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Computer Science

A collection of 32 issues

What’s the Next Best Thing to Learn?

With Gaurav Gandhi Recommendation engines are everywhere. These systems recommend what shows to watch on Netflix and what products to buy on Amazon. Since at least the Netflix Prize, the conventional wisdom is that recommendation engines have become very good. Except that they are not. Some of the deficiencies are

everywhere: meeting consumers where they are

Content delivery is not optimized for the technical stack used by an overwhelming majority of people. The technical stack of people who aren’t particularly tech-savvy, especially those who are old (over ~60 years), is often a messaging application like FB Messenger or WhatsApp. They currently do not have a

Growth Funnels

You spend a ton of time building a product to solve a particular problem. You launch. Then, either the kinds of people whose problem you are solving never arrive or they arrive and then leave. You want to know why because if you know the reason, you can work to

Quality Data: Plumbing ML Data Pipelines

What’s the difference between a scientist and a data scientist? Scientists often collect their own data, and data scientists often use data collected by other people. That is part jest but speaks to an important point. Good scientists know their data. Good data scientists must know their data too.

Automating Understanding, Not Just ML

Some of the most complex parts of Machine Learning are largely automated. The modal ML person types in simple commands for very complex operations and voila! Some companies, like Microsoft (Azure) and DataRobot, also provide a UI for this. And this has generally not turned out well. Why? Because this

About 85% Problematic: The Trouble With Human-In-The-Loop ML Systems

MIT researchers recently unveiled a system that combines machine learning with input from users to ‘predict 85% of the attacks.’ Each day, the system winnows down millions of rows to a few hundred atypical data points and passes these points on to ‘human experts’ who then label the few hundred

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,

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

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