autranep

Hey dude have you ever thought of doing some dishes from Hannibal the TV show? Barring the human meat of course

As someone who works in modern AI research (deep reinforcement learning, especially applied to continuous control) I really am not convinced by your arguments. For one you seem to assume that AI research is limited to first order logic but that's not the case. We know now that we can learn continuous embeddings of symbolic relationships (even in a theoretically turing complete way) through simple continuous optimization (and literally anything can be modeled as continuous optimization; what we know now that we didn't know 10 years ago is that a lot more complex, and interesting, optimization problems are tractable than we previously thought or had any reason to believe). I don't think this continuous approach to "reasoning" has any of the pitfalls of first order logic other than our severe lack of understanding of the mathematical properties of these extremely high dimensional problems.

I think most people now believe that what you call cartesian logic is not a component of general intelligence, but rather that models of the universe, and complex systems to reason about those models, will be able to be learned autonomously in an almost unsupervised way and be represented purely as very, very high dimensional nonlinear functions of stimuli (including continuous turing complete dynamical systems that are a function of their own output). We already have plenty of mathematical theorems that say that any information or relationship can be represented this way and any algorithm can be computed this way (e.g. via LSTMs, provided you have data sufficient to approximate the manifolds, a model class flexible enough to represent the relationships and parameter space large enough to minimally encode the information of the system) and we now have reason to believe that these representations might be learnable too with a clever enough choice of model and learning algorithm. We don't know how, but the unexpected success of deep learning gives us hope that we're vaguely squinting at what may be the right direction.

Was he doing a thesis in security? Otherwise that just sounds like it was denied because it’s not novel or thesis worthy...

They're not comparable libraries at all. Totally different purposes. Tensorflow is a very low level deep learning library and sklearn is very high level "classical" machine learning library.

If you don't have a good math background and understand deep learning architectures then tf won't be very useful to you at all.