Interviewer: Dr. Siegel, thank you
very much for sparing the time today.
Let me go directly to the subject
of this interview, and
ask you to explain: what is it you do,
how do you do it and why do you do it?
Dr. Siegel: Sure, I've been in
the field of machine learning and
predictive analytics since the early 90s,
first as an academic, and
then since 2003 as an independent
consultant, applying machine learning for
running a conference series, and
I wrote a book called
Machine learning is when computers learn
from examples and somehow find patterns or
generalize from those examples,
typically in order to make a prediction or
some kind of discrimination about
individual cases, so: 'are you going to
buy this product, are you going to cancel
your subscription, is there a picture
of a traffic light somewhere within
this image?', and for self-driving cars.
The kinds of tasks that we want
organizations and their machines to
perform repeatedly, put probabilities
on potential outcomes and
behaviors of individuals or categories of
images, sounds and speech recognition.
Things that occur in great quantities and
which we have a lot of data to learn from,
in order to improve the ability for
machines to do these things.
That field's called 'machine learning' and
when you're applying it to business
problems, like with customer prediction,
it's also often called
Interviewer: Behind the words
'predictive analytics' I assume that there