I can't understand this life style. To be the best at thinking deeply I need my mind to relax and focus in the goal, so to obtain a flux that point me in the direction to progress. Any anxiety or hurry is only going to make me blind to new opportunities. So, my advice is to learn to focus in a good target and forget playing being the number one.
Edited: Anyway, if you are unable to change that life style, I suggest you dedicate your life to something important for the rest of society, perhaps that can ameliorate being second in other fields.
Recently I read somewhere that people prefer "potential" to "factual value". For example some prefer a potential Oscar film rather than a film awarded an Oscar. So perhaps the new algorithm for fostering true telling and honestity is base on the potential of people to construct the new rules not to adhere to them. Perhaps this is the NIH syndrome at work, or just that people prefer rediscovering facts than learning facts. But rediscovering requires a very good teacher and a lot of time.
Nice from one student: "I’m not worried about AI getting to where we are now. I’m much more worried about the possibility of us reverting to where AI is."
In 10.4 A/B testing is just a list with several points and there is not warning about having a deep understanding. For example, the point of selecting a sample is not easy, if you take a sample of something on 1 july of 2020, you have to consider if the weather, the day of week, people on vacation or anyone of thousands of factor is going to make your sample not adequate to generalize the result to other circumstances. Using statistics correctly requires neutralizing many sources of errors. It is not easy to get a good representative sample.
> I am working with people that don't understand even the basics (such as a survey with 49 responses means that the margin of error is over 10%
I don't understand this, if the population if just 49 people then the margin of error is zero. So intuitively the bigger the population the bigger the bound for the margin of error.
When there is a small amount of information the variance of any estimation is very big and this explains what happens in that example. Overfitting implies a different behavior in training and in test and this is related to a big variance in the estimation of the error. So small amount of information implies that any model suffer overfitting and big variance, so is a general result not related especifically with Bayes.
It has inspired other posts, for example (2)
As a hobby Lisper I learned about Peter Seibel as the author of Practical Common Lisp.
(1) https://gigamonkeys.com/flowers/ (2) https://medium.com/@danonrockstar/let-a-thousand-flowers-blo...