

The wonderful and terrifying implications of computers that can learn - jph00
https://www.youtube.com/watch?v=xx310zM3tLs

======
akashtndn
My query deals with the social implications you mentioned at the end of your
talk. Are we, as a society, equipped to deal with the changes the surge in
computational efficiency deep learning is about to bring when a large portion,
including the policy-makers, aren't even able to properly acknowledge them?
Can you expand on your last slide's point where you mention that "better
education" won't help in dealing with the upcoming upheaval?

------
gradschool
Thanks for this interesting talk and best wishes for the success of your
company. Here are a few questions it raised for me. What references would you
recommend for someone who wants to know more about machine learning algorithms
from a technical point of view? Does the coming economic upheaval you envision
with its exponential growth depend on the assumption that machine learning
will be applied to the design of computers themselves? The reason I ask is
that the applications you demonstrated concentrate on pattern classification,
which doesn't seem to me like it would be much help for the work of electrical
engineers and computer scientists (albeit a game changer in other fields). Are
you aware of any evidence to the contrary?

~~~
jph00
I would start with the Intro to Machine Learning Coursera course, and then the
Intro to Neural Networks course. That will get you to a point that you can
understand current papers.

The demo I showed actually showed that it's possible to design an algorithm in
minutes that previously would have taken years, but combining machines and
people. So I do strongly believe that machine learning allows us to create
new, better, programs more quickly than before!

------
jph00
I'm the speaker from this TEDx talk (and Enlitic CEO / Kaggle Past President)
- happy to answer any questions or respond to any comments about it here.
Sorry for some rather extreme simplifications in the talk... I only had 18
mins to cover a lot of territory!

