Readers of this and our other blogs will be aware that we have long been sceptical of the idea that 'big data' - coupled with clever machine learning algorithms - will be able to achieve improved decision-making and risk assessment as claimed (see links below). We believe that a smart data approach that combines expert judgment (including understanding of underlying causal mechanisms) with relevant data is required and that Bayesian Networks (BNs) provide an ideal formalism for doing this effectively.
Turing award winner Judea Pearl, who was the pioneer of BNs, has just published a new book "The Book of Why: The New Science of Cause and Effect", which delivers essentially this same message. And there is a great interview with Pearl in the Atlantic Magazine about the book and his current views. The article includes the following:
As he sees it, the state of the art in artificial intelligence today is merely a souped-up version of what machines could already do a generation ago: find hidden regularities in a large set of data. “All the impressive achievements of deep learning amount to just curve fitting,” he said recently.Read it all.
- How a Pioneer of Machine Learning Became One of Its Sharpest Critics
- The Book of Why
- The problem with big data and machine learning
- A short story illustrating why pure machine learning (without expert input) may be doomed to fail and totally unnecessary (2 page pdf)
- Another machine learning fable: explains why pure machine learning for identifying credit risk may result in perfectly incorrect risk assessment (1 page pdf)
- Moving from big data and machine learning to smart data and causal modelling: a simple example from consumer research and marketing (7 page pdf)
|Norman gets his hands on Pearl's new book|