Recent Notes
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Science fiction depicts AIs as computers that can think and take action like humans. This primer is about engineering that into real systems called agents. AI has predominantly been about pattern recognition. Is this photo of a cat or dog? Is that product review positive or negative? But these AIs don’t take action, that’s left…
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Along the path to AI automation lies a chasm. Extreme technical challenges and market development problems combine for a formidable innovation challenge. Three automation examples around transportation, legal contracts, and mental health illustrate the Automation Chasm mental model.
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Generative AI has a lot of unrealized potential. Most demos show incredible new abilities around computer usage. But that is different than computer programming. What can we do if we treat generative AI as a new way to program?
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Language models could offer a new way of writing NLP apps, unlocking the vast amounts of knowledge in text all around us.
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The biggest problems in ML are with data. But labeling data is the least effective way to solve data problems.
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Depth is a critical part of modern neural networks. They enable efficient representations through constructions of hierarchical rules. By now we all know it so I’ll assume I don’t need to convince anyone, but in case you need a refresher it’s basically because we cannot efficiently model many data distributions that appear in the wild…
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