MIT CSAIL project shows that neural nets contain "subnetworks" 10x smaller that can just learn just as well - and often faster These days, nearly all AI-based products in our lives rely on "deep neural networks" that automatically learn to process labeled data. For most organizations and individuals, though, deep learning is tough to break into. To learn well, neural networks normally have to be quite large and need massive datasets. This training process usually requires multiple days of training and expensive graphics processing units (GPUs) - and sometimes even custom-designed hardware. But what if they don't actually have to be all that big after all?

Artificial intelligence solutions are now essential weapons in the insurers' battle against fraud. FREMONT, CA: The insurance industry is held responsible for a mass of sensitive data concerning both its customers and employees. Any data breach in an insurance firm could compromise the personal information of multiple users in no time. But insurers now have the option of attaining better cybersecurity posture by utilizing groundbreaking technologies available to them. Artificial Intelligence (AI) among those, is truly reforming insurance systems by making it more secure and enhancing the interaction between humans and machines.

By Shray Ambe This post was written as part of a class assignment from students who took a neuroethics course with Dr. Rommelfanger in Paris of Summer 2016. My name is Shray Ambe and I am a rising senior at Emory University. I am a Neuroscience and Behavioral Biology major who is pursuing a career in the medical field. Outside of the classroom, I am involved in organizing the booth for Emory's Center for The Study of Human Health at the Atlanta Science Festival Expo every year and also enjoy volunteering at the Emory Autism Center and the Radiology Department at Emory University Hospital. At the 2016 Neuroethics Network in Paris, France, bioethicist and philosopher John Harris gave a lecture titled "How Smart Do We Want Machines to Be?" During his lecture, Harris discussed the potential impacts of artificial intelligence (AI) and stated "it doesn't matter how smart they are; obviously the smarter the better."