IMO, he's right. LLMs can't be AI because they don't create a model of observations to predict things, they just imitate observations based on their likeness to each other. When you play Quake, you create a simple model of the game physics and use that fast model to navigate the game. Your equivalent of LLM has a role too: it's a fuzzy detector of things you encounter in the game, sounds, images and symbols, but once detected, those things are fed into the fast and rigid physics model.
A bit confusing for sure, but I think (not sure) I get what they're saying. Training a nn (for visual tasks at least) consists of training a model with much more dimensions (params) than the input space (eg: controller inputs + atari pixels). This contrasts with a lot of what humans do, which is take higher dimensional information (tons of data per second combining visual, audio, touch/vibration, etc) and synthesizing much lower dimensional models / heuristics / rules of thumb, like the example they give of the 5 second per mile rule for thunder.
Im pretty sure kid/child ai porn already exist somewhere. But i'm quite lucky despite knowing rotten.com and plenty of other sides, never having seen real so i doubt i will see fake child porn.
Whats the elephant in the room now? Nothing changed. Whoever consumes real will consume fake too. FBI/CIA will still try to destroy cp rings.
We could even think it might make this situation somehow better because they might consume purely virtual cp?
Your family will be target for example, just imagine your daughter in high-school getting bullied by these type of generated AI videos. it's easy to say nothing happen, but when it happen to you you will be aware how fucked is these AI videos.
We already have seen that Opensource can compete which is a lot more than people expected. After all opensource and running huge models?
But what it means, that with time, Opensource will be as good as what commercial offerings now have. Hardware will get cheaper, research is open or delayed open.
Of course its my opinion, its my comment after all.
Nonetheless, survival can't be the life goal after all the moon will drift away from earth in the future, the sun will explode and if we survive that as a species, all bonds between elements will disolve.
It also can't be about giving your dna away because your dna has very little to no impact over just a handful of generations.
And no the goal of our society has to be to have as much energy available as possible to us. So much energy, that energy doesn't matter. There is enough ways of generating energy without a real issue at all. Fusion, renewable energy directly from the sun.
There is also no inherant issue right now preventing us all having clean stable energy besides capitalsm. We have the technology, we have the resources, we have the manufacturing capacity.
To finish my comment: Its not about energy, its about entropy. You need energy to create entropy. We don't even consume the energy of the sun, we use it for entropy and dissipate it back to space after.
No clue why you would say its a major source of danger. We have plenty of mechanism in place to prevent issues and due to the nature of IaC and how we handle state, we could literlay tear down everything and are back up running in around 2h with a complex system with 10 componentes based on k8s.
I'm not sure why people on HN (of all places) are so divided regarding the perception of AI/ML.
I have not seen anything like it before. We literaly had not system or way of even doing things like code generation based on text input.
Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute.
I could list tons of examples which are groundbreaking. The whole Image generation stack is completly new.
That blog article is fair enough, there is hype around this topic for sure, but alone for every researcher who needs to write code for their research, AI can make them already a lot more efficient.
But i do believe, that we have entered a new ara: An ara were we take data again very serious. A few years back, you said 'the internet doesn't forget' then we realized that yes the internet starts to forget. Google deleted pages, removed the cache feature and it felt like we stoped caring for data because we didn't knew what to do with it.
Then ai came along. And not only is now data king again but we are now in the mids of reinforcment ara: We now give feedback and the systems incorporate that feedback into their training/learning.
And the ai/ml topic is getting worked on on every single aspect of it: Hardware, Algorithm, use cases, data, tools, protocols, etc. We are in the middle of incorporating and building for and on it. This takes a little bit of time. Still the progress is crazy exhausting.
We will only see in a few years if there is a real ceiling. We do need more GPUs, bigger Datacenters to do a lot more experiments on AI architecture and algorithm. We have a clear bottleneck. Big companies train one big model for weeks and month.
> Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute.
Thing is we just see that it's copy pasting stack overflow, but now in a fancy way so this is sounding like "I asked Google for a nearby restaurant and it found it in like 500ms, my C64 couldn't do that". It sounds impressive (and it is) because it sounds like "it learned about navigating in the real world and it can now solve everything related to that" but what it actually solved is "fancy lookup in a GIS database". It's useful, damn sure it is, but once the novelty wears off you start seeing it for what it is instead of what you imagine it is.
Edit: to drive the point home.
> claude just generated that
What you think happened is AI is "thinking" and building a ontology over which it reasoned and came to the logical conclusion that this script was the right output. What actually happened is your input correlates to this output according to the trillion examples it saw. There is no ontology. There is no reasoning. There is nothing. Of course this is still impressive and useful as hell, but the novelty will wear off in time. The limitations are obvious by this point.
I'm following LLMs, AI/ML for a few years now and not just on a high level.
There is not a single system out there today which can do what claude can do.
I stil see it for what it is: A technology i can communicate/use with natural language and get a very diverse of tasks done. From writing/generating code, to svgs, to emails, translation etc. etc. etc.
Its a paradigma shift for the whole world literaly.
We finally have a system which encodes not just basic things but high level concepts. And we humans are doing often enough something very similiar.
And what limitations are obvious? Tell me? We have not reached any real ceiling yet. We are limited by GPU capacity or how many architectural experiments a researcher can run. We have plenty of work to do to cleanup the data set we use and have. We need to build more infrastructure, better software support etc.
We have not even reached the phase were we all have local AI/ML chips build in.
We don't even know yet how a system will act if everyone of us has access to very fast inferencing like you already get with groq.
> Its a paradigma shift for the whole world literaly.
That's hyperbolic. I use LLMs daily. They speed up tasks you'd normally use Google for and can extrapolate existing code into other languages. They boost productivity for professionals, but it's not like the discovery of the steam engine or electricity.
> And what limitations are obvious? Tell me? We have not reached any real ceiling yet.
Scaling parameters is the most obvious limitation of the current LLM architecture (transformers). That’s why what should have been called GPT-5 is instead named GPT 4.5, it isn’t significantly better than the previous model despite having far more parameters, a lot more cleaned up training data and optimizations.
The low-hanging fruit has already been picked, and most obvious optimizations have been implemented. As a result, almost all leading LLM companies are now operating at a similar level. There hasn’t been a real breakthrough in over two years. And the last huge architectural breakthrough was in 2017 (with paper "Attention is all you need").
Scaling at this point yields only diminishing returns. So no, what you’re saying isn’t accurate, the ceiling is clearly visible now.
> ... but it's not like the discovery of the steam engine or electricity.
completly disagree. People might have googled before but the human<>computer interface was never in any way as accessable as it is now for a normal human being. Can i use Photoshop? yes but i learned it. My sisters played around with Dall-E and are now able to do simiiliar things.
It might feel boring to you that technology accessability drips down like this, but this changes a lot for a lot of people. The entry barrier to everything got a lot lower. It makes a huge difference to you as a human being if you have rich parents and good teachers or not. You had never the chance to just get help like this. Millions of kids struggle because they don't have parents they can ask certain questions required for understanding topics in school.
Steam Engine = fundamental for our scaling economy
electricity = fundamental for liberating all of us from day time
internet = interconnecting all of us
LLM/ML/AI = liberating knowledge through accessability
> 'There hasn’t been a real breakthrough in over two years.'
DeepSeek alone was a real breakthrough.
But let me ask an LLM about this:
- Mixture of Experts (MoE) scaling
- Long-context handling
- Multimodal capabilities
- Tool use & agentic reasoning
Funny enough your comment comes before claude 4.0 release (again increase in performance, etc.) and the Google IO.
We don't know if we found all 'low hanging fruits'. The meta paper about thinking in latent space came out in February. I would definitly call this a low hanging fruit.
We are limited, very hard, on infrastructure. Every experiement you want to try consumes a lot of it. If you look at the top x GPU AI clusters, we don't have that many on the planet. We have Google, Microsoft, Azure, Nvidia, Baidu, Tesla and xAI, Cerebras. Not that many researcher are able to just work on this.
Google has now its first Diffusion based Model active. 2025! We are so far away from testing out more and more approaches, architectures etc. And we are optimizing on every front. Cost, speed, precision etc.
> My sisters played around with Dall-E and are now able to do simiiliar things.
This is no way shape or form in any actual productive way similar to being skilled at Photoshop. There is absolutely no way these people can mask, crop, tweak color precisely, etc. There are hundreds of these sub-tasks. It's not just "making cool images". No amount of LLMing will make you skilled and no amount of delegation will make you able to ask these specific questions in a skillful way to the LLM.
There is a very real fundamental problem here. To be able to state the right questions you have to have a base of competence that ya'll are so happy about throwing into the wind. The next generation will not even know what a "mask" is, let alone ask an LLM for details. Education is dropping worldwide and these things are not going to help. They are going to accelerate this bullshit.
> liberating knowledge through accessability
Because the thing is, availability of knowledge never was the issue. The existence of ridiculous amounts of copyright free educational material and the hundreds of gigs of books on Project Gutenberg are testament to that.
Even in my youth (90s) there were plenty of books and easy to access resources to learn, say, calculus. Did I peruse them? Hell no. Did my friends? You bet your ass they were busy wasting time doing bullshit as well. Let's just be honest about this.
These problems are not technical and no amount of technology is going to solve them. If anything, it'll make things worse. Good education is everything, focus on that. Drop the AI bullshit, drop the tech bullshit. Read books, solve problems. Focus on good teachers.
I honestly think it's still way too early to say this either way. If your hypothesis that there are no breakthroughs left is right, then it's still a very big deal, but I'd agree with you that it's not steam engine level.
But I don't think "the transformer paper was eight years ago" is strong evidence for that argument at all. First of all, the incremental improvement and commercialization and scaling that has happened in that period of time is already incredibly fast. Faraday had most of the pieces in place for electricity in the 1830s and it took half a century to scale it, including periods where the state of the art began to stagnate before hitting a new breakthrough.
I see no reason to believe it's impossible that we'll see further step-change progressions in AI. Indeed, "Attention is All You Need" itself makes me think it's more likely than not. Out of the infinite space of things to try, they found a fairly simple tweak to apply to existing techniques, and it happened to work extremely well. Certainly a lot more of the solution space has been explored now, but there's still a huge space of things that haven't been tried yet.
LLMs are great at tasks that involve written language. If your task does not involve written language, they suck. That's the main limitation. No matter how hard you push, AI is not a 'do everything machine' which is how it's being hyped.
Written language is very powerful apparently. After all LLM can generate SVG, python code to use Blender etc.
One demo i saw with LLM and code use: "Generate a small snake game" and because the author still had the Blender MCP tool connection, the LLM decided to generate 3D assets through Blender for that game.
> We finally have a system which encodes not just basic things but high level concepts
That's the thing I'm trying to convey: it's in fact not encoding anything you'll recognize and if it is, it's certainly not "concepts" as you understand them. Not saying it cannot correlate text that includes what you call "high level concepts" or do what you imagine to be useful work in that general direction. Again not making claims it's not useful, just saying that it becomes kind of meh once you factor in all costs and not just the hypothetical imaginary future productivity gains. AKA building literal nuclear reactors to do something that basically amounts to filling in React templates or whatever BS needs doing.
If it was reasoning it could start with a small set of bootstrap data and infer/deduce the rest from experience. It cannot. We are not even close as in there is not even theory to get us there forget about the engineering. It's not a subtle issue. We need to throw literally all data we have at it to get it to acceptable levels. At some point you have to retrace some steps and think over some decisions, but I guess I'm a skeptic.
In short it's a correlation engine which, again, is very useful and will go ways to improve our lives somewhat - I hope - but I'm not holding my breath for anything more. A lot of correlation does not causation make. No reasoning can take place until you establish ontology, causality and the whole shebang.
I do understand it but i also think that the current LLMs are the first step to it.
GPT-3 started proper investment into this topic, there was not enough research done in this direction and now it is. People like Yann LeCun already analyse different approaches/architecture but they still use the infrastructure of LLMs (ML/GPUs) and potentially the data.
I never said that LLM is the breaktrhough in consesnes.
But you can also ask LLM strategies for thinking. It can tell you a lot of things. We will see if a LLM will be a fundamental part of AGI or not but GPU/ML will probably be.
I also think that the compression mechanism through LLM lead to concepts through optimization. You can see from the antropic paper, that an LLM doesn't work in normal language space but in a high dimensional one and then 'expresses' the output in a language you like.
We also see that real multi modal models are better in a lot of tasks due to a lot more context available through them. Estimating what someone said due to context.
The necessary infrastructure and power requirement is something i accept too. We can assume, i do, that further progress in a lot of topics will require this type of compute and it also solves our data bottleneck: normal CPU architecture is limited by memory databus.
Also in comparision to a lot of other companies, if the richest companies in the world invest in nuclear, i think this is a lot better than any other companies. They have a lot higher margins and knowledge. co2 is a market separator for them too.
I also expect this amount of compute to be the base for fixing real issues we all face like cancer or optimizing cancer or any other sickness detection. We need to make medicin a lot cheaper and if someone in africa can do a cheap x ray and send it to the cloud to get any feedback, that would / could help a lot of people.
Doing complex and massive protein analysis or mRna research in virtual space, also requires GPUs.
All of this happened in a timespan of only a few years. I have not seen anything progressing as fast as AI/ML currenly does and as unfortunate it is, this needs compute.
Even my small inhouse image recognition fine tuning explodes when you do a handful parameter optimizations but the quality is a lot better than what we had before.
And enabling people to have real natural language UI is HUGE. It makes so much more accessable. Not just for people with a disability.
Things like 'do a eli5 on topic x'. "explain to me this concept" etc. I would have loved that when i tried to be successful in the university math curiculum.
All of that is already crazy and still is. But in parallel what Nvidia and others currently do with ML and Robotics is also something which requires all of that compute. And the progress is again breath taking. The current flood of basic robots standing and walking around is due to ML.
I mean, you're not even wrong ! Most all of these large models are based on the idea that if you put all of the representations that we can of the world into a big pile that you can tease out some kind of meaning. There's not even really a cohesive theory as to that, and surely no testable way to prove that it's true. It certainly seems like you can make a system that behaves as if it could be like that, and I think that's what you're picking up on. But it's actually probably something else and something far shorter of that.
There is an interesting analogy that my Analysis I professor once said: The intersection of all valid examples are also a definition of an object. In many ways this is, at least in my current understanding, how ML systems "think". So yeah it will take some superposition of examples and kind of try to interpolate between those. But fundamentally it is - at least so far - always an interpolation, not an extrapolation.
Whether we consider that "just regurgitating Stackoverflow" or "it thought up the solution to my problem" mostly comes up to semantics
> There is not a single system out there today which can do what claude can do.
Of course there is, it's called Gemini 2.5 Pro and it is also the reason I cancelled my Claude (and earlier OpenAI) subscriptions (I had quite a few of them to go around limits).
Yeah. It’s just fancier techniques than linear regression. Just like the latter takes a set of numbers and produces another set, LLMs takes words and produces another set of words.
The actual techniques are the breakthrough. The result are fun to play with and may be useful in some occasions, but we don’t have to put them on a pedestal.
You have the wrong idea of how an LLM works. Its more like an model that iteratively finds associating / relevant blocks. The reasoning are the iterative steps it takes.
> “I'm not sure why people on HN (of all places) are so divided regarding the perception of AI/ML.”
Everyone is a rational actor from their individual perspective. The people hyping AI, and the people dismissing the hype both have good reasons.
The is justification to see this new tech as ground breaking. There is justification to be weary about massive theft of data and dismissiveness of privacy.
First, acknowledge and respect that there are so many opinions on any issue. Take yourself out of the equation for a minute. Understand the other side. Really understand it.
>but alone for every researcher who needs to write code for their research, AI can make them already a lot more efficient.
scientists don't need to be efficient, they need to be correct. Software bugs were already a huge cause of scientific error, and responsible for lack of reproducibility, see for example cases like this (https://www.vice.com/en/article/a-code-glitch-may-have-cause...)
Programming in research environments is done with some notoriously questionably variation in quality, as is the case for the industry to be fair, but in research minor errors can ruin results of entire studies. People are fed up and come to much harsher judgements on AI because in an environment like a lab you cannot write software with the attitude of an impressionist painter or the AI equivalent, you need to actually know what you're typing.
AI can make you more efficient if you don't care if you're right, which is maybe cool if you're generating images for your summer beach volleyball event, but it's a disastrous idea if you're writing code in a scientific environment.
I basically agree, but want to point out two major differences to other "hype-y" topics that existed in the past that in my opinion make the whole AI discussions on HN a little bit more controversial than other older hype discussions:
1. The whole investment volume (and thus hope and expectations) into AI is much larger than into other hype topics.
2. Sam Altman, the CEO of OpenAI, was president of YCombinator, the company begind Hacker News, from 2014 to 2019.
On (1): Investment volume relative to what? To me, it looks like a very similar pattern of investors crowding into the currently hot thing, trying to get a piece of the winners of the power law.
On (2): I'm honestly not sure I think this is making a big difference at all. Not much of the commentary here is driven by YC stuff, because most of the audience here has no direct entwinement with YC.
>On (1): Investment volume relative to what? To me, it looks like a very similar pattern of investors crowding into the currently hot thing, trying to get a piece of the winners of the power law.
The profile of investors (nearly all the biggest tech companies amongst others) as well as how much they're willing to and have put down (billions) is larger than most.
Open AI alone just started work on a $100B+ datacenter (Stargate)
Yeah maybe I buy it. But it reminds me of the investment in building out the infrastructure of the internet. That predates HN, but it's the kind of thing we would have debated here if we could have :)
The ultimate job of a programmer is to translate human language into computer language. Computers are extremely capable, but they speak a very cryptic overtly logical language.
LLMs are undeniably treading onto that territory. Who knows how far in they will make it, but the wall is breached. Which is unsettling to down right scary depending on your take. It is a real threat to a skill that many have honed for years and for which is very lucrative to have. Programmers don't even need to be replaced, having to settle for $100k/yr in a senior role is almost just a scary.
Google never gave a good reason for why they stopped making their cache public, but my theory is that it was because people were scraping it to train their LLMs.
> Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute.
I agree that this is useful! It will even take natural language and augment the script, and maybe get it right! Nice!
The AI is combing through scraped data with an LLM, and conjuring forth some imagemagick snippets into a shell script. This is very useful, and if you’re like most people, who don’t know imagemagick intimately, it’s going to save you tons of time.
Where it gets incredibly frustrating is tech leadership seeing these trivial examples, and assuming it extrapolates to general software engineering at their companies. “Oh it writes code, or makes our engineers faster, or whatever. Get the managers mandating this, now! Also, we need to get started on the layoffs. Have them stack rank their reports by who uses AI the best, so that we are ready to pull the trigger.”
But every real engineer who uses these tools on real (as in huge, poorly written) codebases, if they are being honest (they may not be, given the stack ranking), will tell you “on a good day it multiplies my productivity by, let’s say, 1.1-2x? On a bad day, I end up scrapping 10k lines of LLM code, reading some documentation on my own, and solving the problem with 5 lines of intentional code.”
Please, PLEASE pay attention to this details that I added: Huge, poorly written codebases. This is just the reality at most software companies that have graduated from series A startup. What my colleagues and I are trying to tell you, leadership, is that these “it made a script” and “it made a html form with a backend” examples ARE NOT cleanly extrapolating to the flaming dumpster fire codebases we actually work with. Sometimes the tools help! Sometimes, they don’t.
It’s as if LLM is just another tool we use sometimes.
This is why I am annoyed. It’s incredibly frustrating to be told by your boss “use tool or get fired” when that tool doesn’t always fit the task at hand. It DOES NOT mean I see zero value in LLMs.
most work in software jobs is not making one-off scripts like in your example. a lot of the job is about modifying existing codebases which include in-house approachs to style and services along with various third party frameworks like Spring driven by annotations, and requirements around how to write tests and how many. AI is just not very helpful here, you spend more time spinning wheels trying to craft the absolute perfect script than just making code changes directly.
There is no single reason. Nobody will argue that LLMs are already quite useful at some tasks if used properly.
As for the opposing view, there are so many reasons.
* Founders and other people who bet their money on AI try to pump up the hype in spite of problems with delivery
* We know some of them are plainly lying, but the general public doesn't
* They repeat their assumptions as facts ("AI will replace most X and Y jobs by year Z")
* We clearly see that the enormous development of LLMs has plateaued but they try to convince the general public it's the contrary
* We see the difference on how a single individual (Aaron Swartz) is treated when making a small copyright infringement, and how the consequences for AI companies like OpenAI or Meta who copied the whole contents of Libgen are non-existent.
* Some people like me just hate AI slop - in writing and imaging. It just puts me off and I stop reading/watching etc.
I was not able to get meeting transcription in that quality that cheap ever before. I followed dictation software for over a decade and tx to ML the open source software is suddenly a lot better than ever before.
Our internal company search with state of the art search indexes and search software was always shit. Now i ask an agent about a product standard and it just finds it.
Image generation never existed before.
Building a chatbot in a way that it actually does what you expect and its more complicated than answering the same 10 theoretical features it can do was hard and never really good and it now just works.
Im also not aware of any software rewriting or even writing documents for me, structer them etc.
If this was 'a simple user error' or 'not using the right tool for the job' than this was an error from smart people and it still got fixed by using AI/ML in an instant.
With this, my argument still stands even if it would be for a different reason which i personally doubt.
Often big companies are the least efficient. And big companies can still make mistakes or have very inefficient processes. There was already a perfectly simple solution to the issue that could have been utilised prior to this and overall still the most efficient solution.
Also, everyone does dumb things, even smart people do dumb things. I do research in a field that many outsiders would say you must be smart to do (not my view) and every single one of us does dumb shit daily. Anyone who thinks they don't isn't as smart as they think they are.
Well, LLMs are the right tool for the job. They just work.
I mean if you are going to deny their usefulness in the face of plenty of people telling you they actually help, it’s going to be impossible to have a discussion.
They can be useful, however for admin tasks, there are plenty of valid alternatives that really take no longer time wise so why bother using all that computing power.
They don't just work though, they are not fool proof and definitely require double checking.
> valid alternatives that really take no longer time wise
That’s not my experience.
We use them more and more at my job. It was already great for most office tasks including brainstorming simple things but now suppliers are starting to sell us agents which pretty much just work and honestly there are a ton of things for which LLMs seem really suited for.
CMDB queries? Annoying SAP requests for which you have to delve through dozens of menus? The stupid interface of my travel management and expense software? Please give me a chatbot for all of that which can actually decipher what I’m trying to do. These are hours of productivity unlocked.
We are also starting to deploy more and more RAG on select core business dataset and it’s more useful than even I anticipated and I’m already convinced. You ask, you get a brief answer and the documents back. This used to be either hours of delving through search results or emails with experts.
As imperfect as they are now, the potential value of LLMs is already tremendous.
How do you check accuracy of these? You stated brainstorming as an example that they are great at. As obviously experts are experts for a reason.
My issue here is that a lot of this is solved by good practice, for example,travel management and expenses have been solved, company credit card. I don't need one slightly better piece of software to manage one terrible piece of software to solve an issue that has a solution.
Because LLMs send you back links to the tools and you still get the usual confirmation process when you do things.
The main issue never was knowing what to do but actually getting the tools to do it. LLMs are extremely good at turning messy stuff into tools manipulation especially where there never was an API available in the first place.
It’s not a question of practices. Anyone who has ever worked for a very large company knows that systems are complicated by need and everything move at the speed of a freighter ship if you want to make significant changes.
Of course we need one slightly better piece of software to manage terrible pieces of software. There are insane value there. This is a major issue for most companies. I have seen millions spent into getting better dashboards from SAP which paid for themselves in actual savings.
Ok take Transcription, they were trying to use free as in cost tools instead of using software that works efficiently that has been effective for decades now.
> Alphafold made a breakthrough in protein folding.
Sort of. Alphafold is a prediction tool, or, alternatively framed, a hypothesis generation tool. Then you run an experiment to compare.
It doesn't represent a scientific theory, not in the sense that humans use them. It does not have anywhere near something like the accuracy rate for hypotheses to qualify as akin to the typical scientific testing paradigm. It's an incredibly powerful and efficient tool in certain contexts and used correctly in the discovery phase, but not the understanding or confirmation phase.
It's also got the usual pitfalls with differentiable neural nets. E.g. you flip one amino acid and it doesn't really provide a proper measure of impact.
Ultimately, one major prediction breakthrough is not that crazy. If we compare that to e.g. Random Forest and similar models, the impact in science is infinitely more with them.
We already have a precise and accurate theory for protein folding. What we don’t have is the computational power to do true precise simulations at a scale and speed we’d like.
In many aspects a huge tangled barely documented code base written by inexperienced grad students of quantum shortcuts, err, perturbative methods isn’t that much more or less intelligible than an AI model learning those same methods.
What "precise and accurate theory for protein folding" exists?
Nobody has been able to demonstrate convincingly that any simulation or theory method can reliably predict the folding trajectory of anything but the simplest peptides.
> What "precise and accurate theory for protein folding" exists?
It’s called Quantum Mechanics.
> Nobody has been able to demonstrate convincingly that any simulation or theory method can reliably predict the folding trajectory of anything but the simplest peptides.
No we don’t have simplified models or specialized theories to reduce the computational complexity enough to efficiently solve the QM or even molecular dynamics systems needed to predict protein folding for more than the simplest peptides.
Granted, it’s common to mix up things and say that not having a computationally tractable models means we don’t have precise and accurate theory of PF. Something like [0] resulting in an accurate, precise, and fast theory of protein folding would be incredibly valuable. This however, may not be possible outside specific cases. Though I believe AlphaFold indicates otherwise as it appears life has evolved various building blocks which enable a simpler physics of PF tractable to evolutionary processes.
Quantum computing however could change that [1]. If practical QM is feasible that is, which it’s beginning to look more and more likely. Some say QC is already proven and just needs scaled up.
I don't think anybody is 100% certain that doing a full quantum simulation of a protein (in a box of water) would recapitulate the dynamics of protein folding. It seems like a totally reasonable claim, but one that could not really be evaluated.
If you have a paper that makes a strong argument around this claim, I'd love to see it. BTW- regarding folding funnels, I learned protein folding from Ken Dill as a grad student in biophysics at UCSF, and used to run MD simulations of nucleic acids and proteins. I don't think anybody in the field wants to waste the time worrying about running full quantum simulations of protein folding, it would be prohibitevly expensive even with far better QM simulators than we have now (IE, n squared or better).
Also the article you linked- they are trying to find the optimal structure (called fold by some in the field). That's not protein folding- it's ground state de novo structure prediction. Protein folding is the process by which an unfolded protein adopts the structured state, and most proteins don't actually adopt some single static structure but tend to interconvert between several different substructes that are all kinetically accessible.
> I don't think anybody is 100% certain that doing a full quantum simulation of a protein (in a box of water) would recapitulate the dynamics of protein folding.
True, until it's experimentally shown there's still some possibility QM wouldn't suffice. Though I've not read anything that'd give reason to believe QM couldn't capture the dynamic behavior of folding, unlike the uncertainty around dark matter or quantum supremacy or quantum gravity.
Though it might be practically impossible to setup a simulation using QM which could faithfully capture true protein folding. That seems more likely.
> It seems like a totally reasonable claim, but one that could not really be evaluated.
If quantum supremeacy holds, my hunch would be that it would be feasible to evaluate it one day.
The paper I linked was mostly to showcase that there seem to be approaches utilizing quantum computing to speed up solving QM simulations. We're still in the early days of quantum computing algorithms and it's unclear what's possible yet. Tackling a dynamic system like a unfolded protein folding is certainly a ways away though!
> Also the article you linked- they are trying to find the optimal structure (called fold by some in the field). That's not protein folding- it's ground state de novo structure prediction.
Thanks! I haven't worked on quantum chemistry for many years, and only tangentially on protein folding, so useful to know the terminology. The meta table states and that whole possibility of folding states / pathways / etc fascinates me as potentially being emergent property of protein folding physics and biology as we know it.
One one hand, maybe it's good to have better searchable records, even if it's hard to quantify the benefit.
On the other hand, now all your meetings produce reams of computer searchable records subject to discovery in civil and criminal litigation, possibly leading to far worse liability than would have been possible in a mostly email based business.
I doubt that, but even if you're right it doesn't change my point: if you're not willing to stop doing that, you'll be less productive than the firms that are willing to operate legally and ethically.
And if in some industry it's really not possible to do business without committing crimes, then let's reform the criminal code to something reasonable.
The problem is that the hype assumes that all of this is a baseline (or even below the baseline), while there are no signs that it can go much further in the near future – and in some cases, it's actually cutting-edge research. This leads to a pushback that may be disproportionate.
I'm sure there's many people out there who could say that they hardly use AI but that crypto has made them lots of money.
At the end of the day searching work documents and talking with computers is only desirable inasmuch as they are economically profitable. Crypto at the end of the day is responsible for a lot of people getting wealthy. Was a lot of this wealth obtained on sketchy grounds? probably, but the same could be said AI (for example, the recent sale of windsurf for an obscene amount of money).
Crypto is not creating anything. Its a scheme that is based on gamble.
Person A gets rich. Person B loses money. It does not really contribute to anything.
> is only desirable inasmuch as they are economically profitable.
The bug difference is that they are profitable because they create value, when cryptocurrencies are a zero sum game between participants. (It is in fact a negative-sum game, since some people are getting paid to make the thing work so that others can gamble on the system).
Openssh should have been a game changer but they made a classic openssh porting bug (not reading all bytes from the channel on close) and have now been sat on the fix in “prerelease” for years. I prodded the VP over the group about the issue and they repeatedly made excuses about how the team is too small and getting updates over to the windows team is too hard. That was multiple windows releases ago. Over on GitHub if you look up git receive pack errors being frequent clone problems for windows users you’ll find constant reports ever since the git distribution stopped using its own ssh. I know a bunch of good people at Microsoft, but this leadership is incapable of operating in a user centric manner and shouldn’t be trusted with embedded OSS forks.
I'm a simple man, if I open the shell and `ssh foo@bar.com` doesn't work, I don't use that computer. Idk if Windows has fixed that yet or why it's so hard for them. Also couldn't even find the shell on a Chromebook.
openssh has been an optional windows component for... almost a decade now? including the server, so you can ssh into powershell as easily as into any unix-like. (last time I set it up there was some fiddling with file permissions required for key auth to work, but it does work.)
OpenSSH on Windows is great for the odd connection and SFTP session, but I still feel strongly that any serious usage should just stick with PuTTY and WinSCP. The GUI capabilities these provide are what Windows users are used to. The only benefit of built-in SSH is if you're working with some minimal image stuff, like Windows Server Core or Tiny11. IMHO.
I would argue that if he wants to do AGI through RL, a LLM could be a perfect teacher or oracle.
After all i'm not walking around as a human and not having guidance. It should/could make RL a lot faster leveraging this.
My logical part / RL part does need the 'database'/fact part and my facts are trying to be as logical as possible but its just not.