Brian D. Womack

Dr. Brian D. Womack

Senior Director, Complementary Learning Products at Intel Saffron

Talk Track: Deep Learning and Artificial Intelligence

Talk Title: The 3rd Wave of Machine Intelligence (3MI)




Dr. Brian Womack is a veteran of defense intelligence and operations communities with a strong combination of data science, analytics algorithm development, robust signal processing, and software engineering experience. He values staying involved with the implementation details of adaptive computing technologies advance the state of the art of human machine intelligence (HMI).

His research and development focus is on the third wave of machine intelligence (3MI) to implement Saffron’s complementary learning vision; which increases system autonomy by creating a true partnership of human and machine.  By integrating both traditional statistical learning methods from the second wave of machine intelligence (2MI) and instance learning methods from cognitive memory-based computing, it is possible to make high impact decisions faster with more relevant data.  Brian received his Ph.D. from Duke in robust signal processing, and his M.S. from Texas A&M in adaptive control systems. He enjoys martial arts, SCUBA, camping, hiking, canoeing, archery, and volunteering.


While tremendous progress has been made since the creation of artificial intelligence (AI) in the 1980s and the advent of the second wave of machine intelligence (2MI) from the 1990s to 2010s, there are a set of technology barriers that have slowed the advancement in the capability of inference applications.  These barriers center around the focus on existing algorithm kernel runtimes and accuracy percentages and attempts to scale algorithms using existing mathematical foundations.  For example, the prevalence of symmetric mode probability density distribution (PDF) assumptions (i.e. zero skewness), time-invariance assumptions, and lack of distributed math and standardized data structures limits both implementation ease and performance capabilities.


In our talk, we will detail the nine key characteristics that differentiate between 2MI and the third wave of machine intelligence (3MI), which is a combination of traditional 2ML that supports both memory based cognitive instance learning models and 2MI statistical learning models.  Our goal is to motivate the community to address these capability gaps in the math, signal processing, modeling, programming, and algorithms that form the building blocks of an integrated 3MI implementation.