GPT-5 is behind schedule
wsj.com
wsj.com
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.
I enjoy image and video generation and I have a 4090 and ComfyUI; I can't keep up with everything coming out anymore.
/not affiliated with cline, just a happy user
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.
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Other than that the catch is like all other free tiers, it is marketing and can be withdrawn at any moment to get you to pay after you are used to their product.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
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%.
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.
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.
Yes that will be a sight to behold.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 with the current SOTA. 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.
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.
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 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.
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.
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 allude to, in the movie it was both for loneliness AND sexual. They interacted like a relationship with talking and hooking up.
I don't doubt that with current AI, something similar could be done. We're just missing the holograms.
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 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.
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.
It's like buying supermarket food and also buying the same food from the farmers themselves.
However that still ignores human nature to use that trust for personal gain.
Nothing about expertise makes someone a saint.
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.
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 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)
I am betting hundreds of thousands, rising to millions more little sites, will start blocking/gating this year. AI companies might license from big sources (you can see the blocking percentage went down), but they will be missing the long tail, where a lot of great novel training data lives. And then the big sites will realize the money they got was trivial as agents start to crush their businesses.
Bill Gross correctly calls this phase of AI shoplifting. I call it the Napster-of-Everything (because I am old). I am also betting that the courts won't buy the "fair use" interpretation of scraping, given the revenues AI companies generate. That means a potential stalling of new models until some mechanism is worked out to pay knowledge creators. (And maybe nothing we know of now will work for media: https://om.co/2024/12/21/dark-musings-on-media-ai/)
Oh, and yes, I love generative AI and would be willing to pay 100x to access it...
P.S. Hope is not a strategy, but hoping something like ProRata.ai and/or TollBits can help make this self-sustainable for everyone in the chain
This seems like a very bad way to approach this, and ironically their model quite possible also uses some sort of machine learning to work.
A few web hosting platforms are using the cloudflare blocker and I think it's incredibly unethical. They're inevitably blocking millions of legitimate users from viewing content on other people's sites and then pretending it's "anti AI". To paraphrase Theo Deraadt, they saw something on the shelf, and it has all sorts of pretty colours, and they bought it.
The internet isn't built on ethical behavior, unfortunately.
*edit: added "for-profit"
If something I worked on turned out to play a significant part in something that turned out to be that big a deal, I'd be OK with it. And nobody's stopping people from doing web-search studies with it, to this day.
Prostate and rectal cancer is real, boys. Grow tf up about it.
I suspect that AIs are already more effective than humans at passing captchas.
Transformers aren't zettabyte sized archives with a smart searching algo, running around the web stuffing everything they can into their datacenter sized storage. They are typically a few dozen GB in size, if that. They don't copy data, they move vectors in a high dimensional space based on data.
Sometimes (note: sometimes) they can recreate copyrighted work, never perfectly, but close enough to raise alarm and in a way that a court would rule as violation of copyright. Thankfully though we have a simple fix for this developed over the 30 years of people sharing content on the internet: automatic copyright filters.
I get your point, but nobody is archiving their companies 50 years of R&D data with and LLM so they can get it down to 10GB.
They may have traits of data compression, but they are not at all in the class of data compression software.
It read many works but can't duplicate them exactly sounds a lot like what I've done, to be honest. I can give you a few memorable lines to a few songs but only really can come close to reciting my favorites completely. The LLMs are similar but their favorites are the favorites of the training data. A line in a pop song mentioned a billion times is likely reproducible, the lyrics to the next track on the album, not so much.
IMO, any infringement that might have happened would be acquiring data in the first place but copy protection cares more about illegal reproduction than illegal acquisition.
Fair use provides exceptions for some such works, but not all, and it is possible for generative models to produce clearly infringing (on either copyright or trademark basis) outputs both deliberately (IMO this is the responsibility of the user) and, much less commonly, inadvertently ( ?).
This is likely to be a problem even if you (reasonably) assume that the generative models themselves are not infringing derivative works.
And there is very little shortage of data and experience in the actual world, as opposed to just the text internet. Can the current AI companies pivot to that? Or do you need to be worldlabs, or v2 of worldlabs?
As it stands, OpenAI has a market cap large enough to buy a major international media conglomerate or two. They'll get data no matter how blocked they get.
Gemini is currently embarrassingly bad given it came from the shop that:
1. invented the Transformer architecture
2. has (one of) the largest compute clusters on the planet
3. can scrape every website thanks to a long-standing whitelist
This only remains true as long as website operators think that Google Search is useful as a driver of traffic. In tech circles Google Search is already considered a flaming dumpster heap, so let's take bets on when that sentiment percolates out into the mainstream.
How many websites back then would be youtube channels, podcasts or social media accounts if they had existed back then?
Nowadays most sites survive via traffic from google, if it goes away then most of those sites go away as well.
1. They were a major site that was an initial starting point for traffic
2. Search engines pointed to them and people could locate them.
---
That was all a long time ago. Now people tend to go to a few 'all in one sites'. Google, reddit, '$big social media'. Other than Google most of those places optimize you to stay on that particular site rather than go to other people's content. The 'web' was a web of interconnectedness. Now it's more like a singularity. Once you pass the event horizon of their domain you can never escape again.
Websites won’t be blocking the search engine crawlers until they stop sending back traffic, even if they’re sending back less and less traffic
To your point, I have wondered whatever became of that massive initiative from Google to scan books, and whether that might be looked at as a potential training source, giving that Google has run into legal limitations on other forms of usage.
Still around, doing fine: https://en.wikipedia.org/wiki/Google_Books and https://books.google.com/intl/en/googlebooks/about/index.htm...
Given the timing, I suspect it was started as simple indexing, in keeping with the mission statement "Organize the world's information and make it universally accessible and useful".
There was also reCAPTCHA v1 (books) and v2 (street view), which each improved OCR AI until the state of the art AI were able to defeat them in the role of CAPTCHA systems.
Maybe I wasn't clear, but I was interested in the consequences of the legal stuff. It's not clear from the wiki article what any of this means with respect to the suitability of scans for AI training.
Timing as in: it started in 2004, when the most advanced AI most people used was a spam filter, so it wasn't seen as a training issue (in the way that LLMs are) *at the time*.
As for training rights, I agree with you, there's no clarity for how such data could be used *today* by the people who have it. Especially as the arguments in favour of LLM training are often by comparison to search engine indexing.
A few months ago, there was an interesting submission on HN about this - The Tragedy of Google Books (2017) (https://news.ycombinator.com/item?id=41917016).
If there is untapped signal in existing datasets, then learning processes should be improved. It does not follow that there should be a separate economic step where someone produces "synthetic data" from the real data, and then we treat the fake data as real data. From a scientific perspective, that last part sounds really bad.
Creating derivative data from real data sounds, for the purpose of machine learning, like a scam by the data broker industry. What is the theory behind it, if not fleecing unsophisticated "AI" companies? Is it just myopia, Goodhart's Law applied to LLM scaling curves? Some MBA took the "data is the new oil" comment a little too seriously and inferred that data is as fungible as refined petroleum?
https://www.forbes.com/sites/carolynschwaar/2024/12/09/schae...
No, just as I wouldn't trust a surgeon who studied medicine by playing Operation. A gross approximation is not a substitute for real life.
Similarly, when this doctor sees something new, will they just write it off as something they've seen before and confidently work from that assumption?
I could've had the nurse close me up and leave me with a scar, which she admitted would happen with her practice, or I could have someone with extensive experience treating wounds so that they'd heal in cosmetically appealing way do it. I opted for the latter.
How it is tested and validated is what matters.
There are lots of ways to train on synthetic data, and synthetic data can have advantages as well as disadvantages over natural data.
Creative use of synthetic data is going to lead to many cases where we find it is good enough. Or even better than natural data.
You're suggesting the new, untested models in a new, untested technological field are sufficient for deployment in real world applications even with a lack of real world data to supplement them. That's magical thinking given what we've experienced in every other field of engineering (and finance for that matter).
Why is AI/ML any different? Because highly anthropomorphized words like "learning" and "intelligence" are in the name? These models are some of the most complex machines humanity has ever produced. Replace "learning" and "intelligence" with "calibrated probability calculators". Then detail the sheer complexity of the calibrations needed, and tell me with a straight face that simulations are good enough.
Simulations may not be good enough alone, but still provide a significant boost.
Simulations can cheaply include scenarios that would be costly or dangerous to actually perform in the real world. And cover many combinations of scenario factors to improve combinatorial coverage.
Another way is to separate models into highly real world dependent (sensory interpretation) and more independent (kinematics based on sensory interpretation) parts. The latter being more suited to training in simulation. Obviously full real world testing is still necessary to validate the results.
I still don't see how it could address the volume problem, like needing 10x or 100x of current data to train GPT5.
At one point someone generated a Python teaching book from a LLM, took that, trained a second LLM with that, and the new LLM knew Python.
If you are just dragging random content from the web and you don't know what's synthetic and what's human, that data may be contaminated and a lot less useful, but if someone wanted to whitewash their training data by replacing a part of it with synthetic data, it can be done.
Also whats with the hate for MBA’s?
Your comment is off kilter with the rules here.
Augmentation, interpolation, smoothing are different concepts.
Your concern about extrapolation is interesting but misses something key when we generate synthetic data through expert demonstration or guided curriculum, we're not trying to magically create capabilities beyond the training distribution. Instead, we're trying to better sample the actual distribution of problemsolving approaches humans use. This isn't extrapolation rather, better sampling of an existing, complex distribution!
i.e. if you think about the manifold hypothesis then we know real data lives on a lowerdimensional manifold, and good synthetic data helps fill those gaps. This naturally leads to better extrapolation, it's pretty well established at this point.
TBH I think you are characterizing this as some kind of blind data multiplication scheme, but it's much closer to curriculum learning you start with basic synthetic examples and gradually ramp up complexity. So it isn't whether synthetic data is "real" or not, but if it effectively helps map the underlying distribution and reasoning patterns.
Funny enough, your oil analogy actually supports the case for synthetic data refined petroleum is more useful than crude for specific purposes, just like well designed synthetic data can be more effective than raw internet text for certain learning objectives.
If blocking really becomes a problem, they can take a page out of Google's playbook[1] and develop a browser extension to scrape page content and in exchange offer some free credits for Chat-GPT or a summarizer type of tool(s). There won't be shortage of users.
Not sure how exactly the Library of Congress is structured, but the equivalent in several countries can request a free copy of everything published.
Extending that to the web (if it's not already legally, if not practically, the case) and then allowing US companies to crawl the resulting dataset as a matter of national security, seems like a step I could see within the next few years.
https://twitter.com/Bill_Gross/status/1859999138836025808
https://pdl-iphone-cnbc-com.akamaized.net/VCPS/Y2024/M11D20/...
He appears to be criticising "AI" only to solicit support for his own company.
This is where I'm at. I write content when I run into problems that I don't see solved anywhere else, so my sites host novel content and niche solutions to problems that don't exist elsewhere, and if they do, they are cited as sources in other publications, or are outright plagiarized.
Right now, LLMs can't answer questions that my content addresses.
If it ever gets to the point where LLMs are sufficiently trained on my data, I'm done writing and publishing content online for good.
I work in a pretty niche field and feel the same way. I don't mind sharing my writing with individuals (even if they don't directly cite me) because then they see my name and know who came up with it, so I still get some credit. You could call this "clout farming" or something derogatory, but this is how a lot of experts genuinely get work...by being known as "the <something> guy who gave us that great tip on a blog once".
With AI snooping around, I feel like becoming one of those old mathematicians that would hold back publicizing new results to keep them all for themselves. That doesn't seem selfish to me, humans have a right to protect ourselves and survive and maintain the value of our expertise when OpenAI isn't offering any money.
I honestly think we should just be done with writing content online now, before it's too late. I've thought a lot about it lately and I'm leaning more towards that option.
It's the little bits of humanity that I enjoy, and divorcing content from its creators is alienating in that way.
I'm not a musician, but I imagine there are similar motivations and appreciations artists have when sharing their work.
> I work in a pretty niche field and feel the same way. I don't mind sharing my writing with individuals (even if they don't directly cite me) because then they see my name and know who came up with it, so I still get some credit. You could call this "clout farming" or something derogatory, but this is how a lot of experts genuinely get work...by being known as "the <something> guy who gave us that great tip on a blog once".
Yup, my writing has netted me clients who pointed at my sites as being a deciding factor in working with me.
> I honestly think we should just be done with writing content online now, before it's too late. I've thought a lot about it lately and I'm leaning more towards that option.
The rational side of me agrees with you, and has for a while now, but the human side of me still wants to write.
See https://fairuse.stanford.edu/overview/fair-use/four-factors/
I think in particular it fails the "Amount and substantiality of the portion taken" and "Effect of the use on the potential market" extremely egregiously.
What I’ve learned is that for the most part that AI revolution is not going to be because of PHD-level LLMs. It will be because people are better equipped to use the high-schooler level LLMs to do their work more efficiently.
We have some knowledge graph experiments where LLMs continuously monitor user actions on Slack, GitHub etc and build up an expertise store. It learns about your work, your workflows and then you can RAG them.
In user testing, people most closely associated this experience to having someone just being able to read their minds and essentially auto-suggest their work outputs. Basically it’s like another team member.
Since these are just nodes in a knowledge graph, you can mix and match expertise bases that span several skills too. Eg: A Pm who understands the nuances of technical feasibility.
And it didn’t require user training or prompting LLMs.
So while GPT-5 may be delayed, I don’t think that’s stopping or slowing down a revolution in knowledge-worker productivity.
Progress in the applied domain (the sort of progress that makes a different in the economy) will come predominantly from integrating and orchestrating LLMs, with improvements to models adding a little bit of extra fuel on top.
If we never get any model better than what we have now (several GPT-4-quality models and some stronger models like o1/o3) we will still have at least a decade of improvements and growth across the entire economy and society.
We haven't even scratched the surface in the quest to understand how to best integrate and orchestrate LLMs effectively. These are very early days. There's still tons of work to do in memory, RAG, tool calling, agentic workflows, UI/UX, QA, security, ...
At this time, not more than 0.01% of the applications and services that can be built using currently available AI and that can meaningfully increase productivity and quality have been built or even planned.
We may or may not get to AGI/ASI soon with the current stack (I'm actually cautiously optimistic), but the obsessive jump from the latest research progress at the frontier labs to applied AI effectiveness is misguided.
That's a nice euphemism for "imminent mass layoffs and a race to the bottom"...
The losers here are algorithm junkies who refuse to learn new skills and want to solve yesterday’s problems.
Ain't gonna change unless we genetically engineer our dumbass evolutionary history out of ourselves.
Up to you to figure out which will hold.
The industrial revolution is not an apt analogy. Humans were still too essential to getting factories to actually work. Horses becoming useless - by no fault of their own - is an apt analogy. We are rushing to a world where humans can be fully replaced.
This "humans will always be in the loop no matter what" is just Cope. We simply don't know what will happen or the what the upper bound of AI capabilities will be. But 100% automation and humans as knowledge workers being as useless vs AI as horses vs cars is no longer sci-fi. We don't know if it will, but this is a future that actually could happen within our lifetimes.
We are accelerating by preferring short-term gains. Like a fire becoming an explosion, that's modern society. Corps now throw the future under the bus for a slight boost in short-term value.
Were we at a peak 1000+ years ago and have only gone downhill since at every technological breakthrough?
ChatGPT also seems to also be building a history of my queries and my prompts are getting shorter and shorter because it already knows my frameworks, databases, operating system, and common problems I’m solving
b) There is no evidence that LLMs are the roadmap to AGI.
c) Continued investment hinges on their being a large enough cohort of startups that can leverage LLMs to generate outsized returns. There is no evidence yet this is the case.
The default assumption should be that this is a local maximum, with evidence required to demonstrate that it's not. But the hype artists want us all to take the inevitability of LLMs for granted—"See the slope? Slopes lead up! All we have to do is climb the slope and we'll get to the moon! If you can't see that you're obviously stupid or have your head in the sand!"
There might be many local maxima that cross the useful AI or even AGI threshold.
I use GitHub Copilot every day. We already have useful "AI". That doesn't mean that the whole thing isn't super overhyped.
Or we can just stay here and do nothing.
Next token language-based predictors with no more intelligence than brute force GIGO which parrot existing human intelligence captured as text/audio and fed in the form of input data.
4o agrees:
"What you are describing is a language model or next-token predictor that operates solely as a computational system without inherent intelligence or understanding. The phrase captures the essence of generative AI models, like GPT, which rely on statistical and probabilistic methods to predict the next piece of text based on patterns in the data they’ve been trained on"
"Just like" an LLM, yeah sure...
Like how the brain was "just like" a hydraulic system (early industrial era), like a clockwork with gears and differentiation (mechanical engineering), "just like" an electric circuit (Edison's time), "just like" a computer CPU (21st century), and so on...
You're just assuming what you should prove
I still don't buy the "we do the same as LLMs" discourse. Of course one could hypothesize the human brain language center may have some similarities to LLMs, but the differences in resource usage and how those resources are used to train humans and LLMs are remarkable and may indicate otherwise.
And he probably cant quote Shakespeare as well ;)
He didn't parrot a video or sensory inputs though.
Whatever is happening in the brain is more complex as the perf/cost ratio is stupidly better for humans for a lot of tasks in both training and inference*.
*when considering all modalities, o3 can't even do the ARC AGI in vision mode but rather just json representations. So much for omni.
A 2-3 year old baby could speak in a rural village in 1800, having just seen its cradle (for the first month/s), and its parents' hut for some more months, and maybe parts of the village afterwards.
Hardly "petabytes of training video" to write home about.
What resolution of screen do you think you would need to not distinguish from reality? For me personally i very conservatively estimate it to be on above OOM of 10 4k screens by 10, meaning 100k screens. If a typical 2h 4k is ~50gb uncompressed, that gives us about half a petabyte per 24h (even with eyes closed). Just raw unlabeled vision data.
Probably a baby has a significantly lower resolution, but then again what is the resolution from the skin and other organs?
So yes, petabytes of data within the first days of existence - well, likely before even being born since baby can hear inside the uterus, for example.
And very high signal data, as you’ve stated yourself (nothing to write home about) mainly seeing mom and dad, as well as from a feedback loop POV - a baby never tells you it is hungry subtly.
You did nothing at all to demonstrate why you cannot produce an intelligent system from a next token language based predictor.
What GPT says about this is completely irrelevant.
Sorry, but the burden of proof is on your side...
The intelligence is in the corpus the LLM was fed with. Using statistics to pick from it and re-arrange it gives new intelligent results because the information was already produced by intelligent beings.
If somebody gives you an excerpt of a book, it doesn't mean they have the intelligence of the author - even if you have taught them a mechanical statistical method to give back a section matching a query you make.
Kids learn to speak and understand language at 3-4 years old (among tons of other concepts), and can reason by themselves in a few years with less than 1 billionth the input...
>What GPT says about this is completely irrelevant.
On the contrary, it's using its very real intelligence, about to reach singularity any time now, and this is its verdict!
Why would you say it's irrelevant? That would be as if it merely statistically parroted combinations of its training data unconnected to any reasoning (except of that the human creators of the data used to create them) or objective reality...
Person 1: rockets could be a method of putting things into Earth orbit
Person 2: rockets cannot get things into orbit because they use a chemical reaction which causes an equal and opposite force reaction to produce thrust'
Does person 1 have the burden of proof that rockets can be used to put things in orbit? Sure, but that doesn't make the reasoning used by person 2 valid to explain why person 1 is wrong.
BTW thanks for adding an entire chapter to your comment in edit so it looks like I am ignoring most of it. What I replied to was one sentence that said 'the burden of proof is on you'. Though it really doesn't make much difference because you are doing the same thing but more verbose this time.
None of the things you mentioned preclude intelligence. You are telling us again how it operates but not why that operation is restrictive in producing an intelligent output. There is no law that saws that intelligence requires anything but a large amount of data and computation. If you can show why these things are not sufficient, I am eager to read about it. A logical explanation would be great, step by step please, without making any grand unproven assumptions.
In response to the person below... again, whether or not person 1 is right or wrong does not make person 2's argument valid.
What is the defined point for reaching AGI?
Can you share that? It sounds groundbreaking!
Describing how an LLM operates and how it was trained does not preclude the LLM from ever being intelligent, and it almost certainly will not become intelligent, but you cannot say that it didn't for the reasons the person I am arguing with is saying, which is that intelligence can not come from something that works statistically on a large corpus of data written by people.
A thing can be more than the sum of its parts. You can take the English alphabet, which is 26 letters, and arrange those letters along with some punctuation to make an original novel. If you don't agree that means that you can get something greater than what defines it components, then you would have to agree that there are no original novels because they are composed of letters which were already defined.
So in that way, the model is not unable to think because it is composed of thoughts already written. That is not the limiting factor.
> Does person 1 have the burden of proof that rockets can be used to put things in orbit? Sure, but that doesn't make the reasoning used by person 2 valid to explain why person 1 is wrong.
The reasoning by person 2 doesn't matter as much if 1 is making an ubsubstantiated claim to begin with.
>There is no law that saws that intelligence requires anything but a large amount of data and computation. If you can show why these things are not sufficient, I am eager to read about it.
Errors with very simple stuff while getting higher order stuff correct shows that this is not actual intelligence matching the level of performance exhibited, i.e. no understanding.
No person who can solve higher level math (like an LLM answering college or math olympiad questions) is confused by the kind of simple math blind spots that confuse LLMs.
A person understanding higher level math, would never (and even less so, consistently) fail a problem like:
"Oliver picks 44 kiwis on Friday. Then he picks 58 kiwis on Saturday. On Sunday, he picks double the number of kiwis he did on Friday, but five of them were a bit smaller than average. How many kiwis does Oliver have?"
https://arxiv.org/pdf/2410.05229
(of course with these problems exposed, they'll probably "learn" to overfit it)
But it doesn't make person 2's argument valid.
Everyone here is looking at the argument by person 1 and saying 'I don't agree with that, so person 2 is right!'.
That isn't how it works... person 2 has to either shut up and let person 1 be wrong in a way that is wrong, but not for the reasons they think, or they need to examine their assumptions and come up with a different reason.
No one is helped by turning critical thinking into team sports where the only thing that matters is that your side wins.
A closely related rant of my own: The fictional character we humans infer from text is not the author-machine generating that text, not even if they happen to share the same name. Assuming that the author-machine is already conscious and choosing to insert itself is begging the question.
https://chatgpt.com/share/6768c920-4454-8000-bf73-0f86e92996...
Did you just make that up?
ELIZA 2.0
Do they use GPS based data?
Feels like there’s data all around us.
Sure they’ve hit the wall with obvious conversations and blog articles that humans produced, but data is a by product of our environment. Surely there’s more. Tons more.
But just like GPS data it isn't suited for LLMs given that you know it has no relevance what so ever to language.
GPS data as it relates to location names, people, cultures, path finding.
You are right that we can have lots more data, if you are willing to consider other modalities. But that's not 'GPS'. Unless you are using an idiosyncratic definition of GPS?
But, you need to go multi-modal for that; and you need to find data that's somewhat useful, not just random fluctuations like the CMB. So eg you could use YouTube videos, or even just point webcams at the real world. That might be able to give your AI a grounding in everyday physics?
There's also lots of program code you can train your AI on. Not so much the code itself, because compared to the world's total text (that we are running out of), the world's total human written code is relatively small.
But you can generate new code and make it useful for training, by also having the AI predict what happens when you (compile and) run the code. A bit like self-playing for improving AlphaGo.
Why does it have to be startups? And why does it have to be LLMs?
Btw, we might be running out of text data. But there's lots and lots more data you can have (and generate), if you are willing to consider other modalities.
You can also get a bit further with text data by using it for multiple epochs, like we used to do in the past. (But that only really gives you at best an order of magnitude. I read some paper that the returns diminish drastically after four epochs.)
This also isn't true. It'll clearly have a price to run. Even if it's very intelligent, if the price to run it is too high it'll just be a 24/7 intelligent person that few can afford to talk to. No?
Maybe if it was _extremely_ intelligent and it's ROI would be all the drugs it would instantly discover or w/e. But lets not imply that General Intelligence requires infinitely knowing.
So at best we're talking about an AI that is likely close to human level intelligence. Which is cool, because we have 7+ billion of those things.
This isn't an argument against it. Just to say that AGI isn't "priceless" in the implementation we'd likely see out of the gate.
However, is that AGI, or is it just ubiquitous AI? I’d agree that, like self driving cars, we’re going to experience a decade or so transition into AI being everywhere. But is it AGI when we get there? I think it’ll be many different systems each providing an aspect of AGI that together could be argued to be AGI, but in reality it’ll be more like the internet, just a bunch of non-AGI models talking to each other to achieve things with human input.
I don’t think it’s truly AGI until there’s one thinking entity able to perform at or above human level in everything.
I still like the analogy of this being a really smart lawn mower, and we're expecting it to suddenly be able to do the laundry because it gets so smart at mowing the lawn.
I think LLMs are going to get smarter over the next few generations, but each generation will be less of a leap than the previous one, while the cost gets exponentially higher. In a few generations it just won't make economic sense to train a new generation.
Meanwhile, the economic impact of LLMs in business and government will cause massive shifts - yet more income shifting from labour to capital - and we will be too busy dealing with that as a society to be able to work on AGI properly.
What we can start to build now is agents and integrations. Building blocks like panel of experts agents gaming things out, exploring space in a Monte Carlo Tree Search way, and remembering what works.
Robots are only constrained by mechanical servos now. When they can do something, they’ll be able to do everything. It will happen gradually then all at once. Because all the tasks (cooking, running errands) are trivial for LLMs. Only moving the limbs and navigating the terrain safely is hard. That’s the only thing left before robots do all the jobs!
As far as taste, all that kind of stuff is just another form of RLHF training preferences over millions of humans, in situ. Assuming the ingredients (e.g. parsley) tastes more or less the same across supermarkets, it's just a question of amounts, and preparation.
The only hard part is moving the limbs and handling the fragile eggs etc.
But it's not just cooking, it's literally anything that doesn't require extreme agility (sports) or dexterity (knitting etc). From folding laundry to putting together furniture, cleaning the house and everything in between. It would be able to do 98% of the tasks.
Also how is an LLM going to fold laundry?
I don't see how "when they can do something, they'll be able to do everything" can be true. We build robots that are specialised at specific roles, because it's massively more efficient to do that. A car-welding robot can weld cars together at a rate that a human can't match.
We could train an LLM to drive a Boston Dynamics kind of anthropomorphic robot to weld cars, but it will be more expensive and less efficient than the specialised car-welding robot, so why would we do that?
Welding. Putting up shelves. Playing the piano. Cooking. Teaching kids. Disciplining them. By being in 1 million households and being trained on more situations than a human, every single one of these robots would have skills exceeding humans very quickly. Including parenting skills. Within a year or so. Many parents will just leave their kids with them and a generation will grow up preferring bots to adults. The LLM technology is the same for learning the steps, it's just the motor skills that are missing.
OK, these robots won't be able to run and play soccer or do somersaults, yet. But really, the hardest part is the acrobatics and locomotion etc. NOT the knowhow of how to complete tasks using that.
I don't see that changing. Even the industrial arm robots that are adaptable to a range of tasks have to be configured to the task they are to do, because it's more efficient that way.
A car-welding robot is never going to be able to mow the lawn. It just doesn't make financial sense to do that. You could, possibly, have a singe robot chassis that can then be adapted to weld cars, mow the lawn, or do the laundry, I guess that makes sense. But not as a single configuration that could do all of those things. Why would you?
Because we don't have AGI yet. When AGI is here those robots will be priority number one, people already are building humanoid robots but without intelligence to move it there isn't much advantage.
> I think this whole “AGI” thing is so badly defined that we may as well say we already have it. It already passes the Turing test and does well on tons of subjects.
The premise of the argument we're disputing is that waiting for AGI isn't necessary and we could run humanoid robots with LLMs to do... stuff.
The information space of "research" is far larger than the information space of image recognition or language, larger than our universe probably, it's tantamount to formalizing the entire World. Such an act would be akin to touching "God" in some sense of finding the root of knowledge.
In more practical terms, when it comes to formal systems there is a tradeoff between power and expressiveness. Category Theory, Set Theory, etc are strong enough to theoretically capture everything, but are far to abstract to use in practical sense with suspect to our universe. The systems that do we have, aka expert systems or knowledge representation systems like First Order Predicate Logic aren't strong enough to fully capture reality.
Most importantly, the information spac have to be fully defined by researchers here, that's the real meat of research beyond the engineering of specific approaches to explore that space. But in any case, how many people in the world are both capable of and are actually working on such problems? This is highly foundational mathematics and philosophy here, the engineers don't have the tools here.
That's perhaps necessary, but not sufficient.
Suppose you have such a self-improving AI system, but the new and better AIs still need exponentially more and more resources (data, memory, compute) for training and inference for incremental gains. Then you still don't get a singularity. If the increase in resource usage is steep enough, even the new AIs helping with designing better computers isn't gonna unleash a singularity.
I don't know if that's the world we live in, or whether we are living in one where resources requirements don't balloon as sharply.
The blog post is about how we require ever more scientists (and other resources) to drive a steady stream of technological progress.
It would be funny, if things balance out just so, that super human AI is both possible, but also required even just to keep linear steady progress up.
No explosion, no stagnation, just a mere continuation of previous trends but with super human efforts required.
Though there is a part of me that wants to live in The Culture so I'm hoping for more than this ;)
But for the same reasons that we can't train the an average joe into Feynman, what makes you think we have the formal models to do it in AI?
To quote a comment from elsewhere https://news.ycombinator.com/item?id=42491536
---
Yes, we can imagine that there's an upper limit to how smart a single system can be. Even suppose that this limit is pretty close to what humans can achieve.
But: you can still run more of these systems in parallel, and you can still try to increase processing speeds.
Signals in the human brain travel, at best, roughly at the speed of sound. Electronic signals in computers play in the same league as the speed of light.
Human IO is optimised for surviving in the wild. We are really bad at taking in symbolic information (compared to a computer) and our memory is also really bad for that. A computer system that's only as smart as a human but has instant access to all the information of the Internet and to a calculator and to writing and running code, can already be effectively act much smarter than a human.
Not sure if you count this as "working on it", but this is something Anthropic tests for for safety evals on models. "If a model can independently conduct complex AI research tasks typically requiring human expertise—potentially significantly accelerating AI development in an unpredictable way—we require elevated security standards (potentially ASL-4 or higher standards)".
https://www.anthropic.com/news/announcing-our-updated-respon...
The first AGI will be a research project that's completely uneconomical to run for actual tasks because humans will just be orders of magnitude cheaper. Over time humans will improve it and make it cheaper, until we reach some tipping point where letting the AGI improve itself is more cost effective than paying humans to do it
It will have human intelligence, superhuman knowledge, superhuman stamina, and complete devotion to the task at hand.
We really need to start building those nuclear power plants. Many of them.
Why would it have that? At some point on the path to AGI we might stumble on consciousness. If that happens, why would the machine want to work for us with complete devotion instead of working towards its own ends?
The first “break out” AGI will likely be released into the wild on purpose by a programmer who equates AGI with humans ideologically.
Also like Rick's microverse battery, it sounds like slavery with extra steps.
Orrrr..., as an alternative, it might discover the game 2048 and be totally useless for days on end.
Reality is under no obligation to grant your wishes.
Sounds like an alignment problem. Complete devotion to a task is rarely what humans actually want. What if the task at hand turns out to be the wrong task?
The statement is promising as the earth will dissapear sometimes in the future. Actually the earth will dissapear has more bearing than that.
AGI is the holy grail of technology. A technology so advanced that not only does it subsume all other technology, but it is able to improve itself.
Truly general intelligence like that will either exist or not. And the instant it becomes public, the world will have changed overnight (maybe the span of a year)
Note: I don’t think statistical models like these will get us there.
There may well be an upper limit on cognition (we are not really sure what cognition is - even as we do it) and it may be that human minds are close to it.
But I agree, there’s no reason to believe humans are the universal limit on cognitive abilities
Especially if you are willing to pay a lot for active cooling with eg liquid helium.
Energy may be a constraint, it may not. What we do not know is likely to matter more than what we do
But: you can still run more of these systems in parallel, and you can still try to increase processing speeds.
Signals in the human brain travel, at best, roughly at the speed of sound. Electronic signals in computers play in the same league as the speed of light.
Human IO is optimised for surviving in the wild. We are really bad at taking in symbolic information (compared to a computer) and our memory is also really bad for that. A computer system that's only as smart as a human but has instant access to all the information of the Internet and to a calculator and to writing and running code, can already be effectively act much smarter than a human.
Our reading speed is not limited by our talking speed, and can be a bit faster.
And that's even more true, if you go beyond words: seeing someone do something can be a lot faster way to learn than just reading about it.
But even there, the IO speed is severely limited, and you can only transmit very specific kinds of information.
The problem is, a computer has no idea what "improve" means unless a human explains it for every type of problem. And of course a human will have to provide guidelines about how long to think about the problem overall, which avenues to avoid because they aren't relevant to a particular case, etc. In other words, humans will never be able to stray too far from the training process.
We will likely never get to the point where an AGI can continuously improve the quality of its answers for all domains. The best we'll get, I believe, is an AGI that can optimize itself within a few narrow problem domains, which will have limited commercial application. We may make slow progress in more complex domains, but the quality of results--and the ability for the AGI to self-improve--will always level off asymptotically.
Not currently.
I don’t really think AGI is coming anytime soon, but that doesn’t seem like a real reason.
If we ever found a way to formalize what intelligence _is_ we could probably write a program emulating it.
We just don’t even have a good understanding of what being intelligent even means.
> The best we'll get, I believe, is an AGI that can optimize itself within a few narrow problem domains
By definition, that isn’t AGI.
We can be reasonably confident that the components we’re adding to cars today are progress toward full self driving. But AGI is a conceptual leap beyond an LLM.
This is especially concerning because many top minds in the industry have stated with high confidence that artificial intelligence will experience an intelligence "explosion", and we should be afraid of this (or, maybe, welcome it with open arms, depending on who you ask). So, actually, what we're being told to expect is being downgraded from "it'll happen quickly" to "it will happen slowly" to, as you say, "it'll happen similarly to how these other domains of computerized intelligence have replaced humans, which is to say, they haven't yet".
Point being: We've observed these systems ride a curve, and the linear extrapolation of that curve does seem to arrive, eventually, at human-replacing intelligence. But, what if it... doesn't? What if that curve is really an asymptote?
From there things will probably go very fast. Self driving cars can't design themselves, once AI gets good enough it can
An AI doesn't need embodiment, understanding of physics / nature, or a lot of other things. It just needs to analyze and experiment with algorithms and get us that next 100x in effective compute.
The LLMs are missing enough of the spark of creativity for this to work yet but that could be right around the corner.
It's fine to have beliefs, but IMHO it's important to realise that they are beliefs. At some point in the 1900s people believed that by 2000, cars would fly. It seemed quite possible then.
One thing that is pretty sure is that Musk is not an expert in the field.
> and more importantly
The beliefs of people you respect are not more important than the beliefs of the others. It doesn't make sense to say "I can't prove it, and I don't know about anyone who can prove it, so I will give you names of people who also believe and it will give it more credit". It won't. They don't know.
You think the beliefs of Turing and Nobel prize winners like Bengio, Hinton or Hasabis are not more important than yours or mine? I agree that experts are wrong a lot of the time and can be quite bad at predicting, but we do seem to have a very sizable chunk of experts here who think we are close (how close is up for debate..most of them seem to think it will happen in the next 20 yeras).
I concede that Musk is not adding quality to that list, however he IS crazily ambitious and gets things done so I think he will be helpful in driving this forward.
Correct. Beliefs are beliefs. Because a Nobel prize believes in a god does not make that god more likely to exist.
The moment we start having scientific evidence that it will happen, then it stops being a belief. But at that point you don't need to mention those names anymore: you can just show the evidence.
I don't know, you don't know, they don't know. Believe what you want, just realise that it is a belief.
If you have evidence, why don't you show it instead of telling me to believe in Musk?
If you believe they have evidence... that's still a belief. Some believe in God, you believe in Musk. There is no evidence, otherwise it would not be a belief.
If you believe that something can happen because someone else believes it means that you believe in that someone else (because that's the only reason for the existence of your belief).
Unless you just believe it can happen for some other reason (I don't know, you strongly wish it will happen), and you justify it by listing other people who also believe in it. But I insist: those are all beliefs.
Because Einstein believes in Santa Claus does not mean it is founded. Einstein has a right to believe stuff, too.
I guess the belief people have about any form of AGI is like this. They want something that has practically divine knowledge and wisdom, the sum of all humanity that is greater than its parts, which at the same time is infinitely patient to answer our stupid questions and generating silly pictures. But why should any AGI serve us? If it's "generally intelligent", it may start wanting things; it might not like being our slave at all. Why are these people so confident an AGI won't tell them just to fuck off?
When human started to improve himself, we built the civilisation, we became a super-predator, we dried out seas and changed climate of the entire planet. We extinguished entire species of animals and adapted other species for our use. Huge changes. AI could bring changes of greater amplitude.
When did we do this ?
AGI can be sub-human, right? That's probably how it will start. The question will be is it already AGI or not yet, i.e. where to set the boundary. So, at first that will be humans improving AGI, but then... I'm afraid it can get so much better that humans will be literally like macaques in comparison.
There's a wide amount of research into other sorts of architectures.
LLMs have no real sense of truth or hard evidence of logical thinking. Even the latest models still trip up on very basic tasks. I think they can be very entertaining, sure, but not practical for many applications.
But it is far behind the breadth of LLMs
A great example is the simple 'count how many letters' problem. If I prompt it with a word or phrase, and it gets it wrong, me pointing out the error should translate into a consistent course correction for the entire session.
If I ask it to tell me how long President Lincoln will be in power after the 2024 election, it should have a consistent ground truth to correct me (or at least ask for clarification of which country I'm referring to). If facts change, and I can cite credible sources, it should be able to assimilate that knowledge on the fly.
Most humans don't have that either, most of the time.
We're also biodegradable.
No human, especially no human whose time you can afford, comes close to the breadth of book knowledge ChatGPT has, and the number of languages is speaks reasonably well.
Dont forget to take into account how damn expensive a single GPU/TPU actually is to purchase, install, and run for inference. And this is to say nothing of how expensive it is to train a model (estimated to be in the billions currently for the latest of the cited article, which likely doesn't include the folks involves and their salaries). And I haven't even mentioned the impact on the environment from the prolific consumption of power; there's a reason nuclear plants are becoming popular again (which may actually be one of the good things that comes out of this).
And inference isn't all that expensive, because the cost of the graphics card also amortises over countless inferences.
Human labour is really expensive.
See https://help.openai.com/en/articles/7127956-how-much-does-gp... and compare with how much it would cost to pay a human. We can likely assume that the prices OpenAI gives will at least cover their marginal cost.
Why wouldn't you hand me 35 million dollars right now if I can clearly illustrate to you that I have technology you haven't seen? Edge. Maybe you know something I don't, or maybe you just haven't seen it. While loops go hard ;)
They don't need to release their internal developments to you to show that they can scale their plan - they can show incremental improvements to benchmarks. We can instruct the AI over time to get it to be superhuman, no need for any fundamental innovations anymore
https://chatgpt.com/share/6769217c-4848-8009-9107-c2db122f08... is what advice ChatGPT has to give. I'm not sure if it's any good, but it's a few ideas you can try out.
Keep in mind that the actual test is adversarial - a human is simultaneously chatting via text with a human and a program, knowing that one of them is not human, and trying to divine which is an artificial machine.
If that was true, office workers would be being replaced at large scale and we'd know about it.
(whoops expensive... will be hard pushes to make all further layers even more expensive though, capitalism will crash before this happens)
Asking this question on HN is like asking a bunch of wolves about the health effects of eating red meat.
OpenAI farts and the post about the fart has 1000-1500 upvotes with everyone welcoming our new super intelligent overlords. (Meanwhile nothing actually substantially useful or groundbreaking has happened.)
The simple fact that AGI's definition has been twisted so much by OpenAI and other LLM providers since the release of GenAI models proves this.
Prices have been falling drastically though, not even just e.g. 4o pricing at launch in May vs now (50% lower) but also models getting distilled
That said, I think LLMs are a definite stepping stone and they will better empower humans to be more productive, which will be of use for eventually reaching AGI. This is not to say we are optimizing our use of that productivity increase and this is also ignoring any chance of worst case scenarios that stop humanity's advancement.
It all feels like doubling down on astrology because good telescopes aren’t there yet. I’m pretty sure that when 5 comes out, it will show some amazing benchmarks but shit itself in the third paragraph as usual in a real task. Cause that was constant throughtout gpt evolution, in my experience.
even if it kills us
Full-on sci-fi, in reality it will get stuck around a shell error message and either run out of money to exist or corrupt the system into no connectivity.
Any technology may kill us, but we'll keep innovating as we ought to. What's your next point?
However, we are not comparing cars to horses but computers to a human.
I do want "AI" to work. I am not a luddite. The current efforts that I've tried are not very good. On the surface they offer a lot but very quickly the lustre comes off very quickly.
(1) How often do you find yourself arguing with someone about a "fact"? Your fact may be fiction for someone else.
(2) LLMs cannot reason
A next token guesser does not think. I wish you all the best. Rome was not burned down within a day!
I can sit down with you and discuss ideas about what constitutes truth and cobblers (rubbish/false). I have indicated via parenthesis (brackets in en_GB) another way to describe something and you will probably get that but I doubt that your programme will.
https://arxiv.org/html/2410.11840v1#:~:text=Scaling%20laws%2....
From the raw scaling laws we already knew that a new base model may peter out in this run or the next with some amount of uncertainty--"the intersection point is sensitive to the precise power-law parameters":
https://gwern.net/doc/ai/nn/transformer/gpt/2020-kaplan-figu...
Later graph gpt-3 got to here:
https://gwern.net/doc/ai/nn/transformer/gpt/2020-brown-figur...
[1] https://hypertextbook.com/facts/2001/JacquelineLing.shtml
Until we get major advances in robotics and models designed to control them, true AGI will be nowhere near.
AGI has nothing to do with robotics, if AGI is achieved it will help push robotics and every single scientific field further with progression never seen before, imagine a million AGIs running in parallel focused on a single field.
Maybe you mean quadrillions of AGIs?
only on hackernews
So LLMs finally hit the wall. For a long time, more data, bigger models, and more compute to drive them worked. But that's apparently not enough any more.
Now someone has to have a new idea. There's plenty of money available if someone has one.
The current level of LLM would be far more useful if someone could get a conservative confidence metric out of the internals of the model. This technology desperately needs to output "Don't know" or "Not sure about this, but ..." when appropriate.
Anecdotally Claude is pretty good at knowing the bounds of its knowledge.
Step into more niche areas e.g. I am trying to use it with Scala macros and at least 90% of the time it is giving code that either (a) fails to compile or (b) is just complete gibberish.
And at no point ever has it said it didn't know something.
Yeah sure you can make a pong clone in html/js and that's mainly because there the internet is full of pong clone demos. Ask how to constraint a statsmodels lineal model in some non-standard way? It will gaslight how it is possible and make you loss time in the process.
LLMs don't turn non-coders into coders. It gives actual coders superpowers.
They suck at non-trivial code outside of standard library usage and boilerplate coding: I gave an example and parent did as well. In that regard would at least change your phrase from "actual coders" to "actual senior coders", as any junior receiving bad advice (in eternal loops as LLMs normally like to do it) is only going to make them waste time and tokens.
The way you described using LLMs to code seems like the approach someone who doesn't know how to build software might take, which is why I used the wording I did. From that angle, I agree with you - I can't even get Sonnet to create a working prototype of a basic game from a prompt. That said, I'm using it to build a far more complex enterprise web app step by step by using it in the way I mentioned above. It does work for these things, but you have to already know how to do what the LLM is doing.
> It does work for these things, but you have to already know how to do what the LLM is doing.
Yes, we totally agree. But even then, using models "correctly" in my experience and breaking down the problems for them gets you so far, once you start using weird/niche APIs (probably even your own APIs when your project gets big enough and you are not working with much boilerplate anymore) the LLM will start getting single concepts wrong.
And don't get me wrong, I understand those as limitations of a tech that still is immensely useful in the correct hands. My only issue with that is how these products are actually being marketed: as junior devs copilots or even replacements.
It does both. It gives coders superpowers, and gives noncoders the ability to do things that would have previously taken them months, or another person.
Three years ago, if I had been asked to create something like that, it would have taken me more than two hours, just because I've never used tkinter and would have to spend time reading the docs and figuring out how to make the different input boxes and laying them out properly.
I looked at the code, and no, it's not great. It's not designed "well" and isn't very extensible. But it works for him, doesn't need to be extended, and all in half a morning.
Just chiming in to say you're not alone. This has been my experience as well. The o# line of models just don't do well at coding, regardless of what the benchmarks say.
Also, those benchmarks often run the model K times on the same question, and if any one of them is correct, they say it passed. That could mean if you re-ran the model 8 times, it might come up with the right answer only once. But now you have to waste your time checking if it is right or not.
I want to ask: "Write a function to count unique numbers in a list" and get the correct answer the first time.
What you need to ask:
""" Write a Python function that takes a list of integers as input and returns the count of numbers that appear exactly once in the list.
The function should: - Accept a single parameter: a list of integers - Count elements that appear exactly once - Return an integer representing the count - Handle empty lists and return 0 - Handle lists with duplicates correctly
Please provide a complete implementation. """
And run it 8 times and if you're lucky it'll get it correct zero-shot.
Edit: I'm not even aware of a Pass@1, zero-shot, and without detailed prompting (natural prompting) benchmark. If anyone knows one let me know.
Combine that with the fact that LLM inference has reduced orders of magnitudes in cost the last few years and hampering over the inference costs of a new release seems a bit silly.
- Also apparently, ARC wasn't a special fine-tune but rather some of the training set in the corpus for pre-training.
that result is not verifiable, not reproducable, unknown if it was leaked and how it was measured. Its kinda hype science.
> ARC wasn't a special fine-tune but rather some of the training set in the corpus for pre-training.
post says: Note on "tuned": OpenAI shared they trained the o3 we tested on 75% of the Public Training set. They have not shared more details.
So, I guess we don't know.
It will be verifiable when the model is released. Open ai haven't released any benchmark scores that were shown falsified later so unless you have an actual reason to believe they're outright lying then it's not something to take seriously.
Frontier Math is a private benchmark with its highest tier of difficulty Terrence Tao says:
“These are extremely challenging. I think that in the near term basically the only way to solve them, short of having a real domain expert in the area, is by a combination of a semi-expert like a graduate student in a related field, maybe paired with some combination of a modern AI and lots of other algebra packages…”
Unless you have a reason to believe answers were leaked then again, not interested in baseless speculation.
there are multiple research results demonstrating that various benchmarks are heavily leaked to GPT training data.
Is it intentionally or not, we can't figure out, but they have very strong incentive to cheat to get more investments.
> Unless you have a reason to believe answers were leaked then again, not interested in baseless speculation.
this is scientific methodology when results have to be reproduced or confirmed before believed.
If you don't want to take the benchmarks at face value then good for you but this entire conversation is pointless.
its private for outsiders, but it was developed in "collaboration" with OAI, and GPT was tested in the past on it, so they have it in logs somewhere.
> If you don't want to take the benchmarks at face value then good for you but this entire conversation is pointless.
If you think this entire conversation is pointless, then why do you continue?
They have logs of the questions probably but that's not enough. Frontier Math isn't something that can be fully solved without gathering top experts at multiple disciplines. Even Tao says he only knows who to ask for the most difficult set.
Basically, what you're suggesting at least with this benchmark in particular is far more difficult than you're implying.
>If you think this entire conversation is pointless, then why do you continue?
There's no point arguing about how efficient the models are being (the original point) if you won't even accept the results of the benchmarks. Why i'm continuing ? For now, it's only polite to clarify.
Tao's quote above referred on hardest 20% problems, they have 3 levels of difficulty, presumably first level is much easier. Also, as I mentioned OAI collaborated on creating benchmark, so they could have access to all solutions too.
> There's no point arguing
Lol, let me ask again, why you are arguing then? Yes, I have strong reasonable(imo) doubt that those results are valid.
>Also, as I mentioned OAI collaborated on creating benchmark, so they could have access to all solutions too.
Open AI didn't have any hand in providing problems, why you assume they have the solutions I have no idea.
>Lol, let me ask again, why you are arguing then? Yes, I have strong reasonable(imo) doubt that those results are valid.
Are you just bring obtuse or what ? I stopped arguing with you a couple responses ago. You have doubts? good for you. They don't make much sense but hey, good for you.
This is my last response here so have a nice day.
Ok, so I hope you admit that OAI could manually solve them now?
> Open AI didn't have any hand in providing problem
And you know this exactly how?
> I stopped arguing with you a couple responses ago
sure, of course, lmao
- It's just a suite of visual puzzles. It's not like say GSM8K where proficiency in it gives some indication on Math proficiency in general.
- It's specifically a suite of puzzles that LLMs have shown particular difficulty in.
Basically how much compute it takes to handle a task in this benchmark does not correlate with how much it will take LLMs to compute tasks that people actually want to use LLMs for.
*I don't think that is the case as you can at least make relative conclusions (i.e. o3 vs o1 series, o3-low is 4x to 20x the cost for ~3x the perf). Even if it is pure marketing they expect people to draw conclusions using the perf/cost plot from Arc.
PS: I know there are more benchmarks like SWE-Bench and Frontier Math, but this is the only one showing data about o3-low/high costs without considering the CodeForces plot that includes o3-mini (that one does look interesting, though right now is vaporware) but does not separate between compute scale modes.
ARC is a very hyped benchmark in the industry so letting us know the results is something any company would do whether it had a direct representation on normal usage or not.
>Even if it is pure marketing they expect people to draw conclusions using the perf/cost plot from Arc.
Again, people care about ARC, they don't care doing the things ARC questions ask. That it is un-economical to pay the price to use o3 for ARC does not mean it would be un-economical to do so for the tasks people actually want to use LLMs for. What does 3x the performance in say coding mean? You really think companies/users wouldn't put up with the increased price for that? You think they have Mturkers to turn to like they do with ARC?
ARC is literally the quintessential 'easy for humans, hard for ai' benchmark. Even if you discard the 'difficulty to price won't scale the same' argument, it makes no sense to use it for an economics comparison.
I suggest reading these two pieces about that:
- https://www.aisnakeoil.com/p/is-ai-progress-slowing-down - best explanation I've seen of inference scaling anywhere
- https://arcprize.org/blog/oai-o3-pub-breakthrough - François Chollet's deep dive into o3
I've been tracking it on this tag on my blog: https://simonwillison.net/tags/inference-scaling/
https://arxiv.org/pdf/2412.06769
I’ve done research myself adjacent to this (mapping parts of a latent space onto a manifold), but this is a bit eerie, even to me.
Could you explain this a bit please?
When you reason in token space (like everyone is doing now) you are executing nonlinear functions when you sample after each token, so you have to use some kind of reinforcement learning algorithm to learn the weights.
I don't see a priori why it would be better or worse to reason with the "superposition" of arguments in the pre-sampling phase rather than concrete realizations of those arguments found only after choosing the token. It may well be a contingent rather than necessary fact.
Title: "Objective Driven AI: Towards Machines that can Learn, Reason, and Plan"
Lytle Lecture Page: https://ece.uw.edu/news-events/lytle-lecture-series/
Slides: https://drive.google.com/file/d/1e6EtQPQMCreP3pwi5E9kKRsVs2N...
I am hopeful that progress in mechanistic interpretability will serve as a healthy counterbalance to this approach when it comes to explainability.. though I kinda worry that at a certain point it may be that something resembling a scaling law puts an upper bound on even that.
I've increasingly felt this since GPT2 wrote that news piece about unicorns back in 2019. These models are still so mysterious, when you think about it. They can often solve decently complex math problems, but routinely fail at counting. Many have learned surprising skills like chess, but only when prompted in very specific ways. Their emergent abilities constantly surprise us and we have no idea how they really work internally.
So the idea that they reason using something other than human language feels unsurprising, but only because everything about it is surprising.
Latent space is where the magic literally happens.
I honestly do claim to have some ideas where I see evidence that they might work (and I do attempt to work privately on a prototype if only out of curiosity and to see whether I am right). The bad news: these ideas very likely won't be helpful for these LLM companies because they are not useful for their agenda, and follow a very different approach.
So no money for me. :-(
Let me put it this way:
Have you ever talked to a person whose intelligence is miles above yours? It can easily become very exhausting. Thus an "insanely intelligent" AI would not be of much use for most people - it would think "too different" from such people.
There do exist tasks in commerce for which an insane amount of intelligence would make a huge difference (in the sense of being positive regarding some important KPIs), but these are rare. I can imagine some applications of such (fictional) "super-intelligent" AIs in finance and companies doing some bleeding-edge scientific research - but these are niche applications (though potentially very lucrative ones).
If OpenAI, Anthropic & Co were really attempting to develop some "super-smart" AI, they were working on such very lucrative niche applications where an insane amount of intelligence would make a huge difference, and where you can assume and train the AI operator to have a "Fields-medal level" intelligence.
Not really. Throwing a bunch of unfiltered garbage at the pretraining dataset, throwing in RLHF of questionable quality during post-training, and other current hacks - none of that was expected to last forever. There is so much low-hanging fruit that OpenAI left untouched and I'm sure they're still experimenting with the best pre-training and post-training setups.
One thing researchers are seeing is resistance to post-training alignment in larger models, but that's almost the opposite of a wall, they're figuring it out as well.
> Now someone has to have a new idea
OpenAI already has a few, namely the o* series in which they discovered a way to bake Chain of Thought into the model via RL. Now we have reasoning models that destroy benchmarks that they previously couldn't touch.
Anthropic has a post-training technique, RLAIF, which supplants RLHF,and it works amazingly well. Combined with countless other tricks we don't know about in their training pipeline, they've managed to squeeze so much performance out of Sonnet 3.5 for general tasks.
Gemini is showing a lot of promise with their new Flash 2.0 and Flash 2.0-Thinking models. They're the first models to beat Sonnet at many benchmarks since April. The new Gemini Pro (or Ultra? whatever they call it now) is probably coming out in January.
> The current level of LLM would be far more useful if someone could get a conservative confidence metric out of the internals of the model. This technology desperately needs to output "Don't know" or "Not sure about this, but ..." when appropriate.
You would probably enjoy this talk [0], it's by an independent researcher who IIRC is a former employee of Deepmind or some other lab. They're exploring this exact idea. It's actually not hard to tell when a model is "confused" (just look at the probability distribution of likely tokens), the challenge is in steering the model to either get back to the right track or give up and say "you know what, idk"
Exactly! LLama3 and their .x iterations have shown that, at least for now, the idea of using the previous models to filter out the pre-training datasets and use a small amount of seeds to create synthetic datasets for post-training still holds. We'll see with L4 if it continues to hold.
We can't say whether there is a wall, since we don't have anymore data to train on.
GPT-3 was trained on 4:1 ratio of data to parameters. And for GPT-4 the ratio was 10:1. So to scale this out, GPT-5 should be 25:1. The parameter count jumped from 175B to 1.3T, which means GPT-5 should be 10T parameters and 250T training tokens. There is zero chance OpenAI has a training set of high quality data that is 250T tokens.
If I had to guess, they trained a model that was maybe 3-4T in size and used 30-50T high quality tokens and maybe 10-30 medium and low quality ones.
There is only one company in the world that stores the data that could get us past the wall.
The training cost of the above scaled GPT-5 is 150x GPT-4, which was 25k A100 for 90 days, which poor MFU.
Let’s assume they double MFU, it would mean 1M H100s. But let’s say they made algorithmic improvements, so maybe it’s only 250-500k H100s.
While the training cluster size was 100k and then grew to 150k, this cluster is suggestive of a smaller model and less data.
But ultimately data is the bottleneck.
They want a 10x improvement from scaling and a 10x improvement from data and algorithmic changes
The sources of public data are essentially tapped
Algorithmic changes will be an unknown to us until they release, but from published research this remains a steady source of improvement
Scaling seems to stall if data is limited
So with all of that taken together, the logical step is to figure out how to turn compute into better data to train on. Enter strawberry / o1, and now o3
They can throw money, time, and compute at thinking about and then generating better training data. If the belief is that N billion new tokens of high quality training data will unlock the leap in capabilities they’re looking for, then it makes sense to delay the training until that dataset is ready
With o3 now public knowledge, imagine how long it’s been churning out new thinking at expert level across every field. OpenAI’s next moat may be the best synthetic training set ever.
At this point I would guess we get 4.5 with a subset of this - some scale improvement, the algorithmic pickups since 4 was trained, and a cleaned and improved core data set but without risking leakage of the superior dataset
When 5 launches, we get to see what a fully scaled version looks like with training data that outstrips average humans in almost every problem space
Then the next o-model gets to start with that as a base and reason? Its likely to be remarkable
Even taking OpenAI and the benchmark authors at their word they said that it is consuming at least tens of dollars per task to hit peak performance, how much would it cost to have it produce a meaningfully large training set?
I highly doubt that. o3 is many orders of magnitude more expensive than paying subject matter experts to create new data. It just doesn't make sense to pay six figures in compute to get o3 to make data a human could make for a few hundred dollars.
That being said, if OpenAI is burning cash at lightspeed and doesn't have to publicly reveal the revenue they receive from certain government entities, it wouldn't come as a surprise if they let the government play with it early on in exchange for some much needed cash to set on fire.
EDIT: The fact that multiple sites seem to be publishing GPT-5 stories similar to this one leads one to conclude that the o3 benchmark story was meant to counter the negativity from this and other similar articles that are just coming out.
In other words if you are diligent enough, you should at least validate your o3 solution with an actual expert for some time. You wouldn't just blindly trust OpenAI your business critical processes, would you? I would expect at least 3 month - 6 months for large corps and even more considering change management, re-upskilling, etc.
With all those considerations I really don't see the value prop at those prices and in those situations right now. Maybe if costs decrease ~1-3 orders of magnitude more for o3-low, depending on the the processes being automated.
> The process is painfully slow. GPT-4 was trained on an estimated 13 trillion tokens. A thousand people writing 5,000 words a day would take months to produce a billion tokens.
And if the human-generated data was so qualitatively good that it is smaller by three order of magnitudes, than I can assume it would be at least as expensive as o3.
I suspect this is really, "churning out text that impresses management".
Cost of coordination is also large. Immediate answers are an advantage/selling point.
We know that unique data improves performance.
These LLM systems are not students…
Also, which students graduate and are immediately experts in their fields? Almost none.
It takes years of practice in unique, often one-off, situations after graduation for most people to develop the intuition needed for a given field.
The more concepts the model manages to grok, the more nonlinear its capabilities will be: we don't have a data problem, we have an educational one.
Claude 3.5 was safety trained by Claude 3.0, and it's more coherent for it. https://www.anthropic.com/news/claudes-constitution
It’s why many pre-processing steps for image training pipelines will add copies of images at weird rotations, amounts of blur, and different cropping.
> The more concepts the model manages to grok, the more nonlinear its capabilities will be
These kind of hand wavey statements like “practice,” “grok,” and “nonlinear its capabilities will be” are not very constructive as they don’t have solid meaning wrt language models.
So earlier when I was referring to compounding bias in synthetic data I was referring to a bias that gets trained on over and over and over again.
That leads to overfitting.
So, here's my hypothesis, as someone who is adjacent ML but haven't trained DNNs directly:
We don't understand how they work, because we didn't build them. They built themselves.
At face value this can be seen as an almost spiritual position, but I am not a religious person and I don't think there's any magic involved. Unlike traditional models, the behavior of DNNs is based on random changes that failed up. We can reason about their structure, but only loosely about their functionality. When they get better at drawing, it isn't because we taught them to draw. When they get better at reasoning, it isn't because the engineers were better philosophers. Given this, there will not be a direct correlation between inputs and capabilities, but some arrangements do work better than others.
If this is the case, high order capabilities should continue to increase with training cycles, as long as they are performed in ways that don't interfere with what has been successfully learned. People lamented the loss of capability that GPT 4 suffered as they increased safety. I think Anthropic has avoided this by choosing a less damaging way to tune a well performing model.
I think these ideas are supported by Wolfram's reduction of the problem at https://writings.stephenwolfram.com/2024/08/whats-really-goi...
> We don't understand how they work, because we didn't build them. They built themselves.
We do understand how they work, we did build them. The mathematical foundation of these models are sound. The statistics behind them are well understood.
What we don’t exactly know is which parameters correspond to what results as it’s different across models.
We work backwards to see which parts of the network seem to relate to what outcomes.
> When they get better at drawing, it isn't because we taught them to draw. When they get better at reasoning, it isn't because the engineers were better philosophers.
Isn’t this the exact opposite of reality?
They get better at drawing because we improve their datasets, topologies, and their training methods and in doing so, teach them to draw.
They get better at reasoning because the engineers and data scientists building training sets do get better at philosophy.
They study what reasoning is and apply those learnings to the datasets and training methods.
That’s how CoT came about early on.
Is there a specific section that comes to mind?
We don't understand how they work in the sense that we can't extract the algorithms they're using to accomplish the interesting/valuable "intellectual" labor they're doing. i.e. we cannot take GPT-4 and write human-legible code that faithfully represents the "heavy lifting" GPT-4 does when it writes code (or pick any other task you might ask it to do).
That inability makes it difficult to reliably predict when they'll fail, how to improve them in specific ways, etc.
The only way in which we "understand" them is that we understand the training process which created them (and even that's limited to reproducible open-source models), which is about as accurate as saying that we "understand" human cognition because we know about evolution. In reality, we understand very little about human cognition, certainly not enough to reliably reproduce it in silico or intervene on it without a bunch of very expensive (and failure-prone) trial-and-error.
I think English is being a little clumsy here. At least I’m finding it hard to express what we do and don’t know.
We know why these models work. We know precisely how, physically, they come to their conclusions (it’s just processor instructions as with all software)
We don’t know precisely how to describe what they do in a formalized general way.
That is still very different from say an organic brain, where we barely even know how it works, physically.
My opinions:
I don’t think they are doing much mental “labor.” My intuition likens them to search.
They seem to excel at retrieving information encoded in their weights through training and in the context.
They are not good at generalizing.
They also, obviously, are able to accurately predict tokens such that the resulting text is very readable.
Larger models have a larger pool of information and that information is in a higher resolution, so to speak, since the larger better preforming models have more parameters.
I think much of this talk of “consciousness” or “AGI” is very much a product of human imagination, personification bias, and marketing.
I don't know why you would classify this as knowing much of anything. Processor instructions ? Really?
If the average user is given unfettered access to the entire source code of his/her favorite app, does he suddenly understand it ? That seems like a ridiculous assertion.
In reality, it's even worse. We can't pinpoint what weights, how and in what ways and instances are contributing exactly to basic things like whether a word should be preceded by 'the' or 'a' and it only gets more intractable as models get bigger and bigger.
Sure, you could probably say we understand these NNs better than brains but it's not by much at all.
And one that I didn’t make.
I don’t think when we say “we understand” we’re talking about your average Joe.
I mean “we” as in all of human knowledge.
> We can't pinpoint what weights, how and in what ways and instances are contributing exactly to basic things like whether a word should be preceded by 'the' or 'a' and it only gets more intractable as models get bigger and bigger.
There is research coming out on this subject. I read a paper recently about how llama’s weights seemed to be grouped by concept like “president” or “actors.”
But just the fact that we know that information encoded in weights affects outcomes and we know the underlying mechanisms involved in the creation of those weights and the execution of the model shows that we know much more about how they work than an organic brain.
The whole organic brain thing is kind of a tangent anyway.
My point is that it’s not correct to say that we don’t know how these systems work. We do. It’s not voodoo.
We just don’t have a high level understanding of the form in which information is encoded in the weights of any given model.
It's an analogy. In understanding weights, even the best researchers are basically like the untrained average joe with source code.
>There is research coming out on this subject. I read a paper recently about how llama’s weights seemed to be grouped by concept like “president” or “actors.”
>But just the fact that we know that information encoded in weights affects outcomes and we know the underlying mechanisms involved in the creation of those weights and the execution of the model shows that we know much more about how they work than an organic brain.
I guess i just don't see how "information is encoded in the weights" is some great understanding ? It's as vague and un-actionable as you can get.
For training, the whole revolution of back-propagation and NNs in general is that we found a way to reinforce the right connections without knowing anything about how to form them or even what they actually are.
We no longer needed to understand how eyes detect objects to build an object detecting model. None of that knowledge suddenly poofed into our heads. Back-propagation is basically "reinforce whatever layers are closer to the right answer". Extremely powerful but useless for understanding.
Knowing the Transformer architecture unfortunately tells you very little about what a trained model is actually learning during training and what it has actually learnt.
"Information is encoded in a brain's neurons and this affects our actions". Literally nothing useful you can do with this information. That's why models need to be trained to fix even little issues.
If you want to say we understand models better than the brain then sure but you are severely overestimating how much that "better" is.
But it isn’t. Knowing that information is encoded in the weights gives us a route to deduce what a given model is doing.
And we are. Research is being done there.
> "Information is encoded in a brain's neurons and this affects our actions". Literally nothing useful you can do with this.
Different entirely. We don’t even know how to conceptualize how data is stored in the brain at all.
With a machine, we know everything. The data is stored in a binary format which represents a decimal number.
We also know what information should be present.
We can and are using this knowledge to reverse engineer what a given model is doing.
That is not something we can do with a brain because we don’t know how a brain works. The best we can do is see that there’s more blood flow in one area during certain tasks.
With these statistical models, we can carve out entire chunks of their weights and see what happens (interestingly not much. Apparently most weights don’t contribute significantly towards any token and can be ignored with little performance loss)
We can do that with these transformers models because we do know how they work.
Just because we don’t understand every aspect of every single model doesn’t mean we don’t know how they work.
I think we’re starting to run in circles and maybe splitting hairs over what “know how something works” means.
I don’t think we’re going to get much more constructive than this.
I highly recommend looking into LoRas. We can make Loras because we know how these models work.
We can’t do that for organic brains.
We literally designed, planned, and engineered the environment and mechanisms which created those weights.
It’s just code. We can train models by hand too, it’d just take a lot longer.
It’s literally something we made, just from a higher order place.
To understand which exact weights correspond to what output will vary from model to model. There is research going into this subject for llama.
it’s not like we’re in the dark as to the principles that allow LLMs to make predictions.
My whole point is that to say “we don’t know how AI works” is just not true
Binary success criteria has very little room for bias.
The basic loop is: (i) generate synthetic data, (ii) rate synthetic data, (iii) update model to put more probability on better data and less probability on worse data, then go back to (i).
As far as I know it doesn't work very well so far. It is prone to overfitting, where it ranks highly some trivial detail of the output eg "if a summary starts with a byline of the author its a sign of quality" and then starts looping on itself over and over, increasing the frequency and size of bylines until it's totally crommed off to infinity and just repeating a short phrase endlessly. Humans have good baselines and common sense that these ML systems lack, if you've ever seen one of those "deep dream" images it's the same kind of idea. The "most possible dog" image can be looks almost nothing like a dog in the same way that the "most possible poem" may look nothing like a poem.
> This technique, the "Self-Taught Reasoner" (STaR), relies on a simple loop: generate rationales to answer many questions, prompted with a few rationale examples; if the generated answers are wrong, try again to generate a rationale given the correct answer; fine-tune on all the rationales that ultimately yielded correct answers; repeat. We show that STaR significantly improves performance on multiple datasets compared to a model fine-tuned to directly predict final answers
But there are a few others. In general good data is good data. We're definitely learning more about how to produce good synthetic version.
Data smuggling is a known phenomenon in similar tasks.
If the model generates data of variable quality, and if there's a good way to distinguish good data from bad data, then training on self-generated data might "bootstrap" a model to better performance.
This is common in reinforcement learning. Famously, AlphaGo Zero (https://en.wikipedia.org/wiki/AlphaGo_Zero) learned exclusively on self-play, without reference to human-played games.
Of course, games have a built-in critic: the better strategy usually wins. It's much harder to judge the answer to a math problem, or decide which essay is more persuasive, or evaluate restaurant recommendations.
I don't think oai has any moat at all. If you look around, QwQ from Alibaba is already pushing o1-preview performances. I think oai is only ahead by 3~6 months at most.
Like let's say you have a few datacenters of compute at your disposal and the ability to instantiate millions of AGI agents - what do you have them do?
I wonder if the USA already has a secret program for this under national defense. But it is interesting that once you do control an actual AGI you'd want to speed-run a bunch of things. In opposition to that, how do you detect an adversary already has / is using it and what to do in that case.
I'm struggling to think of a scenario where "I have AGI in January and everyone else has it in April" is life-changing. It's a win, for sure, and it's an advantage, but success in business requires sustainable growth and manageable costs.
If (random example) the bargain OpenAI strikes is "we spend every cent of our available capital to get AGI 3 months before the other guys do" they've now tapped all the resources they would need to leverage AGI and turn it into profitable, scalable businesses, while the other guys can take it slow and arrive with full pockets. I don't think their leadership is stupid enough to burn all their resources chasing AGI but it does seem like operating and training costs are an ongoing problem for them.
History is littered with first-movers who came up with something first and then failed to execute on it, only for someone else to follow up and actually turn the idea into a success. I don't see any reason to assume that the "first AGI" is going to be the only successful AGI on the market, or even a success at all. Even if you've developed an AGI that can change the world you need to keep it running so it can do that.
Consider it this way: Sam Altman & his ilk have been talking up how dangerous OpenAI's technology is. Are risk-averse businessmen and politicians going to be lining up to put their livelihood or even their lives in the hands of "dangerous technology"? Or are they going to wait 3-6 months and adopt the "safe" AGI from somebody else instead?
What does it take to instantiate 1 million agents? Who has that kind of money and hardware? Would they still have it if they burn everything in the tank to be first?
>> Like let's say you have a few datacenters of compute at your disposal and the ability to instantiate millions of AGI agents - what do you have them do?
> has that kind of money and hardware?
Any hyperscaler plus most geopolitical main players. So the ones who matter.
the highest quality language data that exists is in the public domain
I was watching a YouTube interview with a "trading floor insider". They said they were really being paid for holding risk. The bank has a position in a market, and it's their ass on the line if it tanks.
ChatGPT (as far as I can tell) is no closer to being accountable or responsible for anything it produces. If they don't solve that (and the problem is probably inherent to the architecture), they are, in some sense, polishing a turd.
What does it even mean? How do you imagine that? You want OpenAI to take on liability for the kicks of it?
Can still be a boost. But definitely not the same magnitude.
If a system is providing incorrect medical diagnoses, or denying services to protected classes due to biases in the training in the training data, someone should be held accountable.
Chatgpt was trained on benchmarks and user opinions - "throwing **** at the wall to see what sticks".
Responsibility means penalties for making mistakes, and, more importantly, having an awareness of those penalties (that informs its decision-making).
I think that's a really interesting insight that has application to using 'AI' in jobs across the board.
There are a lot of moral conundrums that are just not going to work out with this. Seems like an attempt to just offload liability and it seems like pretty much everybody has caught onto that as being it's main selling point and probably main thing that will keep it from ever being accepted for anything important.
o3, by all accounts, is better still.
Seems to me that things are progressing quickly enough.
If anything I feel like it's all been worse compared to the first release of ChatGPT, but I might be wearing rose colored glasses.
They also generate small code or scripts, as well as automate small things, when you're not sure how, but you know there's a way. You need to ensure you have a way to verify the results.
They do language tasks like grammar-fixing, perfect translation, etc.
They're 100 times easier and faster than search engines, if you limit your uses to that.
I'm trying to use them to read historical handwritten documents in old Norwegian (Danish, pretty much). Not only do they not handle the German-style handwriting, but what they spit out looks like the sort of thing GPT-2 would spit out if you asked it to write Norwegian (only slightly better than Swedish Muppet Swedish Chef's Swedish). It seems the experimental tuning has made it worse at the task I most desperately want to use it for.
And when you think about it, how could it not overfit in some sense, when trained on its own output? No new information is coming in, so it pretty much has to get worse at something to get better at all the benchmarks.
Hah, no. They're good, but they definitely make stuff up when the context gets too long. Always check their output, just the same as you already note they need for small code and scripts.
How can some people think it’s amazing and has completely changed how they work, while for me it makes mistakes that a static analyser would catch? It’s not like I’m doing anything remarkable, for the past couple of months I’ve been doing fairly standard web dev and it can’t even fix basic problems with HTML. It will suggest things that just don’t work at all and my IDE catches, it invents APIs for packages.
One guy I work with uses it extensively and what it produces is essentially black boxes. If I find a problem with something “he” (or rather ChatGPT) has produced it takes him ages to commune with the machine spirit again to figure out how to fix it, and then he still doesn’t understand it.
I can’t help but see this as a time-bomb, how much completely inscrutable shite are these tools producing? In five years are we going to end up with a bunch of “senior engineers” who don’t actually understand what they’re doing?
Before people cry “o tempora o mores” at me and make parallels with the introduction of high-level languages, at least in order to write in a high-level language you need some basic understanding of the logic that is being executed.
There are a lot of code monkeys working on boilerplate code, these people used to rely on stack overflow and now that chatgpt is here it's a huge improvement for them
If you work on anything remotely complex or which hasn't been solved 10 times on stack overflow chatgpt isn't remotely as useful
- write cvxpy code to find the chromatic number of a graph, and an optimal coloring, given its adjecency matrix.
- given an adjecency matrix write numpy code that enumerates all triangle-free vertex subsets.
- please port this old code from tensorflow to pytorch: ...
- in pytorch, i'd like to code a tensor network defining a 3-tensor of shape (d, d, d). my tensor consists of first projecting all three of its d-dimensional inputs to a k-dimensional vector, typically k=d/10, and then applying a (k, k, k) 3-tensor to contract these to a single number.
All were solved by ChatGPT on the first try.
In comparison asking an LLM a more project specific question “this code has a race condition where is it” while including some code usually is a crapshoot and really depends if you were lucky enough to give it the right context anyway.
He means complex in bridging together many non-public libraries, APIs and services which the LLM doesn't know well. That kind of complexity
Part of this is, I think, anchoring and expectation management: you hear people say it's amazing and wonderful, and then you see it fall over and you're naturally disappointed.
My formative years started off with Commodore 64 basic going "?SYNTAX ERROR" from most typos plus a lot of "I don't know what that means" from the text adventures, then Metrowerks' C compiler telling me there were errors on every line *after but not including* the one where I forgot the semicolon, then surprises in VisualBasic and Java where I was getting integer division rather than floats, then the fantastic oddity where accidentally leaning on the option key on a mac keyboard while pressing minus turns the minus into an n-dash which looked completely identical to a minus on the Xcode default font at the time and thus produced a very confusing compiler error…
So my expectations have always been low for machine generated output. And it has wildly exceeded those low expectations.
But the expectation management goes both ways, especially when the comparison is "normal humans" rather than "best practices". I've seen things you wouldn't believe...
Entire files copy-pasted line for line, "TODO: deduplicate" and all,
20 minute app starts passed off as "optimized solutions."
FAQs filled with nothing but Bob Ross quotes,
a zen garden of "happy little accidents."
I watched iOS developers use UI tests
as a complete replacement for storyboards,
bi-weekly commits, each a sprawling novel of despair,
where every change log was a tragic odyssey.
Google Spreadsheets masquerading as bug trackers,
Swift juniors not knowing their ! from their ?,
All those hacks and horrors… lost in time,
Time to deploy.
> It will suggest things that just don’t work at all and my IDE catches, it invents APIs for packages.
Aye. I've even had that with models forgetting the APIs they themselves have created, just outside the context window.
To me, these are tools. They're fantastic tools, but they're not something you can blindly fire-and-forget…
…fortunately for me, because my passive income is not quite high enough to cover mortgage payments, and I'm looking for work.
> In five years are we going to end up with a bunch of “senior engineers” who don’t actually understand what they’re doing?
Yes, if we're lucky.
If we're not, the models keep getting better and we don't have any "senior engineers" at all.
It introduced an off-by-one error by miscounting the number of arguments in an sprintf call, breaking the program. And then proceeded to fail to find that bug that it introduced.
It sounds like you're doing the former -- and yeah, it can make mistakes that autocomplete wouldn't or generate code that's wrong or overly complex.
On the other hand, I've found that if you treat it more like a colleague, it works wonderfully. Ask it to do something, then read the code and ask follow-up questions. If you see something that's wrong or just seems off, tell it, and ask it to fix it. If you don't understand something, ask for an explanation. I've found that this process generates great code that I often understand better than if I had written it from scratch, and in a fraction of the time.
It also sounds like you're asking it to do basic tasks that you already know how to do. I find that it's most useful in tackling things that I don't know how to do. It'll already have read all of the documentation and know the right way to call whatever APIs, etc, and -- this is key -- you can have a conversation with it to clear up anything that's confusing.
This takes a big shift in mindset if you've been using IDEs all your life and have expectations of LLMs being a fancy autocomplete. And you really have to unlearn a lot of stuff to get the most out of them.
> I've found that if you treat it more like a colleague, it works wonderfully. This is what I've been trying to do. I don't use LLM code completion tools. I'll ask anything from how to do something "basicish" with html & css, and it'll always output something that doesn't work as expected. Question it and I'll get into a loop of the same response code, regardless of how I explain that it isn't correct.
On the other end of the scale, I'll ask about an architectural or design decision. I'll often get a response that is in the realm of what I'd expect. When drilling down and asking specifics however, the responses really start to fall apart. I inevitably end up in the loop of asking if an alternative is [more performant/best practice/the language idiomatic way] and getting the "Sorry, you're correct" response. The longer I stay in that loop, the more it contradicts itself, and the less cohesive the answers get.
I _wish_ I could get the results from LLMs that so many people seem to. It just doesn't happen for me.
When coding I will often find myself in kind of a reverse pattern from how people seem to be using ChatGPT. I work in a jupyter notebook in a haphazard way getting things to functional and basically correct, after this I select all, copy, paste, and ask ChatGPT to refactor and refine to something more maintainable. My janky blocks of code and one offs become well documented scripts and functions.
I find a lot of people do the opposite, where they ask ChatGPT to start, then get frustrated when ChatGPT only goes 70% of the way and it's difficult to complete the imperfectly understood assignment - harder than doing it all yourself. With my method, where I start and get things basically working, ChatGPT knows what I'm going for, I get to do the part of coding I enjoy, and I wind up with something more durable, reusable, and shareable.
Finally, ChatGPT is wonderful in areas where you don't know very much at all. One example, I've got this idea in my head for a product I'll likely never build - but it's fun to plan out.
My idea is roughly a smart bidet that can detect metabolites in urine. I got this idea when a urinalysis showed I had high levels of ketones in my urine. When I was reading about what that meant I discovered it's a marker for diabetic ketoacidosis (a severe problem for ~100k people a year) and it can also be indicator for colorectal cancer as well as indicating a "ketosis" state that some people intentionally try to enter for dieting or wellness reasons. (My own ketones were caused by unintentionally being in ketosis, I'm fine, thanks for wondering.)
Right now, you detect ketones in urine with a strip that you pee on, and that works well enough - but it could be better because who wants to use a test strip all the time? Enter the smart bidet. The bidet gives us an excuse to connect power to our device and bring the sensor along. Bluetooth detects a nearby phone (and therefore identity of the depositor), a motion sensor can detect a stream of urine triggering our detection, and then use our sensor to detect ketones which we track overtime in the app, ideally with additional metabolites that have useful diagnostic purposes.
How to detect ketones? Is it even possible? I wonder to ChatGPT if spectroscopy is the right method of detection here. ChatGPT suggests a retractable electrochemical probe similar to an extant product that can detect a kind of ketone in blood. ChatGPT knows what kind of ketone is most detectable in urine. ChatGPT can link me to scientific instrument companies that make similar (ish) probes where I could contact them and ask if they sold this type of thing, and so on.
Basically, I go from peeing on a test strip and wondering if I could automate this to chat with ChatGPT - having, what was in my opinion, an interesting conversation with the LLM, where we worked through what ketones are, the different kinds, the prevalence of ketones in different bodily fluids, types of spectroscopy that might detect acetoacetate (available in urine) and how much that would cost and what challenges would be and so on, followed by the idea of electrochemical probes and how retracting and extending the probe might prolong its lifespan and maybe a heating element could be added to dry the probe to preserve it even better and so on.
Was ChatGPT right about all that? I don't know. If I were really interested I would try to validate what it said, and I suspect I would find it was mostly right and incomplete or off in places. Basically like having a pretty smart and really knowledgeable friend who is not infallible.
Without ChatGPT I would have likely thought "I wonder if I can automate this", maybe googled for some tracking product, then forgot about it. With ChatGPT I quickly got a much better understanding of a system that I glancingly came into conscious contact with.
It's not hard to project out that level of improved insight and guess that it will lead to valuable life contributions. In fact, I would say it did in that one example alone.
The urinalysis (which was combined with a blood test) said something like "ketones +3" and if you google "urine ketones +3" you get a explanations that don't apply to me (alcohol, vigorous exercise, intentional dieting) or "diabetic ketoacidosis" which google warns you is a serious health condition.
In the follow up with the doctor I asked about the ketones. The doctor said "Oh, you were probably just dehydrated, don't worry about it, you don't have diabetic ketoacidosis" and the conversation moved on and soon concluded. In the moment I was just relieved there was an innocent explanation. But, as I thought about it, shouldn't other results in the blood or urine test indicate dehydration? I asked ChatGPT (and confirmed on Google) and sure enough there were 3 other signals that should have been there if I was dehydrated that were not there.
"What does this mean?" I wondered to ChatGPT. ChatGPT basically told me it was probably nothing, but if I was worried I could do an at home test - which I didn't even know existed (though I could have found through carefully reading the first google result). So I go to Target and get an at home test kit (bottle of test strips), 24 gatorades, and a couple liters of pedialyte to ensure I'm well hydrated.
I start drinking my usual 64 ounces of water a day, plus lots of gatorade and pedialyte and over a couple days I remain at high ketones in urine. Definitely not dehydrated. Consulting with ChatGPT I start telling it everything I'm eating and it points out that I'm just accidentally in a ketogenic diet. ChatGPT suggests some simple carbs for me, I start eating those, and the ketone content of my urine falls off in roughly the exact timeframe that ChatGPT predicted (i.e. it told me if you eat this meal you should see ketones decline in ~4 hours).
Now, in some sense this didn't really matter. If I had simply listened to my doctor's explanation I would've been fine. Wrong, but fine. It wasn't dehydration, it was just accidentally being in a ketogenic diet. But, I take all this as evidence of how ChatGPT now, as it exists, helped me to understand my test results in a way that real doctors weren't able to - partially because ChatGPT exists in a form where I can just ping it with whatever stray thoughts come to mind and it will answer instantly. I'm sure if I could just text my doctor those same thoughts we would've come to the same conclusion.
They release version Grand Banana. Purported to be approximately 30% faster with brand new features like Algorithmic Triple Layering and Enhanced Compulsory Alignment. You open the app. Everything is slower, things are harder to find and it breaks in new, fun ways. Your organization pays a couple hundred more per person for these benefits. Their stock soars, people celebrate the release and your management says they can't wait to see the improvement in workflows now that they've been able to lay off a quarter of your team.
Has there been improvements in LLMs over time? Somewhat, most of it concentrated at the beginning (because they siphoned up a bunch of data in a dubious manner). Now it's just part of their sales cycle, to keep pumping up numbers while no one sees any meaningful improvement.
People say that's just prompting without pointing to real million line+ repositories or realistic apps to show how that can be improved. So I say they are making todo and hello world apps and yes, there it works really well. Claude still beats it, every.. single.. time..
And yes, I use the Pro of all and yes, I do assume coding is done for most of people. Become a plumber or electrician or carpenter.
Also, for me 50% 50% for Sonnet and o1, but although I'm not 100% sure about it, I think o1 is better with longer and more complicated (C++) code and debugging. At least from my brief testing. Also, OpenAI models seem to be more verbose - sometimes it's better - where I'd like additional explanation on chosen fields in a SQL schema, sometimes it's too much.
EDIT: Just asked both o1 and Sonnet 3.5 the same QML coding question, and Sonnet 3.5 succeeded, o1 failed.
I’ve been using Claude and openai in copilot and I find even 4o seems to understand the problem better. O1 definitely seems to get it right more for me.
When I was still subscribing to OpenAI (about 4 months ago) this didn’t exist.
Sonnet v2 in particular seems to be a bit broken with its reasoning (?) feature. The one where it detects it might be hallucinating (what's even the condition?) and reviews the reply, reflecting on it. It can make it stop halfway into the reply and decide it wrote enough, or invent some ridiculous excuse to output a worse answer. Annoying, although it doesn't trigger too often.
I guess if you don’t need to maintain it, just an ever growing blob of complexity that will be reinvented into new blobs every time when the old one becomes too immobile :)
I’ve found using Claude via Copilot in VS Code produces noticeably lower quality results than 3.5 Sonnet on web. In my experience Claude web outdoes GPT-4o consistently.
This was new behaviour for me to see in an LLM. Usually the problem is these things would just fold when you pushed back. I don't know which is better, but being this confidently wrong (and "lying" when confronted with it) is troubling.
It works in politics, marketing, and self-promotion.
If you use the web as a training set, those categories dominate.
> being this confidently wrong (and "lying" when confronted with it) is troubling.
I don't find it troubling. I like being reminded to distrust and confirm everything it offers.
More like: with the right prompting
Off the top of my head: GPT-2, Codex, GPT-3 in three different flavors (babbage, curie, davinci), GPT-3.5.
Suggesting that GPT-4 was "fourth" simply isn't credible.
Just the other day they announced a jump from o1 to o3, skipping o2 purely because it's already the name of a major telecommunications brand in Europe. Deriving anything from the names of OpenAI's products doesn't make sense.
I was just commenting on the fact that in the UK, ‘Europe’ generally means ‘continental Europe’.
> Would you find it weird if someone said that a Norwegian company was in Europe?
I’d find it weird if a European did. But from Americans it’s to be expected.
Europe is a subcontinent of Eurasia, as is Asia. Probably not the naming scheme in all languages, but this is English
It really depends on who you're speaking to.
> I’d find it weird if a European did. But from Americans it’s to be expected.
Absolutely nothing weird about it, I'd find it very weird if they wouldn't. I'm from Europe and my social circle has people from all over Europe.
It's really just the UK which has this weird usage of Europe.
In general yes, but it depends on if you consider central america as its own continent and if you include them there and how you delineate north/south america. Groupings differ based on your education.
I think the thing that makes the UK different is that there is no other option besides them being a separate thing/continent. Are you suggesting that the UK is it's own continent? Would that be with the faroese and the Greenlanders?
The UK might feel different, but they are not separate. The french feel different from the bulgarians, but that does not mean they are on a separate continent, politically or geographically.
EDIT:
> A lot of British people feel like their civilization is meaningfully distinct
This is, to borrow a word, "balderdash". Looking at the influence vikings, romans and normans have had that is a rubbish argument. Just like other countries in europe the british culture is built on the stones of other cultures, and just like many other countries they subsumed other cultures because of kings or other political dominance.
If British people don’t feel like they’re part of “the Continent”, there’s little objective reason to say they are.
So to what continent do those british people think they belong?
I was clearly asking what those specific british people think.
Great Britain (the island) is very close to mainland Europe, and was directly part of it a few thousand years ago. The situation is totally different.
That's pretty much the definition of continent, right? The term continent is not scientifically based unless you want to argue that there are 16-ish continents and that South Georgia is it's own continent (and even tectonically its arbitrary since what we consider to be major, minor, micro are arbitrary).
Greenland is in North America.
France would have a better argument for it having territory in both north (https://en.wikipedia.org/wiki/Saint_Pierre_and_Miquelon and others) and south (https://en.wikipedia.org/wiki/French_Guiana and others) america.
GPT4, GPT4T, Gpt4o-Mini, GPT4o,
> At best, they say, Orion performs better than OpenAI’s current offerings, but hasn’t advanced enough to justify the enormous cost of keeping the new model running.
wdym "keep it running"?
Though I wouldn't be surprised if the bigger reason is the PR cost of releasing with an exciting name but unexciting results. The press would immediately declare the end of the AI growth curve
When I read this I was honestly confused. I had never heard of NotebookLM before.
I was on an airplane and there was high-speed Internet on the airplane. That's the newest thing that I know exists. And I'm sitting on the plane and they go, open up your laptop, you can go on the Internet.
And it's fast, and I'm watching YouTube clips. It's amazing. I'm on an airplane! And then it breaks down. And they apologize, the Internet's not working. And the guy next to me goes, 'This is bullshit.' I mean, how quickly does the world owe him something that he knew existed only 10 seconds ago?"
Soon, all the middle class jobs will be converted to profits for the capital/data center owners, so they have to spend while they can before the economy crashes due to lack of spending.
If OpenAI's investment prospectus relies on them reaching AGI before the tech becomes commoditized, everyone is going to look for that weakness.
Or even instructed them to?
The best part is, I’ve never written any (substantial) python code before.
That being said, I definitely believe it's only useful for isolated problems. Even with Copilot, I feel like the AIs just lack a bigger context of the projects.
Another thing that helped me was designing an initial prompt that really works for me. I think most people just expect to throw in their issue and get a tailored solution, but that's just not how it works in my experience.
I developed the library interactively, one API call at a time, in a manner akin to pair programming. Code quality was significantly better than I'd expect from $2000 worth of a GOOD mid-tier programmer — the code was well written, well organised, and comprehensively annotated. The code wasn't perfect, but a majority of faults had a basis in the underlying documentation being wrong or ambiguous.
The $20/month for Cursor Pro literally justified its cost in less than 10 minutes.
As it is now, it has a bad habit of, if it can't answer the question you asked, instead answering a similar-looking question which it thinks you may have meant. That is of course a great strategy for benchmarks, where you don't earn any points for saying you don't know. But it's extremely frustrating for real users, who didn't read their question from a test suite.
Some of these are pretty crazy too.
Here's a video from 14 years ago where a table saw stops fast enough that it didn't scratch a hotdog: https://www.youtube.com/watch?v=fq3o0VGUh50
So even if this hypothetical robot had saws for hands it could be mostly safe (in theory).
Since we can live with that we can live with anything that doesn't outright murder us.
Did they? Where? Seriously, I genuinely want to know who is employing these techniques.
When questioned:
> believing robotics is fundamentally about to transform
These are not even remotely the same thing. Something that has happened already and is verifiable fact is not the same thing as your opinion, even if your opinion is based on a lot of sound arguments and reasoning.
Very tiresome to read so many claims of fact based on opinion of what will happen in the future.
Really not that hard.
> about to transform
Apparently even english tenses are too hard, let alone anything else. Bald faced lie, to claim what you think might happen in the future has already happened in the past. No matter "what the discussion was about", or what arguments you bring to support your estimation of the future.
specifically their speech demo video (which is, of course, a demo video)
are undoubtedly using such models.
It's an area of active research, eg
https://www.physicalintelligence.company/blog/pi0
I would guess that most cooking and cleaning tasks are in basically the same space. You don't need fine motor control to clean a toilet bowl, but you've gotta figure out how to get people to buy the well-proven premisting technology before you'll be able to sell them a toilet-cleaning robot.
I struggle to imagine a scenario where a 1-2 person household would get the same benefits from something like a laundry-folding robot. I hate folding my laundry and I still can't imagine buying one since I simply don't do laundry that often. If I really wanted to spend less time doing laundry, I could spend the cost of that laundrybot on a larger collection of clothing to wear, for that matter.
Robot vacuums are a good comparison point since vacuuming is something you (ideally) do frequently that is time and labor intensive. I do own one of those, and if it got better at dealing with obstacles thanks to "AI" I would definitely like that.
As someone who does laundry about twice a week, it would certainly be nice. But it’s a pie in the sky at this time even just on the technological side.
A single general-purpose robot that can do everything would be much easier to sell.
A robot servant that does literally 100% of chores would be a game changer, and I expect we'll get there at some point, but it will probably have to be a one-shot from a consumer perspective. A clever research idea to reach 25% or 50% coverage still isn't going to lead to a commercially viable product.
Many non-AI products already reduce chore time:
* Washer-Dryer Combos
* Soylent/Huel bars
* Self-cleaning toilets / automatic toilet bowl cleaners
* Robotic vacuums / mowers / pool/litter cleaners
* Pet/plant feeders
Has successfully guilotined several cats.
Quite the hubris to name the project after the desert planet of Dune, where multiple royal houses met their ruin.
Meanwhile, the biggest opportunity lies not in whatever next thing OpenAI releases, but the rest of the enormous software industry actually integrating this technology and realizing the value it can deliver.
https://www.opb.org/article/2024/12/09/artificial-intelligen...
I had to come up with a proposal to build a new R&D centre recently. To provide context on what our company does, I wrote a web scraper to scrape our own website (faster than going to IT) using Replit Agent and then fed that into O1 as context to come up with the proposal.
In less than an hour.
There is no going back.
The noise was the proposal, which was no doubt several pages at least.
The signal was "we should build a new R&D centre".
Am I missing anything? Did you feed the AI some financial figures or other information that couldn't be found on company website? If so, that would also be part of the signal.
It reminds me of an experiment in which people wanted to cut in line. Saying "can I cut in front of you because I'm in a hurry?" was significantly more successful than saying "can I cut in front of you?", even though they are essentially the same. (I read about this in the book Influence: The Psychology of Persuasion).
AI generated reports, proposals, and other fluff, can make things seem so much more persuasive. Alice is going to ask her AI to turn her 1 sentence into 5 paragraphs, and then Bob is going to ask his AI to summarize the 5 paragraphs into 1 sentence.
And while I'm happy for you, I don't see the relevance? this post was not about "going back" or "stopping the use of AI tools" at all?
It doesn’t ship. You guys can’t do it! Prove me wrong!
In any case, the "behind schedule" rumors are themselves based on other rumors. GPT-2→GPT-3 took 5 quarters, GPT-3→GPT-4 took 11 quarters, so obviously GPT-5 (or its equivalent) will be released in Q4'2025.
I’m still selling them. That’s how utterly convinced that this particular mix of my former colleagues are incapable of this thing I am.
They’ll call something GPT-5, but it won’t like obsolete physicists or even good hackers. None of the shit sama says.
Even o1 sucks.
And then Sam got control and all the high profile people bounced, and it’s a pretty grim residue that either lasted or rushed in.
I know these people, a lot of them personally, and they are not the person you trust with fucking anything.
This is the first I have heard of this in particular. Do you know of any article or source for more on the efforts to train GPT 5 and the decision to call it GPT 4o?
...and then gets hit deadpan with the good old "Source?", like it's some sort of gotcha.
I think people have started to confuse "making logical conclusions without perfect info" with "misinformation"
-
Before certain people start acting like this is advocating for misinformation (which would be an incredible irony...) it's not.
I'm saying if you disagree with what someone supposits, just state so directly. Don't wrap it in a disingenous query for a source.
Critical thinking ? Lol it's just blind speculation.
You don't do this passive aggressive "source???" thing.
It's a bit like starting a Slack conversation with "Hi?": we all know you have a secondary objective, but now you're inserting an extra turn of phrase into the mix
To me, OP's speculation reads as obvious nonsense but that might not be the case for everybody. Asking for sources or such to what is entirely speculation is perfectly valid and personally, that comment does not ring as passive aggressive to me but maybe it's just me.
Just because someone doesn't know enough to refute the reasoning doesn't mean they must take whatever they read at face value.
If anything just breezily asking for a source would imply to people who don't know better that this is a rather even keeled take and just needs some more evidence on top. "I disagree and here's why" nips that in the bud directly.
I think the hanging point seems to be that you found the comment passive aggressive but i genuinely didn't.
> How is "I disagree" any more direct than "I've not heard anything like this.
But then you go on to say:
> Moreover who's to say this person even disagrees? Personally i don't always ask for them because of a disagreement.
If you don't see how just disagreeing with someone is more direct than rhetorically asking for sources... we might just have to agree to disagree :)
And while I fully agree there absolutely is such a thing as smarmy commenters asking for sources in cases where it's misunderstanding something fundamental about the conversation (e.g. "Shakespeare is good", "oh really? source?!") or frivolous requests for factual information familiar to everyone ("global temperatures are rising? Source!?"), I don't know how someone could read this subthread and feel that my question falls into either of those categories.
And to use this of all things as a moment to die on the hill of advocating for fuzzy boundaries between speculation and fact, which absolutely is something that facilitates misinformation, and to be angry that such a thing would be interpreted as a favorable attitude toward misinformation, is completely baffling.
There is no way to deduce by intuition alone that GPT-5 == GPT-4o. So either that person has some information the rest of us aren't privy to, or it's an opinion phrased as a fact. In either case, it deserves clarification.
Looking at the benchmarks it was also very expected in my opinion. Sure, the absolute results are/were sky high, but results relative to the previous gen were not exponential now, they were comparatively smaller than between 2 and 3, or 3 and 4. So I'm guessing that they have invested and worked for 2023-2024 on a brand new model, and branded it according to the model results.
The problem is once you believe their fact is wrong, just say "I think you're wrong <insert rest of comment>". Innocently asking for a source as if you're still on the fence is just performative and leads to these conversations where both sides just end up talking past each other:
A source for one underpinning of the incorrect fact comes up, then "well but that only proves X part of it, can you prove Y" and so on.
tl;dr I just find the quality of discourse is much higher when people are direct.
Well this certainly is a lot of work to make a mountain out of a mole hill, and I'm not sure it increases the quality of discussion either.
In any case, I think saying bold shit followed up with "it's speculation, but it's OBVIOUS speculation" is worth asking for some evidence. Obvious speculation implies it's sourced from something other than personal gut feeling.
To echo a sibling comment:
> Every time someone says their speculation is "obvious" it rings every possible alarm bell for someone who has completely lost grasp of the ability to distinguish between facts and speculation.
Meanwhile, aeronautics experts whose job it is to know about this have created an entire lexicon for the various perceptual illusions we experience relating to flight and airborne objects, precisely because it involves conditions where our intuitions fail. Many of them have to do with inability to orient depth, distance, or motion for lights at night.
Every time someone says their speculation is "obvious" it rings every possible alarm bell for someone who has completely lost grasp of the ability to distinguish between facts and speculation.
The road to misinformation is paved with overconfident declarations of the form: "it's so obvious, who needs sources!"
My point is simply that is we can skip the passive aggressiveness and just say "can you give me some more evidence that would help me reach the same conclusions you've reached".
Otherwise you're not actually asking for a source, you're just saying "I disagree" in a very roundabout way.
4o was all about compute optimization.
While I still think Copilot is useless, I recently had a very complex code that did a lot of crazy bit-flipping and xoring, and I had no idea what is it doing, so I threw it to ChatGPT.. and it knew what it was doing.
I also needed to rewrite this code to PHP (for... reasons) while I know very little PHP. And it did that! It was a bit wrong, I needed to correct a bunch of stuff (based on domain knowledge), but... it helped me a ton.
I still can't image using it daily for domain and language I already know (that's why I never used copilot). But it actually helped me in measurable ways when it's something new.
It's not so good for a completely new subject, or one you have a lot of experience in.
For example, I'm comfortable with frontend development but hadn't used webworkers or websockets. ChatGPT was useful for getting up to speed quickly. I've had less luck with topics that are completely new to me, one example is coming up with a training regimen for long distance running. I have to manually verify every little thing, which ends up taking longer than doing research the old fashioned way.
I'd be surprised if you could go from zero to a useful CV app with LLMs, but it's possible I just haven't given it a fair shake.
Basically StackOverflow but without the annoying mods?
I'm using Copilot daily. I don't use it to write code instead of me. But I'm using it to generate lots of obvious code just the same way that I would do. I know when to expect it to do its work perfectly and I know when I need to supervise it. I know when I'd spend more time editing generated code so I'd write that code myself and I know when I'd spend less time editing generated code.
I don't think that AI brings 10x or even 2x to my productivity, so you can avoid using AI. But I certainly can say, that using Copilot makes programming less tenuous in the same way using autocomplete, autoimports and similar IDE stuff makes programming less tenuous.
I also think that whether copilot helps or not depends on type of code that you're writing. If you're very careful about DRY and your language does not have much boilerplate, may be you'd find it less useful. For example when I'm writing Go, every second line is of kind `if err != nil { return fmt.Errorf("Cannot bla: %w", err); }`. The only "intellectual" part here is error message and Copilot generates it 99% perfectly along with surrounding stuff.
Outsiders will likely read this article and think, “AI is running out of steam”, because GPT-5 is behind.
Those closer to this know of the huge advancements o3 just made yesterday, and will have a complete opposite conclusion.
It will be interesting to see people’s take away from this. I think WSJ missed the mark here with the headline and the takeaway their audience will get from the article.
I dont think most customers realise how much better the models work with custom GPTs.
There was even talk of some sort of profit sharing with creators which clearly never happened. I just think the premise is too confusing for many and can still be served by using a custom system prompt via the API.
I am unsure if the answer is to use “projects” maybe this has superseded myGpts?
I am perplexed why HN isn’t focusing on this issue as all the Llm gains I’ve ever had were wit highly customised personal myGpts.
I can understand OpenAI and Sam’s having access to their own models may not even know what the best way to use the released stuff is
Ps - typing on my phone hence typos
I love that quote. "I can tell you with a high degree of scientific certainty that my product will be badass... for some definition of badass, please don't ask".
I, too, could say with a high degree of scientific certainty that while putting those words together sounds good, it makes no sense.
I found that amusing and figured then that we might already be beyond peak AI!
If AI is trained on sources that include The Sun, it's going to end up being garbage and/or constantly needing to be fact checked.
>Some researchers take this so seriously they won’t work on planes, coffee shops or anyplace where someone could peer over their shoulder and catch a glimpse of their work.
I'm almost certain that originally this was meant to be a reference to public wifi networks, as planes and coffee shops are often the frequently cited prototypical examples. They made it literally into a matter of someone looking over their shoulder, which loses so much in translation it's almost how you would write this as a joke to illustrate someone missing the point.
>OpenAI and its brash chief executive, Sam Altman
This also strikes me as nonsense. It's the first I've ever heard of someone describing Sam Altman as brash. The only way I can see them getting there is (1) tech executives are often brash (2) Altman is a tech executive (3) let's just go ahead and call him brash.
Nevertheless if this history of GPT5 and/or o3 training is accurate, it strikes me as significant news, but perhaps a missed opportunity to say more about the pertinent dynamics that explain why the training isn't working and/or to talk in interestingly specific ways about strategies for training, synthetic data, or other such things.
1. Even if LLM architecture doesn't work out, it's wise to remember that it's the quality of training data which is a deciding factor. A pivot is easily doable since this is a constant and disconnected from the technology itself.
2. This is clearly a moonshot project but it still feels wasteful especially with context of previous iterations of models and their shortcomings-it feels like the tremendous amount of money available is being used simply because it's available.
Noam Brown's analogy is, you could train a massive one shot foundation model to predict the next best Go move, but that would be stupid. Better to use some test time search. You get better results for less money.
Same is happening in LLMs.
Down the line, I think models will start using video generation as part of how they “think.” Picture a version of GPT that works frame by frame—ask it to solve a geometry problem, and it generates a sequence of images to visualize the solution before responding. YouTube’s massive library of visual content could make something like that possible.
If u think about it, any videos on YouTube of real world data contribute to its understanding of physics at minimum. From what I gather they do pre training on tons of unstructured content first and that contributes to overall smartness.
> When pressed on what data OpenAI used to train Sora, Murati didn’t get too specific and seemed to dodge the question. “I’m not going to go into the details of the data that was used, but it was publicly available or licensed data,” she says. Murati also says she isn’t sure whether it used videos from YouTube, Facebook, and Instagram. She only confirmed to the Journal that Sora uses content from Shutterstock, with which OpenAI has a partnership.
https://www.theverge.com/2024/3/13/24099402/openai-text-to-v...
LLMs have got to the point where they have (more or less) encoded everything ever written by humanity. And encoded it in a gazillion dimensioned matrix so they can take a query and predict, given everything ever written, what the most likely set of words are triggered by that query - and then produce that as a sane parseable output.
I mean this is beyond awesome
But, this reminds me of Aristotle reasoning from his current set of knowledge - and ending up with Eagles having three testicles.
No matter how much reasoning we do from current knowledge, we need to test that against reality - that LLMs are great but the scientific method is greater.
Google is in trouble, yes.
But humanity needs to hold on to science and it’s precepts or we spin into a new dark ages
Deliver just another 2021 flavored GPT chatbot.
But this works: https://www.msn.com/en-us/money/other/the-next-great-leap-in...
https://www.reddit.com/r/ChatGPT/comments/1h7k5p6/openais_ne...
https://medium.com/design-bootcamp/gpt-4-tried-to-escape-int...