Machine learning: The future

Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains.What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field's energy and attention,and we discuss existing obstacles that must be addressed. I will first discuss current work in machine learning in particular,feedforeword Artificial Neural Nets (ANN), Boolean Belief Nets (BBN). Among techniques employing recursion, Recurrent Neural Nets, Context Free Grammar Discovery, Genetic Algorithms, and Genetic Programming have been prominent.