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Prof. Murray Shanahan - Machines Don't Think Like Us

Machine Learning Street Talk (MLST)

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Unpacking Features in AI Models

This chapter examines a groundbreaking paper on using autoencoders for feature extraction in AI, focusing on their ability to identify significant features albeit with some concerns over the abstractness of these features. The discussion also highlights the complexities surrounding an AI model named Golden Gate Claude, particularly its fixation on the Golden Gate Bridge and the implications of agency in machine learning.

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