MATRIX Spring Seminar Series – Dr. Ehsan Hoque

Advancing Health Equity and Access Using AI

Ehsan Hoque

University of Rochester

https://utsa.zoom.us/j/92387759081

Friday, February 11, 2022

11 AM – 12 PM CST

Did you know using 43 muscles of our face, we can create 10,000 unique expressions? Imagine combining it with language, prosody, body language, physiology, and varying the conversational context (e.g., face to face, computer-mediated, social network). The complexity of modeling such dynamics is still an unsolved problem in computing, making it a grand challenge.

In this talk, I will demonstrate how we can computationally tackle some aspects of the grand challenge with the explicit goal of improving health equity and access. I will provide examples of how AI’s ability to quantify human behavior enables compelling health applications in movement disorders, PTSD, Autism, aging, and terminal illness. I will describe how we can create high-quality and population-representative input data outside of the lab and produce accurate, generalizable, and unbiased results. I will reflect on our experience of engaging the stakeholders early in the process, paving the way for AI into clinical care needs, resulting in practical solutions that end-users, clinicians, and patients find value in.

LINK

MATRIX Spring Seminar Series – Dr. Isabel Muzzio

Regaining our bearings: Neural representations and circuits underlying spatial reorientation.

Isabel Muzzio

University of Texas San Antonio

https://utsa.zoom.us/j/92387759081

Friday, February 18, 2022

11 AM – 12 PM CST

Reorientation – regaining one’s bearings after becoming lost – is a fundamental navigation process wherein the internal sense of direction becomes unreliable, forcing disoriented navigators to reorient only using external cues. Across species the geometry of the surrounding space plays a dominant role in reorientation, even when other directionally informative cues, such as landmarks, are available. Consequently, the modular theory has proposed that reorientation is driven by a mechanism that is sensitive to geometry and impermeant to non-geometric landmarks. In support of this theory, we have shown that the hippocampal map aligns to the geometry of a layout during reorientation. However, the modular theory’s exclusive reliance on geometry fails in situations of contextual ambiguity (identity of a context is unclear) as well as following overtraining, after animals learn the directional value of landmarks and use these cue to minimize errors in geometrically equivalent locations. In these situations, disoriented animals must identify the environment in which they are lost (context recognition) and recover facing direction within the context (heading retrieval). Disoriented animals initially use non-geometric landmarks to perform context recognition, but exclusively rely on the shape of the layout to compute heading retrieval; however, over time they incorporate features to recover heading in order to maximize reward. The neural mechanisms associated with these processes exhibit unique patterns of activity and dynamics. Following disorientation, heading retrieval cells rapidly align to environmental geometry, while context recognition cells align to local landmarks over days, as animals learn the value of these cues. We further demonstarte that the retrosplenial cortex (RSC), a region involved in scene perception containing neurons responsive to location, borders, and head-direction, is required for the use of geometry during reorientation. These inputs are modulated by a long-range GABAergic projection from CA1 to RSC, which inhibits the use of geometry, thereby enhancing the use of landmarks, as animals learn their directional value. This research uncovers important aspects of navigation with critical applications to the fields of robotics and health.

LINK

MATRIX Spring Seminar Series – Dr. Roland Green

Training of Giant Neural Networks with Weight Streaming

Roland Green

Cerebras Systems

 https://utsa.zoom.us/j/92387759081

Friday, March 4, 2022

11 AM – 12 PM CST

State-of-the-art language models have grown in parameter count by three orders of magnitude over the last two years. This growth has presented challenges for training both in terms of storage and compute requirements. In this talk, we survey existing approaches used to scale training to clusters of compute units and explore the limitations of each in the face of giant models. One thing that all these approaches have in common is the storage of model parameters on the compute units, which we find to be a primary driver of complexity and communication overhead. We present a new paradigm for giant model training, called weight streaming, based on the disaggregation of model storage and compute, and describe an implementation of this paradigm using Cerebras wafer-scale systems. Our weight streaming architecture enables the training of models two orders of magnitude larger than the current state-of-the-art, with a simple scaling model. Combined with built-in support for weight sparsity, our solution can make training giant networks tractable for the first time.

LINK