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The Future of the Transformer Part 1 with Trey Kollmer | H100 Chips will Supercharge AI Hardware

"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

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Exploring Neural Network Behavior with Out-of-Distribution Data

This chapter explores a study on neural networks' behavior with out-of-distribution data, revealing that they often resort to equal-weight predictions in unfamiliar situations. Through an example of rotated handwritten digits, it underscores the need for system designers to account for diminishing model confidence as inputs deviate from their training experience.

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