[amelius]

Not sure what you are saying here. Perhaps an analogy helps.

Psychology is a science. You can make falsifiable statements about the human brain. You will need experiments to build and test theories. It's the same with deep learning.

With computer "science" (and math) it's not the same. You can reason completely about your subjects, i.e. you can determine if something will or will not work just by reasoning, no experiments needed.

For more information on the differences between math and science I recommend reading: https://en.wikipedia.org/wiki/Scientific_method#Relationship...

[swexbe]

Seems like the distinction is mainly between which tools are available to you as a scientist (at least if we stick to comp-sci, math is in a league of it's own). When, or if, we can completely model a human brain, a psychologist would no longer need to perform experiments to test their theories.

Given enough computing power, most theories could theoretically by proven or falsified purely through reasoning.

[amelius]

The point is: being able to run a brain inside a computer is not the same as understanding that brain. If you wanted to build a new brain, you'd have to reach for the tool all the time in an iterative way and hope for the best. Only tools that aid in understanding matter. We have only very few tools that help DL researchers better understand what they are doing. Hence DL is more akin towards science than towards math/CS or engineering.