Complexity of the Internet—An AI Observation Science Perspective w Jeremy Kepner

What does “normal” look like in a system that grows, adapts, and scales at extraordinary speed? How do its underlying patterns shift as the network expands from its early days to a billion-fold increase in scale?

Dr. Kepner will explore how advances in high-performance, privacy-preserving AI graph analysis tools open new windows into the Internet’s behavior. His work sheds light on emergence, structure, and stability within this constantly changing global system.
Dr. Kepner will explain the deep connections between graphs and matrices and more general mathematical concepts of semirings and associative (token) arrays that are the foundations of modern large language model (LLM) agentic AI systems. These mathematical concepts form the basis of the high performance GraphBLAS sparse matrix standard and the D4M (Dynamic Distributed Dimensional Model) associative array library that can analyze the largest networks in the world while preserving privacy.

Please register in advance for this seminar even if you plan to attend in person at > https://acm-org.zoom.us/webinar/register/5117750009506/WN_Z5KGSMQBSg2dzjM7s_X0mw

Indicate on the registration form if you plan to attend in person. This will help us determine whether the room is close to reaching capacity. We plan to serve light refreshments from about 6:30 pm.

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We may make some auxiliary material such as slides and access to the recording available after the seminar to people who have registered.

This is a joint meeting of the GBC/ACM (http://www.gbcacm.org) and the Boston Chapter of the IEEE-CS.