alt Hacker NewsWe Stopped Using the Mathematics That Works
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> A Bayesian decision-theoretic agent needs explicit utility functions, cost models, prior distributions, and a formal description of the action space. Every assumption must be stated. Every trade-off must be quantified. This is intellectually honest and practically gruelling. Getting the utility function wrong doesn’t just give you a bad answer; it gives you a confidently optimal answer to the wrong question.
I was talking somebody through Bayesian updates the other day. The problem is that if you mess up any part of it, in any way, then the result can be completely garbage. Meanwhile, if you throw some neural network at the problem, it can much better handle noise.
> Deep learning’s convenience advantage is the same phenomenon at larger scale. Why specify a prior when you can train on a million examples? Why model uncertainty when you can just make the network bigger? The answers to these questions are good answers, but they require you to care about things the market doesn’t always reward.
The answer seems simple to me - sometimes getting an answer is not enough, and you need to understand how an answer was reached. In the age of hallucinations, one can appreciate approaches where hallucinations are impossible.
Just because you can analyse it doesn't mean that it is better. Deep learning theory is unbelievably garbage compared to the empirical results.
In particular, please show me a worked example of a decision tree meta learning. Because its trivial to show this for DNNs.
We are at the age of alchemy, wait for the age of chemistry and physics. New mathematical foundations are yet to be found.
Heading down the links of this blog ends up at https://github.com/gfrmin/credence, which claims to be an agentic harness that keeps track of usefulness of tools separately and beats LangChain at a benchmark.
LangChain… Now that’s a name I haven’t heard in a long, long time..
Anyway, that’s a cool idea. But also his blog posts include phrases like “That’s not intelligence, it’s just <x> with vibes.” Urg. Slop of the worst sort.
But, like I said, I like the idea of keeping a running tally of what tool uses are useful in which circumstances, and consulting the oracle for recommended uses. I feel slightly icky digging into the code though; there’s a type of (usually brilliant) engineer that assumes when they see success that it’s a) wrong, and b) because everybody’s stupid, and sadly, some of that tone comes through the claude sonnet 4.0 writing used to put this blog together.
See also: https://gfrm.in/posts/agentic-ai/
> I’ve spent the last few months building agents that maintain actual beliefs and update them from evidence — first a Bayesian learner that teaches itself which foods are safe, then an evolutionary system that discovers its own cognitive architecture. Looking at what the industry calls “agents” has been clarifying.
> What would it take for an AI system to genuinely deserve the word “agent”?
> At minimum, an agent has beliefs — not hunches, not vibes, but quantifiable representations of what it thinks is true and how certain it is. An agent has goals — not a prompt that says “be helpful,” but an objective function it’s trying to maximise. And an agent decides — not by asking a language model what to do next, but by evaluating its options against its goals in light of its beliefs.
> By this standard, the systems we’re calling “AI agents” are none of these things.
a voice of reason cries out in the howling maelstrom
Tldr: the author is annoyed at the Bitter Lesson.
Join the crowd dude. It's still true, no matter how inconvenient it is.
I found the article confusing. Its premise seems to be that alternative methods to deep learning “work”, and only faded out due to other factors, yet keeps referencing scenarios in which they demonstrably failed to “work”. Such as:
> In 2012, Alex Krizhevsky submitted a deep convolutional neural network to the ImageNet Large Scale Visual Recognition Challenge. It won by 9.8 percentage points over the nearest competitor.
Maybe there’s another definition of “works” that’s implicit and I’m not getting, but I’m struggling to picture a definition relevant to the history-of-deep-learning narrative they are trying to explain.