citation_please

This is a very challenging view-point, and I appreciate the effort you've put into backing it up. I think my biggest hesitation of accepting its results is the difference between annual income and wealth. By this study's standard, I'm nearly upper-middle-class, but I have a negative net-worth due to student loans. If my employment status came into question, I would be very quickly impoverished, and would have to rely on many social and governmental safety nets. Can this really be considered rich?

Maybe I'm an outlier as an individual in my mid-20s, but it's enough to make me question the definition of class on annual income.

Then Juul should be put next to the nicotine patches and gum in the pharmacy, and be marketed as a medication. The FDA heavily regulates substances that can be abused when not used as medication (things like meth ingredients).

Frankly, letting a company aggressively market a highly addictive compound to an impressionable segment of society because of a single-use excuse ("it can be used as medication!") is a bit disappointing. It also reads as astroturfing because it's such a limp talking point.

But this is somewhat an admission of using it as medication, which is far from what Juul is being marketed as. It should be next to the nicotine patches at the pharmacy. Can you even buy nicotine patches in bulk? Substances that can be abused for something other than their primary use as medication are heavily regulated by the FDA (think meth ingredients).

Huh. Well let's assume causality points in only one direction, genetics->tissue->ability, then I'm screwed and there's nothing I can do about it. Let's assume that it at least points partially in the other direction, ability->tissue, now I can keep my tissue healthy by exercising.

Since I don't know for sure which direction causality points, I'm going to take the path of maximal self-determinism and try to stay fit as long as possible.

This is probably heavily affected by at least two things:

1. increase in healthcare costs and how health insurance is bound to employment

2. increase in wealth concentration and a shift from cash compensation to asset compensation (Bezos is only absurdly rich if he liquidates his Amazon holding instantly)

So, I would say your portrayal of hourly compensation is disingenuous as well. All of these statistics probably need to be calculated as medians instead of means ("compensation per hour" sounds suspiciously like "total compensation / total hours", which is a mean) in order for it to come close to accurately describing the situation of the median American, because wealth concentration has skewed the mean American into something a lot more optimistic than one would think.

Made me think: if we cut corporate taxes, raised the minimum wage, and reduced means-tested welfare, where would we end up? Same position but with less bureaucracy?

Something tells me that the welfare safety net has more societal benefits than a wage floor, but right now we don't really have a respectable form of either compared to 20 years ago.

Hmm, electronic genres are definitely clustered around 128, 140, and 170 (+/-2), and those are all reasonable heart rates when you're partying...But a "human heart beat" can be literally anything so I'm not convinced.

Well I'm not the one who does resume sorting, so usually if it makes it to my desk I know there's a good chance they're qualified. If you give me (personally) two resumes, one with a master's degree from almost anywhere, and one with two years of experience doing /exactly/ what we do, I'll choose the latter first. At this point, master's degrees don't carry the weight they used to in my mind based on (1) people I've interviewed (2) my coworkers and (3) my friends and acquiantances.

I would argue that abstaining from voting in a formal setting is significantly different than not "voting" in modern elections. There's significant historical evidence of voter suppression, including suffragist movements. Voter motivation is not something that I think we should brush away lightly, because it has an impact on democracy just like voter manipulation and voter suppression.

Adding onto this, every physics PhD I've talked to has really impressed me with how much problem-solving they tackled during their stint in academia. You really said it succinctly with "a physics student could find themselves having to learn practically any technical skill".

Running and extending numerical simulations with supercomputer clusters is just something you have to learn to do in order to solve your problem in some cases, apparently.

Their ability to code is usually sub-par, but they ask the right questions about the data, which is critical in the data setting that we work in.

We don't work in advertising / marketing / business analytics, which is very easy to have an intuition about; and we don't strictly work with images, which, again, isn't terribly difficult to have an intuition about. So a strong scientific background is actually a major plus for us.

I could see that if we were doing purely deep learning image classification or advertising prediction then the physics degrees would be less useful, but thankfully we don't.

Probably the wrong fit if the industry experience isn't right, in which case they would be on a fast-track to a senior dev role in the non machine-learning part of our company.

You're right about "research", although I'd hesitate to use the word hardcore hah. We get tons of candidates with ~2 years of slightly relevant work, but rarely do we get candidates with the exactly right relevant work. The "qualified" candidates I spoke of have ~4 years of slightly relevant work, but there are only a couple of them, and they're either out of our hiring budget or currently working for our team.

I'm on a hiring committee for a job with the title of "Machine Learning Engineer". The most qualified candidates we have come through the process are physics PhDs who did a significant amount of coding during their PhD and have been able to segue into machine learning because they were interested.

Our other tier of hireable candidates have been individuals with 4+ years of industry experience, usually with CS PhDs with a machine learning specialty.

The rest of our team came from internal transfers, people who were less qualifed but proved that they would have a positive impact on the output of the team.