Biases in machine learning

Today we will be discussing “Capturing failures of large language models via human cognitive biases” by Jones and Steinhardt. The rationality community is organized around trying to compensate for systematic biases in human thought. This movement was arguably started by “Thinking Fast and Slow” by Kahnemann and Tvarksy, and has reached its current state majorly

Yet another stab at image recognition

Like every other idiot with an internet connection, I am fascinated by machine learning and neural nets. My favorite aspect of AI is image recognition, and I’ve written about it in the past. I am going to try and talk about it in reference to a book I’ve recently been reading. The book that I’ve

Why Grothendieck would say machine learning is mostly overfitting

Introduction Grothendieck is, by far, the single most influential mathematician of the 20th century. He solved long standing mathematical problems, created whole new fields of human thought, and then spectacularly abandoned it all when his institute refused to accept military funding. The overarching theme of all his research was that when we study mathematical concepts,

We gotta have some more pop philosophy- Mathematics, machine learning and Wittgenstein

In my quest to read all the pop neuroscience available online, I read this fascinating article on Gerald Edelman. It was full of profound quotes like We don’t have goals. We just have values. More importantly, it talked about the concept of polymorphous sets as proposed by Wittgenstein. “Typical Wittgenstein,” Edelman mused. “There is a