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Natesh Ganesh*Electrical and Computer Engineering, Univ. of Massachusetts, Amherst*

Thursday, July 25, 1:00-2:00
	Building 101, Lecture Room D  (VTC from Boulder)
	Gaithersburg

Thursday July 25, 11:00-12:00
	Building 1, Room 4550
	Boulder

**Host:** Andrew Dienstfrey

**Abstract:**  The shift in the focus of the computing industry towards learning applications, and the inevitable end to Moore's law have empowered the search for novel substrates and architectures to build computing systems that can learn like the human brain. Machine learning techniques have made tremendous progress in narrow tasks using large datasets and compute power. These systems are a far cry from the human brain in terms of energy efficiency and performance across several domains. In order to build energy efficient AI systems, we need to identify the optimal devices, architectures and design techniques. To achieve this, it is necessary to have a solid theoretical foundation for intelligence and computing, and what they both entail. In this talk I will review some of the fundamental ideas that underlie the current computational approach to intelligence, and explore the foundational question - is computing the optimal path forward to artificial intelligence? A thermodynamic framework of intelligence will be proposed as an alternative option - one that describes intelligence as a physical process in terms of homeostasis, entropy flow and energy dissipation. I will discuss this exciting path moving forward examining recent advances from non-equilibrium thermodynamics and their relationship to predictive inference in self-organized systems. I will end with a discussion on how these results will encourage rethinking of our design philosophies needed to engineer a 'computer' based on thermodynamic principles using novel substrates and network architectures.

**Bio:** Natesh Ganesh is a PhD student in the Electrical and Computer Engineering Dept. at the Univ. of Massachusetts, Amherst working under Prof. Neal Anderson. His research interests include physical intelligence, complex systems, information theory, non-equilibrium thermodynamics, brain inspired hardware and artificial consciousness. He has been working on the fundamental non-equilibrium conditions for intelligence in physical systems and proposed the novel  framework of Thermodynamic Intelligence for his graduate research. He is currently working on the new engineering of thermodynamic computing to build energy efficient AI using novel substrates.

**Note:** Visitors from outside NIST must contact Cathy Graham; (301) 975-3800; at least 24 hours in advance.
  Mathematics and statistics

    
    Contacts
      
        

Andrew Dienstfrey
    
      
  andrew.dienstfrey@nist.gov
  (303) 497-7485

  Stephen Langer
    
      
  stephen.langer@nist.gov
  (301) 975-5423

Created July 9, 2019, Updated November 15, 2019
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