I have used synthetic data sets many times for simulation purposes, most recently in my articles Six degrees of Separations between any two Datasets and How to Lie with p-values. Many applications (including the data sets themselves) can be found in my books Applied Stochastic Processes and New Foundations of Statistical Science. For instance, these data sets can be used to benchmark some statistical tests of hypothesis (the null hypothesis known to be true or false in advance) and to assess the power of such tests or confidence intervals. In other cases, it is used to simulate clusters and test cluster detection / pattern detection algorithms, see here. I also used such data sets to discover two new deep conjectures in number theory (see here), to design new Fintech models such as bounded Brownian motions, and find new families of statistical distributions (see here).

Tech giants, as well as smaller startups, have been investing in emotion AI for over a decade, using either computer vision or voice analysis to recognize human emotions. Many of these companies started with a focus on market research, analyzing and capturing human emotions in response to a product or TV commercial. Commercial deployments are slowly emerging in virtual personal assistants (VPAs), cars, call centers, robotics and smart devices.

Following the recent election cycle, voter turnout issues are at the forefront of the American consciousness. Several chatbots have recently been developed to help citizens quickly and easily register to vote. The bots GoVoteBot, VotePlz, and HelloVote all let users complete their voter registration via SMS and Facebook Messenger.