The book continues to drift between wonder about the world and weakly motivated bias towards human scale, which is a pity because it cites some elegant analyses.
This weekend I took Tarot as a little test case for the OpenAI API.
It turned into a quick lesson in how not to use Chat-GPT (in so many ways 🙂 ).
Fortune telling by ‘reading the cards’ seemed like a good use-case for generative AI: ChatGPT can draw random cards and explain their meaning in the convincing tone of a Tarot Zealot.
I had to tweak the prompt a bit when Chat-GPT explained in a condesending tone that it was an AI and could not actually ‘draw cards’. But after that, the whole fortune telling business could be swiftly disrupted in a few lines of code.
Card 1 is 'The Wheel of Fortune' Card 2 is 'The Moon' Card 3 is 'The Star' Wait one minute for your fortune teller to give her explanation. She has to align with higher powers on the interpretation of these cards... Hear your fortune: Ah, my dear client, what an interesting trio of cards you have drawn! The Wheel of Fortune represents life's constant changes and cycles, indicating that a major turning point is approaching. The Moon signifies a time of uncertainty, where emotions and intuition guide your path. However, fear not, for The Star brings hope and inspiration, shining light on your journey. This whimsical combination suggests that you should prepare for unexpected shifts, trust your instincts, and stay optimistic, as ultimately, a bright and promising future lies ahead. Remember, my friend, laughter is the best crystal ball gazer!
But after trying my “Tarbot” a couple of times, an odd pattern emerged: most of the ‘random’ cards were from the major arcana. That is a special category of 22 of the 78 Tarot cards that is considered more interesting, deep, and complex by Tarot aficionados.
In a longer experiment, I got to no less than 76% major arcana. A true random draw whould have resulted in c. 28%.
Ouch! How is that for a bias towards what seems interesting?
The explanation of combining online and offline methods is insightful, though somewhat superficial
Charming book with some nice perspectives that preaches to the converted
Entertaining plea for rationality and scenario thinking.
The practical and relevant examples (health effect of smoking, impact of humanity on climate change) of causal inference alone make the book worthwhile.
Great exercise in spotting biases, and understanding how these manifest themselves in how the world around us is shaped.
Overly simplified presentation of basic statistics that cuts some corners, as superbly pointed out by Andrew Gelman.
A cornucopia of charming mathematical anecdotes and facts
In the US election system, geographic concentration puts democrats at a fundamental disadvantage.
Economists should stay away from pseudo-philosophical assertions, in particular when these hinge on misinterpretation of Bayesian methods, use flawed logic, and do not lead to realistic recommendations.
A lot of Fermi-type deconstruction of drivers, Monte Carlo simulations, and value estimates .
Shocking to realize how much controversy surrounded Bayesian statistics before the explosion of computing power.
Despite all mathematical considerations, the prerequisite for a truly successful betting strategy inside knowledge.
Sage advise from the man who beat the dealer at blackjack and outperformed the market as one of the world’s first quants (but feel free to skip the chapters about Edward’s youth as a prodigy).
Essential reading for everyone who uses statistics on a regular basis, for policy making or otherwise.
Entertaining rant on shortsightedness in many guises, backed-up by a statistical world-view.