In Cognilytica Research’s briefing note on simMachines, January, 2018, it highlights the black box challenge of today’s AI machine learning technologies and the fundamental problems lack of explainability causes.
simMachines, Inc., the leader in Explainable AI / Machine Learning applications, announces the launch of their latest product—Dynamic Predictive Audiences designed for data companies, publishers and media platforms.
Today’s digitally-empowered customers have high expectations for relevant, highly personalized customer experiences. Companies must keep pace with these growing demands or they will be left behind by their more perceptive competition.
Similarity is a machine learning method that uses a nearest neighbor approach to identify the similarity of two or more objects to each other based on algorithmic distance functions.
It’s well known that the “Amazon effect” is wreaking havoc on retailers’ ability to lure and retain shoppers because of their inability to predict what motivates customers to buy and to be relevant in the moment of interaction.
Most marketers are just beginning to explore machine learning applications. Machine learning is already providing tremendous analytic efficiency gains and increased precision.
Machine learning helps level the playing field for all marketers if applied effectively, because it can quickly be deployed at speed and scale within critical areas of the business – such as preventing high value customers from leaving or more effectively attracting and delighting high value customers through more relevant dialogs.
Demand for explainable AI over the last year has started to ramp up from a variety of perspectives. DARPA, the Defense Advanced Research Projects Agency, for example, has been calling for more explainable machine learning models that human users can understand and trust.
The General Data Protection Regulation (or GDPR) going into effect in the EU in 2018 requires that algorithms used to make credit or insurance decisions must be explainable to consumers.