Google DeepMind recently unveiled AlphaGo, a body of artificial intelligence
(AI) work that purportedly beat world champion Go player, Lee See-Do. It is brilliant
insofar as it uses Monte Carlo Search in conjunction with AI to work an optimal path
through a near infinitesimal number of moves to triumph over its human adversary. At
first glance Monte Carlo Search may seem to be the same as Monte Carlo Simulation
but it is far more useful for finding a path to a particular classification. In our example,
the classification of interest is the likelihood that a customer will convert to an active
customer given a high bid on a digital media advertisement, subject to knowing the
potential customer's environment and journey steps thus far. Using neural networks
we are able to produce ‘fiercely accurate' models that can predict the likelihood of
conversion, bringing together enormous amounts of customer journey data, enriched
with the marketing expertise native to a business. It is not possible with neural networks,
out of the box, to understand why or how the output was formulated. While Monte Carlo
Simulation can help unlock some explanatory value in neural networks, the techniques
showcased by Google DeepMind bring about a new branch of game theory that can
alter the manner in which we approach the discipline of data science in response to
business problems. Jube.io has been able to aggregate these techniques into Predictive Analytics as a Service, bringing creating a solution that can help individuals who don't know how to program nor have a background in artificial intelligence, across a wide variety of industrial domains, create highly predictive models which can be used in optimisation as well. While not as sophisticated as AlphaGo, it is AI for the commercial masses. Predictive Analytics Training