I have data. I need insights. Where do I start?

This question comes up often.

It is typically asked by starting data scientists, analysts and managers new to data science. Their bosses are under pressure to show some ROI from all the money that has been spent on systems to collect, store and organize the data (not to mention the money being spent on data scientists).

Sometimes they are lucky – they may be asked to solve a very specific and well-studied problem (e.g., predict which customer is likely to cancel their mobile contract). In this situation, there are numerous ways to skin the cat and it is data science heaven.

But often they are simply asked to “mine the data and tell me something interesting”.

Where to start?

This is a difficult question and it doesn’t have a single, perfect answer. I am sure experienced practitioners have evolved many ways to do this. Here’s one way that I have found to be useful … (read the rest of the post on Medium)

Handy Command-Line One-liners for Starting Data Scientists

[6/5/2017 update: I was asked if I had a PDF version of the one-liners below. Here it is. Data-Science-One-Liners.pdf ]

Experienced data scientists use Unix/Linux command-line utilities (like grep, sed and awk) a great deal in everyday work. But starting data scientists, particularly those without programming experience, are often unaware of the power and elegance of these utilities.

When interviewing candidates for data scientist positions, I ask simple data manipulation questions that can be done with a command-line one-liner. But often the answer is “I will fire up R, import the CSV into a data frame, and then …” or “I will load the data into Postgres and then …”.

The command-line can be much simpler and faster, especially for getting large data files ready for consumption by specialized tools like R. For example, rather than try to load a million-row CSV into R and sample 10% of it, you can quickly create a 10% sample using this one-liner … (read the rest of the post on Medium )

Building startups in an exit-friendly way

I recently spoke at StartMIT, MIT’s student entrepreneurship bootcamp. The topic was exit strategies and how to build startups in an exit-friendly way.

Slides below. I had quite a bit of content in the talk-track that’s not reflected in the slides so if you have questions, please feel free to ask in the comments. Slide #41 (“How to talk to an acquirer”) evoked the most interest both during the talk and in the Q&A afterwards.

[1-17-2017 update: not everyone is able to view the embedded slides below for some reason, so here’s a downloadable/printable PDF version: Exit Strategies PDF]

AlphaGo is Here. What’s Next?

One of the most dramatic events in 2016 was the triumph of Google DeepMind’s AlphaGo AI program against Lee Sedol of South Korea, one of the world’s top Go players.

This was a shock to many. Chess fell to AI many years ago but Go was thought to be safe from AI for a while and AlphaGo’s success set off a flurry of questions. Is AI much further along than we think? Are robots with human-level intelligence just around the corner?

Experts have lined up on both sides of these questions and there’s no shortage of perspectives. I wanted to share two that particularly resonated with me.

In an Edge interview on big data and AI (which is a great read in its entirety, btw), Gary Marcus of NYU highlights a key requirement of systems like Google DeepMind’s Atari AI and AlphaGo AI.

You’d think if it’s so great let’s take that same technique and put it in robots, so we’ll have robots vacuum our homes and take care of our kids. The reality is that in the [Google DeepMind] Atari game system, first of all, data is very cheap. You can play the game over and over again. If you’re not sticking quarters in a slot, you can do it infinitely. You can get gigabytes of data very quickly, with no real cost.

If you’re talking about having a robot in your home? – I’m still dreaming of Rosie the robot that’s going to take care of my domestic situation – you can’t afford for it to make mistakes. The DeepMind system is very much about trial and error on an enormous scale. If you have a robot at home, you can’t have it run into your furniture too many times. You don’t want it to put your cat in the dishwasher even once. You can’t get the same scale of data.

This is certainly true in my experience. Without lots and lots of data to learn from, the fancy machine learning/deep learning stuff doesn’t work as well (this is not to say that data is everything; many math/CS tricks contributed to the breakthroughs but lots of data is a must-have).

So is that it? In situations where we can’t have “trial-and-error on an enormous scale”, are we basically stuck?

Perhaps not. Machine learning researcher Paul Mineiro acknowledges this …

In the real world we have sample complexity constraints: you have to perform actual actions to get actual rewards.

… and suggests a way around it.

However, in the same way that cars and planes are faster than people because they have unfair energetic advantages (we are 100W machines; airplanes are much higher), I think “superhuman AI”, should it come about, will be because of sample complexity advantages, i.e., a distributed collection of robots that can perform more actions and experience more rewards (and remember and share all of them with each other).

AIs remembering and sharing with each other. That’s a cool idea.

Perhaps we can’t reduce the total amount of trial-and-error necessary for AIs to learn, but maybe we can “spread the data-collection pain” to thousands of AIs, learn from the pooled data, and push the learning back out to all the AIs and run this loop continuously. If my robot bumps into the furniture, maybe yours won’t have to.

Come to think of it, this “remembering and sharing with each other” is one of the arguments that have been put forth for how homo sapiens evolved from their humble beginnings to today where they can build things like AlphGo.

One More Reason to Prefer Simple Models

When building models for classification and regression, the question arises often: Go with a simple model that’s easy to understand but doesn’t have the highest accuracy? Or go with the model that’s impressively complex but much more accurate?

The needs of the situation often force one choice over another. If explainability is important, the simple model may win. If black-boxes are fine and it is all about accuracy, the complex model may be chosen. If the accuracy is roughly the same, Occam’s Razor may point to the simpler model.

I recently came across a different reason for preferring the simpler model.

In Classifier Technology and the Illusion of Progress, David Hand argues that the accuracy advantage of the complex model may not persist for long [note that he refers to the data used to train and validate the model as the “design distribution”]

The performance difference between two classifiers may be irrelevant in the context of the differences arising between the design and future distributions … more sophisticated classifiers, which almost by definition model small idiosyncrasies of the distribution underlying the design set, will be more susceptible to wasting effort in this way: the grosser features of the distributions (modeled by simpler methods) are more likely to persist than the smaller features (modeled by the more elaborate methods).

The apparent superiority of the more sophisticated tree classifier over the very simple linear discriminant classifier is seen to fade when we take into account the fact that the classifiers must necessarily be applied in the future to distributions which are likely to have changed from those which produced the design set … the simple linear classifier captures most of the separation between the classes, the additional distributional subtleties captured by the tree method become less and less relevant when the distributions drift. Only the major aspects are still likely to hold.

Data scientists are often cautioned that future data may be different from the data used for training the model. This advice isn’t new.

What I found interesting was the notion that, even when the data changes in the future, its major features are likely to hold up for longer or change more slowly than its minor features. And this, in turn, favors simpler models since they tend to use the major features.