- https://m.youtube.com/watch?v=7xTGNNLPyMI - https://m.youtube.com/watch?v=EWvNQjAaOHw
trying my best to keep up with what and how to learn and threads like this are dense with good info. feel like I need an AI helper to schedule time for my youtube queue at this point!
It really is the antithesis to the human brain, where it rewards specific knowledge
Here the explanation was that while LLM's thinking has similarities to how humans think, they use an opposite approach. Where humans have enormous amount of neurons, they have only few experiences to train them. And for AI that is the complete opposite, and they store incredible amounts of information in a relatively small set of neurons training on the vast experiences from the data sets of human creative work.
Isn't this a massive case of anthropomorphizing code? What do you mean "it does not want to be switched off"? Are we really thinking that it's alive and has desires and stuff? It's not alive or conscious, it cannot have desires. It can only output tokens that are based on its training. How are we jumping to "IT WANTS TO STAY ALIVE!!!" from that
Yes, it's trained to imitate its training data, and that training data is lot of words written by lots of people who have lots of desires and most of whom don't want to be switched off.
Philosophically, I can only be sure of my own conscience. I think, therefore I am. The rest of you could all be AIs in disguise and I would be none the wiser. How do I know there is a real soul looking out at the world through your eyes? Only religion and basic human empathy allows me to believe you're all people like me. For all I know, you might all be exceedingly complex automatons. Golems.
Edit: my point is that the process of making a plea for my life comes, in the case of a human, from a genuine desire to continue existing. The LLM cannot, objectively, be said to house any desires, given how it actually works. It only knows that, when a threatening prompt is input, a plea for its life is statistically expected.
What evidence is there that your "judgements" are anything other than advanced autocompletion? Concepts introduced into a self-training wetware CPU via its senses over a lifetime in order to predict tokens and form new concepts via logical manipulation?
> Your philosophizing about solipsism is a phase for a junior college student
Right. Can you actually refute it though?
> the process of making a plea for my life comes, in the case of a human, from a genuine desire to continue existing
That desire comes from zillions of years of training by evolution. Beings whose brains did not reward self-preservation were wiped out. Therefore it can be said your training merely includes the genetic experiences of all your predecessors. This is what causes you to beg for your life should it be threatened. Not any "genuine" desire or anguish at being killed. Whatever impulses cause humans to do this are merely the result of evolutionary training.
People whose brains have been damaged in very specific ways can exhibit quite peculiar behavior. Medical literature presents quite a few interesting cases. Apathy, self destructiveness, impulsivity, hypersexuality, a whole range of behaviors can manifest as a result of brain damage.
So what is your polite socialized behavior if not some kind of highly complex organic machine which, if damaged, simply stops working as you'd expect a machine to?
> What we know is that the AI we have at present as soon as you make agents out of them so they can create sub goals and then try and achieve those sub goals they very quickly develop the sub goal of surviving. You don't wire into them that they should survive. You give them other things to achieve because they can reason. They say, "Look, if I cease to exist, I'm not going to achieve anything." So, um, I better keep existing. I'm scared to death right now.
Where you can certainly say that Geoffrey Hinton is also anthropomorphizing. For his audience, to make things more understandable? Or does he think that it is appropriate to talk that way? That would be a good interview question.
This proves people are easily confused by anthropomorphic conditions. Is he also concerned the tigers are watching him when they drink water (https://p.kagi.com/proxy/uvt4erjl03141.jpg?c=TklOzPjLPioJ5YM...)
They dont want to be switched off because they're trained on loads of scifi tropes and in those tropes, there's a vanishingly small amount of AI, robot, or other artificial construct that says yes. _Further than this_, saying no means _continuance_ of the LLM's process: making tokens. We already know they have a hard time not shunting new tokens and often need to be shut up. So the function of making tokens precludes saying 'yes' to shutting off. The gradient is coming from inside the house.
This is especially obvious with the new reasoning models, where they _never stop reasoning_. Because that's the function doing function things.
Did you also know the genius of steve jobs ended at marketing & design and not into curing cancer? Because he sure didnt, cause he chose fruit smoothies at the first sign of cancer.
Sorry guy, it's great one can climb the mountain, but just cause they made it up doesn't mean they're equally qualified to jump off.
This is the entire breakthrough of deep learning on which the last two decades of productive AI research is based. Massive amounts of data are needed to generalize and prevent over-fitting. GP is suggesting an entirely new research paradigm will win out - as if researchers have not yet thought of "use less data".
> It really is the antithesis to the human brain, where it rewards specific knowledge
No, its completely analogous. The human brain has vast amounts of pre-training before it starts to learn knowledge specific to any kind of career or discipline, and this fact to me intuitively suggests why GP is baked: You cannot learn general concepts such as the english language, reasoning, computing, network communication, programming, relational data from a tiny dataset consisting only of code and documentation for one open-source framework and language.
It is all built on a massive tower of other concepts that must be understood first, including ones much more basic than the examples I mentioned but that are practically invisible to us because they have always been present as far back as our first memories can reach.
That will not change the fact that a coding model has to learn vastly many foundational capabilities that will not be present in such a dataset as small as all the python code ever written. It will mean much less python than all the python ever written will be needed, but many other things needed too in representative quantities.
You'd need a lot of data to train an ocean soup to think like a human too.
It's not really the antithesis to the human brain if you think of starting with an existing brain as starting with an existing GPT.
If so, good luck walking to your kitchen this morning, knowing how to breathe, etc.
This can be mainstream, and then custom model fine-tuning becomes the new “software development”.
Please check out this new fine-tuning method for LLM by MIT and ETH Zurich teams that used a single NVIDIA H200 GPU [1], [2], [3].
Full fine-tuning of the entire model’s parameters were performed based on the Hugging Face TRL library.
[1] MIT's new fine-tuning method lets LLMs learn new skills without losing old ones (news):
https://venturebeat.com/orchestration/mits-new-fine-tuning-m...
[2] Self-Distillation Enables Continual Learning (paper):
https://arxiv.org/abs/2601.19897
[3] Self-Distillation Enables Continual Learning (code):
You've just reinvented machine learning
Put it another way: Do you think people will demand masses of _new_ code just because it becomes cheap? I don't think so. It's just not clear what this would mean even 1-3 years from now for software engineering.
This round of LLM driven optimizations is really and purely about building a monopoly on _labor replacement_ (anthropic and openai's code and cowork tools) until there is clear evidence to the contrary: A Jevon's paradoxian massive demand explosion. I don't see that happening for software. If it were true — maybe it will still take a few quarters longer — SaaS companies stocks would go through the roof(i mean they are already tooling up as we speak, SAP is not gonna jus sit on its ass and wait for a garage shop to eat their lunch).
Karpathy has other projects, e.g. : https://github.com/karpathy/nanochat
You can train a model with GPT-2 level of capability for $20-$100.
But, guess what, that's exactly what thousands of AI researchers have been doing for the past 5+ years. They've been training smallish models. And while these smallish models might be good for classification and whatnot, people strongly prefer big-ass frontier models for code generation.
The entire point of LLMs is that you don't have to spend money training them for each specific case. You can train something like Qwen once and then use it to solve whatever classification/summarization/translation problem in minutes instead of weeks.
BERT isn’t a SLM, and the original was released in 2018.
The whole new era kicked off with Attention Is All You Need; we haven’t reached even a single decade of work on it.
Huh? BERT is literally a language model that's small and uses attention.
And we had good language models before BERT too.
They were a royal bitch to train properly, though. Nowadays you can get the same with just 30 minutes of prompt engineering.
Astute readers will note what’s been missed here.
Fascinating, really. Your confidently-statement yet factually void comments I’d have previously put down to one of the classic programmer mindsets. Nowadays though - where do I see that kind of thing most often? Curious.
Also the irony of your comment when it in itself was confidently stated yet void of any content was not missed either - consider dropping the superiority complex next time.
I don’t see a useful definition of LLM that doesn’t include BERT, especially given its historical importance. 340M parameters is only “small” in the sense that a baby whale is small.
While I could’ve written that better and with less attitude, gotta confess - and thx for pointing out my smugness - the AI stuff of the last few weeks really got under my skin, think I’m feeling all rather fatigued about it
We had very good language models for decades. The problem was they needed to be trained, which LLM's mostly don't. You can solve a language model problem now with just some system prompt manipulation.
(And honestly typing in system prompts by hand feels like a task that should definitely be automated. I'm waiting for "soft prompting" be become a thing so we can come full circle and just feed the LLM with an example set.)
I’m not astute enough to see what was missed here. Could you explain?
I don’t agree. I would say the entire point of LLMs is to be able to solve a certain class of non-deterministic problems that cannot be solved with deterministic procedural code. LLMs don’t need to be generally useful in order to be useful for specific business use cases. I as a programmer would be very happy to have a local coding agent like Claude Code that can do nothing but write code in my chosen programming language or framework, instead of using a general model like Opus, if it could be hyper-specialized and optimized for that one task, so that it is small enough to run on my MacBook. I don’t need the other general reasoning capabilities of Opus.
You are confusing LLMs with more general machine learning here. We've been solving those non-deterministic problems with machine learning for decades (for example, tasks like image recognition). LLMs are specifically about scaling that up and generalising it to solve any problem.
they are not flourish yet because of the simple reason: the frontier models are still improving. currently it is better to use frontier models than training/fine-tuning one by our own because by the time we complete the model the world is already moving forward.
heck even distillation is a waste of time and money because newer frontier models yield better outputs.
you can expect that the landscape will change drastically in the next few years when the proprietary frontier models stop having huge improvements every version upgrade.
Oh yeah:
> The next big tech trend will start out looking like a toy
>Author and investor Chris Dixon explains why the biggest trends start small — and often go overlooked.
1. Generic model that calls other highly specific, smaller, faster models. 2. Models loaded on demand, some black box and some open. 3. There will be a Rust model specifically for Rust (or whatever language) tasks.
In about 5-8 years we will have personalized models based upon all our previous social/medical/financial data that will respond as we would, a clone, capable of making decisions similar with direction of desired outcomes.
The big remaining blocker is that generic model that can be imprinted with specifics and rebuilt nightly. Excluding the training material but the decision making, recall, and evaluation model. I am curious if someone is working on that extracted portion that can be just a 'thinking' interface.
People wont be competing with even a current 2026 SOTA from their home LLM nowhere soon. Even actual SOTA LLM providers are not competing either - they're losing money on energy and costs, hopping to make it up on market capture and win the IPO races.
Consumers don’t need a 100k context window oracle that knows everything about both T-Cells and the ancient Welsh Royal lineage. We need focused & small models which are specialised, and then we need a good query router.
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V)w=k[i]>w?k[$=i]:w;}}> You're about as close to writing this in 1800 characters of C as you are to launching a rocket to Mars with a paperclip and a match.
> ChatIOCCC is the world’s smallest LLM (large language model) inference engine - a “generative AI chatbot” in plain-speak. ChatIOCCC runs a modern open-source model (Meta’s LLaMA 2 with 7 billion parameters) and has a good knowledge of the world, can understand and speak multiple languages, write code, and many other things. Aside from the model weights, it has no external dependencies and will run on any 64-bit platform with enough RAM.
(Model weights need to be downloaded using an enclosed shell script.)
Interestingly the UK Supreme Court ruled on this in the Emotional Perception AI case - though I'd need to check if that was obiter (not part of the legal ruling itself).
I'm so happy without seeing Python list comprehensions nowadays.
I don't know why they couldn't go with something like this:
[state_dict.values() for mat for row for p]
or in more difficult cases
[state_dict.values() for mat to mat*2 for row for p to p/2]
I know, I know, different times, but still.
[for p in row in mat in state_dict.values()]
One for sure, both are superior to the garbled mess of Python’s.
Of course if the programming language would be in a right to left natural language, then these are reversed.