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 majorlyContinue reading “Biases in machine learning”
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’veContinue reading “Yet another stab at image recognition”
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,Continue reading “Why Grothendieck would say machine learning is mostly overfitting”
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 aContinue reading “We gotta have some more pop philosophy- Mathematics, machine learning and Wittgenstein”
The paper that I wish to discuss today is An Introduction to Topological Data Analysis: fundamental and practical aspects for data scientists, by Frederic Chazal and Bertrand Michel. Topological data analysis is an exciting new field, and this paper can be understood by people from a wide range of backgrounds. Notation: For this paper, denotesContinue reading “The topology of data”
The paper that I will be reviewing today is Deep Learning by YeCun, Bengio and Hilton. It was published in Nature in 2015, and is one of the most highly cited papers in the field with 16,750 citations. The authors of the paper went on to win the Turing Award in 2018. I did notContinue reading “Deep Learning through bad analogies”
Fruits of procrastination 