SingularityNET: Agent Based Modeling of COVID-19: Next Steps and Broader Implications – Dr. Ben Goertzel
➡️ COVID-19 Simulation Summit Playlist: https://www.youtube.com/playlist?list=PLAJnaovHtaFR5puHCN4W_4o8cgIHdawDb
👀 About the speaker
Dr. Goertzel is one of the world’s foremost experts in Artificial General Intelligence, a subfield of AI oriented toward creating thinking machines with general cognitive capability at the human level and beyond. He has decades of expertise applying AI to practical problems in areas ranging from natural language processing and data mining to robotics, video gaming, national security, and bioinformatics.
Dr. Goertzel has published 20 scientific books and 140+ scientific research papers and is the leading architect and designer of the OpenCog system and associated design for human-level general intelligence. Together with Cassio Pennachin, dr. Goertzel co-authored “Artificial General Intelligence,” published in 2002 by Springer Publishing. He is also the chair of the Artificial General Intelligence (AGI) conference series, advisor to Singularity University and former Director of Research of the Machine Intelligence Research Institute (formerly the Singularity Institute). He also served as Chief Scientist Officer for Hanson Robotics until early 2019.
His recent appearance on the Joe Rogan Experience podcast scored over 2 million views on Youtube and the wide-ranging discussion continues to be viewed, shared and referenced today
On April 30th, 2020, the DAIA Foundation has organized an online COVID-19 Simulation Summit. The summit was focused on the use of agent based simulation models for more effective simulation of COVID-19 spread and evaluation of COVID-19 policies. Consisting of live video talks, Q&A sessions, and panel discussions,
the event gathered together scientists with insight and experience in simulation modeling of complex systems (especially but not only agent based modeling) and complex systems dynamics; along with scientists and physicians with specific insight into COVID-19 and related epidemiological issues.
The aim of the COVID-19 Simulation Summit is to gain understanding of
What tools and approaches should be used to provide the most accurate possible models of COVID-19 and its spread and implications, both in the current phase and in future phases of disease propagation?
What assumptions should be made or questioned within COVID-19 simulation models?
What assumptions have been left unquestioned in current models and need to be more thoroughly explored?
What data sources might best be leveraged in constructing models related to COVID-19?
What data should we be gathering that we aren’t currently, and how may this be done in a way that respects personal privacy and data sovereignty?
What real-world test cases might best be used for initial exploration of more accurate simulation models
The epidemiological models of COVID-19 spreading dynamics that are driving current policy decisions are generally well thought out and carefully implemented, however they also tend to be highly simplistic relative to the complexity of the actual situation.
Among many other factors, they don’t generally factor in the impacts of various interventions and control policies, nor do they account for the different behavior patterns of different classes of people.
The agent based simulation paradigm allows a finer-grained sort of modeling, in which a region (or the world as a whole) is modeled as a specific geometry occupied by interacting autonomous agents with a diversity of specific behavior patterns. An in-depth agent based simulation of COVID-19 spreading would allow better-grounded policy choices to be made regarding how to manage, control and cope with the pandemic.
An agent based simulation, like any other model, depends on the underlying assumptions used to structure it. However, the agent based modeling paradigm provides a more flexible approach to evaluating the consequences of various assumptions, and thus exploring their validity.
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