lemondrops

Also, this? https://twitter.com/SamiraEKahou/status/940085885101412352

I don't get why anyone but the wife herself should be offended by this.

It's silly you're getting downvoted for this well-articulated and insightful comment.

I think much of the negativity towards DS from the programming community is because the Data Scientist is what the programmer used to be ~15 years ago. It's that nerdy thing for a select group of very smart people, whereas being a software developer/engineer/architect/whatever has become just another common job (at least outside of Silicon Valley).

Also, from my experience as the lone developer taking the first steps to implement machine learning techniques in my company - lots of developers also think DS/ML is a cool thing with value, but they simply, absolutely don't understand it (and don't want to put in the effort to learn). These techniques are not hard and not magic, but they require a completely different way to think about problems than "traditional" programming does. I've seen developers up and down the hierarchical ladder struggle with wrapping their heads around these concepts, and it's way easier to dismiss it all as "hype" instead of accepting the fact that these techniques will be a huge part of what software development will look like in the future.

I never saw it that way before -- but as someone who found programming in their early teenage years too, this sounds very familiar. I still feel a sense of excitement when I learn something new, but it wears off very quickly because most new technologies are just another way of doing the things you did before. E.g. I got to learn Type Script recently, but after a couple of days of getting used to the syntax it felt just like all the other frontend frameworks out there... It's true that most regular coding dayjobs mainly consist of gluing together libraries and solving (often) self inflicted problems, and it's so frustrating!

Analyzing data and deriving meaning from it is exactly what we, in science, mean by discovering something.

Though from what I've gathered in recent discussions about Nobel prizes, it is rather common that the project leader is awarded the prize, regardless if his actions directly lead to the "discovery" or not.

Not really, see e.g. the 2012-2013 financial crisis in Cyprus, there was no "army" to enforce that people got all their money back... [1] The only thing that keeps the system working is trust (see other reply to my initial comment that explained it more thoroughly).

Though I agree that there is a lot less "backing" of Bitcoin in that sense, nothing protects you from losing everything if something goes wrong.

https://www.theguardian.com/world/2013/mar/30/bank-of-cyprus...

>> It is only worth something because other people say it is worth something

That is actually the very definition of most currencies currently in use [1]. Money is actually just a promise that someone will provide you goods or services in exchange for it sometime in the future, it has no intrinsic worth. Also, paper money isn't tied to anything [2] either, let alone the "digital" money we have in the banks.

[1] https://en.wikipedia.org/wiki/Fiat_money [2] https://en.wikipedia.org/wiki/Nixon_shock

>> others will do just a monkey job using magical APIs.

Isn't that what most of modern software development is like already? Many common use cases have been implemented in frameworks, and usually a developer's job consists mostly of tacking pieces of framework together. The days where a person single-handedly implements a state-of-the-art game from scratch are long over. On the other hand, you could still create a game by yourself using all the available open source tools. You can still be creative and do exiting things all you want, the type of work is just different.

Also, you don't have to be a genius to understand machine learning either. But you do have to learn some math!

Creating features has not been automated by Deep Learning at all. Even for image recognition tasks, where your "features" are simply the pixels of an image, there's still lots of preprocessing work to get those images into a form that NNs can deal with well.

Feature engineering is actually still the hardest part of most ML tasks, because it can not be optimized by a simple grid search like the hyperparameters of a model.

> annoyingly out of place

Agree. I can't take an article seriously when the author is too lazy to type out Facebook.

I live on ~30% of my income, which I would consider a modest "upper entry level developer" in my country. I don't feel like I sacrifice anything, though it is true that I'm lucky to share an already cheap apartment with my SO. (But we also only have heating in two of our rooms, and not even a sink in the bathroom - we brush our teeth in the kitchen. I guess this would already be inacceptable for many...) I don't own a car because I can bike or walk everywhere. I only use prepaid cards for my mobile phone, no data plan, because I always found the idea of paying for mobile internet ridiculous when there is wifi all around us. (Plus, I don't like how being online all the times makes me check my phone so often.) I don't need a new shiny phone every two years and will use my Fairphone 2 until it dies on me. I do eat out 3-4 times a week for lunch at work, but when at home I cook 99% of the time. Sometimes I eat out with friends, go to the cinema etc. I buy clothes maybe once or twice a year but avoid expensive brands like the plague, except for things like running shoes or other "long term equipment".

When it comes to my hobbies, I don't sacrifice anything, but I naturally like to do things that are inexpensive or for free like excercising and spending time outdoors, reading, watching movies... My most expensive hobby is probably indoor rock-climbing, which I don't do that often sadly, and the subscription to the martial arts club that I train at weekly. I just spent almost 500€ to attend PyCon this year, and nearly three times that much for my yearly holiday.

For me it's not even a concious act of saving, I just naturally live this way and don't need much. I don't like owning too many things, they feel like a burden. The money keeps accumulating (I saved 30K€ the last two years, living as described above), and I honestly don't know what to do with it or how to use it wisely in regards of the future... Real estate seems like a good investment, but comes with its own problems.

> Even the calmer takes on this are anthromorphizing the machines too much and attributing intelligence to the complete lack thereof.

Exactly. I haven't read the details of the implementation of the system mentioned in the article, but the outputs remind me a lot of what was returned by a text generating neural network of this tutorial that I did once:

http://machinelearningmastery.com/text-generation-lstm-recur...

Especially with fewer epochs (<10) the generated text was part gibberish, part endless repetitions of common phrases or basic words like "the" - simply because (surprise!) "the" is one of the more frequently used words in speech.

Pulling this out of context, one could also say "This AI is inventing its own language, just by reading Alice in Wonderland!", which is of course utter bullshit.

Would it implode if the conjecture was disproved?

On the other hand I think it's quite strange that a talented entrepreneur and a physicist, among others, are considered as a source of expertise in a field they have nothing to do with, per se. I don't see any of the top AI/ML researchers voicing these kind of concerns. And while I highly respect Musk and Hawking, and agree that they are rational people, their concerns seem to be driven by "fear of the unknown" more than anything else, like another comment pointed out.

Whenever I see discussions about the dangers of AI, they are always about those Terminator-like AI-overlords that will destroy us all. Or that humans will be made redundant because robots will take all our jobs. But there are never concrete arguments or scenarios, just vague expressions of fear. Honestly, if I think about all the things HUMANS have done to each other and the planet, I can hardly imagine anything worse than us.

Somewhat related: https://www.nytimes.com/2017/06/24/opinion/sunday/artificial...

Not sure how to feel about this; but I also don't consider myself a women who suffers heavily under period symptoms.

If I feel bad enough that I can't do my work properly, I would simply leave, periods or not. (Normal paid sick leave - this is relatively easy in my country.) It has only happened once since I started working full time five years ago, that I was in so much pain from periods that I had to leave work. (And that was after taking two painkillers and even those didn't help.)

I certainly think a policy like this would feed into wage gap issues - it would definitely be unfair to pay the same amount of money to two employees, when one of them potentially works 12 days more in a year.

It sounds plausible that it would work better in a female dominated company -- I would rather suck up the pain than deal with stupid remarks of my male colleagues because they know I'm on my periods...

In general, I think women grow up with having to deal with their periods and the symptoms since childhood/teenage years, so it's just a normal part of life for us. Sure, some women have it way worse that others, but I'd say 90% can cope with it without problems and don't need extra holidays.

Though I do like the idea of giving those women in bad pain the opportunity to work from home! It doesn't necessarily help with the symptoms, but at least one can sit somewhere comfortable in PJs with a warm water bottle, which makes it a lot more bearable IMO.