The ability to “talk to an expert” about any topic I’m curious about and ask very specific questions has been invaluable to me.
It reminds me of being a kid and asking my grandpa a million questions, like how light bulbs worked, or what was inside his radio, or how do we have day and night.
And before anyone talks about accuracy or hallucinations, these conversations usually are treated as starting off points to then start googling specific terms, people, laws, treaties, etc to dig deeper and verify.
Last year during a visit to my first Indian reservation, I had a whole bunch of questions that nobody in person had answers to. And ChatGPT was invaluable in understanding concepts like where a reservation’s autonomy begins and ends. And why certain tribes are richer than others. What happens when someone calls 911 on a reservation. Or speeds. Or wants to start a factory without worrying about import/export rules. And what causes some tribes to lose their language faster than others. And 20 other questions like this.
And most of those resulted in google searches to verify the information. But I literally could never do this before.
Same this year when I’m visiting family in India. To learn about the politics, the major players, WHY they are considered major players (like the Chief Minister of Bengal or Uttar Pradesh or Maharashtra being major players because of their populations and economies). Criticisms, explanations of laws, etc etc.
For insanely curious people who often feel unsatisfied with the answers given by those around them, it’s the greatest thing ever.
I was wondering which effects were at play and the graph matched my subjective experience well.
Like as in your example, I would previously asked people "how would 911 handle an US Reservation Area", and watch how my friends think and reason. To me getting a conclusive answer was not a point. Now they just copy & paste Chat GPT, no fun haha.
Sometimes I have to say “no! just use your words to describe it! I want to hear your description”
It's useful as a starting point, not as a definitive expert answer.
Most lawyers, when you ask them to write a brief, will cite only real cases.
LLMs are too new to have such a thing. It sounds like you’re an “LLM opponent” (whatever that means) who believes the appropriate standard is infallibility? I don’t even get that line of thinking, but you’re welcome to it. But let’s not pretend this is a decades-long topic with a social consensus that people try to influence.
> I don’t even get that line of thinking, but you’re welcome to it
I would not say "LLM oponent". Rather "LLM critic". I'm not against LLMs as a technology. I'm worried about how the technology is deployed and used, and what the consequences are. Specifically, copyright issues, power use issues, inherent biases in the traning data that strengthen existing discrimation against minorities, raciscm and sexism. I'm not convinced by the hype created by LLM proponents (mostly investors and other companies and people who financially benefit from LLMs). I'm not saying that machine learning doesn't bring any value or does not have use cases. I'm talking more about the recent AI/LLM hype.
More than once I got eloquent answer that are completely wrong.
I'm asking it a question about social dynamics in the USSR, what's the worst thing that'll happen?! I'll get the wrong impression?
What are people using this for? are you building a nuclear reactor where every mistake is catastrophic?
Almost none of my interactions with LLMs "Matter", they are things I'm curious about, if 10 out of 100 things I learnt from it are false, then I learned 90 new things. And these are things which mostly I'd have no way to learn about otherwise (without spending significant money on books/classes etc.)
As a simple example, LLMs will happily incorporate "facts" learned from marketing material into it's knowledgebase and then regurgitate it as part of a summary on the topic.
1. You ask someone you can trust for facts and opinions on topics, but you keep in mind that the answer might only be right in 90% of the cases. Also people tend to tell you if the are not sure.
2. For answers you need to rely on you ask people who are legally or professionally responsible if they give you wrong advice: doctors, lawyers, car mechanics, the police etc.
ChatGPT can‘t lose it‘s job if it informs you incorrectly.
https://danluu.com/nothing-works/
The idea that competition is effective, is often in spherical cow territory.
There’s tons of real world conditions which can easily let a firm be terrible at their core competency, and still survive.
I have had language models tell me it doesn't know. Usually when using a RAG-based system like Perplexity, but they can say they don't know when prompted properly.
If you had written "notaries don't even get insurance because giving bad advice is not something you can be sued for" you would be consistent.
If they lie on purpose I don’t ask them anything anymore.
The real experts give reliable answers, LLMs don’t.
The same question can yield different results.
Most people don't even know any experts personally, much less have one they could call for help on demand. Meanwhile, the unreliable, occasionally tripping pseudo-experts named GPT-4 and Claude are equally unreliably-expert in every domain of interest known to humanity, and don't mind me shoving a random 100-pages long PDF in their face in the middle of the night - they'll still happily answer within seconds, and the whole session costs me fractions of a cent, so I can ask for a second, and third, and tenth opinion, and then a meta-opinion, and then compare&contrast with search results, and they don't mind that either.
There's lots to LLMs that more than compensates for their inherent unreliability.
Most people can read original sources.
Kurzgesagt had something along the same lines: https://www.youtube.com/watch?v=bgo7rm5Maqg
(Incidentally, not that different from LLMs, once again.)
I trust some LLMs more than most people because their BS rate is much much lower than most people I know.
For my work, that is easy to verify. Just try out the code, try out the tool or read more about the scientific topic. Ask more questions around it if needed. In the end it all just works and that's an amazing accomplishment. There's no way back.
You ask an actual expert.
I don’t treat any water cooler conversation as accurate. It’s for fun and socializing.
This is also not related to the problem being trivialized in the presented solution.
Lack of access to experts, doesn’t improve the quality of water cooler conversations.
Think this thread has gone from "how to Trust AI", to "how do we Trust Anything".
I can't do this with an LLM because it does not have identity and may make random mistakes.
LLMs also lack the ability to say "I don't know", which my fellow humans have.
There’s no reason a source has to be authoritative, just because it’s a computer.
It is a bit of an adjustment, though. We are used to our machines being accurate, or failing loudly.
But, looks like the future is opinionated machines.
Usually, stasis is the enemy that is much great than false information. If people with 90% truth can take a step forward in the world, even if they mistakenly think they have 100% truth, what does it matter? They're learning more and acting more for that step taken. If the mistaken ground truth is false and importantly enough false, they'll learn it bc their experience is grounded in the reality the navigate anyhow. If they don't learn it, it's of no consequence.
This is on my mind because I work in democratic reform, and I am acutely aware (from books like "Democracy for Realists", that eviscerate common assumptions about "how democracy works") that it often doesn't matter if we understand how democracy is working, so long as we feel like we do, enough to take steps forward and keep trying and learning. We literally don't even know how democracy works, and yet we've been living under it for centuries, to decent enough ends.
I think often about the research of Donald Hoffman. His lab runs evolutionary simulations, putting "creatures" that see "reality" (of the simulation) against creatures that see only "fitness" (the abstraction, but also the lie, that is more about seeing what gets the creature living to the next click of the engine, whether that's truth or falsehood about the reality). https://www.youtube.com/watch?v=oYp5XuGYqqY
Basically, creatures that see only fitness (that see only the lie), they drive to extinction every creature that insists on seeing "reality as it is".
I take this to mean truth is in no way, shape, or form favoured in the universe. This is just a convinient lie we tell ourselves, to motivate our current cultural work and preferences.
So tl;dr -- better to move forward and feel high agency with imperfect information, than to wait for a full truthful solution that might never come, or might be such high cost as to arrive too late. Those moving forward rapidly with imperfect information will perhaps drive to extinction those methods that insist on full grounding in reality.
Maybe this is always the way the world has worked... I mean, does any mammal before us have any idea how any of reality worked? No, they just used their senses to detect the gist of reality (often heuristics and lies), and operated in the world as such. Maybe the human sphere of language and thought will settle on similar ruthlessness.
We've come too far since the age of enlightenment to just give it all up.
Also, the vision heuristics that brains use to help us monitor motion as another obvious example. They lie. They work. They won.
https://x.com/foone/status/1014267515696922624?s=46
> Objective truth is crucial to science
Agreed. We define science and science is truth about base reality.
> Objective truth is crucial to [...] progress.
More contentious imho. Depends if progress is some abstract human ideal that we pursue, or simply "survival". If it's the former, maybe objective truth is required. If it's the latter, I find the simulation evidence to be that over-adherence to objective truth (at least information-theoretically) is in fact detrimental to our survival.
Frank Herbert, Dune
The industrialization of content generation, misinformation, and inauthentic behavior are very problematic.
I’ve hit on an analogy that’s proving very resilient at framing the crossroads we seem to be at - namely the move to fiat money from the gold standard.
The gold standard is easy to understand, and fiat money honestly seems like madness.
This is really similar to what we seem to be doing with genAI, as it vastly outstrips humanity’s capacity to verify.
There’s a few studies out there that show that people have different modes of content consumption. A large chunk of content consumption is for casual purposes, and without any desire to get mired into questions of accuracy. About 10% of the time (some small %, I don’t remember the exact) people care about the content being accurate.
What a shift from twenty years ago when optimism over “information superhighways” on the “world wide web” would end knowledge gatekeeping and educate the masses, to now— worries of AI slop and finely tuned ML algorithms frying older and younger generations’ brains, while information of human value gets buried, siloed, and paywalled, with no way to verify anything at all.
Similar to reading a pop sci book, you're getting an entertainment from a thing with no actual understanding of the source material rather than an education.
A question asked to an AI is not reviewed by anyone, and it's ephemeral. The AI can answer "yes" today, and "no" tomorrow, so it's not possible to build a consensus on whether it answers specific questions correctly.
LLM generated answers are more comparable to ad-hoc human expert's answers and not to written books. But it's much simpler to statistically evaluate and correct them. That is how we can know that, on average, LLMs are improving and are outperforming human experts on an increasing number of tasks and topics.
More specifically: I've never gotten an answer from an LLM to a tricky or obscure question about a subject I already know anything about that seemed remotely competent. The answers to basic and obvious questions are sometimes okay, but also sometimes completely wrong (but confidently stated). When asked follow-up questions the LLM will repeatedly directly contradict itself with additional answers each as wrong as the first, all just as confidently stated.
Of course don't forget that "writing up an answer off the top of their head based on that, filling in any missing material by just making something up" is what everyone does all the time, and in particular it's what experts do in their areas of expertise. How often those snap answers and hasty extrapolations turn out correct is, literally, how you measure understanding.
EDIT:
There's some deep irony here, because with LLMs being "all system 1, no system 2", we're trying to give them the same crutches we use on the road to understanding, but have them move the opposite direction. Take "chain of thought" - saying "let's think step by step" and then explicitly going through your reasoning is not understanding - it's the direct opposite of it. Think of a student that solves a math problem step by step - they're not demonstrating understanding or mastery of the subject. On the contrary, they're just demonstrating they can emulate understanding by more mechanistic, procedural means.
If instead you are reading the expert's blog post or hastily composed email or chatting with them on an airplane you get a different level of polish and care, but again you can use context to evaluate the source and claims made. Often the result is still "oh yeah this seems pretty insightful" but sometimes "wow, this person shouldn't be speculating outside of their area of expertise because they have no clue about this".
With LLM output, the appropriate assessment (at least in any that I have tried, which is far from exhaustive) is basically always "this is vaguely topical bullshit; you shouldn't trust this at all".
Edited: I just submitted an ASK HN post about this.
Certainly not my experience. Without being more specific, it's hard to discuss. Feel free to name something that can be looked at.
It's even more so with humans! Most of our conversations are, and has always been, ephemeral and unverifiable (and there's plenty of people who want to undo the little of permanence and verifiability we still have on the Internet...). Along the dimension of permanence and verifiability, asking an LLM is actually much better than asking a human - there's always a log of the conversation you had with the AI produced and stored somewhere for at least a while (even if only until you clear your temp folder), and if you can get ahold of that log, you can not just verify the answers, you can actually debug the AI. You can rerun the conversation with different parameters, different prompting, perhaps even inspect the inference process itself. You can do that ten times, hundred times, a million times, and won't be asked to come to Hague and explain yourself. Now try that with a human :).
If you want to compare AI with ephemeral unverifiable conversations with uninformed people, go ahead. But that doesn't make them sound very valuable. I believe they are more valuable than that for sure, but how much, I'm not sure.
I don’t even trust the summaries after watching LLMs think we have meetings about my boss’s cat just because I mentioned it once as she sniffed the camera…
Legal Eagle has a great video on how ChatGPT was used to present a legal argument, including made up case references! Stuff like this is why I’m wary to rely on it in areas outside of my expertise.
You wouldn’t write a legal argument based on what some random stranger told you, would you?
I’m responding to this comment, where I think it’s clear that an LLM can’t event achieve the goal the poster would like.
> You wouldn’t write a legal argument based on what some random stranger told you, would you?
I wouldn’t but a lawyer actually went to court with arguments literally written by a machine without verification.
I know it can't - the one thing it's missing is the ability to generate coherent and correct (and not ugly) domain-specific illustrations and diagrams to accompany the text. But that's not a big deal, it just means I need to add some txt2img and img2img models, and perhaps some old-school computer vision and image processing algos. They're all there at my fingertips too, the hardest thing about this is finding the right ComfyUI blocks to use and wiring them correctly.
Nothing in the universe says an LLM has to do the whole job zero-shot, end-to-end, in a single interaction.
> I wouldn’t but a lawyer actually went to court with arguments literally written by a machine without verification.
And surely a doctor somewhere tried to heal someone with whatever was on the first WebMD page returned by Google. There are always going to be lazy lawyers doctors doing stupid things; laziness is natural for humans. It's not a valid argument against tools that aren't 100% reliable and idiot-proof; it's an argument for professional licensure.
All the examples you give require domain knowledge which is the opposite of what OP wants, so I’m not sure what your issue is with what I’m saying.
The task is to replace hazelcast with infinispan in a stand-alone IMDG setup. You're interested in Locks and EntryProcessors.
Ghat GPT 4, o1 tell you with their enthusiastic style Infinispan has all those features.
You test it locally and it does....
But the thing is infinispan doesn't have explicit locks in client-server mode, just in embedded mode, but that's something you find out from another human who has tied doing the same thing.
Are you better off using Chat GPT in this case?
I could go on and on and on, on times Chat GPT has bullshitted me and wasted days of my time, but hey, it helps with one-liners and Copilot occasionally has spectacular method auto-complete and learns on the fly some stuff and it makes my cry when it remembers random tidbits about me that not even family members do
But only "probably" — the very fact that I've not heard of those things means I don't know if there's a potential risk from trying to push this onto a test server.
You do have a test server, and aren't just testing locally, right? Whatever this is?
Of course I didn't test in a client-server setup, that's why chat gpt manage to fool me, because I know all those terms, and that was not the only alternative I looked up. Before trying Infinispan I tried Apache Ignite and the api was the same for client-server and embedded mode; in hazelcast the api was the same for client-server and embedded mode, so I just presumed it would be the same for Infinispan AND I had Chat GPT re-assuring me.
The takeaway about Chat GPT for me is -- if there's plenty of examples/knowledge out there, it's ok to trust it, but if you're pushing the envelope, the knowledge is obscure, not many examples, DO NOT TRUST it.
DO NOT assume that just because the information is in the documentation, chat GPT has the knowledge or insight and you can cut corners by asking chat GPT.
And it's not even obscure information -- we've asked Chat GPT about the behavior of PostgreSql batchupserts/locking and it also failed to understand how that works.
Basically, I cannot trust it on anything that's hard -- my 20 years of experience have made me weary of certain topics and whenever those come up, I KNOW that I don't know, I KNOW that that particular topic is tricky, obscure, niche and my output is low confidence, and I need to slow down.
The more you use Chat GPT, the more likely it will screw you over in subtle ways; I remember being very surprised about how could so very subtle bugs arise EXACTLY in the pieces of code I deemed very unlikely to need tests.
I know our interns/younger folks use it for everything and I just hope there's got to be some ways to profit from people mindlessly using it.
horseshoes and hand-grenades?
Where "close" does not count, it suggests there's some artificial threshold at play. Some are unavoidable, some might be desirable to push through, but in general, life sucks when you surround yourself or enforce artificial hard cut-offs.
Which it is, of course.
Similarly, while you can't fully trust everything a journalist says, it's obviously better to have journalism than to have nothing: the "Ikon Value Problem" doesn't mean that journalism should be eradicated. Pre-LLMs, we really had nothing like LLMs in this way.
The runway on this one seems to be running out fast - how long before all the google results are also non-expert opinions regurgitated by LLMs?
Google has given up on fighting the SEO crowd long time ago. I worry they give up on the entire idea of search and will just serve answers from their LLM.
And least when it's about YouTube specific topics, like where the like button and the subscribe button is.
They will tell me. Every. Single. F*cking. 5. Minute. Clip. Again. And. Again.
Not soooo much for anything actually important or interesting, though.... ;)
PS: Also which of the always same ~5 shady companies their sponsor is, of course.
And then he does not mention the web in general (or even Reddit - it wouldn't be worth more than an eyeroll to me), but YouTube.
On the one hand, yeah, well, the web was probably in a better shape in the past. (And YT even is a major aspect of that, imho, but anyways...) On the other hand, you really must be a die hard YT fanatic to only mention that single website (which by the way is mostly video clips, and has all the issues of the entire web), instead of just the web.
It's really well outside of the sphere of my imagination. The root cause of my reply wasn't even disagreement at first, but surprise and confusion.
>They will tell me. Every. Single. F*cking. 5. Minute. Clip. Again. And. Again.
Do you know why you got that video. Because people liked and subscribed to them and the 'experts' with the best information in the universe are hidden 5000 videos below with 10 views.
And this is 100% Googles fault for the algorithms they created that force these behaviors on anyone that wants to use their platform and have visibility.
Lastly, if you can't find anything interesting or important on YT, this points at a failure of your own. While there is an ocean of crap, there is more than enough amazing content out there.
> And this is 100% Googles fault for the algorithms they created that force these behaviors on anyone that wants to use their platform and have visibility.
Wrong assumptions. It's not their fault, and a lot of it is probably by intent. It's just that they and you are not in the same boat. You are the product at big tech sites. It's 100% (impersonally) your fault to be sooo resistant understanding that. ;)
LLMs do already a lot better job at this. A lot faster, accurate enough and easy to use.
I can now study something alone which i was not able to do before.
Ask it something non-trivial about a subject you are an expert in and get back to me.
Besides, when i ask it about things like SRE, Cloud etc. its a very good starting point.
Could you elaborate on this? What happened before when you had that type of questions? What was stopping you from tamping "911 emergency indian reservation" into google and learning that the "Prairie Band Potawatomi Nation" has their own 911 dispatch?
In my youth, before the internet was everywhere, we were taught that we could always ask the nearest librarian and that they would help us find some useful information. The information was all there, in books, the challenge was to know which books to read. As I got older, and Google started to become more available, we were taught how to filter out bad information. The challenge shifted from finding information into how not to find misinformation.
When I hear what you say here, I'm reminded of that shift. There doesn't seem to be any fundamental change there, expect may that it makes it harder not to find misinformation by obscuring the source of the information, which I was taught was an important indicator of its legitimacy.
The alternative is that when I return from vacation, I get back to the busy life and am only reminded of these questions in casual conversations.
While my main use case for LLMs is coding just like most people here, there are lots of areas that are being ignored.
Did you know llama 3.X models have been trained as psychotherapists? It's been invaluable to dump and discuss feelings with it in ways I wouldn't trust any regular person. When real therapists also cost more than what people can afford (and will have you committed if you say the wrong thing), this ends up being a very good option.
And you know how escorts are traditionally known as therapists lite? Yeah, it works in reverse too. The main use case most are sleeping on is, well, emotional porn and erotic role play. Let me explain.
My generation (i.e. Z) doesn't do drugs, we don't drink, we don't go out. Why? Because we can hang on discord, play games, scroll tiktok and goon to our heart's content. 60% of gen Z men are single, 30% women. The loneliness epidemic hit hard along with covid. It's basically a match made in heaven for LLMs that can pretend to love you, like everything about you, ask you about your day, and of course, can sext on a superhuman level. When you're lonely enough, the fact that it's all just simulated doesn't matter one bit.
It's so interesting that the porn industry is usually on the forefront of innovation, adopting blueray and hddvd and whatnot before anyone else, but they're largely asleep on this and so is everyone else who doesn't want to touch of it with a 10ft pole. Well except maybe c.ai to some extent. The business case is there and it's a wide open market that OAI, Anthropic, Google and the rest won't ever stoop down to themselves, so the bar for entry is far lower.
Right now the best experience is known to be heading over to r/locallama by doing it yourself, but there's millions to be made for someone who improves it and figures out a platform to sell it on in the next few years. It can be done well enough with existing properly tuned, open weight, apache licensed LLMs and progress isn't stopping.
The movie was about replicants of course, but in the background, the technology shown with the AI being a companion, it was a huge corporate hit, a big seller. In the background you see ad's for it, and they reference it as their most popular product.
And as you say, I'm sure the porn industry will catch on.
Kind of crazy how Porn isn't leading this tech wave like past ones. Maybe because people are scared of tracking?
It's in our nature to crave outside acceptance of who we are. But may be taken to extreme, when we stop being wanting to be challenged at all we could lose touch with reality, society...
The thing is though, LLMs do whatever you tune them to do. If you train them on a sycophantic corporate drone butler dataset, you get the average assistant model that's obviously a bad fit for this use case. If you train them on something else, you get whatever you want, even someone that challenges you. I wouldn't be surprised if having some sort of simulated soulmate partner thing that also does the job of an educator and life guide will be the norm in the future.
Isn't that exactly what you suggest? A paid one-sided relationship that helps people feel better about themselves, with a bit of naughtiness mixed in.
Isn’t that the result of major credit card companies banning certain uses, thus pruning branches from the tree of possible futures ?
What we need is digital central bank money in some form, to get rid of that type of censorship.
I always do a double take when I read such statistics. How can they possibly add up? Are gen Z men considered particularly undesirable leading to lots of relationships with large age gaps? Is there a ridiculously large overhang of gay women (over men)? Is there a huge number of men with multiple partners?
These gender disparities are difficult enough to believe when they come to sexual relations, it gets even harder when talking about relationships.
I guess what I'm saying is: I don't believe those numbers as stated and would be interested in an explanation or at least a source.
From what I can find after a brief search, there's this one [0] that claims 63% for men, 34% for women, and [1] there's a a generally known toxicity around dating these days that makes these numbers entirely believable. I don't pretend to have a large enough network of acquaintances to make a good guess, but hardly anyone I know isn't single, and I know maybe two or three religious types that are actually married.
As for gen Z men being especially undesirable, there's well... [2].
[0] https://www.pewresearch.org/short-reads/2023/02/08/for-valen...
[1] https://old.reddit.com/r/GenZ/comments/1eo9bzj/interesting_b...
[2] https://www.ft.com/content/29fd9b5c-2f35-41bf-9d4c-994db4e12...
Other factor.
As the other post suggested. There are large age gaps. Women date older, men date younger. This is also long known. Does it add up to 60/30? That does seem high, but maybe with every other factor thrown in, it explains it?
Or, you know, are leading women on.
I could certainly invent explanations for them. For example, I can say "no man would date until they've earned enough money to buy a house." This means younger males won't be dating but that doesn't appear to describe the world we live in.
I could say "Every man who dates is dating 2 women" but that also doesn't appear to describe the world we live in.
It is possible, for example, to create a cunning and manipulative schemer that is entirely devoted to mentoring you with no romantic component whatsoever.
It is dangerous to assume that LLMs are experts on any topic. With or without quotes. You are getting a super fast journalist intern with a huge memory but inability to reason critically, lacking understanding about anything and huge unreliability when it comes to answering questions (you can get completely different answers to the same question depending on how you answer it and sometimes even the same question can get you different answers). LLMs are very useful and are a true game changer. But calling that expertise is a disservice to the true experts.
I think you overestimate the ability of a typical 'expert'. You can earn a PhD without the ability to reason critically. You can testify as an expert in a courtroom without understanding conditional probability. Lawyers and accountants in real life also totally contradict themselves when they get asked the same question twice but phrased slightly differently.
Books might not be great, but they are at least some minimum bar to reach. You had to do some study and analysis.
So
Top 10% of Books. Ok
90 % of Books. marginal, lot of bad.
Internet. Just millions of pages of junk.
- Books still take some effort. So why not start there.
It isn't either/or, binary, a lot of books are bad, so guess I'll learn my medical degree from browsing the web because I don't trust those 'experts'.
What is the top 10% of the Internet that isn't part of some publishing arm of existing media? And, how can you tell? Some dudes blog about vaccines verses Harvard? Which do you believe.
Where are the self funded scientific studies that are occurring outside of academia? And thus not 'biased' by the 'elites'.
For internet only writing. There aren't a ton of "Astral Codex Ten"'s to draw upon as independent thinkers. And even then, he didn't sprout out of the ether fully formed, he has a degree, he was trained in academia.
Why does that even matter?
So you brought up the top 10% of the Internet, possibly as argument against books? That maybe there is valuable information on the Internet.
I was just saying, that 10% is also created by the same people that create books. So if you are arguing against books, then the top 10% of the Internet isn't some golden age of knowledge coming from some different more reliable source.
I'll get you started with one of the great works from the 1600s:
https://www.gutenberg.org/cache/epub/49513/pg49513-images.ht...
The scientific method relies on evidence and reproducible results, not authority alone.
Edited to add a reference: see under Appeal to authority. https://writingcenter.unc.edu/tips-and-tools/fallacies/
Which definition of theory includes that it can never be 100% true? It can't be proven to be true, but surely it could be true without anyone knowing about it.
And as a consequence, they know where that area of expertise ends. And they know what half-knowing something feels like compared to really knowing something. And thus, they will preface and qualify their statements.
LLMs don't do any of that. I don't know if they could, I do know it would be inconvenient for the sales pitch around them. But the people that I call experts distinguish themselves not by being right with their predictions a lot, but rather by qualifying their statements with the degree of uncertainty that they have.
And no "expert system" does that.
How do you count examples like Musk, then?
He is very cautious about rockets, and all the space science people I follow and hold in high regard, say he's actually a domain expert there. He regularly expectation-manages experimental SpaceX launches downward.
He's also very bold and brash about basically everything else; the majority of people I've seeing saying he's skilled in any other area have turned out to not themselves have any skills in those areas, while the people who do have expertise say he's talking nonsense at best and is taking wild safety risks at worst.
The rest of the genius persona is growing up with enough ego that he could become a good salesman, and also badly managed autism and also a badly managed drug habit.
Seeing him dabble in politics and social media shows instantly how little he understands the limits of his knowledge. A scholar he is not.
That's not to say I think it is rationalizing it's own level of understanding, but that somewhere in the vector space it seems to have a Gradient for speculative language. If primed to include language about it, it could help cut down on some of the hallucination. No idea if this will effect the rate of false positives on the statements it does still answer confidently however
(And in neural network terms, that error bar could be represented by the number of connections, by congruency of separate paths of arguing, by vividness of memories, etc ... it's not above human reasoning either, no need for new data structures ...)
> People who are experts (PhD and 20 years of experience) often have very dumb opinions in their field of expertise.
The conventional wisdom is that experts are dumb OUTSIDE of their fields of expertise.
I don't know about you, but I would be very insulted by someone passing judgement like this on my own work in my field. I am sure that I would doubt their qualifications to even make the judgement.
Are there experienced fools? Sure. We both probably work with some. To me they are not experts, though.
Also, expert historians, philosophers psychs, etc. aren't judged based on their correctness, but on their breadth and depth of knowledge and their capacity to derive novel insights. Some of the best works of history I've read are both detailed and polemical, trying to argue for a new framework for understanding a historical epoch that shifts how we understand our modern world.
I don't know, I think I know very little about the world and there are people who know far more and I appreciate reading what they have to say, of course with a critical eye. It seems to me that disagreeing with that is just regurgitated anti-intellectualism, which is a coherent position, but it's good to be honest about it.
And the training data contains all those dumb opinions.
And all in the hand of a few big tech corporations...
The open source, local LLM community is absolutely buzzing right now.
Yes, the big companies are making the models, but enough of them are open weights that they can be fine tuned and run however you like.
I think LLMs genuinely do present an opportunity to be neutral experts, or at the least neutral third parties. If they're run in completely transparent ways, they may be preferable to humans in some circumstances.
And then we're right back to "trusting expert human beings" again.
With good prompting, the SOTA models already act in ways I think most reasonable people would agree with, and that's without trying to build this specifically for that use case.
And how long is that going to last? This is a well known playbook at this point, we'd be better off if we didn't fall for it yet again - it's comical at this point. Sooner or later they'll lock the ecosystem down, take all the free stuff away and demand to extract the market value out of the work they used to "graciously" provide for free to build an audience and market share.
You can't take the free stuff away. It's on my hard drive.
They can stop releasing them, but local models aren't going anywhere.
Now we finally have a tool where all of you can prove every day how strong/smart/funny/foo you are (not actually). How was life even possible without?
So, don't be so pessimistic. ;)
While also noting that "neutral" is not well-defined, I agree. They will be used as if they were.
We humans are very good at rejecting any information that doesn’t confirm our priors or support our political goals.
Like, if ChatGPT says (say) vaccines are good/bad, I expect the other side will simply attack and reject it as misinformation, conspiracy, and similar.
However that still ignores human nature to use that trust for personal gain.
Nothing about expertise makes someone a saint.
It's like buying supermarket food and also buying the same food from the farmers themselves.
Come to think of it, it's really no different than walking into Home Depot and asking "the old guys" working in the aisles about stuff -- you can access some fantastic knowledge if you know the names of all the tools and techniques, and if not, can show them a picture or describe what you're trying to do and they'll at least point you in a starting direction with regards to names of tools needed, techniques to use, etc.
Just like I don't expect Home Depot hourly worker Grandpa Bob to be the end-all-be-all expert (for free, as well!), neither do I expect ChatGPT to be an all-knowing-all-encompassing oracle of knowledge.
It'll probably get you 95% of the way there though!
Querying multiple llms at the same time and being able to compare results is a much better comparison to googling but no one does this.
As I said, you are talking to a super confident journalist intern who can give you answers but you won't know if it is true or partially true until you consult with a human source of knowledge.
It's not even similar to asking the old guys at the Home Depot because they can tell you if they are unsure they have a good answer for you. An LLM won't. Old guys won't hallucinate facts the way an LLM will
It is really is the 21st century Searle's epistemological Chinese room nightmare edition. Grammar checks out but whatever is spit out doesn't necessarily bear any resemblance to reality
Ol' boy at the Depot is constrained by his own experiences and knowledge, absolutely can hallucinate, oftentimes will insert wild, irrelevant opinions and stories while getting to the point, and frankly if you line 6 of them up side by side to answer the same question, you're probably leaving with 8 different answers.
There's never One True Solution (tm) for any query; there are 100 ways to plumb your way out of a problem, and you're asking a literal stranger who you assume will at least point you in the right direction (which is kind of preposterous to begin with)
I encourage people to treat LLMs the same way -- use it as a jumping off point, a tool for discovery that's no more definitive than if you're asking for directions at some backwoods gas station. Take the info you get, look deeper with other tools, work the problem, and you'll find a solution.
Don't accept anything they provide at face value. I'm sure we all remember at least a couple teachers growing up who were the literal authority figures in our lives at the time, fully accredited and presented to us as masters of their curriculum, who were completely human, oftentimes wrong, and totally full of shit. So goes the LLM.
Scientists should be writing papers sealed digitally once they're peer reviewed and considered "truth", same thing with journalist/news articles - sealed once confirmed true or backed up by a solid source in the same way we trust root certificates.
But then again, especially when it comes to journalism, cropping photos, chopping quotes, etc all to misrepresent etc. Turns out we're all the bad actors; it's in our DNA. And tbf, many people when presented with hard evidence to the contrary of the opinion that they cling onto like a babe to a breast, just plug their ears and cover their eyes.
Okay so maybe there's no point seeking truth/factual correctness, our species doesn't want it 99% of the time, unless it affects them directly (eg people that shoot down public healthcare until they have an expensive illness themselves).
I treat LLM answers about the same way I treat wikipedia articles. If it's critical I get it right, I go to the wiki sources referenced. Recent models have gotten good at 'showing their sources', which is helpful.
the problem with this is that humans will likely use it for low key stuff, see that it works (or that the errors don't affect them too badly) and start using it for more serious stuff. It will all be good until someone uses it in something more serious and some time later it ends badly.
Human basic thinking is fairly primitive. If yesterday was sunny, the assumption is that today should too. The more this happens the higher your confidence. The problem is that this confidence emboldens people to gamble on that and when it is not sunny anymore, terrible things happen. A lot of hype driven behaviour is like that. Crypto was like that. The economic crisis of the late 00s was like that. And LLMs look set to be like that too.
It is going to take a big event involving big critical damage or a high profile series of deaths via misuse of an LLM to give policymakers and business leaders around the world a reality check and get them looking at LLMs in a more critical way. An AI autumn if you wish. It is going to happen at some point. Maybe not in 2025 or 2026 but it will definitely happen.
You may argue that it is the fault of the human using the LLM/crypto/giving out loans but it really doesn't matter when those decisions affect others.
Even the ability to talk to a university work placement student/intern in any topic is very useful, never mind true experts.
Even Google's indexing and Wikipedia opened up a huge quantity of low-hanging fruit for knowledge sharing; Even to the extent that LLMs must be treated with caution because the default mode is over-confident, and even to the extent one can call them a "blurry JPEG of the internet", LLMs likewise make available a lot of low-hanging fruit before we get to an AI that reasons more like we do from limited examples.
It was super fast and always provided you with sources. It never hallucinated. It was completely free except for some advertisement. You could build a whole career out of being good at using it.
It was a search engine. Young people might not remember but there was a time when Google wasn't shite but actually magic.
Except for all the times the search results were wrong answers.
https://searchengineland.com/when-google-gets-it-wrong-direc...
At least you could check whether a source was reputable and where the bias was. With LLM's the connection between the answer and the source is completely lost. You can't even tell why it answered a certain way.
I didn't deny either of those things, I said that search engines also hallucinate — my actual link gave several examples, including "King of the United States" -> "Barack Obama".
Just because it showed the link to breitbart doesn't mean it was not hallucinating.
> At least you could check whether a source was reputable and where the bias was.
The former does not imply the latter. You could tell where a search engine got an answer from, but not which answers were hidden — an argument that I saw some on the American right make to criticise Google for failing to show their version of events.
> With LLM's the connection between the answer and the source is completely lost. You can't even tell why it answered a certain way.
Also not so. The free version of ChatGPT supports search directly, so it allows you to have references.
They don't. Google added a weird widget that do hallucinate. But the result list is still accurate, even though it may be biased towards certain sources.
> You could tell where a search engine got an answer from, but not which answers were hidden
A bit pedantic, but a search engine returns a list of results according to the query you posted. There's no question-answer oracle. If you type "King of the United States", you will get pages that have the terms listed. Maybe there will be semantic manipulations like "King -> Head of state -> President", but generally it's on you to post the correct keywords.
It takes more time for me to verify the stuff they output than grabbing a book off Anna's Archive or my payed collections and looking something up immediately. I'd rather spend that time making notes than waiting for the LLM to respond and double checking it.
As an insanely curious person who's often unsatisfied with the answers given by those around me, I can't agree. The greatest thing ever is libraries. I don't want to outsource my thinking to a computer any more than I want to outsource it to the people around me.
Of course LLMs aren't going anywhere, but I do not envy Sam Altman right now.
It’s possible we’re nearing the end of the LLM race, but I doubt that’s the end of the AI story this decade, or OpenAI.
Imagine asking chatgpt to tell you about slopes in Colorado, and the first five answers are about how awesome North Face is and how you can order from them. You probably wouldn't use it as much.
The cash burn and claim of imminent agi is where the valuation trouble could be.
What's the catch though? I was looking at Gemini recently and it seemed too good to be true.
Other than that the catch is like all other free tiers, it is marketing and can be withdrawn at any moment to get you to pay after you are used to their product.
> When you use Unpaid Services, including, for example, Google AI Studio and the unpaid quota on Gemini API, Google uses the content you submit to the Services and any generated responses to provide, improve, and develop Google products and services and machine learning technologies, including Google's enterprise features, products, and services, consistent with our Privacy Policy.
https://aider.chat/docs/leaderboards/ https://aider.chat/2024/12/21/polyglot.html
I enjoy image and video generation and I have a 4090 and ComfyUI; I can't keep up with everything coming out anymore.
https://www.reddit.com/r/ChatGPTCoding/
There are a lot of tools, but only a small pool of tools that are worth checking out. Cline, Continue, Windsurf, CoPilot, Cursor, and Aider are the ones that come to mind.
/not affiliated with cline, just a happy user
There's no specific reason to expect them to get better.
Things that will shift the status quo are: MCST-LLMs (like with ARC-AGI) and Much Bigger LLMs (like GPT-5, if they ever turn up) or some completely novel architecture.
[1] - It's provable; if just chaining LLMs are a particular size into agentic systems could scale indefinitely, then you could use a 1-param LLM and get AGI. You can't. QED. Chaining LLMs with agentic systems has a capped maximum level of function which we basically already see with the current LLMs.
ie. Adding 'agentic' to your system has a finite, probably already reached, upper bound of value.
Perhaps I missunderstand your reply, but that has not been my experience at all.
There are 3 types of "agentic" behaviour that has worked for a while for me, and I don't know how else it would work without "agents":
1. Task decomposition - this was my manual flow since pre-chatgpt models: a) provide an overview of topic x with chapter names; b) expand on chapter 1 ... n ; c) make a summary of each chapter; d) make an introduction based on the summaries. I now have an "agent" that does that w/ minimal scripting and no "libraries". Just pure python control loop.
This gets me pretty reasonable documents for my daily needs.
2. tool use (search, db queries, API hits). I don't know how you'd use an LLM without this functionality. And chaining them into flows absolutely works.
3. coding. I use the following "flow" -> input a paragraph or 2 about what I want, send that + some embedding-based context from the codebase to an LLM (3.5 or 4o, recently o1 or gemini) -> get code -> run code -> /terminal if error -> paste results -> re-iterate if needed. This flow really works today, especially with 3.5. In my testing it needs somewhere under 3 "iterations" to "get" what's needed in more than 80% of the cases. I intervene in the rest of 20%.
If a discrete system can be composed of multiple LLMs the upper bound on the function they provide is by the function of the LLM, not the number of agents.
Ie. We have agentic systems.
Saying “wait till you see those agentic systems!” is like saying “wait til you see those c++ programs!”
Yes. I see them. Mmm. Ok. I don’t think I’m going to be surprised by seeing them doing exactly the same things in a year.
The impressive part in a year will the non agentic part of things.
Ie. Explicitly; if the underlying LLMs dont get any better, there is no reason to expect the system built out of them to get any better.
If that was untrue, you would expect to be able to build agentic systems out of much smaller LLMs, but that overwhelmingly doesn’t work.
Actually o1, o3 are doing exactly this, and very well. I.e. explicitly: by proper orchestration the same LLM can do much better job. There is a price, but...
> you would expect to be able to build agentic systems out of much smaller LLMs
Good point, it should be possible to do it on a high-end pc or even embedded.
MCTS will be the next big “thing”; not agents.
I use zed for this, cursor for my more involved sessions and aider + vscode + continue for local stuff when I want to see how far along local models have come. Haven't tried cline yet, but heard great stuff.
Yes that will be a sight to behold.
What’s it called? Could you post a link please?
Thank you
i think as humans we put too much emphasis on what intelligence means relative to ourselves, instead of relative to nature
Could you elaborate? Was it easy to install?
I just installed the Docker version and created a little wrapper script which starts and stops the container. Installing different models is trivial.
I think I already had CUDA set up, not sure if that made a difference. But it's quick and easy. Set it up, fuck around for an hour or so while you get things working, then you've got your own local LLM you can spin up whenever you want.
https://github.com/TheBlewish/Automated-AI-Web-Researcher-Ol...
Until about 120 years ago, people were happy with horses and horse carriages. Such a great help! Travel long distances, pull weights, I never want to go back! But then the automobile was invented and within a few years little travel was done by horses anymore.
More recently, everybody had a landline phone at home. Such great tech! Talk to grandma hundreds of miles away! I never want to go back! Then suddenly the mobile phone and just shortly after the smart phone came along and now nobody has a landline anymore but everybody can record tiktoks anywhere anytime and share them with the world within seconds.
Now imagine "AI". Sure, we have some new tools right now. Sure we don't want to go back. But imagine the transformative effects that could come if the train didn't stop here. Question is just: will it?
OpenAI may be first to realize and switch, so they still have a chance to recoup some of those billions
The whole sector is a hype bubble artificially inflating stock prices.
The debate stems from a delusion and failure to realize that people are simply picking and choosing different fringe features on what qualifies as agi. Additionally the term exists in a fuzzy state inside our minds as well. It’s not that the concept is profound. It’s that some of the features that define the classification of the term we aren’t sure about. But this doesn’t matter because we are basically just unsure about the definition of a term that we completely made up arbitrarily.
For example the definition of consciousness seems like a profound debate but it’s not. The word consciousness is a human invention and the definition is vague because we choose the definition to be ill defined, vague and controversial.
Much of the debate on this stuff is purely as I stated just a language issue.
I don't believe AGI is possible but if it was and it was as subjective as you say what is and isn't conscious, then it starts to take on an even more altogether evil character.
Akin to cloning slave humans or something for free cheap labor.
Slavery is also a word. Don’t you find it strange that your entire moral framework is constructed on top of the basis of arbitrary definitions of vocabulary? Make what you think is right or wrong based not off of language. Language is a delusion that masquerades as something with actual meaning when it is just an invention, a tool, to facilitate communication.
Right now your concept of right and wrong is a vocabulary issue. Does this make sense? No.
When we discuss the potential AGI we're not talking as consumers, we're talking about the business side. If AGI is not reached, you'll see an absolutely enormous market correction, as it realizes that the product is not going to replace any human workers.
The current generation of products are not profitable. They're investments towards that AGI dream. If that dream doesn't happen, then the current generation of stuff will disappear too, as it becomes impossible to provide at a cost you'd be comfortable with.
Is it on github?
AI right now feels like that MBA person at work.
They don’t know anything.
But because they sound like they are speaking with authority & confidence, allows them to get promoted at work.
(While all of the experts at work roll their eyes because they know the MBA/AI is just spitting out nonsense & wish the company never had any MBA/AI people)
Between Perplexity searching and summarizing, Claude explaining, and qwen (and other tools) coding, I'm already as happy as can be with whatever you want to call this level of intelligence.
Just today I used a completely local AI research tool, based on Ollama. It worked great.
Maybe it won't get much better? Or maybe it'll take decades instead of years? Ok. I remember not having these tools. I never want to go back.