- We can't really know for sure. We have the anecdote about the workbook but what about earlier and did he absorb any of his dad's teachings or aptitude by osmosis.
Even if we accept he didn't he still went to college and had an unusual experience as an undergrad in being mentored by an excellent mathematician.
That he took a gap year in h.s. doesn't seem that noteworthy to me.
- The world is an interdependent eco- system these days. The idea that a country can isolate itself an reproduce expertise that has flourished elsewhere is a bit silly and tilting at windmills.
Globalization is a fact of the world today and the best path to better lives for everyone is through mutual cooperation and policies that lift all boats.
Trump's goals and attempts to change this are foolhardy.
- Did you read the book? Some of those are distortions.
Regarding the negging incident, she left out important context in her summary of this part of the book.
Feynman went to a bar where it was clear that some of the women at that bar were intending to use men to get free drinks and food. In the incident he described, a woman asked him to buy three sandwiches and a drink at a diner and then says she has to run to go meet up with a lieutenant (taking the sandwiches with her). His negging, was to ask for her to pay for the sandwiches if she had no intention of staying and eating with him. Basically, not being a pushover.
Secondly, he states right after that in the book, "But no matter how effective the lesson was, I never really used it after that. I didn't enjoy doing that."
I also think the incident about lying about whether he was a student while at Cornell was exaggerated. Feynman was 26 at the time and his wife had just died. In the anecdote about the dance, he mentions that some girls asked him if he was a student, and after getting rejected by others at the dance, he says "I don't want to say" and two girls go with him back to his place. But later he confesses, "I didn't want the situation to get so distorted and misunderstood, so I let them know I was a professor".
Overall, I don't find strong evidence of the claims that he was a misogynist or abusive to women in the book outside of his frequenting of a strip club, which may be enough for some people, but, I think people don't realize how different people's attitudes were to things like nudity and sex in the 70s and early 80s before AIDs was a thing.
- Whether he endorsed Harris or not is irrelevant to my point.
It was a poorly written article. I am actually very sympathetic with some of the pushback against some things associated with wokeness, like poorly implemented DEI policies that don't address root causes for representation disparities.
And you have no evidence that any of this is why Trump won. From the people I know who supported Trump, they did it for much different reasons.
- Not much I can say other than that was a disappointing piece of trash from Paul.
The whole tirade against wokeness by the far right is nothing more than a bizarre attempt to stigmatize those who want to improve things for segments of society.
A more legitimate article might have focused on tactics such as shaming and cancelling those who disagree which is problematic in many instances, but Graham paints with too broad of a brush and comes across as another conservative whose only interest is to discredit those who think differently.
- Not sure what that even means. It's our best theory at the moment and helps explain multiple different cosmological phenomena.
There are also hypothesized particles that fit within the Standard Model that researchers are searching for experimentally.
Honestly, these articles are upvoted by people who know very little physics and think that dark matter is somehow a very mysterious idea.
- Linear algebra is a relatively straight forward subject. I won't say easy because there are bits that aren't, and I struggled with it when I was first exposed to it in college. But in graduate school revisited it and didn't have a problem.
So, I really agree that it's about timing and curriculum. For one, it appears somewhat abstract until you really understand geometrically what's happening.
So, I surmise that most non-mathematicians don't have quite the mathematical maturity to absorb the standard pedagogy when they first approach the subject.
- Before smart phones or the rise of the internet your information was mined by credit agencies for use by banks, employers and other forms of credit lending.
Credit cards and Banks sold your data to third parties for marketing purposes.
Payroll companies like ADP also shared your data with the credit agencies.
This is not a new phenomenon and has been the currency of a number of industries for a while.
The only thing that has changed is the types of data collected. Personally, I think these older forms of data collection are quite a bit more insidious than some of the data mining done by a game like Niantic for some ml model.
I have a lot more control over and less insidious consequences from these types of data collection. I can avoid the game or service if I like. There isn't much I can do to prevent a credit agency from collecting my data.
- For me it's been fear of impacting friendships. I have some friends who have very different political views than myself, although I consider myself a centrist.
Some of my friends are no longer on speaking terms with each other because there identity is not just wrapped up in their political beliefs but also in opposition of the other side.
It's a sad state of affairs and a fairly recent one, in my opinion.
I don't remember political disagreements being such a big deal before the rise of Trump.
During the Trump Clinton election he changed the game and politics became more about insulting and denigrating your opposition.
- This balls in the urn problem is relatively straightforward with Bayesian Updating.
We have a uniform prior for the number of red balls that were placed in the Urn, P(U) = 1/101 where U ∈ [0, 100].
We then have the probability the first ball was red given a value of U, P(B1=Red|U) = U/100.
Lastly we have the P(B1=Red) = ∑ P(B1=Red|U)P(U), where the sum is over all values of U.
But we don't actually need to compute this sum because from symmetry arguments we can see that P(B1=Red) = 1/2.
This follows because there are the same number of configurations with U red balls as U green balls and each configuration is equally likely.
So now we can compute P(U|B1=Red) using Bayes Rule.
P(U|B1=Red) = P(B1=Red|U)P(U)/P(B1=Red) = (U/100)*(2/101)
But how does that help us? Well, after we remove a red ball the probability the next ball will be red given the first was red and there were U red balls initially in the urn is:
P(B2=red|B1=red,U) = (U-1)/99.
From this we see that if U was in the range of 51 to 100, then P(B2=red|B1=red,U) > 1/2, i.e., the second ball is more likely to be Red.
So let's compute the following where we are summing over U ∈ [51,100]:
P(U ∈ [51,100]|B1=Red) = ∑ P(B1=Red|U)P(U)/P(B1=Red) = ∑ (U/100)*(2/101) = (2/101) ∑ U/100 = (2/101) (50/100) * 75.5
P(U ∈ [51,100]|B1=Red) = 75.5/101
So we find that P(U ∈ [51,100]|B1=Red) = 75.5/101 ≈ 3/4 which means that given the first ball we picked was red, then roughly 3/4 of the time we would expect the next ball being red to be more likely.
This seems like a much more straightforward way to think about it for me.
- There is a lot of evidence that aphants score higher on spatial reasoning tests than those with vivid visual recall and are over represented in fields like math and science.
These two skill sets appear to be divergent, i.e., people are either good at visualizing and recalling fine details or they are good at manipulating/reasoning about spatial objects.
Personally, I have always excelled at the latter and have a strong sense of direction and have scored well on tests that require one to manipulate/rotate objects in my brain.
- I sort of agree and disagree. I wouldn't agree with the idea that most FAANG engineers are not passionate by nature about their work.
What I would say is that the bureaucracy and bullshit one has to deal with makes it hard to maintain that passion and that many end up as TC optimizers in the sense that they stay instead of working someplace better for less TC.
That said, I am not sure how many would make different choices. Many who join a FAANG company don't have the slightest inkling of what it will be like and once they realize that they are tiny cog in a giant machine it's hard to leave the TC and perks behind.
- I find this hard to believe having worked in multiple enterprises and in the FAANG world.
In my anecdotal experience, I can only think of one or two examples of someone from the enterprise world who I would consider outstanding.
The overall quality of engineers is much higher at the FAANG companies.
- East Asian Languages are tough for Westerners, or at least that has been my experience. The biggest problem is really the barrier to reading especially with Chinese and a lesser extent Japanese.
Would love any tips from someone who has mastered all those characters without full immersion or a structured University curriculum.
I think the framing is dead on.