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Comment by jokoon | Hacker News Reader

Science is the only field where ML would truly shine and be really useful.

There are tons of science problems where there are just not enough gray matter because it's just too expensive to train scientists. ML can crunch any data and result and speed up research by guiding experiments, where normal research just doesn't have enough resources to do so.

Of course, it really only works if the scientists are able to understand data and how to use ML, which is why computing becomes just a tool for a scientist, nothing else.

And again, ML is not really "smart", it's just sophisticated, improved statistical methods.

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As someone who's background is biology and physics and who does ML work as well. This is an incredibly optimistic view of ML.

>Of course, it really only works if the scientists are able to understand data and how to use ML, which is why computing becomes just a tool for a scientist, nothing else.

Ideally in science you would like to use literally anything else other than ML if possible, fitting models come with their own challenges and neural networks are even more of a nightmare. Understanding the world well enough to hard code a rule is always preferable to fitting to some data and hoping the model will come up with a rule. While there has been some attempts to use ML for feature detection it then takes a lot of experimenting to generally show if it detected signal or just some noise in your data.

Most of the things that would accelerate science would either require AI much more complex than we currently have (basically replacing lab assistants with AI) or are incredible research undertakings in their own right like Alpha Fold, Deep Potential Neural Networks, etc.

ML is an associative statistical system of function optimisation -- pretty much the opposite of science.

Ie., ML makes the assumption that data points are IID.

The whole purpose of science is to produce models which explain why data isnt IID.

> ML is an associative statistical system of function optimisation

You can also separate cause from effect by using causal inference, under some assumptions.

> ML makes the assumption that data points are IID.

Common ML algorithms do, but it is done for practical reasons rather than a limitation in the mathematics.

> The whole purpose of science is to produce models which explain why data isnt IID.

And ML can greatly help in this, though it is not a silver bullet.

Actually, there might not be a good way to model or describe the difference between causal inference, correlation and causality.

Causality involves a deep understanding of a phenomenon in science.

For example, the standard model of physics is pretty good at describing the real world in a good enough manner because we understand a lot of it. The difference with correlation and causality, in my view, is human, scientific understanding of what things are. Formulas, data or drawing are not enough.

For example there might never be a way to prove natural selection, even if there is a lot of data available, but a lot of scientific consensus is enough to describe causality.

When you say it's the opposite of science, you mean ML is just made of black boxes that completely hide away knowledge that humans can interpret?

Science is derived from scio, which in latin means knowledge.

It's true that in a way, ML allows new things, but which are still obscuring real knowledge...

I'm still curious about analysis of trained networks.

Totally agree.

While AlphaFold 2 is a tremendous achievement, to me the major drawback is the blackbox approach. It means it is very difficult to know when the model is outputting garbage and it also doesn't directly lead to new insights.

A much more interesting approach: "Discovery of Physics From Data: Universal Laws and Discrepancies" [1]

If ML did that, then it would be much more interesting.

[1] - https://www.frontiersin.org/articles/10.3389/frai.2020.00025...

> Science is the only field where ML would truly shine and be really useful.

You know that ML is a really important part of a lot of companies that aren't "Science", right?

Like Google search result rankings rely on ML.

> Science is the only field where ML would truly shine and be really useful.

It's clear to see how ML is useful for science, but why exactly do you think it's *only* useful for science? It seems like in order for that to be true, you'd have to expand the definition of science to basically everything.

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