Neurons, Perceptron and Deep Learning: Milestones toward Artificial General Intelligence

Friday, November 1, 2019 - 2:20pm to 3:30pm
Innovation Center, Room 1400

Speaker: Venkat Kaushik
Affiliation: OpenText Corporation
Location: Innovation Center, Room 1400
Time: Friday 11/1/2019 (2:20 - 3:10pm)

Affiliation: OpenText Corporation

Abstract:
Deep Learning is a culmination of advances in cognitive neuroscience, neurobiology, clinical psychology, mathematics and statistics and logic. The explosion in data coupled with the recent advancements in custom compute and cloud scale storage has brought us super-human narrow AI systems such Watson, AlphaZero and DeepFace. A portion of this talk is an exploration of key ideas and their significance in the context of neural networks which forms the basis of most deep learning systems. Here, I will highlight several milestones that led us to our current vantage point. Remainder of the talk telescopes on the prerequisites that may help pave the way for a safe, human-centered Artifical General Intelligence (AGI).

Bio:
I am a solutions architect and specialize in Enterprise Information Management (EIM) for large enterprises. I received a PhD in physics from University of Texas at Arlington in 2007. My doctoral dissertation and post-doctoral work respectively centered on a search for the Higgs boson at the DZero experiment at Fermilab and precision top quark measurements for ATLAS experiment at CERN. I have witnessed the collection and use petabyte scale dataset and grid computing spanning multiple continents. I used Artificial Neural Networks in search for exotic particles and contributed to building and refining software for advanced multivariate statistics, hypotheses testing and particle/detector Monte Carlo simulations.
Since transitioning to IT industry in 2013, I have assumed several different roles in platform and data engineering, technical leadership in big data technologies specializing in distributed, relational and in-memory databases and message streaming. My current focus areas are leveraging machine learning algorithms to improve business outcomes in EIM and practice of cloud architecture. I have enjoyed being an adjunct faculty in the physics department of University of South Carolina. I am avid technology enthusiast and a voracious consumer of knowledge.