Confession of a so-called AI expert

This is an odd point of view to have. While op may see this as pressure because of imposter syndrome, I would have DREAMED of an opportunity like this in college.

These entrepreneur may not quite understand what they're getting into, but make no mistake, they are very good at recognizing experts, both established and rising [although they do cast a wide net] and this kind of behavior drives life changing opportunities and make it substantially easier, in a difficult world, for the cream of the crop to be unlocked to exceptional success at an early age. And the rising tide helps all of us.

The smartest people in college are usually the ones who ate able to learn what they need to know outside of college on their own, once they have an income, and entrepreneurs unlock that potential.

It doesn't really matter if the OP (or anybody else really) is a super genius or not. People from outside the field can't tell, and so they approach those with a high media profile, and who match some of the preconceived ideas, like the buzzword bingo mentioned in the blog post.

The same is true even inside our field. When I'm interested in some architectural design pattern, I often read an article by Martin Fowler. Why? Because what he writes sounds plausible, and because I've heard his name a hundred times before. I've never seen production code he wrote, or been on a project he worked on. Maybe he feels like an impostor too, sometimes?

I'm not calling Fowler an impostor; I just want to draw the parallels how second-hand knowledge influences our perception of expertise.

Not even at insect level yet. There's no doubt things will improve, and there's already great value, but I hate calling ML "AI". It's been over 70 years of ML research (specifically neural networks) and I don't know how long it's going to take to reach insect-level behavior (which is still far from basic intelligence) let alone so-called AGI (which, BTW, people in the '50s were certain is just around the corner), even though I think we'll get there eventually. We'd better stop using the term "AI" to mean anything other than a field of research or an aspiration, and definitely stop using it to describe existing software.

Playing go or chess or matching patterns are all things intelligent begins can do but that does not imply that doing those thing means you are intelligent.

https://en.m.wikipedia.org/wiki/AI_winter

I mean, the dotcom bubble popped but websites are still here and more profitable than ever. The bubble popping doesn't mean that DL is going to go away. People will just have more reasonable expectations about what it can do.

1. Something works past all expectations.

2. There is craze, overhype, bubble and bust.

The markets have the ability to overvalue things that are great successes. In fact overinvestment and bubbles are often the result of real success.

If something generates 100X return, its completely feasible that markets value it in level that would require 200X return to be profitable investment.

Large amount of data has helped, so AI systems are better than before (with lots of training data), but that's pretty much it. It is not going to replace programming jobs, leave aside solving world problems.

Not equating ML to OR, directly. With today's horsepower and data sets, it is a brave new world. But we're in the elevated expectation phase of the hype cycle.

I've been told Berklee College of Music makes it very easy for students to leave and later come back, even years later, exactly to enable students to grab transient professional opportunities without sacrificing their education. I wonder how engineering schools stack up in comparison.

I've had my own calculus / discrete math / math for bio courses before but that was after several years as a doctoral student and TA at Georgia Tech. I can't imagine that there isn't a PhD candidate with more experience under their belt both teaching and using TensorFlow. The author even admits they volunteered to teach the course to stimulate learning the material themselves.

Stanford allows undergrads to propose and teach some of these more "practical" courses for upcoming technologies - a few examples are classes for NodeJS, cryptocurrency, Spark, and this one for Tensorflow. It's student-lead, very hands-on, and intended to give a specific industry experience.

The classes the author mentioned are first year pre-requisites for the AI specialization and doing research at labs, and gives enough background for a student to be instructive when explaining concepts such as perceptrons and svm's, without necessarily the mathematical rigor. This is the level needed to interact with Tensorflow.

https://registrar.stanford.edu/staff/student-initiated-cours...

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.

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."

Machine Learning is a feature set inside an application inside a market. It's not an industry of it's own where massive swaths of an industry place their money or livelihoods, like e-commerce or derivatives.

So in that sense there isn't any bubble to burst. The majority of ML applications are happening INSIDE massive technology companies, not as stand alone companies. Even then, the stand alone companies have a product that they are selling that ML functions with. So SaaS with ML, or Image Captioning or Translation service etc...