Recommends your series with 3Blue1Brown on the cosmic distance ladder.

Transcript context

…I have to get out of my technical mindset. He spoke in plain English, didn’t use equations, and he synthesized a lot of disparate facts. Little pieces of evolution had been worked out in the past, but he had this very compelling vision. Again, he was still missing things. He didn’t know the mechanism for heredity, he didn’t have DNA. But his writing style was persuasive, and that helped a lot. Newton wrote in Latin. He had invented entire new areas of mathematics just to explain what he was doing. He was also from an era where scientists were much more secretive and competitive. Academia is still competitive, but it was even worse back in Newton’s day. He held back some of his best insights because he didn’t want his rivals to get any advantage. He was also a somewhat unpleasant person from what I gather. It was only a couple of decades after Newton when other scientists explained his work in much simpler terms that they became widespread. The art of exposition and making a case and creating a narrative is also a very important part of science. If you have the data, it helps, but people need to be convinced, otherwise they will not push it further or take the initial investment to learn your theory and really explore it. That’s another thing which is really hard to reinforcement learn on. How can you score how persuasive you are? Well, there are entire marketing departments trying to do this. Maybe it’s good that AI is not yet optimized to be persuasive. There’s a social aspect to science. Even though we pride ourselves on having an objective side to it, where there’s data and experiment and validation, we still have to tell stories and convince our fellow scientists. That’s a soft, squishy thing. It’s a combination of data and painting a narrative, and it’s a narrative of gaps. Even with Darwin, as I said, there were pieces of his theory he could not explain. But he could still make a case that in the future, people would find transitional forms, that they would find the mechanism of inheritance, and they did. I don’t know how you can quantify that in such a precise way that you can start doing reinforcement learning. Maybe that will be forever the human side of science. One takeaway I had from reading and watching your stuff on the cosmic distance ladder… By the way, I highly recommend people watch your series with 3Blue1Brown on the cosmic distance ladder. One takeaway was that the deductive overhang in many fields could be so much bigger than people realize. If you just had the right insight about how to study a problem, you might be surprised at how much more you could learn about the world. I wonder if you think that’s a product of astronomy at the particular times in history that you’re studying. Or is it just that based on the data that is incident on the Earth right now, we could actually divine a lot more than we happen to know? Astronomy was one of the first sciences to really embrace data analysis and squeezing every last possible drop of information out of the information they had because data was the bottleneck. It still is the bottleneck. It’s really hard to collect astronomical data. Astronomers are world-class in extracting all kinds of conclusions from little traces of data, almost like Sherlock. I hear that for a lot of quant hedge funds, their preferred hire is an astronomy PhD, actually. They are also very interested for other reasons in extracting signals from various random bits of data. We do under-explore how to extract extra information from various signals. Just to pick one random study, I remember reading once that people were trying to measure how often scientists actually read the papers that they cite. How do you measure this? You could try to survey different scientists, but they had a clever trick. Many citations have little typos, like a number is wrong or punctuation is almost wrong. They measured how often a typo got copied from one reference to the next, and they could infer whether an author was just copying and pasting a reference without actually checking it. From that, they were able to infer some measure of how much attention people were paying. So there are some clever tricks to extract… These questions you posed earlier of how we can assess whether a scientific development is fruitful, interesting, or represents real progress… Maybe there are really useful metrics or footprints of this phenomenon in data. We can examine citations and how often something is mentioned in a conference. Maybe there’s a lot of sociology of science research to be done that could actually detect these things. Maybe we should get some astronomers on the case, actually.…

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