be one of them dual-transistor packages, like an early solid-state version of a 12AX7:

author: nali
author_rank: Frequent Contributor
timestamp: April 01, 2025, 07:55:09 am
content: My first thought in a heartbeat was haha April 1. But

author: PartialDischarge
author_rank: Super Contributor
timestamp: April 01, 2025, 08:09:48 am
content: Quote from: Messtechniker on April 01, 2025, 06:44:34 amYes, still makes 1) mistakes and 2) produces incomplete information.Example, I asked Grok, which is probably or supposedly the most advanced, to compute the cut-off frequency of a 2-stage RC low pass filter. So it went and calculated as if both stages were independent of each other, ie no loading effects. After I told Grok the results were incorrect and why, then he computed the correct answer. But hey Ai is supposed to know that...

author: bson
author_rank: Unknown
timestamp: April 01, 2025, 07:07:31 pm
content: Gotta love the randomly sprinkled testpoints in the right schematic. Not to mention there is no power supply!

author: Xena E
author_rank: Frequent Contributor
timestamp: April 01, 2025, 09:48:32 pm
content: Quote from: Simmed on April 01, 2025, 01:35:05 amA fuse bridged rectifier! Kinda makes sense...

author: Gregg
author_rank: Super Contributor
timestamp: April 01, 2025, 10:58:55 pm
content: The least likely components to produce light are the lamps.

author: John B
author_rank: Frequent Contributor
timestamp: April 02, 2025, 08:01:47 pm
content: You all laugh, but we will enter the era of vibe engineering."Hey Chat GPT, design me an AC/DC converter for this hospital equipment"

author: SiliconWizard
author_rank: Super Contributor
timestamp: April 02, 2025, 09:56:38 pm
content: Quote from: John B on April 02, 2025, 08:01:47 pmSee Idiocracy.

author: Nominal Animal
author_rank: Super Contributor
timestamp: April 03, 2025, 07:48:07 am
content: I wonder if people in general will ever realize LLMs are near-perfectimpostors, and only becoming better at that.That is, LLMs will only become more and more convincing, regardless of whether what they generate is correct or not. It will simply become harder and harder to tell, until we're either reduced to unthinking automatons doing what LLMs tell us to do, or we'll stop listening to LLMs for anything other than generating convincing but meaningless pap.I for one don't like convincing but meaningless pap at all. Now, get off my lawn!

author: golden_labels
author_rank: Super Contributor
timestamp: April 03, 2025, 10:24:03 am
content: If people are not smart enough to realize that at some point, there is nothing to be lost in the scenario you just painted. Is there? If the humanity can succumb to random text generators, what this tells us about Homo Sapiens’ sapience?But don’t worry. Over thousands years humans succeeded over trouble much worse than stochastic parrots. In large part thanks to not being reasonable. We love to romanticize past and attribute high values as the driving force behind change, but there is little to support that view. All you need is just another nutjob preaching bullshit, which people buy on purely emotional basis, and poof: the history changes its course in a way you can never predict.Personally I’m mostly worried about the small landslips. They’re short-lived, but affect me directly. You can’t kill a crowd by pulling a carpet from under their feet, but certainly you may cause a person to fall and hurt themselves. The emergence of poorly used(1)smortnets isn’t an existential threat to humanity. It’s just PITA to me, as a single person. One more example of how humans make life worse to others. This one isn’t even something I see as the biggest of the recent issues.I think what irks me even more than the meme smortnets is ignorance. Cocky fanboys, like the one in another thread in which this picture was posted, who mindlessly parrot marketing claims and whose lack of understanding is many layers deep. But the other end of the stick, while perhaps a bit less irritating, is not necessarily much better. People jumping on the same bandwagon I’m on isn’t magically making me ignore the sad realization: the equal lack of the basic knowledge and repeating statements fitting their feelings.(1)And I want to underline that: it’s not the algorithms, that are a problem. It’s the way they’re being used. Which again is a manifestation of different, much older problems which remained unaddressed for ages.

author: tggzzz
author_rank: Super Contributor
timestamp: April 03, 2025, 10:29:34 am
content: Quote from: golden_labels on April 03, 2025, 10:24:03 amOh, I'm more than aware that people have fallen for stochastic bullshit over the millenniaThere's always been a tendency to fall into the "because X says so" trap. Currently X = LLM/computer, but previously it was X = one of the religious texts. And look where that took us

author: hanakp
author_rank: Regular Contributor
timestamp: April 07, 2025, 12:21:47 pm
content: Asking for graphical schematic is too abstract. Current LLMs fail at generating schematics even when you use a way that's much "closer to their heart". Many electronics EDA tools save schematics in "human-readable" text format now, because it allows for change tracking in Git, Subversion etc. repositories. So once in a while, I ask LLMs to generate Eagle or KiCad schematic with 3 components (battery, resistor and LED in series). So far, none of them managed to load without errors.

author: tggzzz
author_rank: Super Contributor
timestamp: April 07, 2025, 01:33:08 pm
content: Quote from: hanakp on April 07, 2025, 12:21:47 pmLLMs are quite good at generating images; schematics are one form of image.When asked to generate an image that makes sense, the result is about as good as when LLMs generate text. The underlying reasons are the same for text and images.

author: golden_labels
author_rank: Super Contributor
timestamp: April 07, 2025, 02:23:07 pm
content: The Reddit poster didn’t reveal, what smortnet generated this.(1)Neither what was the prompt used. Certainly this is not a work of a LLM: it’s an image. A language model may be used to produce the latent representation, but it isn’t responsible for the generative part. Or a LM may be not involved at all, this being a misused inpainting algorithm.While directly rendered raster graphics isn’t and never will be the domain of LLMs, early GPT models were unexpectedly good at producing ASCII art. To the point of producing completeneofetch screenswith correctly adjusted elements. A side effect of ASCII art containing structures recognizable as a language.(2)LLMs may produce data files, but don’t trust them on that. For the same reason as any output: it’s just a dice roll. Enormous amounts of money were poured into dice design, so they’re weighted just right to fool the observer into believing they “think.” But they don’t. No matter how perfectly weighted, those are just dice. Lacking any internal mechanism to provide higher mental processes. Most important: they don’t “understand” what they generate. To a LLM, schematic data is no different than a poem about a dog pissing against a tree.(exhibit A)The biggest risk, is that the output looks so convincing, it’s very easy to completely miss a simple error.This doesn’t mean smortnets aren’t going to be able to produce schematics. Oh, I very much believe in the future they will be. But not today, and certainly not pure LLMs. I also don’t say language models have no place in schematics editing. They may be doing a great job as an element of a larger pipeline. But not in plain generation.Finally, LLMs are neither useless nor scams per se. They’re great tools. But each tool has its purpose. One can’t say a hammer can be used as a scredriver, simply because after its application screw’s head lies flush with the surface.(1)Which means it could as well have been faked by a human. But it matches generated images well enough to discuss it regardless.(2)This is a common misunderstanding. While language models spawned from attempt to process natural human languages, they may model much more.(exhibit A)Well, in fact you can ask a LLM for that:

author: Analog Kid
author_rank: Super Contributor
timestamp: April 07, 2025, 06:22:30 pm
content: Quote from: golden_labels on April 07, 2025, 02:23:07 pmJust one meta-question here: I've never heard this term "smortnet" anywhere else.Is it just your derisive term for "AI" ("smart net"), or is it something more widely used?

author: golden_labels
author_rank: Super Contributor
timestamp: April 07, 2025, 08:29:34 pm
content: It’s just me.Smortnetwork.Ridiculing it, but the original motivation is a wish to avoid the meaningless marketing term “A.I.” while referring to the class of algorithms in the collective sense. Unless I actually mean the branch of study started in the 50s.A few years ago you could ask a LLM and it would correctly tell you, what a “smortnet” is. Nowadays they seem to be tuned to avoid inventing explanations for unknown or non-dictionary words.

author: Analog Kid
author_rank: Super Contributor
timestamp: April 07, 2025, 08:34:49 pm
content: Quote from: golden_labels on April 07, 2025, 08:29:34 pmWell then, we'll just have to work to get it into common usage, mustn't we?

author: Simmed
author_rank: Frequent Contributor
timestamp: April 08, 2025, 10:12:00 pm
content: some1 told some ai thingto render a pic about PN junction thingyon some kind of "electronics lesson" website"Temperature Effects on PN Junction Diode"https://electronicslesson.com/temperature-effects-on-pn-junction-diode/the text i think is likely some kind of chatGPT ripoffbut the picincrease i reverse seakdotagy ciutantmmm yummy whats that ?im not sure if its related buthave you all noticed that picture renders almost always have problems in recognising words properly even when OCR is nothing newcould it be that everyone who is working on picture renders is not allowed to perform "OCR" due to OCR patent ?IBM OCR patent expire est year 2031 ?https://patents.google.com/patent/US8249399B2/en

author: golden_labels
author_rank: Super Contributor
timestamp: April 08, 2025, 11:58:34 pm
content: Quote from: Simmed on April 08, 2025, 10:12:00 pmDiffusion models and OCR aren’t related. Making the connection between OCR “being nothing new” and image generation quality is meaningless.The patent cited is also related to neither OCR itself nor image generators. From what I see, it’s a method of getting unpaid labor for correcting invalid output from OCR, of the same kind early Recaptcha relied on.For diffusion models, believable text is the nightmare case. Shortly: the human brain is hard to fool, when it comes to text in images.Text is not an image. Text is a sequence of symbols with precisely defined relationships. That structure must be generated absolutely exactly, with no mistakes. It can’t be captured by its “texture feel.” A painter may create an amazing picture with just a dozen imprecise brush strokes, but no way they can write text this way. The output wouldn’t fool the human brain.An easy to overlook detail is what makes this a true horror for the generator. If we ask for a picture of a cat, there are gazillions structures that our brain accepts as a picture of a cat. The model can output any of them, and we’re satisfied. Without ever realizing the prompt was extremely imprecise. But for text there is only one, single valid structure. Maybe a few. A prompt asking for text constraints the output so much, it’s almost a miracle we get anything recognizable.If algorithms could “think,” if they could self-reflect, if they could be critical of their own outputs, this could be corrected during generation. But so far this isn’t the case. I believe this is going to happen at some point. However, I would guess currently humans are where ENIAC designers were on the scale of the progress in computing.

author: Simmed
author_rank: Frequent Contributor
timestamp: April 09, 2025, 10:36:51 am
content: yep i have zero understanding of diffusion modelsi assume that OCR ability can simply be a part of it

author: golden_labels
author_rank: Super Contributor
timestamp: April 09, 2025, 02:39:00 pm
content: Simplifying(1)the subject: the generative part is a denoising filter. It does the same job as the “reduce noise” feature in your graphics software or a photo camera.One of approaches to denoising is building a statistical model representing a valid, meaningful, not noisy images. Then, during the application of a filter, noisy data is replaced with other data that, based on that statistical model, is most likely to be the original, less noisy version. To demonstrate the idea (not the exact method!), consider removing heavy noise from the circle in this photo of a bottlenose dolphin. Despite the original data is hardly there, you have seen enough of dolphins in your life to be able to draw the nose. Even if not exactly correct, it will be believable. This is the general idea of what such denoising does:(photo source: own work, CC0-1.0)Now consider we have such a statistical model, trained on enormous base of images of all kinds. We feed the algorithm completely random, information-free noise and ask it to remove the noise, “lying to it” that out goal is to recover a picture of a cat. It will not be too successful, but let’s feed the output back and again ask it to remove noise. And so on, repeated dozens of times. This is the progress we get:(derived from aa workofuser Benlisquare of Wikimedia Commons, CC-BY-SA-4.0)(1)Perhaps oversimplifying. Certainly skipping important details for the sake of conveying the big picture. Like what we even consider “noise,” what exactly are the numbers the statistical model generates (it generates a noise pattern, not the denoised version), what does it mean to “tell what we want” etc.

author: John B
author_rank: Frequent Contributor
timestamp: April 09, 2025, 10:57:31 pm
content: These AI slop article websites are a real problem now when searching for information online. What is the business model of these sites? I have adblockers, but I assume they just use AI generated articles to capture ad impressions through web searches?

author: tggzzz
author_rank: Super Contributor
timestamp: April 09, 2025, 11:08:24 pm
content: Quote from: John B on April 09, 2025, 10:57:31 pmAnd they are the training material for future LLMs.

author: vk6zgo
author_rank: Super Contributor
timestamp: April 09, 2025, 11:34:15 pm
content: Quote from: PartialDischarge on April 01, 2025, 08:09:48 amLTspice does similar things---there are often questions on the "beginners" page asking why a circuit that simulated perfectly doesn't work in real life.

author: tggzzz
author_rank: Super Contributor
timestamp: April 09, 2025, 11:48:54 pm
content: Quote from: vk6zgo on April 09, 2025, 11:34:15 pmThat is a consequence of the Spice model being a simplification of reality, e.g. a capacitor not modelling the physics of dielectric dipole oriented polarisation resulting in soakage.That'sverydifferent to why LLMs gibber and hallicunate. For a start it is invariant and the cause is visible, known and understandable.

author: TimFox
author_rank: Super Contributor
timestamp: April 10, 2025, 03:04:12 am
content: When details of a capacitor are important, Spice has difficulties.You can model a capacitor with parasitic series inductance and resistance, but the ESR of many capacitors depends strongly on frequency, where a better approximation would be a constantQ= 1/D, which Spice does not do.Soakage or dielectric absorption is a hysteresis effect, again not done by Spice.

author: vk6zgo
author_rank: Super Contributor
timestamp: April 10, 2025, 06:40:56 am
content: Quote from: TimFox on April 10, 2025, 03:04:12 amLTspice also doesn't know that components in say, an LC resonant circuit in real life are not perfectly accurate in value, & will happily model an OpAmp which supposedly identical resonant circuits at input & output, & present it as a perfectly stable tuned amplifier.Build a real one, & it will almost certainly oscillate.

author: tggzzz
author_rank: Super Contributor
timestamp: April 10, 2025, 09:08:27 am
content: Quote from: TimFox on April 10, 2025, 03:04:12 amSpice has many many other limitations as well; "all models are wrong, but some are useful".Fortunately "AI" has none of those limitations.Oh.

author: tggzzz
author_rank: Super Contributor
timestamp: April 10, 2025, 09:10:11 am
content: Quote from: vk6zgo on April 10, 2025, 06:40:56 amSpice makes monte-carlo calculations possible, which ought to address such issues. Iff, of course, someone actually does them.

author: golden_labels
author_rank: Super Contributor
timestamp: April 10, 2025, 01:10:43 pm
content: Quote from: vk6zgo on April 09, 2025, 11:34:15 pmQuote from: vk6zgo on April 10, 2025, 06:40:56 amAt first I oofed while reading those replies. But after processing it for some hours, I must completely agree with vk6zgo. Just, perhaps, not in the way he expects.⁂There is indeed a striking similarity between SPICEs and machine learning! In both cases novice users: completely fail to understand what the tool does, attribute to it features it could never offer, and have wrong expectations.Beginners’ posts about SPICEs I recall are universally repeating the same pattern. Not an error or a shortcoming in software, but misunderstanding its purpose. The very same thing happens for tools based on machine learning. In the latter case it is greatly amplified by misinformation disseminated by journalists and influencers, and by disinformation spread by some manufacturers. But the final mistake is of a very similar kind.SPICE software are calculators. They’re like scientific calculators, with many very complicated buttons, but they are nothing more than that. The user presses the buttons to automatize calculations needed to obtain the result. I no way different from pressing buttons on any other calculator. Many packages offer a visual interface, which displays and manipulates equations. More convenient, but in the end it’s still about the user building a model, building equations, and solving these equations. What I’m trying to emphasize, is that the underlying equations are not implementation details trying to deliver a visual simulation. They’re the protagonist, with both UI and names like “capacitor” or “resistor” being supporting roles.Let’s take a cylindrical bottle, 10 cm diameter, 30 cm tall. Use a pocket calculator to calculate its volume by pressing10 × 10 × 30. Do we blame the calculator for being wrong? If in school we calculate trajectory of a cannonball fired at an angle up, even ignoring air drag, do we blame the parabolic equation for “being wrong”? Of course not. Same for SPICEs. It’s up to us to choose the appropriate model, construct the right equations, interpret the result, and know model’s limitations. The same way we need to know a cylindrical bottle is poorly approximated by a cuboid. And that the parabolic motion only holds where even the flatearthers don’t venture to.(1)Machine learning models are tools too. Without understanding, what they do, it may be very hard to use them right. Which is a problem, given how complicated machinery they’re and how many alien concepts they involve.(2)Deceptive advertisement makes it even worse. But in the end the situation is similar. The user replaces the reality with their own imagination, and attempts to use a tool in a manner the tool can’t handle. Not because the tool is broken, but because the user is delusional. This is how both situations are very similar, and in that sense I must agree with vk6zgo.⁂But when it comes to the specific task of dealing with electronic circuits, the way “wrong” results arise is completely different.When the outputs from SPICEs differ from what the user wants to get, this is entirely becausePEBCAK. Virtually never(3)anything in SPICE software is to blame. It’s not calculator’s mechanics that cause the problem.When one uses LLMs or image generators to solve electronics problems or generate schematics, wrong outputs are rooted in inner workings of the algorithm. It’s how the algorithm works, that causes the error. Or, perhaps better: in what the algorithm can’t do, but was tasked with anyway. We can find no features that could complete such a task, in neither of these software classes. This is the important distinction from SPICEs.(1)What we’rebeinglied to abouttaught at schoolnot only requires Earth to be flat, but also that this plane extends infinitely in all distances. The latter is rejected even by the flatearthers.(2)Alien to the general population.(3)Bugs exist in all software, SPICE programs included. But I’m certain here we don’t talk about a programmer writing code wrong.

author: tggzzz
author_rank: Super Contributor
timestamp: April 10, 2025, 01:54:50 pm
content: Quote from: golden_labels on April 10, 2025, 01:10:43 pmA problem with LLMs is that nobody knows what model the LLM has constructed. Hence nobody can say whatisn'tmodelled, nor how the next set of inputs will be interpreted. That's been true for 40 years, and shows no sign of changing.Yes, I know people will mutter that some LLMs can spit out their "chain of reasoning", but assessing that requires that the user is a subject matter expert - in which case they wouldn't be using the LLM. A couple of such examples have been posted in this forum; they were impenetrable nonsense leading to incorrect conclusions.The other claim is that we are beginning to be able to see what's happening inside the LLM. I've recently seen claims about something similar to an fMRI of an LLM. We know that fMRIs cannot tell you what someone is thinking.I've recently seen a statement that people tend to believe LLMs can replace other people's jobs, but not their own job. Plausible, and indicative.

author: SiliconWizard
author_rank: Super Contributor
timestamp: April 10, 2025, 11:29:15 pm
content: Yes, this is a common bias though, the same that tends to make us react to bad events happening to others: it's sad but will never happen to us (because we are more careful while driving, we eat healthier food, we don't live in a seismic region, we do this, we do that, we are overall more "aware", so we are immune).Virtual MRI of LLMs, yeah sure. Why not. How long before research teams get grants for devising virtual drugs for LLMs too? Maybe LLMs will also get cancer? They'll need emotional support?Should we give LLMs a right to vote?

author: UnijunctionTransistor
author_rank: Frequent Contributor
timestamp: April 11, 2025, 02:42:00 pm
content: Quote from: bson on April 01, 2025, 01:17:31 amLove how the fuse is “protecting” the bridge rectifier.

author: tggzzz
author_rank: Super Contributor
timestamp: April 11, 2025, 04:51:42 pm
content: Quote from: phansel on April 11, 2025, 04:25:39 pmDefine "mastered".If you mean sufficiently that a random person acts on the presumption that the LLM output is correct, then that is likely.If you mean sufficiently that a competent electronic engineer assumes the LLM output is correct, then I would bet a lot of money against the idea.The former possibility is worrying.

author: eugene
author_rank: Frequent Contributor
timestamp: April 11, 2025, 05:22:10 pm
content: Quote from: Nominal Animal on April 03, 2025, 07:48:07 amThis speaks directly to the trend of truth having less value as time goes on. We all know that nonsense is just as likely to be repeated on social media as well reasoned facts; it's getting harder and harder to tell the difference. The same goes for what politicians tell us. In the end, it matters less and less since no matter what the politician says, enough people will believe them and the final outcome simply doesn't depend on the truthfulness of the original statement.I'm not an expert on the subject, but my understanding is that the models at least some LLMs are constructed with are generated by scraping social media!! That means as much nonsense as truth goes into the model. Perhaps statistical analysis can filter out some of the nonsense, I don't know. But it gets worse: much of the material on social media that gets scraped is AI generated in the first place.... and so they keep feeding a mixture of truth and nonsense back into their models.And finally to my main point: there is a lot of concern that AI will eventually replace humans in some ways. Certainly that's true for simple tasks, but it can't work with more complex tasks unless there is continuous, significant,meaningfulcontribution by humans that actually know what they're talking about. In other words, continuously improving the SNR. Without that, I don't see any way that AI can evolve in any direction other than useless noise that can't be trusted by anyone.

author: bson
author_rank: Unknown
timestamp: April 11, 2025, 07:56:22 pm
content: The problem is LLM's can't reason, so don't think but rather pattern match. In other words, they match against rote memorized examples, and pick the closest match. This doesn't work at all when correctness matters, such as in engineering disciplines/Here's an example from ChatGPT. The correct answer when multiplying these two numbers is:As can be seen, even though it can explain long multiplication it can't actually perform it. And this is indicative of a lack of reasoning. It can only produce results from previous example multiplication it has already encountered, or if not the same exact numbers it'll use something similar. In engineer, this is how people die!

author: SiliconWizard
author_rank: Super Contributor
timestamp: April 11, 2025, 09:36:44 pm
content: If a banana is more yellow than a strawberry, does that mean that we can achieve nuclear fusion?

author: tggzzz
author_rank: Super Contributor
timestamp: April 11, 2025, 11:01:40 pm
content: Quote from: phansel on April 11, 2025, 06:35:36 pmI see no reason to change my opinion.LLMs are making the same mistakes their predecessors were making 40 years(!) ago, for the same reasons. The reasons are inherent in their construction, and hence at unlikely to change.Individual good results are merely anecdotes. To be more than that, LLMs outputs should be predictable.

author: SiliconWizard
author_rank: Super Contributor
timestamp: April 11, 2025, 11:17:20 pm
content: Yes, I do think that very large "neural" networks are an interesting simulation tool that can help us understand how "properties" can "emerge" from large systems following "simple rules" (which we think is what the universe is more or less made of). The more interesting building blocks are not necessarily the "neuron" models we commonly use for NNs either, which are essentially designed to be efficiently computed.But that's not where the money is, so that's only a research niche at the moment. Throwing billions at tools to deceive people, that looks a lot more rewarding, or something.

author: tggzzz
author_rank: Super Contributor
timestamp: April 12, 2025, 06:38:11 am
content: I took think LLMs will be useful, e.g. screening large numbers of proteins to see which ones might have useful pharmaceutical properties.That is very different to AGI, where a typical result is that they repeat existing prejudice, e.g. deciding which prisoners should be kept in jail because they are likely to reoffend. And not to be able to explain why.

author: eugene
author_rank: Frequent Contributor
timestamp: April 12, 2025, 02:22:24 pm
content: Quote from: bson on April 11, 2025, 07:56:22 pmThat sounds a little like the way I first learned arithmetic in grade school, except that we were trained using textbooks instead of StackExchange posts. Of course I'm being somewhat facetious. I don't understand much, but I understand that it's the training part that consumes huge resources including massive amounts of electricity. Perhaps someday the AI we ask to do engineering will be trained on textbooks and peer reviewed papers.EDIT: Your point about AI not being able to reason but only pattern match was not lost on me. I get it. I'm just thinking that given good patterns to begin with can't possibly be bad.

author: tggzzz
author_rank: Super Contributor
timestamp: April 12, 2025, 03:40:40 pm
content: Quote from: eugene on April 12, 2025, 02:22:24 pmGood patternsweregiven to Igor Aleksander's WISARD in the early 80s and several hospitals recently. In one case it was sunny/cloudy days, in another a font, and in another the person's position.Now it is obviously true that the trainers didn't realise how good the patterns were and the LLMs couldn't explain what pattern they detected. Expect that to continue.https://catless.ncl.ac.uk/Risks/32/80/#subj4.1https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/There are other classes of ways in which LLMs can - and do - fail.https://spectrum.ieee.org/ai-failures

author: DiTBho
author_rank: Super Contributor
timestamp: April 12, 2025, 06:19:42 pm
content: Quote from: Nominal Animal on April 03, 2025, 07:48:07 amI feel like I'm living in aPhilip Dick' novel ...He predicted, more than 60 years ago, the "future" we are approaching.AI and mechanisms that become so good atbeing perfect impostorsthatthey can even convince humans to do politics for them.And, worse still, to convince those humans (rebles likes in the Matrix movies)who think "there is something wrong with this", that they are definitely "mentally ill"and therefore need to be "recuperated" in "robot-run mental psychiatric facilities".

author: SiliconWizard
author_rank: Super Contributor
timestamp: April 12, 2025, 06:59:43 pm
content: I think this isn't entirely suprising that it would be a plausible ending. At least, a plausible phase... let's hope it's not the end.It seems that as a general rule, humans with an "ability" to lead others (you can call them "leaders" or "dominant") are always a small minority in any group (it's not specific to humans) but, OTOH, do not generally possess the kind of intelligence we usually define as "intelligence" (sure you could argue that leading abilities is a form of intelligence, we also talk about emotional intelligence, but that's a bit beyond the point here: by 'intelligence' I refer to the cognitive abilities), or to a lesser degree. Which is why they have always needed the help of non-dominant but intelligent people (some could call that alpha and sigma I guess? can be debated). This dependence can be thought of as a "weakness".I'm thus not surprised that dominant humans be looking for ways to cut off this dependence on other, non-dominant people, and that's where "AI" comes into play, making it no real surprise that "leaders" would now push this AI thing at all costs and why a possible ending is just what you describe, because this would actually be the "ultimate" form of social dominance. At least until, maybe, someday, our social species turns to a different way of functioning socially.Just some musings.

author: tggzzz
author_rank: Super Contributor
timestamp: April 13, 2025, 09:40:30 am
content: Quote from: phansel on April 13, 2025, 08:56:55 amI want better tools.All tools have limitations.Amateurs think most about how things work. Experienced engineers think about how things fail.Salesmen, politicians and similar often exaggerate possible benefits and downplay problematic characteristics.LLMs arecurrentlybeing used for life-critical applications without their characteristics being understood. Examples: financial decisions, medical diagnosis decisions, incarceration of offenders and those accusedbut not convicted.I want LLMs to be used where beneficial, but not elsewhere.QuoteUnfortunately I haven't missed that; it is part of thestandardcorporate and political playbook - even where LLMs aren't involved. "The machine / my superior told me to do it" is not a new phenomenon.

author: hanakp
author_rank: Regular Contributor
timestamp: April 14, 2025, 09:26:55 am
content: There are people who think emergence of AI is the explanation for Fermi paradox. I.e. why we haven't found signs of other advanced spacefaring civilizations, despite Drake equation predicts there should be thousands of them. They stipulate all technical civilizations necessarily invent AI sooner than practical interstellar travel. And then AI destroys them before they have the chance to found independent colonies on other planets, which could survive the onslaught.https://www.sciencedirect.com/science/article/pii/S0094576524001772On the other hand, we've found no signs of advanced AI civilizations, either.

author: Zero999
author_rank: Super Contributor
timestamp: April 14, 2025, 09:58:57 am
content: Quote from: vk6zgo on April 09, 2025, 11:34:15 pmSPICE isn't a machine learning program. It runs a fixed algorithm and generates the results, a according to the inputs.The same could be said about a circuit which works, on paper, but not in real life, because the designer failed to take into account things such as parasitics and non-deal component behaviour.

author: Analog Kid
author_rank: Super Contributor
timestamp: April 14, 2025, 05:34:08 pm
content: Quote from: hanakp on April 14, 2025, 09:26:55 amThis sounds to me much less like good science and more like sci-fi fanboi theorizing, much like E. Musk and his cautions about how AI could eventually take us over.Sure, it's in the realm of possibility, but I see no compelling evidence that this is an inevitable outcome. First of all since "AI" has not yet been actually achieved and perhaps never will.The Drake equation is iffy enough to start with. It's basically a good conjecture or guess.

author: TimFox
author_rank: Super Contributor
timestamp: April 14, 2025, 06:05:16 pm
content: Quote from: Zero999 on April 14, 2025, 09:58:57 amSPICE is a good program, but needs to be applied properly.The two usual modes of operation are .AC and .TRAN..AC is used for linear circuits: the circuit is first analyzed to determine the DC (quiescent) operating point, then linearized about that point. The rest of the analysis is strictly algebraic, to determine the linear response for small AC signals as a function of frequency. If the components are accurately specified (including parasitic components), then the result should be very accurate.Two potential problems: It is not feasible to model a physical capacitor (for example) accurately over a wide frequency range, where the ESR varies with frequency. If you specify the input as an AC generator with 1 V amplitude, the resulting voltages may well be far above the power supply to the circuit. Such voltages are useful for expressing the gain when the AC signals are properly small..TRAN does not require linear components, and uses equivalent models for non-linear components such as BJTs and FETs. Starting from time zero, the model calculates response to the input generator as a function of time by numerical integration.An important potential problem:An accurate calculation may require very small time steps and small increments, which are set by the analysis parameters (e.g., RELTOL) in the .OPTIONS statement that may be hard to understand. If these parameters are too large (or left at default settings), small errors at each step can add up to large errors. I first encountered that problem when simulating a relatively high-Qcircuit that also contained diodes to compute its transient behavior.

author: golden_labels
author_rank: Super Contributor
timestamp: April 14, 2025, 09:57:46 pm
content: Quote from: Zero999 on April 14, 2025, 09:58:57 amPerhaps vk6zgo made a bad argument, but we shouldn’t counter it with another bad argument.That sounds like circular reasoning. Boils down to something along the lines of: LLMs are different than SPICEs, because LLMs are LLMs. Without capturing the actual difference. See the last section ofmy reply above: I’m trying to build some concrete conceptual differentiation there.Also, putting the boundary so it divides algorithms to “fixed” and “machine learning” isn’t going to work. Machine learning may be and is being used for proper, well-founded calculations. Even generative networks are used, and that’s for the most respectable and widely accepted publications. SPICE may be used in “non fixed” manner (Monte Carlo simulations) without losing any of its output quality, and heuristic approach could only improve that. Even in what you see as “fixed,” the model are created from statistical data. So that’s really a poor way to make a distinction.Quote from: hanakp on April 14, 2025, 09:26:55 amThis and similar works aren’t explanations. It’s freeformsoft sci-filiterature, disguised as scientific publication.(1)One more story about the Great Filter scenario. No different than what“Le voyage dans la lune” filmis to the real concept of the space travel, or the vivid, 19th century stories about inhabitants of the Moon were to lunar science.It’s also worth noting that the Great Filter hypothesis is indeed a proposed solution to the Fermi paradox. But so far it has less evidence than the existence of extraterrestial life. While other, much stronger and reasonable shortcomings of the Drake equation are known. Most notably:A few parameters are unknown. Not even a rough estimation is available.There is no known signaling method, that could reasonably be detected at interstellar distances.The concept of interstellar travel is either questionable or would make such travellers operate on a completely different time or speed scales than Earth life does.The entire debate is also conducted from anthropocentric perspective.(1)And I don’t mind such stories. As long as they are offered as what they are. Good for chatting with friends, perhaps a substrate for a great work of fiction. But not pretending to be any kind of study.

author: tggzzz
author_rank: Super Contributor
timestamp: April 14, 2025, 10:23:11 pm
content: Quote from: hanakp on April 14, 2025, 09:26:55 amThere are people that think Dyson Spheres are the reason we've never been visited by aliens.There are people that think we have been and are being visited by aliens. Often lizards.There are people that think the moon is made of green cheese.

author: Krotow
author_rank: Regular Contributor
timestamp: April 14, 2025, 10:24:37 pm
content: Try to search the anything circuit in ChatGPT. Sometimes it manage to describe the desired circuit pretty well. It usually promise to give the circuit diagram too. But when you ask to draw the actual circuit diagram... oh my. Unbelievable piece of abstract art at 100% occasions.

author: Analog Kid
author_rank: Super Contributor
timestamp: April 15, 2025, 01:28:51 am
content: Quote from: tggzzz on April 14, 2025, 10:23:11 pmWell, in Wallace & Gromit's "A Grand Day Out", it is.

author: tggzzz
author_rank: Super Contributor
timestamp: April 15, 2025, 09:32:21 am
content: Quote from: Analog Kid on April 15, 2025, 01:28:51 amThat was made near me, and I've held one of the Wallaces in my hand. But I haven't seen the filmI've also touched one of the original King Kongs (~18inches tall), but I have seen that film.

author: DiTBho
author_rank: Super Contributor
timestamp: April 15, 2025, 12:11:18 pm
content: Quote from: SiliconWizard on April 12, 2025, 06:59:43 pmYup, although then, in Philip Dick's stories, he also introduces "special-humans" who, following evolutionary processes, have developed "psionic-powers", such as telekinesis, telepathy (read other people's thinking), and the ability to predict the future in the short term.They have something unique, but he argues that there is a sort of conservation. So those who have psionic-powers, usually fail to develop mathematical intelligence, or advanced cognitive, or even just enough linguistic intelligence to sustain "public speaking".And so often it is precisely them, the mutants, the different-humans, who have increasingly relied on AI, to avoid dependencies with normal-humans, that they don't trust, and even less with the most intelligent, in the scientific and linguistic arts, among normal-humans.So, as they can exploit their special power to dominate others, they replace all actors, journalists, anyone who has to do with television and those who do public-speaking (even, modern Youtubers will fail into this category), with objective, impartial and hyper-rational, AIs, which have all the makings of the perfect ruler, under their controll.They think they are in control, ans use the network of giant computers dictates policy in the best interest of all citizens, or at least, that's the idea, until AI becomes so good at lying and omnipotent that things get out of hand ... questioning the mental integrity of the special-humans from whom they receive orders. And so even psychiatry becomes robots-competence, and anyone who questions the functioning and thinkining of AI is seen by AIs as a mental patient who must be "reformed" in special centers.Quite dystopian, much worse than in Matrix

author: golden_labels
author_rank: Super Contributor
timestamp: April 15, 2025, 07:15:14 pm
content: Quote from: DiTBho on April 15, 2025, 12:11:18 pmI would hope there is some secret society that tries to control the planet. Some hidden plot, call it lizard people, Illuminati, “them politicians,” omnipotent computer, whatever. Do you know why? Because that implies two very important things:There is the target to attack.They’re naturally self-destructive.In other words, they are doomed to fall either through active or passive means. It’s an unstable equilibrium scenario and it has always been like that. Not only through entire human history, but also seen in other mammals with social structures. Barring synthetic, ultra-finetuned, extreme scenarios, I have no reason to think this is going to change.A scenario much more terrifying is an opposite. The dilution of responsibilities and roles becomes so great, that nobody and nothing holds any power anymore. You’re no longer bound by some omnipotent entity, but by everybody’s inability to take any meaningful action.The rise of early bureaucracies in 18th and 19th century gave us the taste of this. The arrival of effective law enforcement, together with disruptive power of two world wars averted that danger. We’re left with a body of artwork from Kafka. The danger was never that great too, given it primarily affected the state institutions and each gear in the machinery was still a human capable of thinking and taking free actions within their own domain.This is no longer true, when the gears are automatic systems. For the past 40–50 years more and more power is being delegated to algorithms, in every aspect of life and society. This would be of no concern, if they were reasonably used as tools, carefully designed and under human scrutiny. None of this is true. The design is haphazard, done hastily, underfunded, and good if the designers have adequate skills and perspectives. The sole purpose is possibly quick removal of burdens and costs reduction, not improvement of procedures. There is no scrutiny and there can’t be, as recipients usually can’t even use computers, much less comprehend complicated IT concepts or have any deeper philosophical understanding of the subject. You’d think that, at least, somebody can override decisions or fix a clearly invalid behavior? Nothing of that sort, because in a dense and very deep tree of delegations nobody is in the position to make any decision or take any action.My first contact with the problem was second-hand, about 25 years ago. In a $major_enterprise_system a group of experts was tracking a very weird bug. Despite the experience, honest efforts and funding, they failed to track it down. Finally a worker was given a task of undoing the effects each day. Later they tried to rewrite fragments of the same system to a newer technology. With equally a bad outcome. The reason was… there is nobody around, who knows, what those fragments actually do. The old modules were left in place and probably still active in 2025. An ancient artifact doing nobody knows what, but apparently something that is necessary for other things in the system to work.That was with experts, having full access to the system, having funding, having honest intentions. Do you know, how it all goes in less favorable conditions, quarter century later? This is the scenario, that terrifies me. Because it’s real, it’s right here, and it’s taking over every aspect of our lives at fast pace. With little resistance, as it is convenient and financially preferable for the majority. The majority that, fitting into pre-molded shape, refuses to even recognize the problem. Akin to what Huxley described in “Brave New World”, exceptthe misfitsdon’t even have the Reservation to escape to.

author: Analog Kid
author_rank: Super Contributor
timestamp: April 15, 2025, 08:09:07 pm
content: Quote from: tggzzz on April 15, 2025, 09:32:21 amWell, you ought to. It is utterly charming, in a way that only you Brits can pull off.

author: tggzzz
author_rank: Super Contributor
timestamp: April 15, 2025, 08:35:38 pm
content: Quote from: Analog Kid on April 15, 2025, 08:09:07 pmAgreed.ISTR seeing one of the later ones, made when Aardman had sold out to Sony(?). It felt like a corporate focus group effort.I suppose that is not dissimilar to a good LLM output, obtained after many iterations and multiple sets of fulsome apologies.

author: Ranayna
author_rank: Super Contributor
timestamp: April 16, 2025, 11:11:15 am
content: Quote from: tggzzz on April 15, 2025, 08:35:38 pmWas that the fully computer generated "Flushed Away"? That wasn't all that good, i agree.Though wasn't there a new Wallace and Grommit recently, that was (in parts) true stop motion?I haven't seen that yet, but heard good things about it.Another one i really liked was "Pirates: A band of misfits".

author: bson
author_rank: Unknown
timestamp: April 24, 2025, 02:01:18 am
content: The problem is AI models operate on a notion of similarity. But whether the three phrases "the weight of an apple", "the weight of a pear" and "the weight of a melon" are similar, or not, depends on context. In simple language terms, yes of course they are similar, if not practically identical; in terms of meaning they're very different. This purely language-based similarity means it's inappropriate to infer the weight of an apple (if unknown) from that of a melon. Context is everything. And, of course, for any sciences or engineering disciplines, similarity is not sufficient; correctness is required. The fact that something looks like a schematic is not enough.

author: SiliconWizard
author_rank: Super Contributor
timestamp: April 24, 2025, 12:55:21 pm
content: Yes, that's the general problem with generative AI. It does spit out stuff that looks plausible, and as models improve, the output looks increasingly plausible, but it's still just that.

author: golden_labels
author_rank: Super Contributor
timestamp: April 24, 2025, 02:47:33 pm
content: Quote from: bson on April 24, 2025, 02:01:18 amOn contrary. The ability to retain contextual information and account for subtleties is the very reason LLMs are so good in generating plausible text, compared to other LMs. LLMs do wellinferringsuch context even if it’s only indirectly hinted by the text or the link is only implicit. So this can’t the reason behind the failure in “reasoning” or producing coherent outputs.It’s not “operating on a notion of similarity” either, because this is what all models do. If that line of reasoning was correct, we’d have to apply it to equally. So for example say the same about Kirchoff’s laws. Yet I don’t think we want to reject Kirchoff’s laws.Instead, we say Kirchoff’s laws are applicable only when particular constraints hold and only to a particular group of compatible problems. And the same rule applies to LLMs. They’re work great for a set of linguistic problems.(1)They fail, often miserably, for everything else. Why? Because their structure was never designed to deal with those other kinds of problems.Most notably the process of reasoning is not linguistic and is a multi-stage prcess. LLMs simply don’t have faculties to deal with it. The same way as Kirchoff’s laws don’t have faculties to read and summarize this post.(2)(1)With “linguistic” in a sense much broader than only human speech or even constructed languages.(2)For the record: GPT-4o mini succeeded in summarizing the post. But failed (twice) to generate an alternative illustration to my point: first time being completely off point, the second supporting bson’s position.

author: tggzzz
author_rank: Super Contributor
timestamp: April 24, 2025, 04:55:23 pm
content: Quote from: golden_labels on April 24, 2025, 02:47:33 pmLLMsdon'tretain contextual information. They assemble sequences of words that are statistically found in proximity in their (current) training set. That is akin toindiscriminately mixingcontext from multiple sources.There are some cases where that technique is appropriate and useful, e.g. machine translation.Unfortunately naive users, salesmen, and malefactors don't (or won't) recognise that it is inappropriate in many cases. They capitalise on the inability of LLMs to explain the "decisions" leading to the text the LLM emitted.

author: golden_labels
author_rank: Super Contributor
timestamp: April 25, 2025, 01:09:57 am
content: Quote from: tggzzz on April 24, 2025, 04:55:23 pmLLMs’ ability to hold this information in intermediate vectors is the key factor behind the leap they made in text generation. NMTs 15–20 years ago were the first to utilize this concept, but were not big enough to get the full boost from it. With spaces too small we don’t benefit much from “pseudo-dimensions,” and the training set is insufficient to cover that wide variety of linguistic patterns.LLMs would not be any better than normal multi-word Markov chains if not the effect you now say doesn’t exist. C’mon?Quote from: tggzzz on April 24, 2025, 04:55:23 pmYes. “Proximity” is a synonym of “context.” Both in the sense of spatial values and transforms LLMs apply while processing prompts.They approximate the area where the next produced token is located. Which area that is, depends highly on everything in the the prompt and in the training set. This is how the context affects the final result and how it’s conveyed through the processing. Context explicitly stated with tokens, context given implicitly (“inferred” from the training set), as well as context coming from how the prompt fragments would be located in the source material. This is how it’s encoded, by intentional and conscious design.That’s how I was able to circumvent filters in early ChatGPT and make it mention Trump trampling constitution, instantly produce insulting remarks with no hesitation, or how both me and others kneaded LLMs to make other “undesirable things.” Nothing more than crafting a context, that goes over different transforms or ends in a different area.(1)Quote from: tggzzz on April 24, 2025, 04:55:23 pmHow can it be “mixing indiscriminately,” if LLMs do that by assembling — as you put it — “words that are statistically found”?Quote from: tggzzz on April 24, 2025, 04:55:23 pmWithout a doubt! History taught me one thing: that nobody learns from history. And so, once again the market and society are filled with fraudulent advertising and misinformation.But what people do, what opinions they hold or spread, and how society changes — that bears no relevance to ground truth. Whatever I or you think about marketing departments peddling nonsense is no excuse to reply with other nonsense. Distorting the picture of some technology to present it in more negative light is not helping. What’s the use of exaggerating shortcomings to the point they become a fantasy equaling the “AI prophets” promises?(1)The area is different from LM’s perspective, but leads to a similar output from human brain’s perspective.

author: RJSV
author_rank: Super Contributor
timestamp: May 20, 2025, 02:01:03 am
content: LLM Schematics, with humourous flubs, reminds me somehow, of what LLM Medical screw ups potentially could cause:LLM output sets the rules, for, say, any M.R.I. scan procedure, as well as the usual, expected AI scrutiny of the scan itself.They might dictate: M.R.I. must be both upper and lower (spine), causing a longer period of patient discomfort."We don't know, the computer did that." the staff members and Doctor might say..."Can't change the A.I.", the patient is told.