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b'Today we\\u2019re joined by Julieta Martinez, a senior research scientist at recently announced startup Waabi.\\xa0\\nJulieta was a keynote speaker at the recent LatinX in AI workshop at CVPR, and our conversation focuses on her talk \\u201cWhat do Large-Scale Visual Search and Neural Network Compression have in Common,\\u201d which shows that multiple ideas from large-scale visual search can be used to achieve state-of-the-art neural network compression. We explore the commonality between large databases and dealing with high dimensional, many-parameter neural networks, the advantages of using product quantization, and how that plays out when using it to compress a neural network.\\xa0\\nWe also dig into another paper Julieta presented at the conference, Deep Multi-Task Learning for Joint Localization, Perception, and Prediction, which details an architecture that is able to reuse computation between the three tasks, and is thus able to correct localization errors efficiently.\\nThe complete show notes for this episode can be found at twimlai.com/go/498.'