(September 5, 2017 at 5:37 am)ThoughtCurvature Wrote: Mathilda, in contrast, research is going in directions largely concerning very general algorithms, or general intelligence.
Don't forget about unsupervised learning models that already exist today (and are only improving):
(1) Manifold learning or Deepmind's "Early Visual Concept Learning with Unsupervised Deep Learning"
(2) Generative Adversarial Networks that uses unsupervised learning (See Wikipedia "Generative_adversarial_networks")
I've been working on unsupervised learning models for over 20 years but thanks for telling me that they exist.
While I agree that unsupervised learning is a step in the right direction towards artificial general intelligence, or strong AI, there is a big difference between a AI that can learn a static data set unsupervised and one that can act, learn and relearn in real time. This is important because the real-world is not a static data set.
I am also well aware of the need for generalisation. As far as I am concerned, this is the key reason why so much AI fails. People get excited by initial results but don't realise that the challenge actually lays in scaling it up because by doing so the problem domain grows exponentially.
You also did not respond to my point that about EU's GDPR which demands that automated decision making needs to provide explanations which will dramatically reduce the profitability of the current deep learning approach. It is precisely because neural networks lack explanatory power that I have stopped using them myself.
