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#038 - Professor Kenneth Stanley - Why Greatness Cannot Be Planned

Machine Learning Street Talk (MLST)

CHAPTER

Evolving Beyond Randomness

This chapter examines the intricacies of evolutionary algorithms, critiquing traditional methods for their reliance on random mutations. It advocates for incorporating gradient-informed mutation operators and emphasizes the significance of divergence linked to information accumulation amidst the challenges of local optima. The discussion also critiques the state of machine learning research, comparing the landscape of academia to medieval periods while highlighting the importance of innovation over mere adherence to conventional objectives.

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