Babak Hodjat is the CTO for AI at Cognizant where he leads a team of developers and researchers bringing advanced AI solutions to businesses. Babak is the former co-founder and CEO of Sentient, responsible for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founder of the world's first AI-driven hedge-fund, Sentient Investment Management.

Abstract: Deep Learning systems are at the heart of many AI breakthroughs in recent years. Another AI discipline, which, like Neural Networks, was conceived in the early years of Computing is Evolutionary Computations (EC). We will show how EC complements Neural Networks and Deep Learning, enabling Neuro-Architecture Search for AutoML, as well as practical multi-objective decision systems. This marriage of Neural Networks and Evolutionary Computation can even improve certain aspects of Reinforcement Learning. Evolutionary Algorithms are representation agnostic and replacing neural networks with rule-sets as the substrate can even allow explainable models. We will go over the how and why, as well as demonstrating a range of application use-cases.

Babak Hodjat is the CTO for AI at Cognizant where he leads a team of developers and researchers bringing advanced AI solutions to businesses. Babak is the former co-founder and CEO of Sentient, responsible for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founder of the world's first AI-driven hedge-fund, Sentient Investment Management.

Abstract: Deep Learning systems are at the heart of many AI breakthroughs in recent years. Another AI discipline, which, like Neural Networks, was conceived in the early years of Computing is Evolutionary Computations (EC). We will show how EC complements Neural Networks and Deep Learning, enabling Neuro-Architecture Search for AutoML, as well as practical multi-objective decision systems. This marriage of Neural Networks and Evolutionary Computation can even improve certain aspects of Reinforcement Learning. Evolutionary Algorithms are representation agnostic and replacing neural networks with rule-sets as the substrate can even allow explainable models. We will go over the how and why, as well as demonstrating a range of application use-cases.