The goal of lda2vec is to make volumes of text useful to humans (not machines!) while still keeping the model simple to modify. It learns the powerful word representations in word2vec while jointly constructing human-interpretable LDA document representations.
Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation.
Build up a network by dragging from an existing node. This will add a node, or connect existing nodes (if you release the drag over another existing node). Clicking on a node or link will select or deselect it. Press delete or backspace to remove the selected node or link.
While most everyone has been saying it, science now supports it: Political partisanship is the worst it’s been in over half a century, and it’s increasing at an exponential rate.
By offering readers a groundbreaking analysis of how trends are sparked and take hold, Malcolm Gladwell’s book The Tipping Point became an exemplification of the very processes he was describing.
As a part of my work for MIRI on the “Can we know what to do about AI?” project, I read Nate Silver’s book The Signal and the Noise: Why So Many Predictions Fail — but Some Don’t. I compiled a list of the takeaway points that I found most relevant to the project.
Single and secretly wondering which of your friends might be able to introduce you to your future soulmate? Sociologists have long studied the dating pool problem, noting that an acquaintance is more likely to introduce you to your mate than a close friend is.