GE today announced that its GE Digital division has acquired machine learning startup Wise.io. Terms of the deal weren't disclosed. Among other things, GE wants to have Wise's team improve its Digital Twin services that are geared toward the industrial Internet of Things (IoT), according to a statement. More generally, the team "will spearhead innovative solutions in GE's vertical markets to develop its machine learning offerings," the statement says. That's different from the startup's primary focus, which was improving customer support with machine learning.
Machine learning and artificial intelligence will take over mundane tasks across the enterprise, freeing people up for more strategic activities. In preparation, more organizations will "press pause" to ascertain their readiness across three dimensions: processes, systems, and talent. All need to be optimized to ensure ROI.
Falkonry releases its 2019 Predictive Operations Readiness Report based on the results of 300 organizations who used a calculator developed by the company to assess their machine learning and artificial intelligence (AI) readiness. The results provide a look at where companies see themselves from a maturity level and were remarkably consistent across the globe, according to Falkonry.
The project aims at increasing the efficiency of the company's games' functionalities, in-game advertising and user acquisition by utilizing machine learning and AI. Through research, the goal is to enhance understanding as well as produce practical technical implementations on Next Games technology platform that support efficient utilization of machine learning and AI. The goals of the project are in line with the company's strategic priorities to focus on improving efficiency and reducing development. The project will start in December 2019 and is expected to end on the 30th of September 2021 at the latest. The project has four phases and Business Finland will issue the grant in four stages based on submitted and approved reports on costs incurred and progression of the project.
R squared, also known as coefficient of determination, is a popular measure of quality of fit in regression. However, it does not offer any significant insights into how well our regression model can predict future values. Instead, the PRESS statistic (the predicted residual sum of squares) can be used as a measure of predictive power. The PRESS statistic can be computed in the leave-one-out cross validation process, by adding the square of the residuals for the case that is left out. As a reminder, in the leave-one-out cross validation, one case of the data set is used as the testing set and the remaining are used as the testing set.