Can Markov Logic Take Machine Learning to the Next Level?

Advances in machine learning, including deep learning, have propelled artificial intelligence (AI) into the public conscience and forced executives to create new business plans based on data. However, the scarcity of highly trained data scientists has stymied many machine learning implementations, potentially blocking future AI development. Now a group of academics and technologist say the emerging fields of Markov Logic and probabilistic programming could lower the bar for implementing machine learning. Markov Logic is a language first described in by two professors in the University of Washington's Department of Computer Science and Engineering, Pedro Domingos and Matthew Richardson, in their seminal 2006 paper "Markov Logic Networks." The work is based on mathematical discoveries made by Andrey Markov Jr., the Soviet mathematician who died in 1979 (his father, who had the same name, is associated with a related field, dubbed Markov chains).