Tag machine learning
Marginalia: Rebooting AI by Gary Marcus and Ernest Davis
With this new year, let’s try a new format. Marginalia will be a series in which I’ll share notes and comments on interesting books I read. The name is directly inspired by the old word indicating the small notes on the margins of books. It will be a chance to discuss my readings without the need to write a full-fledged article. I hope it will be interesting as a review of the book or as a discussion starter.
Questions about Deep Learning and the nature of knowledge
If there is something that can be assumed as a fact in the AI and Machine Learning domain is that the last years had been dominated by Deep Learning and other Neural Network based techniques. When I say dominated, I mean that it looks like the only way to achieve something in Machine Learning and it is absorbing the great part of AI enthusiasts’ energy and attention. This is indubitably a good thing.
Not every classification error is the same
In this article, I would like to talk about a common mistake new people approaching Machine Learning and classification algorithm often do. In particular, when we evaluate (and thus train) a classification algorithm, people tend to consider every misclassification equally important and equally bad. We are so deep into our mathematical version of the world that we forget about the consequences of classification errors in the real world. But let’s start from the beginning.
How hidden variables in statistical models affect social inequality
Use of machine learning is becoming ubiquitous and, even with a fancy name, it remains a tool in the statistical modeler belt. Every day, we leak billions of data from ourselves to companies ready to use it for their affair. Modeling through data get more common every day and mathematical model are the rulers of our life: they decide where we can work, if we can get a loan, how many years of jails we deserve, and more.