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One of the major challenges facing businesses using AI is understanding exactly how these models make decisions. Traditionally, AI has been treated like a black box: Inputs go in, outputs come out, ...
This talk will attempt to demystify, for a non-technical audience, the current state of neural network explainability and interpretability, as well as trace the boundaries of what is in principle ...
The field of interpretability investigates what machine learning (ML) models are learning from training datasets, the causes and effects of changes within a model, and the justifications behind its ...
A practical review of explainable AI examines how transparency and interpretability improve trust in high-stakes applications. By introducing ...
Cognitive computational neuroscience has entered a transformative era. The rapid rise of large multimodal foundation models, state-space architectures, and ...
Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...
What happens when the most powerful tools humanity has ever created begin to outpace our ability to understand or control them? This is the unsettling reality we face with artificial intelligence (AI) ...
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