This article provides a series of forecasts regarding the development of AI and robotics. We have discussed some AI topics in the previous posts, and it should seem now obvious the extraordinary disruptive impact AI had over the past few years. However, what everyone is now thinking of is where AI will be in five years time. I find it useful then to describe a few emerging trends we start seeing today, as well as make few predictions around machine learning future developments. The following proposed list does not want to be either exhaustive or truth-in-stone, but it comes from a series of personal considerations that might be useful when thinking about the impact of AI on our world. The interesting aspect of those is that are predictions made one year ago, and many turned out to be true.
This paper illustrates tips and tools to run a data science practice within an organization. It will also give some tools to understand the stage of data science maturity of the company.
Propensity modelling is a statistical approach and a set of techniques which attempts to estimate the likelihood of subjects performing certain types of behaviour (e.g. the purchase of a product) by accounting for independent variables (covariates) and confounding variables that affect such behaviour.
This article follows the first piece on machine learning describing how AI interacts with neuroscience, as well as how hardware and chips are getting created and modified to be more efficient for specific AI algorithm