AI News, Ask Peter Norvig

Ask Peter Norvig

When we first began working on Leada, we sought to better understand the data science industry by interviewing professionals in the field.

As students simply wanting to learn more about data science, we ultimately created a free resource to inform both undergraduates and professionals about the data science industry.

Some experts took longer to contact than others (I emailed Hal Varian over 8 times) but you would be surprised who you can get 25 minutes of time to help inform others.

As a conclusion to The Data Analytics Handbook, we thought it would be fun to see what questions our readers had about data science, and then had them answered by the #3 ranked most powerful data scientist in the world today by Forbes and Director of Research at Google, Peter Norvig.

So take a University class if that is convenient, or an online class if that makes more sense for you, but then get to work on some real data and figure out how to apply what you learned to a data set, then keep at that.

Obviously there are differences in the size of problems: the number of rows (examples) and columns (features) can vary from dozens to billions, but there are other differences that are less obvious: stationarity (are the examples changing over time), transfer (can we train on one data set and apply what we learned to a different set), sparsity (how many of the possible examples are represented in the data), structure (can the problem be represented as a vector of real numbers, or is some other representation necessary), and so on.

Judea Pearl’s book Probabilistic Reasoning in Intelligent Systems, and before the book his papers and hearing him talk in person, demonstrated to me why I was having so much trouble trying to build systems based on Boolean logic, and showed that probability is the right foundation for dealing with any situation that involves uncertainty.

We want to train some function to set parameters to minimize an expected loss function, and whether the function you are training is called a “neural network” or not just seems like an unimportant detail.

The fact that they are “semi-parametric” – they have a very large number of parameters, but do not rely on keeping all data points around – is certainly important, and I think the semi-parametric space is a very important one.

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