Certainly the more immediate the feedback the better though.

On some level, this is true. People can of course legally react in certain ways to someone's speech, such as deciding not to associate with them.

However, I feel like the phrase is a bit deceiving because it seems give the impression that "freedom" and "consequence" are unrelated. It's designed to give people the feeling that they are justified to impose more and more "consequences" (such as blacklists or even threats of violence).

Here's the definition of the phrase from the dictionary: "the right of people to express their opinions publicly without governmental interference, subject to the laws against libel, incitement to violence or rebellion, etc"

In this definition, "freedom" is directly identified with protection from a certain consequence, i.e "governmental interference." I think it should be obvious that "freedom" isn't really about the ability to move ones' mouth and make noises, but rather the freedom one has is DIRECTLY related to ones' protection from consequences. Extralegal actions to increase the "consequences" of certain types of speech directly reduce free speech.

This isn't a black or white thing, but based on what I've been seeing in social media, the possibility of some people internalizing the mantra "freedom of speech does not mean freedom from consequences" to the point that they justify violent action against speech they dislike doesn't seem implausible.

>Despite that unjustified leap, the document goes on to suggest strongly that women working at Google are less qualified than men

Can you elaborate on this? Just because FEWER women may be qualified to work at Google doesn't mean that the ones that are are any less qualified than the men. The fact that fewer women are tall doesn't mean that tall women aren't tall for example.

>I could argue "water is composed of two hydrogen molecules and one oxygen molecule, so women are bad at software development", and my argument would just be a difference of degree worse than hers.

I fail to see how the science discussed in the memo is as irrelevant as you make it out to be. Is it really that far fetched that psychological makeup (as expressed in big-5 characteristics) and interests play a role in what people choose to pursue and what they like to do? Because software engineering is different than other occupations (such as law and medicine), it makes sense to think about what might attract one to one profession over another. Many intelligent women I know chose careers such as medicine over cs. And why not? It pays better and doesn't involve staring at a computer all day (something that not everybody enjoys). The same could be said for law and finance (investment banking, private equity).

>Among all STEM fields, computer science is distinguished for losing the participation of women over the last 10 years.

http://www.pbs.org/newshour/making-sense/truth-women-stem-ca...

If you scroll down to the two bar charts in the link above, you'll notice that while the % of bachelor degrees earned by women in CS has gone down, the % of PHD degrees earned has actually gone up (looks to be about 40% higher compared to 1991)! I think you would agree that earning a phd in CS is much more difficult than a BS, and I think this actually shows that women are being given more opportunity to excel academically in the subject.

As for bachelor degrees in CS, it seems like it has converged more to the % awarded in engineering. Speaking more on the differences between CS (i.e Bachelors level CS that leads to SWE jobs) and Math, I would say there is a qualitative difference between the two, and certainly one can have personal preferences. Software engineering is much more about creating systems that work and solve real-world problems. It also involves a lot of programming. Pure math (and theoretical CS) is more about investigating an abstract world and looking into interesting patterns and connections. It actually has a lot of similarities with philosophy in this regard. Some of the female math/science majors I knew actually didn't really like programming and ended up being highly successful in other fields even if they went into industry (medicine/finance/business).

>There's a reason she does that: if you don't stipulate that correlation, the argument against gender bias in computer science has to confront another damning fact, which is that gender disparity in the field isn't global. Unless women in Asia are somehow biologically different than those of the US, her argument needs some way to address the fact that women make up the majority of STEM majors in many of those cultures.

In fact, people have done cross-cultural studies. https://www.researchgate.net/publication/23179757_Sex_Differ...

"Regression analyses explored the power of sex, gender equality, and their interaction to predict men's and women's 106 national trait means for each of the four traits. Only sex predicted means for all four traits, and sex predicted trait means much more strongly than did gender equality or the interaction between sex and gender equality. These results suggest that biological factors may contribute to sex differences in personality and that culture plays a negligible to small role in moderating sex differences in personality."

From my personal experience (which I agree is less convincing than the numerous empirical studies that have been done on the topic), I'll say that many women in asian countries are pushed into studying cs/programming even if they don't like it, because those fields often provide a straightforward path to making a decent income.

If you know that men and women differ in a distributional sense with respect to some trait, that gives you a prior to work off-of when you meet a new individual. This is rational from bayes theorem, so simply saying "you should treat everyone as an individual" is not nuanced enough.

However, as you acquire more information about a particular individual (such as passing a difficult google interview, or knowing that they've succeeded in a reputable CS curriculum), this should quickly "swamp" the prior, causing it to contribute very little to the final inference.

The problem is the humans are not great at adjusting like this: we're not perfect at applying bayes theorem in our heads. We tend to overstate the influence of various priors when there are stronger signals at hand. Nevertheless, incorporating prior distributional information is NOT irrational, but generally overdone.

Therefore, it seems like the approach of some is to shout down information that would suggest biological distributional differences, to try guarantee that people don't overuse prior information.

Suppose you lent 10,000 to a friend and the friend promised to return it within a year. A year later, your friend doesn't pay you back and makes a bunch of excuses. Instead, he takes the 5,000 he has left to the casino, gambles it on the roulette wheel and by luck manages to turn that into 30,000 and pays you back 1.5 years later. I don't think you would be happy with that situation...

In general, people have a poor understanding of how to evaluate an investment manager. It's not enough to just look at absolute returns and compare them to the S&P, you need to correct for market exposure (the beta). Even then, it is not that straightforward: this is one of the best overviews I've seen (the author of the blog, Robert Frey, was a former managing director at Renaissance Technologies, the most successful hedge fund of all time)

http://keplerianfinance.com/2013/07/alpha-and-evaluating-inv...

To make the "correcting for exposure" aspect concrete, suppose you have the opportunity to invest in a poker player that generates a 10% return on capital per year. It wouldn't really make sense to compare this return to the S&P 500 returns, because the beta is very close to 0.

First paragraph is:

"In number theory, integer factorization is the decomposition of a composite number into a product of smaller integers. If these integers are further restricted to prime numbers, the process is called prime factorization."

This is a clear and precise statement of the problem. There are some key words that one might need to look up which takes time, but that's what I mean by slowing down.

Consider the sequence a1, a2, a3... with the sum being a1 - a2 + a3..., note we can write the sum as: a1 + (-a2 + a3) + (-a4 + a5) hence the sum is less than a1.

We can also write it as: (a1-a2) + (a3-a4) + (a5-a6) .... hence the sum is greater than (a1-a2).

Some companies will actually stop growing and start paying dividends/sharebuybacks for this reason.