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Clarifai(ng) the Future of Clarifai

Clarifai is a promising AI start-up. In a short(ish) time, it has made major progress on an important problem. And it is rapidly rolling out products with lots of business potential. But there are still some things that it could do. As I understand it, the base version of

I Recommend It: A Recommender System For Scholarship Discovery

The rate of production of scholarship has never been higher. And while our ability to discover relevant scholarship per unit of time has never kept pace with the production of knowledge, it has also risen sharply—most recently, due to Google scholar. The efficiency of discovery of relevant scholarship, however,

Companies With Benefits or Potential Welfare Losses of Benefits

Many companies offer employees ‘benefits.’ These include paying for healthcare, investment plans, company gym, luncheons etc. (Just ask a Silicon Valley tech. employee for the full list.) But why ‘benefits’? And why not cash? A company offering a young man a zero-down health care plan seems a bit like within-company

Beyond Anomaly Detection: Supervised Learning from `Bad' Transactions

Nearly every time you connect to the Internet, multiple servers log a bunch of details about the request. For instance, details about the connecting IPs, the protocol being used, etc. (One popular software for collecting such data is Cisco’s Netflow.) Lots of companies analyze this data in an attempt

Bad Remedies for Bad Science

Lack of reproducibility is a symptom of science in crisis. An eye-catching symptom to be sure, but hardly the only one vying for attention. Recent analyses suggest that nearly two-thirds of the (relevant set of) articles published in prominent political science journals condition on post-treatment variables (see here). Another analysis

Some Aspects of Online Learning Re-imagined

Trevor Hastie is a superb teacher. He excels at making complex things simple. Till recently, his lectures were only available to those fortunate enough to be at Stanford. But now, he teaches a free introductory course on statistical learning via the Stanford online learning platform. (I browsed through the online

Stemming the Propagation of Error

About half of the (relevant set of) articles published in neuroscience mistake difference between a significant result and an insignificant result as evidence for the two being significantly different (see here). It would be good to see if the articles that make this mistake, for instance, received fewer citations post