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You ever watch that South Park episode where they make fun of Hollywood where all the actors love the smell of their own farts? These days I feel that this is a pretty apt of a criticism of SV and Bay Area culture..

“But that’s not what happened, because her aggressive tattletale move immediately threw my Primitive Mind into a rage, plummeting me down the Psych Spectrum. This banished my Higher Mind to the closet of my subconscious, allowing my Primitive Mind to come up with a genius-yet-psychotic plan for revenge. Which worked, and made my Primitive Mind feel deeply satisfied in a very not-grown-up way.”

His entire point is that the average person acts like an asshole some of the time and like a kind person some of the time, and that it is incredibly easy to cherry-pick examples to paint someone in a bad (or good) light.

Which is exactly what you have done here, proving his point!

Tech jobs are probably the best combination of pay/lifestyle balance at the moment.

[1] https://www.quora.com/Why-does-it-cost-more-to-heat-househol...

This is some relevant background reading for anyone who thinks Bernie's tax revenue numbers are realistic.

A transaction tax has been tried before in Sweden[1], had disastrous effects on their stock markets, and had to eventually be repealed.

I don't think most people appreciate how efficient the US markets are today, relative to what they were like 20 years ago. HFTs do make a lot of money, but its a extremely small amount compared to the total size of the financial system [2], and it is decreasing rapidly each year as the competition increases.

If we impose a transaction tax, market makers who are already on razor thing margins will increase bid-offer spreads, volatility will increase, and it will become way more expensive on average to trade. There's a reason why you can trade virtually for free on your personal account today, vanguard is able to offer you 0.1% in management fees, etc. These things did not exist 20 years ago.

Its a common myth that HFT's are "stealing" money from investors, but if these firms were not in the markets, it would almost certainly be detrimental to the ordinary saver/investor.

Bernie definitely wouldn't be able to raise nearly as much money as he would need from this tax, and it would hurt the economy. Him and his advisors know it, but he just wants to pander to the anti-wall street sentiment while also making it seem like his proposal isn't completely fiscally irresponsible, which it is.

[1] https://en.wikipedia.org/wiki/Swedish_financial_transaction_...

[2] https://www.ft.com/content/d81f96ea-d43c-11e7-a303-9060cb1e5...

Lets say you took a population of candidates with some distribution of individual programming skills, and assigned them randomly into two categories, with a 90-10 split between A and B. E(skill of a candidate in A) = E(skill of a candidate in B)

Now you rank all the candidates in A and B respectively from best to worst, and you pick the top N candidates from A, and top N candidates from B. Taking that set, E(skill of picked candidates from A) > E(skill of picked candidates from B)

There is no prejudice here! A and B are randomly assigned, if you pick any one individual from A, they are exactly the same on average as an individual from B. But conditioning on the constraint that you need to pick the same number from A and B, where A and B have different population size, then you will get the result that A is on average better than B.

In a hypothetical world where sexism does not exist, given the current ratio of men to women in tech we would expect to see a skill disparity in the average if we enforced hiring equal numbers of both men and women.

Edited to add some further points:

1) Note that the above does not hold true if the individual programming skills are constant after some threshold, so the top N from A and top N from B all have the same level of skill. I don't think this is true in my personal experience, but I have seen people make this argument.

2) The above also does not hold true if the ranking mechanism (interviewing process) does not actually rank the programming skills of A and B successfully. I've also seen people make this argument, but note that as long as some correlation exists between [interview result] and [programming skill] then the above still holds true. It seems highly unlikely that there is zero correlation.

3) I don't necessarily agree with the author that this is a bad thing - I think in the long run to get equal numbers of women and men in candidate pools, having equal numbers of female and male programmers at places like Google can only help. While theoretically there may be an average skill difference, I'm not sure that you can really notice it on an individual level because of the sheer number of qualified candidates that apply to a place like google.

I think you're misunderstanding a big part of what is "easy" and "difficult" for humans vs ai. Yes, go is absolutely a more challenging games for humans than starcraft (I also play go, although not very well - currently around ~7k on igs). Starcraft is strategically a much simpler game than go. You are correct in stating that mechanics is what makes starcraft hard for most people, and yes if the computer knew exactly what to do, it would be able to execute it faster and without making any multi-tasking mistakes. But strategy is not what makes starcraft a challenge for ai. Tasks that are trivial for humans can be extremely difficult for ai.

Computers are way better at tree searching than humans, for the obvious reason that they run much faster than brains. So games with relatively small state-spaces, like checkers, are solved quickly. But as you increase the state space, it becomes impossible to search all possible future moves, and this was why go was intractable for such a long time.

The big advancement in alphago is that by using deep learning it is able to evaluate different board-states without doing any search, using a neural net. This allows it to massively prune the search space. Humans are able to do this through "intuition" gained through experience - talk to any advanced go player and ask them about specific moves and they will tell you things like "this shape is bad" or "it felt like this was a point of thinness". AlphaGo was able to gain this "intuition" by training on a massive dataset of go board positions.

In go, the rules are very simple - 19x19 board, each turn you can put a stone in any not-surrounded open space. Its also a turn based game. The state at any given time is fully known. Starcraft is real-time, there are tons of different actions you can take, the actions are not independent (pressing attack does something different if you have a unit selected or not), the game state is not fully known and a given state can mean different things depending on what preceeded it. Not to mention that the search space is massively massively larger. To create a representation of this that can be fed into a neural net and give meaningful results (something like at a given tick, score all possible actions and find the best one) is going to be incredibly difficult. An order of magnitude more difficult than go, imo.

Go is a discrete game where the game state is 100% known at all times. Starcraft is a continuous game and the game state is not 100% known at any given time.

This alone makes it a much harder problem than go. Not to mention that the game itself is more complex, in the sense that go, despite being a very hard game for humans to master, is composed of a few very simple and well defined rules. Starcraft is much more open-ended, has many more rules, and as a result its much harder to build a representation of game state that is conducive to effective deep learning.

I do think that eventually we will get an AI that can beat humans, but it will be a non-trivial problem to solve, and it may take some time to get there. I think a big component is not really machine learning but more related to how to represent state at any given time, which will necessarily involve a lot of human-tweaking of distilling down what really are the important things that influence winning.

At the same time I can also see the appeal of a stable income and family, raising your kids in some nice suburbs, going to their soccer games or whatever. It was the life I had growing up and I'd like to give that to my kids someday.

Your comment seems pretty closed-minded, and also belittles the op for no reason.

The best innovation often happens when working under constraints; often a drive for profit results in better service for everyone using the infrastructure. Of course this isn't the case 100% of the time, like your example of medicine, which is why I think that to get the best of both worlds you need a mix of government regulation with privatized capitalism. But I think it would be incorrect to dismiss the profit incentive entirely as being bad, and probably in the case of US infrastructure we would benefit from more privatization and not the other way around.

[1] http://money.cnn.com/2015/03/30/news/hong-kong-mtr-subway-pr... [2] http://www.newsweek.com/post-office-aint-broken-its-profit-s...

With wall street bonuses, as long as you perform well, you get rewarded for it. Its a much more "meritocratic" system, there are cases when a hedge fund will lose money overall but individuals within the fund still get large bonuses because the areas they were responsible for did well.

I'm not saying that people who are displaced will easily be able to retrain to a new job. They will have a rough time, and that's something we need to address as a society.

But assuming that number of "jobs" for humans will decrease to zero is a big stretch. I think that even if we had robots that could take care of all our needs for us, humans would come up with jobs to occupy their time.