E.g. abbreviating deep belief nets with DBM, which is the commonly used acronym for deep boltzmann machines. These are similar, but very different. Calling an RBM an encoder is somehow not far fetched, but there are many differences between auto encoders and RBMs. He eventually claims an RBM minimises reconstruction error, which is just plain wrong and shows that this guy has absolutely no clue what he is writing about.
> Engineering is the application of scientific, economic, social, and practical knowledge in order to invent, design, build, maintain, research, and improve structures, machines, devices, systems, materials and processes.
The author should probably revise his definition of engineering.
Yes. Also the guy does not seem to be that knowledgeable.
He did not get the memo that more input data can lead to worse results; he claims the opposite in a slight variation (i.e. the more precise the input data, the higher the quality of the result).
I think the difference is that Prolog was aimed to solve all kinds of programming problems. PPs on the other hand is very domain specific from the start.
> A 'predictive model' should say 'if you do X then you will end up with Y' - and the X cannot be adjusting some number. The X has to be stuff like 'building ETU's in West Africa', or 'canceling all flights',... A predictive model should be able to say 'don't bother canceling flights, it's no use - instead do this...'.
This is just wrong. A predictive model does not necessarily have any "action" input. Example: weather forecast.
> Brains work probabilistically at least in the sense that the underlying biomechanics have some statistical distribution.
No, it's us that use these distribution to model them. Reality might be deterministic after all (no proof of the contrary), and then brains are not working probabilistic at all.
Deep learning research cares neither about identifiability nor global minima. Mostly, local minima are good enough to get the "job" done; here job is the task you are trying to solve.
In fact, it is easy to show there is an combinatorial amount of equivalent global minima.
This is only interesting if you want to analyze the model. Not if you just want a function f(x) that tells you whether x is a cat or a dog.
Whatever the uni. He was working at IDSIA, one of the main contributors to deep learning. Also, his advisor, Markus Hutter, did some serious stuff on AGI.
Also: aerodynamics is not really hard, anyone can fold paper planes!
Or: programming 3D games is easy, just build new levels for an old game!
Or: I don't know what I am doing here, but look, this photoshop effect looks really cool on my holiday photos!
etc.
Seriously:
The writer would not be able to write anything about K-Means if not for people looking at it from a mathematical view point. This angle is of tremendous importance if you want to know how your algorithm behaves in corner cases.
This does not suffice, if you have an actual application (e.g. a recommendation or a hand tracking or an object recognition engine). These need to work as good as you can make it because every improvement of it will result in $$$.
Well, not that important. It's only important that the buzzword appears in the title!