Great Reads - 03
My list of “must reads” for this week(s).
What Does Any of This Have To Do with Physics? - Nautilus [link]
A long, personal story about a PhD student’s journey through graduate school in theoretical physics. This one really resonated with me.
Is there a simple algorithm for intelligence? - Michael Nielsen (Appendix to Neural Networks and Deep Learning) [link]
When it comes to research, an unjustified optimism is often more productive than a seemingly better justified pessimism, for an optimist has the courage to set out and try new things. That’s the path to discovery, even if what is discovered is perhaps not what was originally hoped. A pessimist may be more “correct” in some narrow sense, but will discover less than the optimist.
This point of view is in stark contrast to the way we usually judge ideas: by attempting to figure out whether they are right or wrong. That’s a sensible strategy for dealing with the routine minutiae of day-to-day research. But it can be the wrong way of judging a big, bold idea, the sort of idea that defines an entire research program. Sometimes, we have only weak evidence about whether such an idea is correct or not. We can meekly refuse to follow the idea, instead spending all our time squinting at the available evidence, trying to discern what’s true. Or we can accept that no-one yet knows, and instead work hard on developing the big, bold idea, in the understanding that while we have no guarantee of success, it is only thus that our understanding advances.
While I haven’t yet worked through the book, this Appendix stands alone and is a very nice speculation about the existence of a concise organizing principle for “intelligence”. Surprisingly, it also ends with a very nice reflection on the nature of scientific progress, which I’ve quoted above.
Why doing a PhD is often a waste of time - The Economist [link]
The Trouble with Quantum Mechanics - Steven Weinberg in The New York Review Of Books [link]
A good “popular science” piece, speculating on the future of quantum mechanics.
The Risk of Discovery - Paul Graham [link]
Because biographies of famous scientists tend to edit out their mistakes, we underestimate the degree of risk they were willing to take. And because anything a famous scientist did that wasn’t a mistake has probably now become the conventional wisdom, those choices don’t seem risky either.