Reoptimization for Great Power Competition

“I’m extremely proud of the Space Force and all the good it has accomplished. But, as good as we are, as much as we’ve done, as far as we’ve come, it’s not enough. We are not yet optimized for Great Power Competition.”

~ Chief of Space Operations
Gen. Chance Saltzman

The establishment of the U.S. Space Force was a direct response to threats arising from Great Power Competition in the space domain. Nevertheless, our legacy roots leave us sub-optimized for the security environment confronting us today, and we must finish fine-tuning the service to continue meeting its National Defense Strategy responsibilities

In early 2024, the Department of the Air Force unveiled sweeping plans for reshaping, refocusing, and reoptimizing the Air Force and Space Force to ensure continued supremacy in their respective domains while better posturing the services to deter and, if necessary, prevail in an era of Great Power Competition. Through a series of 24 DAF-wide key decisions, four core areas which demand the Department’s attention will be addressed: Develop People, Generate Readiness, Project Power and Develop Capabilities.

The space domain is no longer benign; it has rapidly become congested and contested.

We must enhance our capabilities, develop Guardians for modern warfare, prepare for the high intensity fight, and strengthen our power projection to thrive and win in this new era of Great Power Competition.

Video by Kevin D Schmidt

Anna Schapiro - Learning representations of specifics and generalities over time

Air Force Research Laboratory

March 8, 2024 | 01:00:12

In this edition of QuEST, we are pleased to welcome Dr. Anna Schapiro to discuss learning representations of specifics and generalities over time.

Abstract:

There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we learn and represent novel information of specific and generalized types, which we test across statistical learning, inference, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems, with empirical and modeling investigations into how the hippocampus shapes neocortical representations during sleep. Together, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.