Scientists use dimensionality reduction to project high-dimensional AI neural networks into 3D visualizations by selecting a central point in the parameter space, generating random normalized direction vectors to create a 2D plane slice, and evaluating loss values at each point to generate a topographical map that reveals how AI learns and makes decisions.
Deep Dive
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Deep Dive
See into the AI Brain! #neuralnetworksHinzugefügt:
Have you ever wondered what the brain [music] of an AI actually looks like?
Neural networks exist in crazy high-dimensional spaces that humans literally cannot see. And while we [music] trust these bots, we have no idea what their learning process looks like.
Scientists figured out a way to project multi-dimensional AI brains into a beautiful 3D map. Because human spatial intuition is stuck in three dimensions, researchers use a trick called dimensionality reduction to explore AI optimization.
>> [music] >> They select a central point in the AI's parameter space and generate random normalized direction vectors [music] to slice a 2D plane right through the massive multi-dimensional space.
For example, if you evaluate the loss value at every point on this [music] grid, it creates a literal 3D topographical map that shows us exactly how the AI learns and makes decisions.
Save this mind-blowing tip. Share if you love science. [music] Follow and say brain below.
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