The Millennial Generation and Communal Living

An interesting piece in The New York Times profiles how a certain subset of the millennial generation is choosing to live in a communal apartment. While your credit history doesn’t matter, you have to pass an interview to get accepted to live in one of these places:

[A] few companies are assembling bundles of apartments in New York with plans to fill them with cherry-picked inhabitants. Promising “a modern, urban lifestyle that values openness, collaboration and relationship building,” Common has entered into agreements with developers to renovate properties in Crown Heights and Bedford-Stuyvesant. This fall, it will begin renting 19 rooms at a Crown Heights property.

“We live in a super-disconnected city that has tons and tons of people, but it can feel really lonely here,” said Harrison Iuliano, who until last week worked as the programming director of Pure House, which rents out rooms to about 40 people in nine apartments in various buildings around Williamsburg. “Our goal is to make that a nonissue.”

Russell Jackson relinquished a studio six months ago to live in a six-bedroom Pure House apartment with a rotating cast (he presently has three flat mates). “I’m getting exposure to stuff and things that I would not have had sequestered on the Upper West Side,” said Mr. Jackson, a 52-year-old chef.

“Laundry services and cleaners and masseuses — all of that is icing,” he said. The real perks are the people he has met along the way. “How cool is it that I walk in the door and they ask me, ‘How’s your day?’ And I am genuinely interested in hearing from them,” said Mr. Jackson, who considers himself the Den Dad to the other tenants, who generally are two or three decades his junior and stay a month or two at a time.

Mr. Jackson, who has appeared on “Iron Chef America,” also orchestrates Pure House’s food events, including its pop-up dinner parties. At one such party, none of the 30 guests knew one another, but most embraced when the night was over…

I think this kind of thing can take off in large urban center like NYC and San Francisco. I’m less convinced that it could take off in larger, spread out cities like Atlanta.

On the Future of Machine Intelligence

This is a very thought-provoking read on the future of machine intelligence and how we will cope with its advancement. The author, Douglas Coupland, begins the narrative with some hypothetical apps that track data (geolocation, etc.) and then paints a dystopian view:

To summarise. Everyone, basically, wants access to and control over what you will become, both as a physical and metadata entity. We are also on our way to a world of concrete walls surrounding any number of niche beliefs. On our journey, we get to watch machine intelligence become profoundly more intelligent while, as a society, we get to watch one labour category after another be systematically burped out of the labour pool. (Doug’s Law: An app is only successful if it puts a lot of people out of work.)

The darkest thought of all may be this: no matter how much politics is applied to the internet and its attendant technologies, it may simply be far too late in the game to change the future. The internet is going to do to us whatever it is going to do, and the same end state will be achieved regardless of human will. Gulp.

Do we at least want to have free access to anything on the internet? Well yes, of course. But it’s important to remember that once a freedom is removed from your internet menu, it will never come back. The political system only deletes online options — it does not add them. The amount of internet freedom we have right now is the most we’re ever going to get.

I found the notion of Artificial Intuition (as opposed to Artificial Intelligence) worth highlighting:

Artificial Intuition happens when a computer and its software look at data and analyse it using computation that mimics human intuition at the deepest levels: language, hierarchical thinking — even spiritual and religious thinking. The machines doing the thinking are deliberately designed to replicate human neural networks, and connected together form even larger artificial neural networks. It sounds scary . . . and maybe it is (or maybe it isn’t). But it’s happening now. In fact, it is accelerating at an astonishing clip, and it’s the true and definite and undeniable human future.

Worth reading in its entirety.

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Note: I usually don’t link to The Financial Times (because of its stringent paywall), but at the time of this post, the article is free to access.

On Learning Data Science

I’ve been learning more about data science in the last couple of months and recently stumbled upon a very good blog post from Dataquest on how to learn data science.

First, it’s important that there is some inherent motivation to learn data science:

Nobody ever talks about motivation in learning. Data science is a broad and fuzzy field, which makes it hard to learn. Really hard. Without motivation, you’ll end up stopping halfway through and believing you can’t do it, when the fault isn’t with you – it’s with the teaching.

You need something that will motivate you to keep learning, even when it’s midnight, formulas are starting to look blurry, and you’re wondering if this will be the night that neural networks finally make sense.

You need something that will make you find the linkages between statistics, linear algebra, and neural networks. Something that will prevent you from struggling with the “what do I learn next?” question.

My entry point to data science was predicting the stock market, although I didn’t know it at the time. Some of the first programs I coded to predict the stock market involved almost no statistics. But I knew they weren’t performing well, so I worked day and night to make them better.

There are good links throughout, including 100 data sets for statistics.

I like the suggestions on communicating your findings and/or your learning process:

Part of communicating insights is understanding the topic and theory well. Another part is understanding how to clearly organize your results. The final piece is being able to explain your analysis clearly.

It’s hard to get good at communicating complex concepts effectively, but here are some things you should try:

Start a blog. Post the results of your data analysis.

Try to teach your less tech-savvy friends and family about data science concepts. It’s amazing how much teaching can help you understand concepts…

More resources and links here.

The Cascadia Earthquake

A thought-provoking, and frightening, piece by Kathryn Schulz in The New Yorker on the very big earthquake that is likely to strike the Pacific Northwest sometime in the future:

The first sign that the Cascadia earthquake has begun will be a compressional wave, radiating outward from the fault line. Compressional waves are fast-moving, high-frequency waves, audible to dogs and certain other animals but experienced by humans only as a sudden jolt. They are not very harmful, but they are potentially very useful, since they travel fast enough to be detected by sensors thirty to ninety seconds ahead of other seismic waves. That is enough time for earthquake early-warning systems, such as those in use throughout Japan, to automatically perform a variety of lifesaving functions: shutting down railways and power plants, opening elevators and firehouse doors, alerting hospitals to halt surgeries, and triggering alarms so that the general public can take cover. The Pacific Northwest has no early-warning system. When the Cascadia earthquake begins, there will be, instead, a cacophony of barking dogs and a long, suspended, what-was-that moment before the surface waves arrive. Surface waves are slower, lower-frequency waves that move the ground both up and down and side to side: the shaking, starting in earnest.

Soon after that shaking begins, the electrical grid will fail, likely everywhere west of the Cascades and possibly well beyond. If it happens at night, the ensuing catastrophe will unfold in darkness. In theory, those who are at home when it hits should be safest; it is easy and relatively inexpensive to seismically safeguard a private dwelling. But, lulled into nonchalance by their seemingly benign environment, most people in the Pacific Northwest have not done so. That nonchalance will shatter instantly. So will everything made of glass. Anything indoors and unsecured will lurch across the floor or come crashing down: bookshelves, lamps, computers, cannisters of flour in the pantry. Refrigerators will walk out of kitchens, unplugging themselves and toppling over. Water heaters will fall and smash interior gas lines. Houses that are not bolted to their foundations will slide off—or, rather, they will stay put, obeying inertia, while the foundations, together with the rest of the Northwest, jolt westward. Unmoored on the undulating ground, the homes will begin to collapse.

Worth reading in entirety.

The Difficulty in Translating Seinfeld to Other Languages

seinfeld

This Verge piece profiles the difficulty in translating the sitcom Seinfeld to other languages. In particular, the show has had difficulty finding a solid audience in Europe (such as in Germany). Seinfeld often relied on word-based humor, American customs, and Jewish references–difficult to convey to other cultures.

Jokes are the hardest things to translate into another language, another culture, another world. A good script for dubbing an American sitcom for foreign consumption does more than literally translate. It manages to convey the same meaning, the same feeling, the same story — the same direct hit to the lower frontal lobes of the brain that produces a laugh, even though those frontal lobes are steeped in a completely different cultural brew.

More so than the average American sitcom, Seinfeld has had difficulty reaching global audiences. While it’s popular in Latin America, it hasn’t been widely accepted in Germany, France, Italy, and the Netherlands. Two decades after it went off the air, Seinfeld remains relevant to American audiences — thanks in part to omnipresent syndicated reruns — but in much of Europe it is considered a cult hit, and commonly relegated to deep-late-night time slots. Its humor, it seems, is just too complicated, too cultural and word-based, to make for easy translation.

An interesting note on dubbing:

According to Israel-based translation company Trans-That, among European countries, France, Germany, Italy, and Spain tend to opt for the more expensive option of dubbing, while smaller countries like Belgium, Switzerland, and the Netherlands prefer subtitles. Dubbing countries often have a long history with the practice that goes back to the beginnings of the film industry. In the 1930s, when many American films were being exported to Europe, the strong preference for dubbing grew out of nationalist concerns — preserving language meant preserving cultural identity. In these countries, entire industries developed around dubbing. Today, certain voice actors will specialize in playing specific American stars, to the point where audiences expect to hear their voice each time they go to see, say, a Tom Cruise movie.

Lip-synch dubbing, despite its ultimate benefits, can get very complicated. It’s not just that the lines may not translate directly — they also have to take just as long to say in both languages and approximate, to the best of their abilities, the lip movements of the original actors. That can pose an added challenge when translating from laconic languages like English into verbose languages like German. And Seinfeld was already a very wordy show, making accurate translation that much more critical.

Definitely worth reading in entirety if you’re a big fan of Seinfeld.

On the Rise of Podcasts

In an excellent New York Times piece titled “Podcasting Blossoms, but in Slow Motion,” Farhad Manjoo explores the slow rise in the popularity of podcasts. They’ve been around for about a decade, but people listening to them appear to be in the minority. It’s only with the advent of last year’s hugely popular Serial that podcasts have gotten more attention (at least, based on my anecdotal evidence). Here are some facts:

Yet the overall audience for podcasts is growing very slowly. In February, Edison Research reported that 17 percent of Americans had listened to one podcast in the previous month. That is up just slightly from Edison’s 2012 survey, when 14 percent of Americans had done so. The business also has some problems, including a labor-intensive ad-buying process, a shortage of audio producers and the inability to accurately measure who is listening.

Here’s Manjoo on whether podcasting is gaining steam:

So don’t call podcasting a bubble or a bust. Instead, it is that rarest thing in the technology industry: a slow, steady and unrelentingly persistent digital tortoise that could eventually — but who really knows? — slay the analog behemoths in its path.

It appears that those who listen to podcasts really, really enjoy them and devote a significant amount of time to the medium:

The share of podcasts in Americans’ diet of audio programming grew by 18 percent from 2014 to 2015, according to Edison. People who listen to podcasts daily spend about two hours a day, on average, with podcasts, a larger share than for any other form of audio, Edison reported.

A profile of Mystery Show, which is one of the podcasts I just started listening to this week:

For instance, the premise of “Mystery Show,” Gimlet’s newest production, which began playing last month, sounds a bit like a stunt. On each episode, Starlee Kine, a longtime public radio personality, solves mysteries for people. But Ms. Kine does not investigate the kind of serious mysteries addressed by the producers of “Serial.” Instead her inquiries are the sort of ridiculously fun questions that no journalist would ever get paid to answer. Why, for example, was Britney Spears once seen carrying a book by a writer that no one ever reads?

Here are my top five podcasts, which I enthusiastically recommend:

1) Design Matters with Debbie Millman
2) 99% Invisible
3) Exponent
4) Sleep With Me (to help you fall asleep–it works! Here’s one story.)
5) Radiolab

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What podcasts have you been listening to lately? Which ones are some of your favorite?

What’s Causing Global Warming?

Bloomberg has created a nice interactive to explain the cause(s) of global warming. Hint: it’s not the volcanoes or the Sun or ozone pollution. The cause of global warming is almost surely the greenhouse gas emissions.

global_warming

From the caption underneath the graphic:
Researchers who study the Earth’s climate create models to test their assumptions about the causes and trajectory of global warming. Around the world there are 28 or so research groups in more than a dozen countries who have written 61 climate models. Each takes a slightly different approach to the elements of the climate system, such as ice, oceans, or atmospheric chemistry.
The computer model that generated the results for this graphic is called “ModelE2,” and was created by NASA’s Goddard Institute for Space Studies (GISS), which has been a leader in climate projections for a generation. ModelE2 contains something on the order of 500,000 lines of code, and is run on a supercomputer at the NASA Center for Climate Simulation in Greenbelt, Maryland.