strong_ai

What do you see as the downside of creating this organization now, as opposed to in 100 years? Artificial Intelligence in its current state has shown itself to be incredibly useful and effective at small tasks. I see no harm in researching and expanding this field (besides opportunity cost).

Also, I see a distinct lack of "fear-mongering" in this post.

Thumbnails drive engagement -- doesn't seem petty to me.

It would be really interesting to see a visualization (maybe using PCA) of the LDA vectors for each document. The topics are not super convincing that the LDA approach worked well.

Other than that, this is a good intro to NLP and calculating document similarity. Well done!

afaik the two main reasons are 1) the expense of porting from 2.* to 3.* and 2) some libraries do not yet support 3 http://python3wos.appspot.com/

Yes. Note both the article and the paper are from 1992.

Wow! This is really awesome! Mind giving a high-level description of the matching process?

ANNs have been proven to be universal approximators (https://en.wikipedia.org/wiki/Universal_approximation_theore...) which I think is what you meant when you said 'Turing Complete'.

if you want to learn neural nets check out Karpathy's class (cs231n.github.io) and do the assignments. making a github repo and HN post about using neural networks is false self-advertising and illegitimatizes those of us who know what we are talking about.

this doesn't look like a neural net to me. from NeuralNetwork.py

  from sklearn.neighbors import KNeighborsClassifier
  # Create a sperate neural network for each identifier
  for index in range(0, len(NaturalLanguageObject._Identifiers)):
       nn = KNeighborsClassifier()
       self._Networks.append(nn)