"The names keep changing—it used to be unsupervised learning, now it’s called big data or deep learning or AI"
Um, I'm sorry, but unsupervised learning and deep learning are not the same.
Um, I'm sorry, but unsupervised learning and deep learning are not the same.
In other words, terminology can be used to make precise, meaningful distinctions, or it can be used to embellish.
Deep learning refers to a particular type of a particular learning technique: Specifically a neural network that has many hidden (intermediate) layers. Deep learning can be used for either supervised or unsupervised learning.
Furthermore, suppose you have labels for some but not all points on your data (i.e. your model is designed to be robust in the face of things it hasn't been trained for). There are a nontrivial number of people who work on either side of the "semi-supervised" divide, e.g. clustering with examplars or pulling out the generative model for a discriminative task. Personally I like these better, as they're more akin to what people seem to actually do (encounter new things and try to make sense of them).
Anyways. If you look at the delta in performance between "old" techniques like random forests or gradient boosting vs. deep convolutional networks, it tends to be quite small until your datasets grow to very large sizes. For things like images that's not much of a problem. For things like rare diseases it's a huge problem.
Deep learning: a particular type of artificial neural network with many hidden layers (and the associated tech to make this work/trainable)
AI: The field of computer science which aims to make computers smarter. Like most fields, there is much overlap with others, for example, statistics.
Big Data: A buzzword. About the best definition I can find is anything which has the 3 V's: Volume, Velocity & Variety. In general, outside its use as a buzzword, I think big data is generally thought of as "when you need a distributed system to process your data", be it because of volume, velocity or variety.
Supervised and unsupervised learning: whether or not you require example data for training
Machine learning: some people say its the subset of AI that deals with statistical methods, other people say its just another word for AI.
Which is the point he was trying to make.
Just because you lack ability to understand nuances of something does not makes it "garbage".
The point being that, no matter how sophisticated these techniques are, the quality of the results is constrained by the quality of the input data.
As Charles Babbage said: "On two occasions I have been asked, 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."