The Differences Between Local Optimization and Global Optimization

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The evolutionary algorithm we're using was actually what's called like a global optimization algorithm, at least it attempts to be so. And typically these searches are mostly directed by how good the individual points are. That's the signature of the information you use to guide the search. But if you extend that paradigm a little bit, not only do you want to do global optimization, but you also want to be able to use information that's more dense and rich than just the performance with respect to your heuristic.

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Where does innovation come from? How common is it for "lone wolf" scientists to make large leaps in innovation by themselves? How can we imbue AIs with creativity? Or, conversely, how can we apply advances in AI creativity to our own personal creative processes? How do creative strategies that work well for individuals differ from creative strategies that work well for groups? To what extent are models like DALL-E and ChatGPT "creative"? Can machines love? Or can they only ever pretend to love? We've worried a fair bit about AI misalignment; but what should we do about the fact that so many humans are misaligned with humanity's own interests? What might it mean to be "reverent" towards science?

Joel Lehman is a machine learning researcher interested in algorithmic creativity, AI safety, artificial life, and intersections of AI with psychology and philosophy. Most recently he was a research scientist at OpenAI co-leading the Open-Endedness team (studying algorithms that can innovate endlessly). Previously he was a founding member of Uber AI Labs, first employee of Geometric Intelligence (acquired by Uber), and a tenure track professor at the IT University of Copenhagen. He co-wrote with Kenneth Stanley a popular science book called Why Greatness Cannot Be Planned on what AI search algorithms imply for individual and societal accomplishment. Follow him on Twitter at @joelbot3000 or email him at lehman.154@gmail.com.

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