How would you describe the current state of predictive analytics? And what are the biggest challenges you’re facing in that space today?
The problem of data representation
Feature learningLately, next to feature engineering we see another approach where those features and patterns are actually themselves “machine learned”. Deep neural networks and hierarchical machine learning approaches are able to capture and identify semantically relevant features for a given problem in a automated, algorithmic way.
Diving a little bit deeper into this, in a recent talk you outlined machine learning techniques that businesses can implement today and you talked about how predictive models can be embedded as microservices. So what are some of the more accessible techniques that businesses can use and what are some of the more interesting microservices applications you’re seeing?
A cognitive pyramid of API’s
You mentioned marketing and one of your areas of expertise look at solutions around personalized marketing applications. What sorts of applications are you seeing today already and what do you expect to see in the future?
Shifting gears just a little bit, another area that you’ve been exploring is machine learning and financial services. You recently participated on a panel. What interesting applications are you seeing in that space?