Hacker News Reader

Comment by michaelrpeskin | Hacker News Reader

An introductory book on neural networks, written a few decades ago, starts with a short history of the field at that time.

This included a phase where some physicists began having opinions about the subject, anticipating they might quickly find a model for the brain, with fields, or other physics-like paradigms.

Their expectations were that with their superior understanding of all things fundamental, they would rush in, and rush out. Leaving the stunned machine learning researchers dazzled, frazzled, and asking "Who was that masked physicist?!?"

Except their ideas went nowhere.

I can't find the book, but this story made for a memorable foreword.

https://xkcd.com/1831/

That's a great xkcd, but there are 2 upsides to this arrogant approach. First, arrogance is a nerd-snipe maximizer. Second, there is a small chance you're absolutely right, and you've just obviated a whole field from first principles. It doesn't happen often, but when it does happen and there is no clout like "emporer's new clothes" clout.

EDIT: The downside, of course, is that you appear arrogant, and people won't like you. This can hurt your reputation because it is apparently anti-social behavior on several levels. I think its fair to call it a little bit of an intellectual punk rock move that is probably better left to the young. It's an interesting emotional anchor to mapping a new field, though.

> Second, there is a small chance you're absolutely right, and you've just obviated a whole field from first principles.

Mostly when I read about things like this happening, it's happening to a formerly intractable problem in mathematics. Do you have examples outside of math?

Alfred Wegener as the initial author on the theory of plate tectonics comes to mind. He was a trained meteorologist who observed the similarities between geological formations between the South American east coast and African west coast. He was lucky, in that his father in-law was a prominent geologist and helped him defend this thesis.

Oh yeah, revolutionary insights are very important for the advancement of knowledge and the elimination of wrong ideas. But as you wrote, this was the work of a thesis, not a random commenter from another field.

LLMs weren't invented by Naom Chomsky but Math/CS guy with a simple "Attention is all you need"

Creating a good chatbot was never a goal of linguistics, and I don't see what insights into human language have been derived from LLMs.

The only time I’ve seen this happen is AlphaFold. Every other time some math or software guy came into biology it worked exactly like in xkcd.

The biggest innovation of this decade, LLMs was created by Math/CS not linguistics

That's a good one.

Biology has already been transformed by mathematics, statistics, and CS for decades. These days, if something like in that xkcd happens, those people are probably not even familiar with the relevant parts of math / stats / CS.

What exactly has it transformed? Nothing has fundamentally changed in biology, you still have to run western blots and give mice cancer. A great example of “math” solving biology is super resolution - which was just a dud.

Genomics, for example. The entire field could not exist without extensive algorithmic research done over several decades. Even today, many of the key people in the field have a background in CS and mathematics.

Theoretical ecology is a bit more old-school answer. Many mathematicians have been involved in that field.

I am not sure I have seen any meaningfully brilliant breakthroughs in those fields though.

I take that back. One can never forget the brilliance of HiC by Erez. But other than that TBH all other “mathematical” or computational breakthroughs I’ve seen are just at best meticulous application of obvious math and computational algorithms to biological problems (and may I say poorly? Thinking back to the microarray nightmare years).

I guess that depends on what you count as a breakthrough. From my perspective, HiC is a small detail, and even AlphaFold is just the latest improvement to an existing process. A true breakthrough would be something like ancient DNA, which gave the humanity a new tool for studying the past.

Pretty much everything in genomics depends on shotgun sequencing, which in turn depends on CS. The algorithms used for assembling something useful out of the sequence reads are highly non-obvious. In fact, most developments in string algorithms since the 80s have been driven by the needs of DNA sequencing. (Information retrieval used to be another contender, but word tokens turned out to be a better tool for that field.)

Not laughing! /s (physicist here)

Actually most applied physicists like myself go down that path cause we're pretty efficient, lazy folk & skip through as fast as possible--I call it the principle of maximum laziness.

Reply on news.ycombinator.com