Confession of a so-called AI expert

This. I don't think people even get a hint of what is possible nowadays with DL in CV, NLP etc. I am actually depressed when I talk to some friends and they are so pitifully outdated, and then even after showing them how to do some magic in 100 lines, observing they are still not getting it and continuing in their old ways :(

I sincerely feel for the author of this post, and am not really sure how to explain my reaction while still being supportive, but...

Based on what the author is saying, part of me thinks imposter syndrome and bubble are both justifiable ways of thinking about what he's describing, but to me as an outsider the bigger problem it reveals is the way hiring and career development happens.

Without exaggerating anything about me, or without this coming from a place of jealously (although I can't deny I'm a bit jealous), it seems that I could easily teach the course they're teaching, with a deeper understanding of the material, and more justification for teaching it in many ways. I know that if I taught that course it would be fairly easy and not really stressful--fun in fact. I've taught courses on equally complex stats and math, and published in related areas.

And yet, there are no recruiters pounding on my door. If I applied for jobs most places would throw out my application for all sorts of reasons.

This person seems competent enough, so I do think there's impostor syndrome going on. Part of what they're describing is a normal process of teaching higher ed for the first time. And there probably is a bubble--the stuff they're describing is part and parcel of hype that goes along with bubbles, and although extremely useful, I think there's also a lot of problems with AI being swept under the rug.

This post really touches a nerve for me, because it gets at a problem with careers, at least in the US, which is that the bases of hiring decisions (and by hiring I mean broadly, not just as an employee) are so incredibly superficial. My guess is this person would function fine in AI, but I think anyone who knew me would have to bet that, between the two of us, I would be better qualified and better able to work in that area. But because this person taught an AI course at Stanford, they're more sought after than me, who doesn't even have a CS degree (although I do have a PhD) and certainly not a degree from an elite school.

I'm really at a difficult place in my life because I'm at a point in my career where I should be happy, and lots of people would say I'm successful, but to me I feel professionally typecast and trapped, by stereotypes and superficial appraisals. All the time you hear admonishments that degrees don't matter, etc. but then the reality is, they not only matter but matter in the most superficial ways possible, where it's not just having a degree and publishing and doing research in closely related areas, but having a degree covering exactly what is the focus of a hot plasma-magnitude bubble, from an elite university no less.

Most of the time at this point I just want a job that pays enough, and where I can live in a nice, comfortable safe place that I love. I've started to feel like the whole concept of meritocracy is a huge lie, and not because the people benefiting from it are incompetent--not because of false positives--but because of the huge problem of false negatives that lies in the shadows.

I am not sure he even understands complexity of DL itself. DL can be formulated as a non-linear optimization problem; that means it's one of the most difficult computational problems and the few types of topology we know are working with current "simple" non-linear optimizers are quite miraculous; I don't think anybody understands why these simple methods work so well when we restrict/structure the number of connections between layers and why fully-connected networks have such a terrible performance even if theoretically they should be able to handle everything better. So there is IMO a plenty of space for everyone to figure out their own niche with best performing algorithm in production and enable magical things in their apps.

Sort of tangential, but I felt this exact same way when I moved from being a post-doc in neuroscience to being a data scientist. The impostor syndrome was so strong it was painful. It has subsided now a bit because I know I'm able to bring value to my company - despite the fact that I know there are much more capable and qualified data scientists (by a large margin) out there, and despite the fact that by-and-large 'ML' and 'AI' is definitely a buzzword around here. But it really, really motivates me to strengthen where I'm lacking.

The funny thing is, it took me about a year to find this position after a good amount of rejections. About a year after I got my data scientist title, I've been contacted by recruiters from places I would have never expected to be contacted from (Amazon, Microsoft, FB, etc.). Did a few interviews, and realized during those interviews that I still have a lot to learn.

For one of the interviews, they gave me a take home assignment where they literally duplicated a column in the feature matrix... I didn't catch it, and during the phone part of the interview I get asked 'do you know notice something interesting about those two feature distribution plots you have there?'

"Hrmm, no I don't. Oh, wait, they look pretty similar."

"They're exactly the same."

"... shit."