This AI Boom Will Also Bust

We're building an applied AI business by creating an experience through both hardware and software. You don't set out to create something with as big a breadth of vision by worrying about booms and busts. You continue your journey unwavering because the potential impact and fruitfulness of development is worth it.

This is why you should work on something you're passionate about. Your time on earth is limited, so strive to leave good work and contribute to the progress of humanity on a larger scale.

A PhD is, by it's nature, rather self structured. How much value will be added to your skills over that time will depend in no small part to how you spend it.

> AI helps provide good analysis without having to cleaning up data manually.

I don't even know where to start. I suppose you don't really think that what separates ML/AI (whatever that means) from your standard OLS regression is that denoising is not done manually?

> Cleaning up data is very expensive. And without that, the analysis is good for nothing.

I hope you are not saying here that linear regression cannot handle noise.

In the end, as this blog post also points out, ML/AI is just a vague blanket term. What you want is a statistical method that captures the signal as fast and efficiently as possible and often the gain from going beyond simple linear models might be marginal.

you'll be a programmer - that is what counts. How good of a programmer you will be will determine your success. never put your eggs in one basket (not saying you shouldn't become an ML expert though, that's pretty damn nice). as a Phd, you are probably good enough.

as to ML, its adoption is hyped. it is powerful, but not as anyone really talks about.

support vector machines and Bayesian learning have been around since the 70s/80s (ninja edit: SVM's since 1963! Markov Chains 1950s, Bayesian Learning/Pattern recognition sine the 1950's), but adoption has been slow due to the nature of business, which is now drooling over it since neural networks beat a few algorithms.

due to the hype, more business will opt for ML now, but the craze will plateau and ML will become another tool in your arsenal.

so basically, you really have nothing to worry about - use your Phd to do interesting things, come up with novel and new research and/or develop your own product.

don't let your job security worries get in the way of enjoying what you want to do now, you're already good and in STEM (and if you don't feel good enough, work on yourself until you do).

> AI helps provide good analysis without having to cleaning up data manually.

My own experience has shown that dirty data impacts advanced AI just as much as it impacts far more basic ML techniques.

Even for the most advanced AI we work on, we spend just as much time worrying about clean data as we do anything else.