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 technologists 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).
Markov Logic implements the concept of a Markov Random Field (also called a Markov Logic Field), which is a set of random variables that is said to have a Markov Property. A Markov Property, in turn, is described (by Wikipedia) as a stochastic process where “the conditional probability distribution of future states of the process [conditional on both past and present states] depends only upon the present state.”
How does this relatively obscure mathematical concept get translated into the world of data management and AI?
Read the rest of the article at our sister site Datanami to find out.