rm999

Could be thermostatic sea level rise: the water will spread to warmer parts of the ocean and expand.

Funny, I have both and I consider them different products with very different use-cases (although I have the Echo Dots, not the full size Echos).

Also, in a great development, both are going to integrate with each other much better soon; there's a private beta in place right now: http://www.aftvnews.com/sonos-begins-beta-testing-amazon-ale...

Fill in the gaps with blue, and you have something very similar to the population density map of the USA: https://mwlibertyfest.files.wordpress.com/2009/06/us-pop-dis...

This seems to reflect the "graying" of rural America: https://www.theatlantic.com/business/archive/2016/06/the-gra.... Younger generations are moving to denser areas, driving property values up. This isn't a bad thing per se (denser populations are more environmentally friendly), but is probably driving some of the division in the USA today.

Definitely true. I worked for a company that was generating millions of dollars a year from neural networks in the mid 90s (edit: to be clear I didn't work there in the 90s, I joined years after their initial buildouts). The Unreasonable Effectiveness™ of neural networks has been true for a long time. When I worked there I tried switching out some models with SVMs and they were less accurate and took 1-2 orders of magnitude more time to train.

the tldr answer: SVMs are theoretically powerful but either impractical or pointless.

The longer answer:

SVMs comes with two theoretical benefits: 1. A guaranteed optimal solution. You get this with simpler techniques like logistic regression. 2. The ability to use non-linear kernels, which can offer much more power than logistic regression (on par with neural networks).

So, at first, it seemed like SVMs were the best of all worlds, and a lot of people got excited. In practice, though, non-linear kernels slowed training down to the point of being impractical. Linear kernels were fast enough, but removed the second benefit, so most people would prefer to use established linear techniques like logistic regression.

This is true, but only in the academic research world. SVMs had relatively little success on practical problems and in industry, so they never built up the kind of standing that neural networks did. Even in 2003-2005 - arguably the peak time for SVMs - neural networks were much better known to almost everyone (industry practitioners, researchers, and laypeople) than SVMs.

What frustrates me is that people who are starting out in machine learning often never learn that linear/logistic regression dominates the practical applications of ML. I've spoken to people who know the in-and-outs of various deep network architectures who don't even know how to start with building a baseline logistic regression model.

Reddit did the right thing - they realized a majority of their users are on Reddit for the images, which makes it a core competency. Reddit was using Imgur as a free image host, which Imgur was obviously not ok with. I realized this last summer when imgur started redirecting direct image links into its ad-ridden bloated mess of a social image page. It was an obvious attack on reddit. I don't know if imgur ended up undoing this or not, but it made it painfully obvious that reddit and imgur cannot peacefully coexist in the way it used to.

edit: just found this, it looks like they're continuing to move away from being a simple image host for reddit. https://www.reddit.com/r/assholedesign/comments/6eip2q/imgur...

>Plus, these experiences were shitty for the user but they must have worked

I spent years as a data scientist in the online display advertising space, and I was shocked at how much of a disconnect there was between advertiser ROI and clicks/impressions/whatever metric is being sold to the advertiser. There are very few parties whose best interests involved clearing up that connection. I heard one story of an ad executive getting fired for introducing A/B testing to their advertisers and proving the negative ROI.

Big flashy ads certainly improve clicks, but it's not clear if they make a customer want to buy a product.

The article doesn't even argue that it's a race thing, it simply starts with the premise but explains it as socioeconomic. I agree with the article on that one. I am not white and I was never discouraged in any way from programming because of my skin color or ethnic background (I've been doing it in some form since I was 6 in the late 80s with full support from pretty much everyone, including a lot of monetary support from my upper middle class parents).

edit: it actually does get into race, but around blacks/african americans. But it didn't get into Asians - my point really is this isn't a white thing per se.

This article makes some decent points around class and the expense of a computer in the 80s/90s. But it dodges more substantial questions, and a lot of the substance is either circular logic or confirmation bias.

There are plenty of asian men in programming, so this isn't a race thing. It probably is socioeconomic for the reasons the article points out, so that's fine - I think we can leave it at "white and asian people dominate programming because they tend to come from wealthier families".

I think the much harder question is why women aren't in programming. They make up 50% of the population and come from the same exact socioeconomic background as men. This question is important because the repercussions still haunt us today. The article starts this question with "girls weren’t interested in computers or video games at all", and then circles the logic with "people tend to do things that they see other people who are 'like them' doing". Well, I'd like to know why women weren't interested in programming. In my circumstantial experience it wasn't a conscious decision or peer pressure, it was coming top down from society. I saw many parents encouraging their sons to play with computers and completely ignoring that their daughters may want to also. The article completely avoids this topic, sadly.

edit: downvoted within 30 seconds - if you're gonna downvote, please at least reply and start a discussion about it.

Short answer: "scripting"

Longer answer: programming isn't necessarily a job where you build things. I'd argue it's more generally about communicating with and controlling computers. And computers are increasingly where our lives are spent and/or tracked.

I learned to program when I was relatively young, and I've spent my whole life witnessing people doing mundane tasks that a script could have easily automated away. It's everywhere: doctors' secretaries copying forms, grocery clerks scanning through shelves, office workers manually editing excel documents, etc. The reason these tasks remain un-automated is because programmers don't do these jobs; programming is considered a profession rather than a basic skill. I showed my friend's secretary how to easily automate a task she spent hours a week on, but she felt like the solution was foreign and rejected it. That one script could have made freed up 100s of hours a year to do other things, but she didn't get it.

In a world where business people are looking to "disrupt" a lot of jobs through automation and consolidation, I think there's room for people to help automate away parts of their own jobs and share in the wealth (with more free or recreational time). I believe this is how a lot of people will keep jobs in the next 20+ years.

Personally, yes - I like asking about high-level run-downs of a project on their resume. I then choose points to stop them (either randomly or if I know a lot about the subtopic) and dig deeper and see where their comfort level lies.

There are a lot of benefits to this approach:

1. Keeps the topic on something they are presumably comfortable with.

2. Gauges their seniority level: more senior folks can talk deeply about a wider variety of topics.

3. Tests their communication and explanation skills, again about a topic they should be comfortable with.

Some potential pitfalls:

1. Makes it harder to objectively compare candidates. I think this is impossible anyway, so I don't consider it a big deal.

2. Some candidates, especially junior ones, simply don't have many projects on their resume to talk about. In this case I'll often ask them to explain a skill or technology they are experienced with.

3. It's harder to conduct these interviews. Giving a problem and passively watching and helping as needed is a lot easier than keeping up an active dynamic conversation.

I don't think the birthday paradox is relevant here.

A decent statistical test would be to gather all firings within some radius around work anniversaries (let's say 6 weeks), and see if there's a statistically higher chance of the firing happening before the anniversary than after. If it's egregious, you'll see a lot more before than after.

The comments in here are oddly fatalist about the whole thing. We put a lot of trust in airlines; and not just in safety or to get us somewhere, but we also give them an unusual number of legal protections once we're in their care.

When the drama first started I kind of wrote it off: "this must have been a misunderstanding compounded by a single employee's stupidity". But when I found that it was not only condoned, but fully defended by the company top-down, red flags went off. Then, the inevitable outrage led to past stories resurfacing, with a pattern of absurd insensitivity (abused dogs, broken guitars, sexist clothing policies, etc). Then, I started remembering some terrible experiences I've personally had on United. Like the time they left me stranded in India for 2 days (rumor is the staff did this on purpose to protest some new policy around maximum flight times), then made me wait 3+ hours to get approved for a hotel room at 1 am even though I was flying business class (which would have been wrong even if I wasn't business class!) Or the time a gate agent lied to me about contacting a connecting flight, leading me to miss an entire day on my Thailand trip. I have many more of these.

My point here is that United really does seem to have ingrained issues that directly harm their customers. This toxic culture doesn't benefit them in any way, but if nobody cares they'll have no incentive to stop.

I founded and am the sole moderator of r/apachespark. That one's easy because it's a well-behaving crowd and the purpose of the subreddit is obvious. The only real things I do is remove spam/unrelated material and tag big releases as announcements.

More generally, I think the moderators' main job is to set the mission/rules of the subreddit, and enforce them. Subreddits with weak enforcement rarely thrive as they devolve into the least common denominator.

BTW, Amazon India has had cash on delivery since ~2013 (except a time during their recent currency crisis). This is arguably more user-friendly than Amazon Cash.

http://www.zdnet.com/article/amazon-pilots-cash-on-delivery-...

This article is about age discrimination happening at the initial levels of the job application process, before you're evaluated on your merits. The study the article mentions is about identical resumes with different ages. The lawsuit against R.J. Reynolds is about guidelines given to recruiters. In both these cases, it doesn't really matter how good you are if you're trying to get a job through the standard process.

I've been working in machine learning for a relatively long time (~15 years), and most of what I've seen does not pass my AI smell test: "do the results amaze me, even though I built it and know how it works?" I am confident true AI, by any reasonable definition, will pass this test, so I think it's a valid question to ask (to be clear I love machine learning, I just think its tight relationship with the term "AI" is an historical error).

Deep neural networks on visual and audio data are passing my smell test. At my last job I helped design and build neural networks to process music data. The results were shockingly good: the networks could tell us a very wide variety of information about a song at an accuracy that exceeded human experts, including genre and mood. This is in a domain that most people think of as very human ("understanding" and "feeling" music), which is what made the results so impressive.

The goalposts will continue to move, but an inflection point has been hit in the last few years. I believe we'll see at least one or two more inflection points in the next several decades as we move closer and closer to "strong" conscious AIs that are functionally above human intelligence.

>I don't believe the author has more than a day's worth of googling more background on the subject than me

There's a certain irony in this accusation considering 30 seconds on his site led me to find out he's writing a book on the subject of social psychology. http://elephantinthebrain.com. The fact that he took the time out of his book writing to write this article probably means he's researched and thought a lot about the subject.

He may be a secondary/tertiary source on this matter, but that doesn't mean he has nothing insightful to say about it, or that people who read the article are wasting their time (not to say that the reverse isn't true; maybe he hasn't properly learned the topic, but it's wrong to assume he just wrote this article off the cuff).

It seems to work fine for me? I just searched for Nothing Else Matters by Metallica and am listening to it on endless mode.

This is a great point. Python has been a solid data engineer/pipeline language for years now. It makes it easy for data scientists and data engineers (or people who wear both hats) to work in the same environment and codebase.

I'm glad to see the quick convergence on Python. I've evaluated Python every ~2 years since 2006 for "data science" tasks (machine learning, statistics, data munging, and visualization). I'd argue that Python only properly covered this full data science stack 1.5-2 years ago. R covered this stack adequately probably around 2011-2012. Matlab had this before 2005.

What makes Python a superior language to Matlab and R is the ease of software development. It's an easy, pleasant language to work in, and I trust it for production tasks (I've written production R code and it's fairly hard to read and fragile).

What's even better is data science is moving 100% into Python 3 (from 2) by 2020:

http://www.python3statement.org/

Wait a minute... wait. Those service fees I've been opting to pay haven't been going to the shoppers/drivers who are processing my order? On my last order I asked the shopper to specifically go out of her way to check on an out of stock item. I did this with the assumption that my 7 dollar service fee was going directly to her, and that she would therefore be happy to go out of her way. When the delivery person (I think the same as the shopper) came by and I smiled at her, I thought that was a "we're square", not a "sorry for not tipping you" smile.

I'm livid. It's on instacart to pay fair wages with the delivery fees if they think it's a problem. Their misleading double fee system is unfair to both me and (more importantly) the people who are directly providing me with service. I'm sick of the uber-ish "on-demand" service culture abusing their non-employees under my dime.

edit: funny thing is, I just watched the infamous episode of Kitchen Nightmares about Amy's Baking Company. In the episode, Gordon Ramsay becomes furious when he finds out that the customers' tips aren't going to the servers. He goes into the restaurant and asks the patrons if they would have tipped if they knew this and everyone agrees no. The owner meekly explains in a follow-up episode that he does this so he can pay the waiters a "fair fee". https://en.wikipedia.org/wiki/Amy's_Baking_Company. This episode utterly destroyed Amy and her husband's company. Instacart, I love your service. It's changed the way I grocery shop and I planned on being a regular, high-value customer. Please don't make me question my decision on this.

Isn't that a plot of people who search Google for the term "google"? If so, I imagine it's something people who are familiar with computers and the internet do not do often.

The best work setup I've ever had by far was not private offices or cubicles (and definitely not an open office); it was a hybrid where our team of 3-5 people sat in a large private office. This increased collaboration while enforcing respect. It allowed our team to create a work culture democratically (how do we arrange seating? what noise levels are ok?) that simply isn't possible in an open seating arrangement.

I know this won't be popular here, but I find private offices problematic for a few reasons. First, they hurt collaboration and social interaction quite a bit. This is ok from a single developer's perspective (heavily skewed audience on HN), but it shows on cross-functional teams. I know this can be hacked into a private office setup ("my door is always open"), but in my experience there is a clear difference in collaboration when there is no physical separation between people who are working on a project together. Also, private offices create a hierarchy where some people get big corner window offices while others are in shitty interior offices or cubicles. My favorite thing about the trend towards open offices has been an egalitarianism where the CEO and founders sits at a similar desk as the interns.

Not pedantic at all, thanks for the correction!

There's some good discussion in this reddit thread: https://www.reddit.com/r/chess/comments/5rm88g/hou_yifan_res....

As someone who does statistics for a living and used to play tournament chess, a few of my disorganized opinions/thoughts:

* There's no conclusive evidence that her pairings were tampered with, and the pairings are in line with what seeds someone would be given by a computer. The probability of her playing that many women is very low, but the whole point of randomness is rare events happen (as humans we're very good at detecting patterns in randomness). Her accusations are plausible but not a given.

* I think her goal to break down the barriers of gendered chess is great. The chess world needs more women, and if there were more they would be completely competitive with men. She's fighting a good fight.

* One of the reasons I stopped playing chess is the egos. This one was relatively mild, but purposefully losing games is wrong, even if done in protest. Once you sit down at the chessboard and shake the other person's hand, you're agreeing to a good game. Throwing a game against a woman because you're grumpy she's a woman is not a good thing.

* But, her activist tactic worked, here we are talking about it.

This is an example of the economic principle of goodwill. Uber has a poor reputation that leads to actual losses, but the negative value of the reputation is difficult to quantify. Conversely, Lyft has fostered positive goodwill that strengthens it in indirect ways.

This controversy will likely blow over with a small amount of damage, but it's a reminder of the vulnerability that Uber's management style brings to it. I think they should pay careful attention to how much people really want to hate them.

It's not a gimmick, it increases immersiveness when done right. The problem is the technology isn't quite there and directors haven't been trained in it (and usually don't make the effort) - so it's basically never done right.

3D as we know it is probably never going to happen. Most movies don't gain much from more immersiveness, and the tech may always be clunky. I've seen some VR movies lately, and I predict they will supercede 3d entirely. Whether they'll end up being the next fad is an open question.

The US airline industry has a serious problem with these outages. By my count, 4 major airlines have been hit with system-wide computer-related outages in the last year (southest, jetblue, delta, united).

This article goes into some detail of why it happens: http://money.cnn.com/2016/08/08/technology/delta-airline-com.... It seems like human error and fragile computer systems are the biggest issues.