Deep Learning Models
topic on 4 shows · 5 statements across 5 episodes
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5 statements about Deep Learning Models, every show
Izmailov: Neural network operations may not be explainable in human terms
“We want to understand it at a lower level, and it is very possible that that's just not fully possible. Like, it is some computational process that leads to some results. It doesn't have to be the case that you can Kind of describe it in human terms and kind o…”
Morris: Machine learning lacks a fundamental unit of deep learning information
“I don't think we know what a bit is yet in terms of like deep learning models.”
Weisbrot: Deep learning acoustic attacks classify keystrokes with up to 95% accuracy
“When trained on keystrokes recorded by a nearby phone, the classifier achieved an accuracy of 95%. The highest accuracy is seen without the use of a language model. When trained on keystrokes recorded using the video conferencing software Zoom, an accuracy of …”
James: Deep learning models rely on data flow, not instruction-driven loops
“The models we're producing don't work based on instructions. It, it's not a central operator that says do this, do that. Instead it's the data that flows over our model and a simple feedback equation that tells that data how to manipulate and transform the mod…”
Chen: Regulators prohibit black-box AI models in lending without explainability
“The super active area of research right now, right, which is how do I make the deep learning models more transparent so that I can debug them, I can verify them, I can make sure there's no systematic bias in them, right? Because until that, you couldn't do imp…”