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Itron Idea Labs

Animal, Societal and Artificial Cognition

March 03, 2021

How does a society think? For that matter, how does cognition occur in any organism or superorganism? What is the purpose of cognition? What can we learn from biology to make progress in artificial intelligence and to make wiser decisions as a society? Questions like these are driving a convergence of ideas from fields as diverse as complex systems science, evolutionary biology, information theory, cognitive science and computer science. One result is active inference, a Bayesian explanation of cognition and biological self-organization that applies to cognition in cells and animals, as well as to societies of these.

Active inference plays a central role in work I have done at Oregon State University (OSU), Environmental Sciences Graduate Program, where I am courtesy faculty. Perhaps in the future it will also play a role at Itron, as it seeks to improve and expand its capacity to deliver machine learning and artificial intelligence solutions. For example, active inference could be a natural fit for problems where multiple intelligent agents, such as intelligent IoT devices, must coordinate behaviors to reach a common goal.

I’m pleased to announce that ScienceX just published a short article summarizing the work I have done at OSU. The topic is societal cognition as it relates to societal transformation in the face of climate change, biodiversity loss and other pressing social and environmental problems.

As Itron Idea Labs explores opportunities in the world of machine learning and artificial intelligence, we welcome conversations with our utility customers and others regarding challenges that require advanced approaches. If you would like to schedule a conversation with the Idea Labs team on these topics, please feel free to contact us at Itronidealabs@itron.com.

By John Boik

Senior Principle Data Scientist

John Boik received his PhD in biomedical sciences from the University of Texas, Health Sciences Center, Houston, where he studied cancer biology. He completed postdoctoral work at Stanford University, in the Department of Statistics, and is currently courtesy faculty at Oregon State University, Environmental Sciences Graduate Program. His BS is in civil engineering, from the University of Colorado, Boulder. He has broad experience modeling biological and societal processes, including utility processes. His professional interests include Bayesian statistical methods, scientific machine learning (merging dynamical systems theory with machine learning), graph representations of data, machine learning on graphs, and Bayesian approaches to artificial intelligence, in particular, active inference. Active inference has potential to model cooperation and communication between intelligent agents, such as intelligent IoT devices. He is a Senior Principle Data Scientist at Itron Idea Labs, where he constructs machine learning models for use by Idea Labs and others at Itron, and assists in evaluation of data science proposals, strategies, and approaches.

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