The problem with #machinelearning in a nutshell, via extensional vs intensional logic:
A: {2, 4, 6, 8, 10, ... X}
B: {2x: x ∈ N}
There is no way for a statistical, asymbolic machine to arrive at B from A, no matter how large you choose X @GaryMarcus @filippie509 #ai
-
Don't get me wrong: it's great that people are pointing these things out and testing the limits of DL. But it's good to form a balanced view. Things aren't as black and white as "omg neural nets can do everything" vs "omg here's one failure mode let's ditch it all"
Agree w @egrefen here, too, but find that no matter how hard I advocate for hybrid models people always think I am arguing against all of ML... I think ML will play a huge role in AGI, but only w proper biases, including representations of operations over variables.
-
-
Properly understood,
#MachineLearning is the study of the relationship of biases to what can be learned with each (and how to implement them computationally). It's bias, all the way down. -
and yet innateness is a dirty word in ML. amazing.
- 7 more replies
New conversation -
-
-
Well said !!
Thanks. Twitter will use this to make your timeline better. UndoUndo
-
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.