Why offtopic is going to hurt us all... - Page 1
Forum section: electronics / general

author: eTobey
author_rank: Super Contributor
timestamp: October 05, 2024, 11:26:33 am
content: Just a little reminder that is worth thinking about:I guess many of you have already experienced, that google has become a b***. You will find anything, except what you were actually looking for. When you now add irrelevant text to topics, it will get even worse, even with just the forums search engine (which is as "useful" as google unfortunately)!Not to mention the hassle already reading through a lot of irelevant text, that is either offtopic, or does not help to answer questions at all.

author: ebastler
author_rank: Super Contributor
timestamp: October 05, 2024, 11:37:04 am
content: Try that search without the + sign, just with the quotes to force the appearance of "spreadsheet". I don't think the plus sign is still supported in Google searches, and it seems to invalidate the markup with quotation marks.Edit: Double-posting is also going to hurt us all... Why does a failed Google search need two threads?

author: eTobey
author_rank: Super Contributor
timestamp: October 05, 2024, 12:03:42 pm
content: Quote from: ebastler on October 05, 2024, 11:37:04 amI removed any mentions about google in the other thread...

author: brucehoult
author_rank: Super Contributor
timestamp: October 05, 2024, 12:09:16 pm
content: The baton for such simple things has been passed on...

author: schmitt trigger
author_rank: Super Contributor
timestamp: October 05, 2024, 01:18:44 pm
content: Indeed, Google as the search-engine-to-go option is rapidly diminishing.Which means that revenue from searches will decrease.However Google as a corporation, has diversified significantly, I don’t see them becoming irrelevant as Altavista.

author: magic
author_rank: Super Contributor
timestamp: October 05, 2024, 02:21:02 pm
content: Great, an offtopic discussion in an offtopic thread about the harms of offtopic.The only thing still missing at this point is somebody complaining about it

author: Stray Electron
author_rank: Super Contributor
timestamp: October 05, 2024, 02:34:29 pm
content: Quote from: magic on October 05, 2024, 02:21:02 pmYou just did, didn't you?

author: magic
author_rank: Super Contributor
timestamp: October 05, 2024, 03:06:12 pm
content: I had to, because the next obligatory step is somebody complaining about the complaining

author: tom66
author_rank: Super Contributor
timestamp: October 05, 2024, 03:08:46 pm
content: For technical problems I'm finding myself using ChatGPT a lot more now. Maybe around 25-30% of the time. I would previously dive deep into the annals of Google, Stack Overflow etc to get an answer but I don't need to any more. It's great. Of course, you have to be wary of the LLM bullshitting you... but it's not like the internet isn't full of bullshit either.

author: brucehoult
author_rank: Super Contributor
timestamp: October 05, 2024, 10:59:03 pm
content: Quote from: tom66 on October 05, 2024, 03:08:46 pmNever try to get ChatGPT to do something original. It simply can't.But looking up existing information, finding something that some human already wrote, somewhere (online), it's very good.

author: K5_489
author_rank: Regular Contributor
timestamp: October 05, 2024, 11:04:48 pm
content: Quote from: magic on October 05, 2024, 03:06:12 pmI gotchu, bro!Seriously??? No one cares about your whining, and we don't need to see it

author: T3sl4co1l
author_rank: Super Contributor
timestamp: October 06, 2024, 01:05:21 am
content: Think about clustering of terms. "Sheet" is too generic. Get specific, "Excel" for example. Most spreadsheets use common syntax so the formulas will also work.Or if you just want some copypasta, spreadsheet data can be generated trivially in TSV format. Do it in your favorite language, or type it out by hand.But you didn't even ask for formulas, and I guess you don't want formulas, you want an answer even more complete than that; direct generated data perhaps. But what format would you even want, what does "pwm sweep" mean? Is this sampled (time-series) data, perhaps 0 and 1 in cells? GPT for its part, seems to be aware of your question's incompleteness. But you are not--?I wonder if there's some error in expectation, misaligned goal, here?Is your perspective of, trying to ask Google itself, for such a formula, or output? But Google doesn't know anything (except the specific things programmed onto it: certain keywords bring up special results, like dictionary/definitions, or calculator). You're asking Google to find webpages that have such an answer; and such a specific answer has little purpose to exist on the internet (mind, that's not zero, and there are some very tedious/niche webpages out there, as can be seen from searching random numbers for example, and maybe there's pages out there offering such answers already), but even if that were the case, why aren't you searching for the data they contain? Perhaps loading the query with "0 0 0 0 0 0 1 1 1"... isn't going to be very successful anyway, but it seems like you don't know what you're looking for in the first place, or don't know how to use a traditional search engine?Tim

author: RoGeorge
author_rank: Super Contributor
timestamp: October 06, 2024, 03:33:07 am
content: Quote from: T3sl4co1l on October 06, 2024, 01:05:21 amWrong topic, right? Not to be confused with offtopic.Was the reply meant forhttps://www.eevblog.com/forum/chat/create-arbitrary-waveform-values-with-spreadsheet/by any chance? (complaining about wrong topic kind of offtopic in a topic about offtopic wouldn't be offtopic, innit?)

author: T3sl4co1l
author_rank: Super Contributor
timestamp: October 06, 2024, 03:50:19 am
content: Oh posh, take it to the topic councilTim

author: Xena E
author_rank: Frequent Contributor
timestamp: October 06, 2024, 07:36:52 pm
content: My problem with Gurggle is that every 'original' non suggested search I do is met with a friggin' captcha screen.(Now,thatshow we whine and winge in the shires.)Is that OT enough for ya?X

author: langwadt
author_rank: Super Contributor
timestamp: October 06, 2024, 07:41:50 pm
content: Quote from: brucehoult on October 05, 2024, 10:59:03 pmso the odds of it being confident but dead wrong is pretty high ...

author: magic
author_rank: Super Contributor
timestamp: October 06, 2024, 08:00:35 pm
content: Quote from: Xena E on October 06, 2024, 07:36:52 pmReally?How are you doing it?Never happened to me.

author: tggzzz
author_rank: Super Contributor
timestamp: October 06, 2024, 08:04:35 pm
content: Quote from: brucehoult on October 05, 2024, 10:59:03 pmLLMs assembleplausiblesequences of words into grammatically correct sentences. "Plausible" is some vague form of "average" of the words that have previously been found in similar relationship to each other and the question.That is not "finding something that a human already wrote". It is how "LLM hallucinations" are created, e.g. non-existent legal case law presented as evidence to a New York judge. Oops; career limitingThere are people that operate in the same way as LLMs: they are called bullshitters, politicians and CEOs.Next problem already worrying people: what happens when LLMs ingest other LLMoutputhallucinations?

author: tom66
author_rank: Super Contributor
timestamp: October 06, 2024, 08:18:21 pm
content: Quote from: tggzzz on October 06, 2024, 08:04:35 pmWe've been over this before I'm sure, you refuse to acknowledge LLM's cannot produce anything useful, I disagree.Just the other day I needed to use a program to zoom and pan around an image without any blended interpolation algorithm, ie. nearest neighbour only. I did some Googling and did not find an exact result. Perhaps there is some way to get a Linux image viewer to do that but the 3-4 I tried did not have any way to turn off interpolation. But, ChatGPT inliterally 30 secondscreated me a tool that did that in Python using Pygame. It worked perfectly. Yes, I could have written the same tool myself, but it would have taken probably 15-30 minutes of my day.Those who refuse to acknowledge the capability of LLMs are just being silly at this point. They are clearly not purely bullshit machines.Now yes, it's funny to ask it about things that obviously don't exist and observe the bullshit because the models do have issues in places... but on the other hand I've had plenty of bullshit from Google results, and the amount of SEO tuning now done by websites mean that it's very easy to find something that's completely wrong. As an example, just search for the text in a Windows BSOD error message. A solid 90% of the results from Google will be absolutely useless: download this scammy tool to fix vague 'driver' issues, install this scammy antivirus, some random post on the Microsoft forum which suggests that rebooting will cure all ills.

author: magic
author_rank: Super Contributor
timestamp: October 06, 2024, 08:33:58 pm
content: Quote from: tggzzz on October 06, 2024, 08:04:35 pmExpanding the analogy, it becomes something akin to a political partyNothing we haven't dealt with before, but uglier than one LLM alone.

author: tggzzz
author_rank: Super Contributor
timestamp: October 06, 2024, 08:50:24 pm
content: Quote from: tom66 on October 06, 2024, 08:18:21 pmStrawman argument. Please point to where I have said thatQuoteI have no reason to doubt that happened. There is no conflict with what I wrote.My statements stand unchanged.If you wish to correct my statements, please do so. But it doesn't make you look good if you "correct" statements I haven't made.

author: coppice
author_rank: Super Contributor
timestamp: October 06, 2024, 08:57:38 pm
content: Quote from: tom66 on October 05, 2024, 03:08:46 pmI think its more realistic to say Google have trashed their system to the point where even a flaky piece of garbage, like ChatGPT, can do better.

author: tom66
author_rank: Super Contributor
timestamp: October 06, 2024, 09:31:41 pm
content: Quote(emphasis mine, though this post was by brucehoult above, to be clear.)QuoteThis is the statement I am correcting. What was created was original as far as any human is concerned, because you will not find anything close to that method available on GitHub et al. Was it based on some statistical model of data somewhere? Sure. But so is what most programmers write because we all learn off other software engineers. There is very little genuinely original code to write these days, and only so many ways to solve some problems. The real intelligence of a programmer comes in solving unexpected problems and debugging systems. Writing code is the easy part.The question does come when novel problems are given to advanced LLMs, that do not have a solution in the dataset at all, how well do they cope?Surprisingly well. Not quite as good as an average software engineer, but better than a junior engineer. These models are not yet available to the wider public, primarily because the compute power required is monstrous (and likely to remain so for some time) but they show that a model based on statistical text and graphic processing is capable, at least when tested, ofnovel reasoning. Getting very close to AGI. But sure, it's just predicting the next token. So it can't be intelligent, right?As I said, those who deny the capability of LLMs are just being silly because the actual results are stunning. And the world had better figure out how to adapt. In the next decade or so we will be overwhelmed with fake LLM generated prose (already we see spammers with obvious-GPT text, not too long before we see better versions of that), and many jobs will be replaced. I think programmers are probably safe for now, but I'm keeping one eye on how things go, because there could be a lot of jobs shed when they get to the level of being able to replace junior/midlevel engineering roles. On the other hand, new jobs are created by new technology all the time. So, who knows.

author: tszaboo
author_rank: Super Contributor
timestamp: October 06, 2024, 09:38:49 pm
content: Quote from: coppice on October 06, 2024, 08:57:38 pmI don't think so. I asked that garbage LLM to give me some ICs with a specific feature. And it invented non-existing features for ICs with slightly similar functions. You ask it what's the second biggest capital in Europe, and it tells you that it's Istanbul. And you call it out on it's bullshit, it apologizes and it makes up some more bullshit.

author: tggzzz
author_rank: Super Contributor
timestamp: October 06, 2024, 09:47:58 pm
content: Quote from: tom66 on October 06, 2024, 09:31:41 pmQ.E.D.I did not write that(emphasis mine). Bruce Holt wrote it in his posthttps://www.eevblog.com/forum/chat/why-offtopic-is-going-to-hurt-us-all/msg5667293/#msg5667293QuoteTo repeat: if you wish to correct my statements, please do so.But it doesn't make you look good if you "correct" statements I haven't made.The points I made accurately describe how LLMs work.Your lack of understanding completely ignores the phenomenon of LLM hallucinations, both their existence and cause.Conventional searches do, of course, pull up lots of incorrect/unhelpful information that has been previously written, e.g. a search result could link to your post. Conventional searches wouldn'tinventa post that youhadn't made; LLMsdodo that kind of thing -and nobody knows how to prevent that. infamous example: a NY lawyer used an LLM to write documents presented to a judge which included case law favourable to their client. The LLM apparently explicitly assured the lawyer that case laws were correct! The opposing party's lawyers could not find the cited cases because they did not exist; the LLM had invented/hallucinated them. The judge was most unimpressed, and the lawyer is in trouble. FFI, see comp.risks and elsewhere.

author: tggzzz
author_rank: Super Contributor
timestamp: October 06, 2024, 09:52:24 pm
content: Quote from: tszaboo on October 06, 2024, 09:38:49 pmAnd to drive home the point to tom66, a conventional search wouldn'tcreatesuch features for animaginaryIC. It might irrelevantly indicate an existing IC that had similar features - very different.The "apologise and emit more bullshit" is what you expect from non-responsive customer service helplines (service in the way bulls service cows, and help in the sense of fob off).

author: tom66
author_rank: Super Contributor
timestamp: October 06, 2024, 09:53:23 pm
content: Quote from: tggzzz on October 06, 2024, 09:47:58 pmYes - My bad, I edited the post to correct this error. The BBCode quote formatting caught me out as I cropped your quote out from the inner quote in the wrong order, incorrectly attributing you to the inner quote.(Perhaps that was a human hallucination?)No part of my post denies the reality of LLM hallucinations. But I contend that LLMs are still very useful and better than Google in many respects. I'm well aware of the lawyer case - that's quite a good one. But, that doesn't make LLM's useless or evenbad. Like all tools, they have their use cases. Yes, I suppose you could describe them as statistical bullshit models, that have been trained to not bullshit so much. But so are humans.

author: tggzzz
author_rank: Super Contributor
timestamp: October 06, 2024, 10:03:00 pm
content: Quote from: tom66 on October 06, 2024, 09:53:23 pmOf course, but what are the tools' limitations, therefore what are valid/invalid use cases, and what is the consequence of people using them for invalid use cases.LLMs have been used to interpret medical scans (e.g. XRays) to determine the likelihood that treatment will be successful. If unlikely, treatment is denied.Unfortunately LLMs cannot indicatewhythey come to such decisions. In one case it was eventually determined that they were detecting thefonton the medical scan! One hospital had poor results because it was in a poor area and poorly funded, and used font A. Another hospital with better results in a more affluent area used font B.The LLM thus - as a consequence of its fundamental operation - perpetuated bias by unwittingly assessing hospitals rather than patients. Patient' lives are at risk. Your families' lives may be at risk.Simple searches couldn't make that mistake. People could explain their reasoning, so that the validity could be determined.

author: tom66
author_rank: Super Contributor
timestamp: October 06, 2024, 10:07:23 pm
content: I would not use an LLM for anything that involved life safety or where the failure of that LLM's output led to significant economic harm to my employer/clients.I will use an LLM where the failure of the output will be e.g. slightly longer to debug a problem (so I only wasted a little bit of time), or otherwise of little to no consequence. So far, my experience has been a net gain in time, in other words, while I may have spent 30 minutes before Googling an obscure linker or compiler error, I can paste a subset of code into ChatGPT and get an answer for why gcc has given me an explicit error in under 30 seconds.As a more specific example, C++ templating errors can sometimes be hard to debug and gcc can produce a huge number of errors due to one missed declaration. I am still somewhat new to C++, more of a C guy, so I have found the breakdown of causation for the errors to be very useful and in the majority of cases (9-out-of-10, roughly?) it has been useful.As for explaining how they got there, look at GPT-o1 preview. It is capable of reasoning its way to a solution. And when it makes a mistake, you can see the mistake in the reasoning, so it's not a post-hoc reconstruction of the reasoning path, if that makes any sense.

author: tszaboo
author_rank: Super Contributor
timestamp: October 06, 2024, 10:22:36 pm
content: Quote from: tggzzz on October 06, 2024, 09:52:24 pmThere are better LMMs like bing, that will actually use current information on the web to give you a correct answer.And we argue that an LMM is good or not, while they are reducing the processing power in the background. So it uses less resources to give an answer. ChatGPT has been declining since the last year considerably.

author: tggzzz
author_rank: Super Contributor
timestamp: October 06, 2024, 10:55:25 pm
content: Quote from: tom66 on October 06, 2024, 10:07:23 pmGood. But not relevant to whether LLMs hallucinate bullshit/rubbish.But that won't stop other people/companies. Even without LLMs, they hide scrutiny of their product's output under the veil of commercial secrecy. LLMs fit that behaviour perfectly. (Examples: US courts "should this accused/convicted person be put in jail")LLMs should be avoided until they can indicate why they emitted a result. That's been a problem for 40 years, and is still a "active research topic". Igor Aleksander's 1983 WISARD, effectively the forerunner of today's LLMs, demonstrated a key property of modern LLMs: you didn't and couldn't predict/understand the result it would produce, and they can't indicate their "reasoning". WISARD correctly distinguished between cars and tanks in the lab, but failed dismally when taken to Luneberger Heath in north Germany. Eventually they worked out the training set was tanks under grey skys and car adverts under sunny skies.Different fonts, anyone?LLMs give The Answer, and the lazy/ignorant won't question that.Conventional search gives a Set (or Bag) of related answers, and you must examine and select them.QuoteI first used C in ~1982, when there was only two books on the language.In 1988 I kicked the tyres with Objective-C and C++. I rapidly decided C++ was to difficult and awkward to be productive; Objective-C was much better. (Objective-C is Smalltalk -GC +C-like syntax, and is the underpinning of all modern Apple products.)In the 90s I occasionally revisited C++, before deciding "if C++ is the answer, you need to revisit your question".IMNSHO, use C for low-level stuff, and a modern productive language for everything else. Pleasingly, many influential US bodies are making the same point, albeit more circumspectly)

author: langwadt
author_rank: Super Contributor
timestamp: October 06, 2024, 11:05:57 pm
content: Quote from: tggzzz on October 06, 2024, 09:52:24 pmhttps://abovethelaw.com/2024/02/airline-said-its-not-responsible-for-terrible-advice-from-its-own-customer-service-ai-bot-the-court-disagreed/

author: SiliconWizard
author_rank: Super Contributor
timestamp: October 06, 2024, 11:13:43 pm
content: Quote from: langwadt on October 06, 2024, 11:05:57 pmInteresting. I see two key points: good thing the court said no, but the other one is that the company definitely tried to pull this off. Showing that companies will definitely keep trying until they get new, more favorable laws through lobbying. The intent is there, it's just a matter of time.

author: DavidAlfa
author_rank: Super Contributor
timestamp: October 06, 2024, 11:26:07 pm
content: Because Google gathers and analyzes everything about you to provide the most BS results possible.Same as YouTube, AliExpress...I often do the searching in a incognito window... The difference is crazy sometimes.

author: SiliconWizard
author_rank: Super Contributor
timestamp: October 07, 2024, 12:13:20 am
content: Quote from: tszaboo on October 06, 2024, 10:22:36 pmBing uses OpenAI's GPT-4. It's been "customized" to be a better fit for search engine needs than ChatGPT, but is based on the same thing, really.Currently, an alternative for those not willing to use MS stuff (at least directly) is Perplexity:https://www.perplexity.ai/it provides references for all statements it makes. Not too bad.Still, while these tools can save you some time, they are no full replacement for conventional search engines - indeed, the fact that they extract the information they "consider you'll find useful" is inherently biased. Conventional search engines (even when they push per-profile links, as most do these days unless you use private browsing) give you more varied search results. Which, again, sure can be a waste of time, but also the opportunity to find info you didn't directly think of (or that other people would not have asked for), and that's also a plus.Quote from: DavidAlfa on October 06, 2024, 11:26:07 pmTrue. Haven't really noticed for Aliexpress, as its search engine overall is pretty bad anyway.But don't get the idea that LLM-based engines will be immune to that: this makes no sense, that's where the money is.For now they are, more or less, although already biased. But the fact they do summarize info and give you only a small subset of the information that, again, it "thinks" is what you are after, is even more of a future opportunity to shove ads to you in ways that are much more subtle (and so will be much more expensive for advertisers).

author: brucehoult
author_rank: Super Contributor
timestamp: October 07, 2024, 01:42:21 am
content: Quote from: tszaboo on October 06, 2024, 09:38:49 pmIsn't that correct, or at least arguable? It's complicated.It's 100% certain that Moscow and Istanbul are the two largest European cities. They're close enough that different definitions (boundaries, commuters...) probably put them in different orders. Istanbul is larger than Moscow in many measures, but you can also argue that only about 70% of the population is geographically in Europe, 30% in Asia. Even with that, the part in Europe is still probably larger than Paris or London. Also, 100% of Turkey is in the European Customs Union.In maybe 2016 (when I was living there) the mobile phone companies got together and said there were something like 25 million active SIM cards in Moscow during business hours but a LOT fewer at night. With its extensive commuter train network I suspect Moscow has more people living outside the city but working inside it than Istanbul. For example, one of my co-workers in northern central Moscow (Mar'ina Roshcha) commuted daily about 90 km from south of not only Moscow city but also Moscow region, in Kaluga region. One that I *know* of.

author: DavidAlfa
author_rank: Super Contributor
timestamp: October 07, 2024, 01:56:28 am
content: It's not that AliExpress search engine is bad, they're intentionally showing all your past searches and visited items to hopefully trigger your interest.In incognito mode the system can't relate you so it will show purely what you went for.I'll repeat my Ali "incident" where I clicked a giant dick to see what the comments would be (No homo, just curious).I was spammed formonthswith forearm-sized weenies, I couldn't open the app in public, any simple search like "battery" would interleave those things every now and then.I learned to not be curious in AliExpress

author: T3sl4co1l
author_rank: Super Contributor
timestamp: October 07, 2024, 02:07:43 am
content: Quote from: brucehoult on October 05, 2024, 10:59:03 pmYou say that like the human creative process doesn't involve large amounts of copying, mashing up, and minor variations.Quote from: tggzzz on October 06, 2024, 08:04:35 pmIt's funny you use a negative tone to describe these systems... yet hedged in such a way that precisely the same definition applies to all human speech.You allude to this, of certain groups, but that seems rather unfair when the net you've cast is far more inclusive.The real insight seems to be by matter of degree, for which we need some way to measure bullshit, but we can imagine that such a method exists. Now we find a different truth: humans in general do it when expedient (or indeed necessary for rhetorical purpose, as often the case for the highlighted groups, but also more broadly, or relatedly, narcissism finds great value in gaslighting, and by extension, domains where narcissists feature prominently), and LLMs do it less and less as time goes on -- or at least, as egregiously.A different perspective I've had is, compare a proper PC to an embedded CPU of comparable processing power, but far fewer peripherals, memory constrained. Worse still, one that lacks hardware virtualization / memory mapping. There are a lot of tasks they are comparable at, but anything that requires enough memory, will at the very least be much harder to compose (e.g. more code (and execution time) to bring in external storage, rather than everything being memory mapped to begin with), and at some point will have to turn into a VM (or plural..!) atconsiderablecost to execution speed -- or if you strip down a lot of the code, compromises on feature set, or accuracy (or even if both). Suppose we have an application that requires some function calculated in given maximum time; to the extent that computations can be approximated, and for sufficient degree of restriction, approximations must be employed by the limited system. In terms of linguistic output, we might consider this bullshitting, an approximation that may or may not be accurate to underlying (available, if given enough time or access) information.Comparing average human to LLM vocabulary, clearly the LLM wins, trained on basically every language (including programming and markup). It's "wider" at this level. Maybe not to ultimate human ability, but I mean the average human is barely trained in one or two languages (depending how strict/technical one makes "well trained"). Perhaps, to the extent we can map vocabulary to processing power, this is the case of the embedded system being faster (e.g. compare a typical STM32F4 to a 486 or early Pentium).Comparing average human to LLM coherency, semantics, synthesis, I would argue LLMs are already superior on several fronts. A wide variety of simple, shallow, space-filling, or formatting (e.g. as standard news format) tasks are essentially solved problems. At least to the mediocre quality of copy that passes for the average case in industry.The present limitation on buffer length, and depth of training at that length (I would assume--?), limits semantics in a similar way as low memory limits performance of a computer: without external resources, or time to access them, or some such limitation like that, some approximation is required, and thus error -- in this case, unexpected, inconsistent or incoherent text. It seems clear from progression in LLM development, that this is the hardest limitation, and is analogous with memory limitations.And, not that it's an insurmountable problem, you could probe an LLM repeatedly and stitch together a more complete and knowledgeable overall response; but that takes more effort, and quickly becomes not worthwhile, just as running a VM vs. getting a better CPU/RAM/system overall, is. Conversely, we could liken the coherence length to various human responses, pathological or otherwise; brains with amnesia for one, but even just lack of attention (literally as in ADHD), might have similar experiences. And there too, one can augment their brain with external tools (e.g., keeping notes to remember long-term information), to varying degrees of success.I suspect the amount of memory they're using for LLMs, let alone processing power, is already more than enough for the task, the problem is we don't know how to solve the problem algorithmically, so it's brute forcing tensors and training data to do a merely poor job of it. And, which doesn't scale well, or at least AFAIK it can't be directly sized up (but has to be fully retrained; not that that might matter much, if you're increasing model size/scope by order of magnitude, it's not like you're saving much training effort).That, and the lack of ways to integrate semantic information -- and of outputs, to interface with the wider world (with good reason, but they're already being put into places they shouldn't, and people will simply try more as time goes on..).Tim

author: brucehoult
author_rank: Super Contributor
timestamp: October 07, 2024, 02:18:11 am
content: Quote from: tggzzz on October 06, 2024, 10:55:25 pmSimilar. I don't recall whether it was 82 or 83. C was very much a minority language on VAX/VMS, which didn't integrate with the native languages (MACRO, Bliss, Fortran, Cobol, Pascal), though we did have it as part of the "Eunice" BSD emulator/compatibility environment.  I actually used BCPL more. My first real use of C & Unix was on the Zilog System 8000 which arrived late 83 or early 84.QuoteKind of similar. I bought MSDOS Zortech C++  (on 9999 floppies) just to use it in an emulator on my Mac (IIcx, I guess). I played with that until Apple had their own CFront-based C++ in MPW.QuoteEarly C++ was good. Certainly at the CFront 2.0 / ARM stage. It had a lot of simply "better C" features which made it highly desirable. And when Apple picked up NeXT you could use "Objective-C++" and mix and match as you wanted.Modern C has picked up all or almost all the "better C" parts of C++. And the C++ committee has gone mad. I once hacked superuser privileges on the university VAX when I noticed that after a Eunice upgrade thepsprogram (which needed superuser rights) had been installed without disabling the ability to attach a debugger. I set a breakpoint, poked code forsetPriv(-1)into memory, ran it, forked a shell. BOOM. Walter Bright is only four years older than me, but I considered him some kind of a god. I first used his "Empire" game on the VAX at university, then Zortech C++ which turned into Symantec C++ (which the company destroyed with v6, but fortunately Metrowerks came along at just the right moment). It's been shocking the last month or two seeing Walter struggling to understand ARMv8-A and try to port the D compiler to it. Had he been x86-only the last 40 years?

author: shabaz
author_rank: Frequent Contributor
timestamp: October 07, 2024, 03:33:22 am
content: Mini case study: I recently solved a problem initially in a non-ChatGPT-oriented way because I sometimes forget to give that a shot.The problem was non-trivial (not going to write in detail, but, long story short, it concerns calculating results from geographic-like data. Google and raw effort took me days to get anywhere, although I did make progress and I coded up a possible solution. My solution consumes several Mbytes of storage and needs an excessive amount of additional storage if I want to increase performance. It works, but it's not pretty.ChatGPT, on the other hand, pointed me toward things I would not have found by myself or would have thought too hard to implement, in the very first query to it. And then, with some experimenting, I realized I could scale resources down immensely, to a couple of hundred kbytes, without losing much precision! It will actually provide better performance than my several Mbyte solution, thankfully, but also annoyingly!

author: SiliconWizard
author_rank: Super Contributor
timestamp: October 07, 2024, 03:34:34 am
content: I remember my first hands-on experience with C++ was with Think C on a Mac. That was rather simple though and very far from the C++ language we have now.Quote from: brucehoult on October 07, 2024, 02:18:11 amYes. The thing (really?) missing in C is adding namespaces - although explicit namespaces such as in C++ do bite IMO. But people often use classes in C++ (at least for embedded dev) as a replacement for 'modules'. And the modules introduced in "recent" C++ are a joke. But developers are used to using classes as modules anyway, so they probably don't care all that much. In C, having neither kind of sucks, as it requires using pretty long identifiers (unless you don't care about name clashes and thus about anyone else that would use your code but you).C++ is an all-you-can-eat buffet. It looks like its only goal is to add all features it can from other languages, in order to silence developers saying "but C++ doesn't have this or that". I wouldn't be surprised if C++ added lifetimes and ownership management in future standard revisions. If it already has, I must have missed it and will have a good laugh.

author: brucehoult
author_rank: Super Contributor
timestamp: October 07, 2024, 05:29:34 am
content: Quote from: T3sl4co1l on October 07, 2024, 02:07:43 amNot all.While many people has no doubt uses variables named "E", "m", "c" in mathematical formulas, would an LLM ever arrange them as "E = mc2", along with justification, before the first human did that?I could also propose as examples certain small contributions I've made myself, such as theorc.binstruction in the RISC-V Zbb extension which is, to the best we've been able to research it, the first time an instruction with those semantics (and a family of related but not (yet) ratified variations) had been either proposed or implemented in a computer.Certainly almost all humans do nothing original ever, and the remainder very rarely do or say anything original. But I think for LLMs it's a big fat precise zero.

author: Andy Chee
author_rank: Super Contributor
timestamp: October 07, 2024, 08:20:30 am
content: Quote from: T3sl4co1l on October 07, 2024, 02:07:43 amNot all human speech.For example, you never see a LLM mistake the plural of "foot" as "foots" (feet), or the plural of "boot" as "beet" (boots). The reason is obvious, such mistakes do not exist in human adult nor the LLM vocabulary.HOWEVERSuch mistakes are quite common in childhood learning. Given that such mistakes do not occur in adult language (or LLM), kids cannot be exposed to incorrect words. Without exposure, how did kids learn these language mistakes in the first place?It suggests that there is more to human learning than exposure to training material. For LLMs, exposure to training material is it.

author: langwadt
author_rank: Super Contributor
timestamp: October 07, 2024, 09:01:11 am
content: Quote from: DavidAlfa on October 07, 2024, 01:56:28 amand show you a 90% discounted price, max buy one

author: ebastler
author_rank: Super Contributor
timestamp: October 07, 2024, 09:01:47 am
content: Quote from: Andy Chee on October 07, 2024, 08:20:30 amThat argument does not seem logical to me. All human learning is based on outside stimuli (training material) as well. What you described before is related to the way that training material is processed internally -- the mind looking for patterns and "rules" which it can apply to similar problems.Whether or not an AI model is capable of the same internal processing, looking for patterns it can generalize, it totally independent from the fact that it is exposed to training material. I don't see why that should not be possible, and would expect that it already happens to some extent in today's LLMs. It's an interesting challenge to think up some experiments to prove or disprove that!

author: tszaboo
author_rank: Super Contributor
timestamp: October 07, 2024, 09:24:05 am
content: Quote from: ebastler on October 07, 2024, 09:01:47 amThe difference between human learning and machine learning is very clear to me. When you try to feed new information to a human, the human will first evaluate this information assigns a value to it, discriminates. A LLM will treat all information equally. So humans assign value on the forward path, a feedback based learning LLM on the feedback path.This means that humans are effected with different issues when learning.For example when a human is meeting disinformation, they might completely ignore it. Or if they received misinformation their entire life, they might ignore the truth. The machine will integrate both if the training data contains misinformation.Also LMM's suffer from human interaction on the input. You ask google's geminy to show you a fireman, and the LMM is actually asked to show you a "fireman and woman of diverse ethnic background". Or it calls you a racist in a thousand words. So when asking a LLM coming from a company, you have to evaluate the company first if it's trying to push you some agenda. Same with people BTW. There are people that consistently will give you misinformation in their responses.

author: tggzzz
author_rank: Super Contributor
timestamp: October 07, 2024, 09:33:32 am
content: Quote from: brucehoult on October 07, 2024, 02:18:11 amBefore using C++/Objective-C, I had kicked the tyres of Apple Smalltalk on a FatMac (glacially slow!). The quality, breadth and depth of the Smalltalk standard libraries was a stunning revelation. They just worked, and everybody used them, and multiple projects were easily "composed" into a single project where everything played well together.Contrast that with C++: how many variants of String and Boolean were there? One per project, all incompatible. Container classes were a dream beyond imagining: roll your own, again and again. Keeping track of complex data structures as they were used in a codebase was impractical, except (potentially) with large amounts of copying. As for adding libraries from multiple vendors in a single project, that was a crapshoot and significant timesink.Objective-C still had the GC problem, but it came with a set of usable standard libraries. Not in the Smalltalk class, but still a major step up from C. Mixing them with C++ was not something I have ever considered.A decade later C++ was still deficient, and even the C++ committee couldn't understand what they had created.The next language to have decent standard libraries and to enable libraries from multiple vendors to be used together without issues was, of course, Java.QuoteC isn't much better, even though they have (quarter of a century late) added a memory model.Remind me, is it possible to "cast away constness"? The ability to do that enables very subtle intermittent errors when code from multiple sources is used. OTOH if you can't do it, then some programs becomes more or less impossible, e.g. debuggers.

author: Andy Chee
author_rank: Super Contributor
timestamp: October 07, 2024, 09:33:38 am
content: Quote from: ebastler on October 07, 2024, 09:01:47 amThe logic is simple. There is nospecificoutside stimuli of "foots" or "beet", so these words were never learnt via replicating outside stimuli. A different learning process must've been involved, one that LLMs are currently incapable of.That said, it might be a good thing that LLMs are not capable of this alternate learning path, as this alternate learning path is implicated in how biases are learnt e.g. beliefs in conspiracy theories or religious cults.

author: ebastler
author_rank: Super Contributor
timestamp: October 07, 2024, 09:36:46 am
content: Quote from: tszaboo on October 07, 2024, 09:24:05 amI think you are underestimating LLMs. In my experience they do place the information they have gathered in the context where it occurred, and can make proper use of that context when working with the information. They don't "treat all information equally".

author: tom66
author_rank: Super Contributor
timestamp: October 07, 2024, 09:38:49 am
content: Quote from: tggzzz on October 06, 2024, 10:55:25 pmIt's possible to test the output of an LLM to the point where you can have very high confidence in the result. This is what researchers are actively studying with the likes of o1 for instance. Any fuzzy model will not have a guaranteed 'truthiness' to it, but the larger the dataset and the larger the test, you can gain progressive confidence in the accuracy of the model.Once again, comparing what a neural network was capable of in 1983 to what it is capable of now is just wrong and misleading. We've been over this.And, LLMscan nowshow a reasoning path, which is unique to neural networks as far as I am aware.Quote from: tggzzz on October 06, 2024, 10:55:25 pmConventional search gives the opportunity for averse agents to alter the input data (e.g. keyword hacking) to make their results more prominent. Conventional search cannot distinguish between truth and fiction either. Any user of GPT or search would do well to perform a sanity check - e.g. by checking against the competing engine, or just a general feeling of "sounds-right-ism".Quote from: tggzzz on October 06, 2024, 10:55:25 pmGood for you, but since we have about 200,000 SLoC of C++ in our product already, I have to write other code in C++.I think C++ is maligned as a language. It is not perfect but I much prefer it to the competitors like Java. And it is considerably better than pure C for complex applications, you need only look at something like gcc's source which has to manually implement all sorts of data structures, memory management, refcounting, mutexing etc. This is necessary of course because gcc is self-hosting, but it shows how cumbersome complex programs that manage data can be when written in C. With C++ you can do something like this:Code:std::list<int> someFunction(){std::list<int> myList;myList.push_back(7);myList.push_back(8);someOtherFunction(myList);// myList contains 7,8,9 and is freed after this function call if nothing is returned, including all of its inner children// but if I 'return' it, it's safe to do so and it won't be freed until its parent destroys it:return myList;}void someOtherFunction(std::list<int> &myList2){myList2.push_back(9);// if I tried to 'del' myList2 here I would have a compiler error}...and know that the memory safety of your list is guaranteed. The list will be free'd when it is no longer used. No risk of double freeing or pointer errors. No need to manually refcount.I'll assume you're hinting towards Rust there. I have not used Rust enough to be sure of it, but I get the feeling that it could eventually replace C++. I've used Objective-C and didn't much like it because it differed too much from the conventional idioms of a programming language (but maybe I'm just awkward.)

author: tggzzz
author_rank: Super Contributor
timestamp: October 07, 2024, 09:39:51 am
content: Quote from: tszaboo on October 07, 2024, 09:24:05 amThe other absolutely key point is that you can ask a personwhythey chose their course of action. That is still -after 40 years- an LLM "active research topic". Translation: good people have repeatedly tried and failed.It smells of the attitude which lead to the HP/Intel Itanic debacle. There the hardware people presumed and required that the compilers would become able to generate good code for EPIC machines. HP/Intel was warned that many people had tried and failed, but chose to ignore the warnings. Oops.At least EPIC compiler failures didn't/couldn't end with people being incarcerated, run over, denied medical treatment, denied mortgages etc etc.

author: ebastler
author_rank: Super Contributor
timestamp: October 07, 2024, 09:40:45 am
content: Quote from: Andy Chee on October 07, 2024, 09:33:38 amThe fact that you don't see "foots" and "beet" in LLM output just means that (in this particular respect) the model has gone beyond the toddler stage of learning, and has already picked up the correct words.As mentioned above, it would be an interesting challenge to think up questions to an LLM which try to check whether it does its own inference and generalisation. These would have to be about "fresh" topics which the LLM can't answer based on its factual training.The "foots" and "beet" observation is not suitable in that respect.

author: Andy Chee
author_rank: Super Contributor
timestamp: October 07, 2024, 09:45:13 am
content: Quote from: ebastler on October 07, 2024, 09:40:45 amThat sounds like the making of an echo chamber feedback loop.Indeed this is what human conspiracy theorists do!

author: Xena E
author_rank: Frequent Contributor
timestamp: October 07, 2024, 09:45:48 am
content: Quote from: magic on October 06, 2024, 08:00:35 pmI contacted Google recaptcha support:Here's what came back:Quote... total wank.

author: tszaboo
author_rank: Super Contributor
timestamp: October 07, 2024, 09:55:24 am
content: Quote from: ebastler on October 07, 2024, 09:36:46 amHumans do that for the LLMs. It's called data set labelling or annotation. Like marking pictures of animals which animal it is.For these LLMs it's possible they use parts of the LLM to label the info automatically, but it needs a source. The smaller the source, the worse the interpretations the AI makes. Some of the reason we get garbage output is because they automated these processes (to save money) and there is no oversight.

author: tggzzz
author_rank: Super Contributor
timestamp: October 07, 2024, 10:08:58 am
content: Quote from: tom66 on October 07, 2024, 09:38:49 amNot true.If you add a new training example to an LLM, it can easily and silently cause previously working examples to fail. Not good if an overnight update causes a driverless car to behave differently at a busy road junctionBasic problem: there is no way ofpredictingtheenvelopein which an LLMs results are acceptable. A new test example may change the envelope significantly, and you won't know it until the LLM fails.QuoteReferences, please.The differences between the 80s and now are scale, not kind. The problems still remain.QuoteSure, search results are manipulated (incognito mode helps!), and the multiple results need to be evaluated.Common sense isn't common, and people/corporations love to use the excuse "because the computer/LLM says so". Plainly impossible to resort to that with multiple results from conventional search engines.QuoteYeah, Soustroupisbleating such pious hopes. While strictly true, it ignores the elephants in the room: other people's libraries, existing code, and developer frailty. Few people are listening.QuoteNewer languages (including Rust) start by presuming multiprocessor and NUMA systems.C (and therefore C++) started byexplicitlyleaving such problems to the libraries, but not providing the language guarantees that would enable such libraries to be created (without relying on explicit knowledge of the target architecture). C and C++ have added sticking plasters and duct tape; not something you want to see on a critical system.Whether Rust will be successful remains to be seen. I suspect it will supplant many traditional C applications, but that will take a generation to happen. C and C++ will remain, just as COBOL remains.QuoteI liked Objective-C since it was "stood on the shoulders" of an existing languageand libraries: Smalltalk.The C++ mob regarded it as a sign of manliness that they Knew Right and Better than everyone else. The consequence is that they didn't stand on other people's shoulders but repeated known mistakes; tripping on their own toenails. That is extremely visible in research papers: C++ papers only referred to other C++ papers, whereas papers for other languages typically referred to knowledge gained from several other languages.C++ had the infamous mercenary attitude: kill them all and let God sort them out. How so? By throwing kitchen sinks into the language and letting the developers sort it out. There's an old saying: "by failing to choose, you choose to fail".

author: tggzzz
author_rank: Super Contributor
timestamp: October 07, 2024, 10:14:58 am
content: Quote from: Andy Chee on October 07, 2024, 09:45:13 amIf "check and evaluate" worked then LLMs would bootstrap themselves towards a perfect nirvana. Anybody believe that can/will happen? To me it feels more like the halting problem.There are already research paper indicating that LLM output "degrades" when fed output from other LLMs. Unsurprising really.Forward thinkers are already worried about what will happen when more of the stuff o the web has been generated by LLMs.

author: ebastler
author_rank: Super Contributor
timestamp: October 07, 2024, 10:46:29 am
content: Quote from: Andy Chee on October 07, 2024, 09:45:13 amYou must have misunderstood my point entirely. Reaching learnings by inference and generalisation is exactly whatallhumans do. It's how small children arrive at the "foots" and "beet" words in your very example -- by figuring out an underlying rule how to form a plural form, based on other examples they learned.

author: T3sl4co1l
author_rank: Super Contributor
timestamp: October 07, 2024, 12:41:07 pm
content: Quote from: brucehoult on October 07, 2024, 05:29:34 amNot sure where this argument is going: clearly we developed -- well, give or take what meaning is attached to those variables, it could be many things, but we can take the most familiar case to us here, relativity -- well before LLMs. It seems likely that any society would; while relativity is not a strict prerequisite for advanced computers, it seems wildly unlikely it won't be uncovered along the way.Or if you mean, not strict chronologically, but just at a given time where both coexist [humans and LLMs, give or take the potential knowledge of relativity]; clearly the presence of one, in the training data of the other, makes it much more likely -- give or take coherence length of course. But then ordering doesn't make sense...Or perhaps, assuming a state of society where humans exist and relativity doesn't, but the conditions are ripe to discover it; versus a society in the same state, but somehow equipped with LLMs, trained on current scientific and mathematical progress and investigated as an oracle upon that; perhaps, as a matter of incremental change. Structurally significant changes seem unlikely (like the choice of tensor notation in GR), but it could still bring insights and attention to certain overlooked combinations of ideas.I don't know that an LLM can't produce semantic insights as such. (Is there even a way to prove that?) Perhaps a more complex design is required (not merely scaling things up), than what we're been trying so far. Eventually, we will crack the problem of feedback poisoning the training, though whether we still call such a model "LLM" at that point, I have no idea.Or, put another way: what structural differences exist, between biological neural networks and current-tech equivalents, that specifically implement or rule out (as the case may be) such a nature?I suspect no one knows enough about either case, as yet, to make a reasonably confident and meaningful statement on this. But I haven't been following either subject in detail, and I know there's been recent developments in e.g. human connectome.Quote from: tggzzz on October 07, 2024, 09:39:51 amYou can ask a person that, but you may not get a truthful answer. Or even a meaningful one.Even just asking for a process of reasoning, presupposes one existed; it's a loaded question! A bias we need to be extremely careful about indeed, when working with systems that don't learn and "think" the way we do.Often a much more useful line of questioning is: "How do you feel? What caused you to feel this way?"(It will be, it seems, a few degrees higher complexity (in whatever structural or scaled way applies) before an LLM can generate reasonable reflections. Perhaps a sort of self-awareness can arise out of feedback loops, without corrupting overall system state; the real challenge is training such a system without an internet-sized corpus of self-reflection!)Most humans do happen to be at least conversant in syllogisms (if not strictly technically accurate ones), but even when so, people are often not aware of their own thought processes. Emotions and hormones precipitate actions from stimuli, but fail to produce any such (strict) log of [rational justification] -->  --> [further justification] (etc.). Or worse still, they do, and the reasons are completely irrelevant on closer inspection.The subconscious doer / conscious observer model shows up from time to time, with particularly exaggerated discrepancies in pathological cases (perhaps, disorder or trauma breaking connections between parts of the brain), such that the consciousness confabulates its own explanation of actions without knowing how or why they were taken. When these two aspects are working together, we perceive a convincingly coherent, cooperative whole (or, so we are conditioned to!), but it's equally possible that that's all we ever were: a pasted-together hack of very (mutually-)convincing behaviors that happens to do well enough, and -- importantly -- that evolution can turn enough knobs on, to optimize for survival under so and so many conditions.It seems to me there are two loud camps on the LLM conversation: those that consistently deny that an LLM (as we know it currently) can surpass a human in (a) any or (b) all traits, or (c) that any model (current or foreseeable) ever can; and, those that are head-over-heels impressed with the successes, so much that they are willfully ignorant to the blemishes of current tech (but, point in case, that error is indeed decreasing as models improve, and it's yet to be seen where it ends).The deniers will willfully refuse to accept the inevitable advance, until they are subsumed by it, obsoleted. The enthusiasts play the gamble of riding the curve, hoping that those current blemishes are slight enough, or can be ignored to adequate (read: marketable) satisfaction by enough users, to point to it and say "see how cool this is?", without it crashing and burning in the near term -- or, to hope to make big bucks by investing in it. Meanwhile, the enthusiasts may hope it keeps going up long term; but that's a devil's bargain, as their best long-term prospect is also to be obsoleted by it.I would humbly suggest a centrist-flavored trilemma, which -- granted, as centrism often is, may reflect my abject ignorance on this topic -- but to say that both of these things can be true to varying degrees, and that there exists a far more terrifying insight that both camps have missed and should instead be focusing on:Sooner or later, wewillrealize the total extent of the human psyche; we will map out the brain, "explain" consciousness, and -- as much by designing and probing these models, and how they reflect upon (analogize, or indeed even emulate) the behavior and nature of the meat kind, as well as by probing the meat kind directly. A dark, Lovecraftian sort of horror, awaits us here: there will be no discovery of "soul", no deep spiritual insight, only the deep and forbidden knowledge of the self on a fundamental working level (if to a very loose level of abstraction, as a system cannot possibly understand itself *fully* from within itself, but at certain levels of understanding, or approximations thereto, sure). Rather than the maddening infinite, we will be faced with the utter banality of algorithmic existence. Will this drive some to solipsism? Perhaps others will seek refuge in belief, denying until the end that such knowledge can even exist? Will some simply make peace with existence as a finite and knowable being? Perhaps there will there be a spiritual awakening, working outward rather than inward: having disproven metaphysics, we might redefine "spirit" or "soul", "consciousness", "free will", etc. into descriptive processes or perceptions, rather than the unfalsifiable belief systems people create around them today.Tim

author: magic
author_rank: Super Contributor
timestamp: October 07, 2024, 01:13:58 pm
content: Quote from: ebastler on October 07, 2024, 09:40:45 amYep, right. It probably wouldn't be hard (likely: would be easier) to create a model which makes such mistakes.Quote from: ebastler on October 07, 2024, 09:40:45 amAI fanboys could start with trying to solve any of the open problems in mathematics or computation theory. And get an answer any better than a summary of current state of the art or an obviously hallucinated non-proof.Bonus: see how easy it is to hallucinate a wrong proof vs a correct one. This could be fun.

author: tggzzz
author_rank: Super Contributor
timestamp: October 07, 2024, 01:17:03 pm
content: Quote from: T3sl4co1l on October 07, 2024, 12:41:07 pmOf course. But people treat computers differently.I guess you are lucky and have you been on the wrong end of something equivalent to "the computer says so and the computer is infallible/unbiassed. Therefore go forth and multiply"? I hope your luck continues.That would never happen with conventional search results. It does happen with LLMs and other computer systems that hide their reasons for the results, often invoking commercial confidentiality.Please read comp.risks, a high quality moderated mailing list which has been going for almost 40 years. The contributors are giants in the fields of reliability and how things and systems fail. Searchable archive athttp://catless.ncl.ac.uk/Risks/QuoteI agree, and dislike both extremes. I admit I distrust the technology shills and acolytes more, because the consequences of their statements are likely to be more deleterious.Nonetheless, it is possible to understand the general principles behind LLM's operation, and then to use that understanding to enquire about inherent failure modes. Same is true for other technology/politics/etc.N.B. OpenAI specifically forbid users to try to discover their "models", which can be interpreted as trying to figure out the reasoning and the envelope limits of the LLM's output. I don't think that has been tested in court, and I would expect manufacturers to try to avoid having it tested in court.QuotePhilosophy and religion often intermingle with hopes and fears. Sometimes the resulting emissions are entertaining, but they are rarely useful.

author: EEVblog
author_rank: Unknown
timestamp: October 07, 2024, 01:50:38 pm
content: Quote from: schmitt trigger on October 05, 2024, 01:18:44 pmI pretty much now only use google search if I'm after a link to a specific thing.If I have an actual question I'm looking for an answer for I go straight to ChatGPT.Gone are the days of trying to ask google a question and hoping you get some resource page that might answer it.

author: tggzzz
author_rank: Super Contributor
timestamp: October 07, 2024, 01:55:37 pm
content: Quote from: EEVblog on October 07, 2024, 01:50:38 pmThere's too much auto-generated crap around (not even LLM, yet) plus search engine optimisation.The former is a harbinger, the latter will mutate into LLMOptimisation.(7s long; if only all yootoob videos were as terse)

author: brucehoult
author_rank: Super Contributor
timestamp: October 07, 2024, 02:24:01 pm
content: They don't like it up 'em.One of my last memories of my maternal grandfather, who died when I was quite young, was him taking me to the 1971 Dad's Army movie, shown in the hall of the village near his farm.

author: bte
author_rank: Regular Contributor
timestamp: October 29, 2024, 08:20:51 pm
content: Quote from: brucehoult on October 07, 2024, 01:42:21 amConsidering that Istanbul is not (and was ever not) the capital of Republic of Turkey, I would say it is incorrect and not arguable. I wonder about the sources ChatGPT got trained on so that it can consider Istanbul as a capital city.

author: SiliconWizard
author_rank: Super Contributor
timestamp: October 29, 2024, 08:26:29 pm
content: It's degenerative AI.

author: brucehoult
author_rank: Super Contributor
timestamp: October 29, 2024, 08:30:22 pm
content: Quote from: bte on October 29, 2024, 08:20:51 pmOh! I'd never considered that the problem might lie in "capital" not "in Europe" or "biggest".

author: EEVblog
author_rank: Unknown
timestamp: October 30, 2024, 01:51:39 am
content: Quote from: SiliconWizard on October 29, 2024, 08:26:29 pmI predicted this:https://twitter.com/eevblog/status/1633292695677595649