meow1032

I would say most research, to an ever growing degree, is so heavily dependent on software that it's tough to make that claim anymore. It makes no sense to me. It's like saying Zillow doesn't need software engineers because they are in the Real Estate business, not the software business.

Not that I'm saying using excel is bad either. I use excel plenty to look at data. But scientists need to know how to use the tools that they have.

If you're deciding who gets a large-scale computational biology grant, and you're choosing between a senior researcher with 5000 publications with a broad scope, and a more junior researcher with 500 publications and a more compuationally focused scope, most committees choose the senior researcher. However, the senior researcher might not know anything about computers, or they may have been trained in the 70's or 80's where the problems of computing were fundamentally different.

So you get someone leading a multi-million dollar project who fundamentally knows nothing about the methods of that project. They don't know how to scope things, how to get past roadblocks, who to hire, etc.

1. Studies can be much more expensive than most people think. In my field, a moderately sized study can easily cost $100,000+ if you're only accounting for up front cost (e.g. use of equipment, compensating participants). Someone would have to foot the costs of this.

2. Studies can be incredibly labor-intensive. PI's can get away with running studies that require thousands of man-hours because they have a captive market of PHD students, Post-docs, and research assistants all willing to work for low wages or for free. PHD students usually don't have the same amount of man-power.

3. For obvious reasons, studies that require high cost, high man-power work tend to get replicated naturally less. In other words, the least practical studies to replicate happen to also be the most necessary to replicate.

A couple of things I would dispute:

> it seems unlikely that replication studies would be devalued merely because they're done by students

I think academics value work in a particularly skewed way. There is "grant work" and there is "grunt work". Grant work is anything that actively contributes to getting grants for one's institution. Grunt work is everything else. PHD's can do grunt work, but that doesn't mean it will be valued on the job market. For example, software development is actively sought after in (biology) grad students, because it's a very useful skill. However, I've also seen it count against applications as professors because it shows they spent too much time on "grunt work". Software development skills don't win grants.

> Few students want to stay in academia

In some fields there aren't any options except to stay in academia or academia adjacent fields.

Sort of, a huge portion of income is from grants, particularly after the first few years from being hired. More importantly, a huge portion of the University income is from grants. When a researcher recieves a grant, there is an "overhead" percentage that goes to the University. Universities hire, in part, to maximize those overheads, which means getting the researchers with the best chance at getting big grants.

Changing the hiring process may affect how PHD students act, but once they're "in the system", they are subject to all the same problematic incentives.

I don't think this will work. All it will do is devalue the value of replication studies because only PHD students do replication studies. It's also not in their best interest especially if they dispute findings of established researchers.

Also, we have to get away from the idea that the scientist's job is to think and write, and literally all of the other work can be shuffled off onto low wage (or no wage), low status workers. This is one of the biggest reasons that science is going through such a crisis. If you want enough papers to consistently get grants you probably need at least 4/5 PHD students every few years. This causes a massive glut in the job market. It also dissociates scientists from their work. I've met esteemed computational biologists who could barely work a computer. All of their code was written, run, and analyzed by graduate students or post docs. They were competent enough at statistics, but that level of abstraction from the actual work is troubling.

Frankly, having worked in academia long enough to see at least a couple shifts in culture, the only thing I can see that comes out of this is a couple more things get added on to the ever growing checklist of publishing a paper/submitting a grant application.

I think we need to get away from the sort of thinking where large structural problems can be solved by tiny incremental improvements. If you really want to solve the problem, one or more of [Granting Agencies|Journals|Universities] has to be completely torn down and built back up.

This is absolutely not assuring. All this means is that the wrong people are going to be surveilled, arrested, and punished.

I remember getting these sorts of questions and having to write the sort of queries that you just wrote, but then having to write like 10 other ones to solve related questions with subtle differences(such as the event_type would change or something).

To be clear, I don't think it's strictly wrong, just that it adds nothing of value to a conversation. It just gives a very specific example of when a rule holds.

It's kind of like if the employment statistics come out, and they show improvement, but someone comments that they just got laid off that week. They aren't wrong, but they're also not adding anything to the conversation.

If the cost of most things depended only on quality then I'd find it more convincing, but especially when we're talking about consumer electronics, things like brand reputation, novelty, and user perception matter a lot.

I'd say maybe 10% of students who take psychology actually go on to do anything with research. Most of my friends in psychology are now counsellors, social workers, occupational therapists etc., and are good at their jobs, and this would not change if they had to take multivariate calculus.

At the same time, I do think psychological research would benefit if the people performing that research were better trained in math (actually when I say math, I specifically mean statistics and linear algebra).

I think the problem is that psychology is so broad that it can't possibly do a good job at catering to all these different concerns. Even at the graduate level, in order to become a clinical psychologist, you have to do a lot of research. The scientist-clinician model sounds good, but in practice I found that a lot of people who just didn't care about research were doing research. I found the same thing with medical students who were doing research to pad their resumes.

I also think that you can't just take a physicist or mathematician and plop them in psychology and start fixing everything. The problems often require a ton of theory, are really expensive to test, and the data quality is often terrible (because of human error, measurement error etc). Relevant XKCD: https://xkcd.com/1831/

In most of what I've read from him, it always feels like he's more concerned with trying to make the reader feel smug about understanding his writing than actually trying to communicate anything meaningful. In this article, he has an aside where he complains that Agricultural Companies are running a smear campaign against him and are "idiots" and "naive".

He seems to acknowledge that clearly there is some bound on this "minority rule" argument, but doesn't bother to further explore this, and instead goes on and writes as though no bound exists, or if it does, only exists when it benefits his argument.

I agree, I don't mean to imply that Hinton did all of the work on NN's, but he certainly contributed a lot to the field, even as the popularity waxed and waned.

Even if you just click on a few of the names in your link, you can see most of those authors submitted ~1-6 papers. Hinton, Bengio, and Jordan have submitted >50 each. I wouldn't say that NN's were exactly thought of as a joke before 2011, but from the people I talked to about them, they weren't considered very promising as practical statistical systems.

Neural Networks weren't really thought of very highly until CNNs started winning image recognition competitions in the early 2010's.

I think most people had the feeling that they were interesting tools to learn how the brain worked, but too slow and opaque to be practical statistical tools. I've been following Hinton for a while (because of Hinton and Shallice 1991), and my understanding is that it was really hard for him to get funding especially when he was just starting out.

The fact that so much of the work from the mid-80's to 2000 came from just a few labs should tell you how hard it was to get funding for that kind of research.