strong_ai

4 ms·

1.
by strong_ai 11y ago
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
2.
by strong_ai 11y ago
Thumbnails drive engagement -- doesn't seem petty to me.
3.
by strong_ai 11y ago
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 calculatin
4.
by strong_ai 11y ago
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/
5.
by strong_ai 11y ago
Yes. Note both the article and the paper are from 1992.
6.
by strong_ai 11y ago
Wow! This is really awesome! Mind giving a high-level description of the matching process?
7.
by strong_ai 11y ago
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'.
8.
by strong_ai 11y ago
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
9.
by strong_ai 11y ago
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._Identifi