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Comment by throwawaywego | Hacker News Reader

The article states positive impacts on science, but there are also negative impacts on science. For instance, the hype of AI has caused a brain-drain on related fields (such as cognitive science or applied mathematics). AI research itself suffers from companies buying up the academic talent. And researchers slap AI (which is usually deep learning) on a decade-old problem, without any care for complexity/benchmarks, implementation/usage, and proper validation methods, just to get published or receive funding.

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It's also makes for a lot of terrible talks. In my field (bioinformatics) it's frustratingly common for a PI to give a keynote which boils down to "some grads students made DeepX and got an AUC of 0.85 on this problem and some others made ML-Y and got an AUC of 0.78 on this other problem and a postdoc did this other ML thing." There's no details or insight, basically just a sales pitch for their software package. Nothing in the talk can be transferred to other topics. The only time the talk is useful is if you happen to need to solve problem X and they've got a tool to solve it for you. You couldn't give a talk about a statistics model without explaining the model, but it seems to be totally OK to give talks about ML projects without saying anything more than that it's a deep net or random forest.

That kind of bullshit has been happening in bioinformatics from the beginning, though. You can't really put it on deep learning. It's a feasible way to get publications and grants, because no one is ever held to account for it.

I also wish people would stop using AUC, and start using a measure reflecting realistically useful specificities. I don't care if you have 99% sensitivity at 90% specificity.

I'm really afraid that ML is mainly just going to become automated p-hacking, and bring about a dark age to much of science. In a publish or perish world, how can you compete with someone with enough budget to set a bunch of models looking for any specious correlations in data sets and publishing what comes out the other end? Like we'll still have great breakthroughs from the top of the field, but a lot of grunt work style studies are going to lose their ability to be trusted.

>ML is mainly just going to become automated p-hacking, and bring about a dark age to much of science

It is already happening. A lot of people who can contribute to actual Science are moving into AI/ML field for the money and the industry/media hype are reinforcing this. Everything is "Deep${NONSENSE}" nowadays whether it is relevant or not. As a beginner, when i started to learn NNs, i couldn't get past my initial hurdle on how to validate the results on actual real-world data. What Statistical metrics do i use to "know" that the blackbox is working correctly? What are the assumptions and limitations that i need to be aware of to understand and have faith in the output? Most people don't seem to know or care; it is "magic" to them. In a world awash with data, reckless application of NN models to any and every problem is only going to drown us in spurious results and muddying all Scientific endeavours.

Contributing to this problem is the fact that professors are grimly aware of needing to confer some marketable skills on their students. And ML is perceived as being a meal ticket right now.

I love how many paper titles nowadays follow the pattern: "Deep-<topic>: <Actual title of the paper>". And often they aren't doing anything "deeper" than a fully-connected multilayer neural network--a machine learning algorithm competitive with SVMs and been around well over a decade.

That's true, but there's a lot of value in waking people up to the idea that ML works, even if what they're doing has worked for a long time.

There are a lot of situations where before people would have assumed their best option is to carefully tweak a custom statistical model, whereas now they're just happy to throw a black box at it and see what happens. This is as much a cultural change as a technological change, and it's good that it is finally happening. That's what a "paradigm shift" is after all.

But why is "throwing a black box at it" good?

The goal of research is usually to rip those boxes open to figure out what's inside and how it works. Moving away from that towards opaque predictions doesn't make a lot of sense to me, especially when the predictions aren't even that much better. Plus, a lot of this work seems weirdly disconnected from what the rest of the field knows to be (im)plausible.

Obviously, black boxes can be useful tools. DeepLabCut is incredibly helpful and will save a lot of grad students a lot of tedium, and that probably wouldn't happen if it involved a lot of tuning. Predictions can also be very useful--frankly, we'll take anything we can get for most neuropsych conditions--but mechanisms and targets for intervention are so much more useful. I know there is some work on this, but it's drown out by the 0.99AUC!!1!! (in a small, cherrypicked group) stuff.

Black boxes are better than nothing. The ultimate black box is the universe, where experimental scientists can fiddle to try and understand. They give a great starting point to make progress, if that's what you want, and if not (like in some commerical applications) you have something that works (ish).

I think something is lost when doing this. I'd bet the researcher who first builds a model and then reaches for ml will outperform the researcher who goes straight for ml.

Building a custom model will help with feature selection. It will provide a baseline to compare the ml model to which can help debug problem points of the ml model. And finally it serves as a sanity check that you aren't leaving a lot of performance on the table.

More money is always good for researchers. At the end of the day, being paid more is the free market doing resource allocation when basic research in other fields isn't being appropriately subsidized.

In classical economic theory, one pre-condition for efficient market allocation of resources is accurate information providing a basis for rational levels of investment.

If AI is subject to crazes, with investors as a whole drastically overestimating its potential, then it's certainly possible to over-allocate capital (human and otherwise) to it in the hopes of a payoff.

Consider the Dutch tulip mania of the 1630s. Imagine if it had lasted a bit longer, long enough for promising scientists and scholars of every type to be trained solely to optimize the growth of tulips.

This allocation of capital would provide a benefit to tulip investors for as long as the craze lasted, but would prove to be a detriment to society once the craze ended.

https://en.wikipedia.org/wiki/Tulip_mania

To be fair, the connectionist variety of ML is extremely compatible with the majority of the hard sciences (Linear Alg, Calculus, not really much CS/discrete math if you think about it). A physics/rigorous CogSci background prepares you just as well as CS for most of the interesting AI stuff. The fact that AI and CS in general has such a low barrier of entry is something to be celebrated. Be glad that our domain do not suffer from the gatekeeping in Medicine and Law.

Lol what about CS then ? So many STEM students go for CS rather than pure sciences. You can't not have new fields because other fields may suffer.

Companies don't care what it is called. People are hired to do jobs.

'AI' is just math + programming. Don't overthink it.

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