Navigating the Frontiers of AI Research

Deep dive into my PhD research on neural network interpretability and the future of artificial intelligence.

My doctoral research centered on the "black box" problem in deep learning. For years, AI models have demonstrated superhuman performance in tasks like image recognition and natural language processing, but how they arrive at their conclusions has remained largely opaque. My work introduces a novel technique called "Concept-Activation Vector Analysis" (CAVA), which allows us to peer inside these complex networks and understand the high-level concepts they learn. For example, we can identify specific neurons or groups of neurons that activate in response to "stripes," "fur," or even abstract ideas like "joy" in an image. This not only makes AI more trustworthy and debuggable but also opens up new avenues for building more robust and fair-minded systems. The implications are vast, from improving medical diagnoses to ensuring autonomous vehicles make safe decisions. This post breaks down the core papers, the math behind CAVA, and my vision for a more transparent AI future.