A 13-year-old used my artificial nose to diagnose pneumonia

Yes, but if you listen to the video, it's clear Caleb has a clear understanding of what's going on.

His vocabulary rivals that of a 20 year old software engineer. It's pretty impressive.

I used Larry as an example because we're from the same city, ironically. My grandparents (the ones I knew anyway) are from the class that repaired electronics; we're the mechanics to their automotive engineers. My grandfather (born in 1919) was obsessed with televisions and repaired them as a side business, so the tools were all around for my dad when he started buying broken microcomputers and fixing them so he could play games. Which in turn taught him enough of the basics (ha) of BASIC and meant that when I was a kid and had basic coding questions he could help. But since we all learned on our own over 3 generations, our facility with tech doesn't 'count'.

I can't claim to be completely without privilege: I'm about as well-off as you can get and still be considered from a disadvantaged background: My mother dropped out of high school and ran away at 15 (ironically her family included engineers and at least one MSU professor), so while I never had the money, my socio never really matched my economic, class wise. It's more that if you were any lower on the ladder than I was, you just didn't have a computer or MAYBE you might have something like the 5 free hours of AOL in the late 90s. A computer with hours of internet access in 93 required some privilege.

There were a couple of G+T things I did, but I got the money we did scrounge for those from my dad, and once he married my stepmom that stopped because she believed very strongly in gender roles (she wanted me to like clothes shopping and makeup like my sisters and her female relatives) and she also wanted to live in the middle of nowhere.

> That said, I guess if you don't get a leg up as a kid and go on to be successful through grit you can always appreciate that your hard work and determination paid off?

Eh, I'm not going to be successful, because doing so would require neglecting either my own health or that of my family. I have MS, and that just explodes the ability to do any kind of career planning.

That has its own silver lining, though, because I can say whatever the hell I want. Which is its own kind of freedom: I have a 'get out of hustle free' card. It's almost like skipping from being 25 to 65: I had to do a lot of reckoning with my own mortality, what I was worth if I wasn't able to have a high powered career, what did I actually want out of life, etc.

a high end high school will have a biology teacher who can teach high school students to do crispr (crispr is quite easy).

My path into biology was looking into a microscope and seeing a world of non-computer state machines that behaved like cellular automata. and then my path led back to computers because real biology is much harder than CS.

And I think some kids get into astronomy if I demo my telescope showing them planets and stars. But I don't really push kids too hard in one or another direction; my parents thought I should learn german so I could excel at organic chemistry

go slugs

The vast majority of work is plumbing, but there are still new things.

What matters isn't making new things, it's making new things that work, and bringing them all the way to completion.

The vast majority of innovation isn't exciting discovery, it's coming up with tests to convince yourself you actually did what you thought you did and sharing those results with regulators. That's the difference between Theranos and GRAIL.

I just built a tardigrade detector neural network; nobody has done that exact thing before, and now i'm talking to the world's leading tardigrade researcher because my tool might be helpful in answering an unanswered important tardigrade question.

But thousands if not millions of individuals worked over hundreds to thousands of years to bring us that tardigrade detector; all I did was take advantage of that to label 100 images! I've tried being an ML researcher; my conclusion is the best models are distilled from postdoc tears, and we merely retrain those models without pain.

Having designed sensor systems, I've lost more than a few hours of my life having to explain "why do we need that big expensive sensor when you can do everything with machine learning?"

The idea that a magic math technique can replace expensive sensors predates NN's by a few decades. Dozens of start-ups have gone bankrupt trying to do non-invasive blood glucose with portable sensors.

This is a very crude but at least conceptually useful rule of thumb: It's all of the above, but ultimately the analysis result is a mathematical function of an array of values produced by the sensor. Very few math functions do not have the property, that variation in the output increases with the level of variation in the input.