MemComputing’s co-founder, Max Di Ventra, has been granted half-a-million dollars over 18 months from the Defense Advanced Research Projects Agency (DARPA) to further develop MemComputing’s technology and its applications to AI.
“Our project, if successful, would have a large impact in the field of machine learning and artificial intelligence by showing that physics approaches can be of great help in fields of research that are traditionally dominated by computer scientists,” said Di Ventra.
With the DARPA funds, the team will apply memcomputing to the unsupervised learning, or pre-training, of Deep Belief Networks. These are systems of multi-layer neural networks (NNs) used to recognize, generate and group data. DiVentra will also propose a hardware architecture, using current technologies, to perform this task. Pre-training of NNs is a notoriously difficult problem, and researchers have all but abandoned it in favor of supervised learning. However, in order to have machines that adapt to external stimuli in real time and make decisions according to the context in which they operate—the goal of the third wave of AI—powerful new methods to train NNs in an unsupervised manner are required.