So, way back in the last millenium, I did my Master's thesis (way smaller deal than a Ph.D. thesis) on neural networks. Since then, I have looked in on it every few years. I think they're cool, I like using them, and writing multi-level backpropagation neural networks used to be one of the first things I'd do in a new language, just to get a feel for how it worked (until pytorch came along and I decided for the first time that using their library was easier than writing my own).
So, it's not like I dislike ML. But, saying an investment is an "AI" startup, ought to be like saying it's a python startup, or saying it's a postgres startup. That ought not to be something you tell people as a defining characteristic of what you do, not because it's a secret but rather because it's not that important to your odds of success. If you used a different language and database, you would probably have about the same odds of success, because it depends more on how well you understand the problem space, and how well you architect your software.
Linear models or other more traditional statistical models can often perform just as well as DL or any other neural network, for the same reason that when you look at a kaggle leaderboard, the difference between the leaders is usually not that big after a while. The limiting factor is in the data, and how well you have transformed/categorized that data, and all the different methods of ML that get thrown at it all end up with similar looking levels of accuracy.
There used to be a saying: "If you don't know how to do it, you don't know how to do it with a computer." AI boosters sometimes sound as if they are suggesting that this is no longer true. They're incorrect. ML is, absolutely, a technique that a good programmer should know about, and may sometimes wish to use, kind of like knowing how a state machine works. It makes no great deal of difference to how likely a business is to succeed.
So, it's not like I dislike ML. But, saying an investment is an "AI" startup, ought to be like saying it's a python startup, or saying it's a postgres startup. That ought not to be something you tell people as a defining characteristic of what you do, not because it's a secret but rather because it's not that important to your odds of success. If you used a different language and database, you would probably have about the same odds of success, because it depends more on how well you understand the problem space, and how well you architect your software.
Linear models or other more traditional statistical models can often perform just as well as DL or any other neural network, for the same reason that when you look at a kaggle leaderboard, the difference between the leaders is usually not that big after a while. The limiting factor is in the data, and how well you have transformed/categorized that data, and all the different methods of ML that get thrown at it all end up with similar looking levels of accuracy.
There used to be a saying: "If you don't know how to do it, you don't know how to do it with a computer." AI boosters sometimes sound as if they are suggesting that this is no longer true. They're incorrect. ML is, absolutely, a technique that a good programmer should know about, and may sometimes wish to use, kind of like knowing how a state machine works. It makes no great deal of difference to how likely a business is to succeed.