Jason Z. Resnikoff’s Seeing the Sixties and Seventies Through 2001 and Alien is a wonderful essay about his father’s experiences as a computer scientist growing up in the era of 2001: A Space Odyssey and Alien. Here’s a taste:

My father was so buried in computers that when he saw 2001 he very much liked HAL, the spaceship Discovery’s villainous central computer. To this day, he enjoys quoting the part of the movie where HAL tries to explain away his own mistake—the supposed fault in the AE35 unit—by saying, “This kind of thing has cropped up before, and it has always been due, to human error,” an excuse that more or less sums up my father’s considerably erudite understanding of computers. According to my father’s interpretation of the film, HAL wanted to become something more than he was. Becoming, always and ever becoming, is in my father’s eyes a worthy, nay, a noble way to go through life, always trying finally to be yourself, that most elusive of goals. The mission to Jupiter was a mission to take the next step in evolution, and HAL wanted to be the one to evolve. My father made this sound like a very reasonable desire, one that makes HAL the hero of the movie.

It’s a story about two iconic movies, but also about how we used to see the future. Turns out we won’t be space babies after all.

A couple of articles about work and technology caught my eye this week. First, Claire Cain Miller describes how Technology, Aided by Recession, Is Polarizing the Work World:

[A new working paper from the National Bureau of Economic Research], which analyzed data from the Current Population Survey from 1976 to 2012, illustrates that the recession had a disproportionately large effect on routine jobs, and greatly sped up their loss. That is probably because even if a new technology is cheaper and more efficient than a human laborer, bosses are unlikely to fire employees and replace them with computers when times are good. The recession, however, gave them a motive. And the people who lost those jobs are generally unable to find new ones, said Henry E. Siu, an associate professor at the University of British Columbia and an author of the study.

Now, combine that problem in the mid-paying job market with an issue Thomas B. Edsall pointed out a few weeks ago in The Downward Ramp:

Just one example: the drying up of cognitively demanding jobs is having a cascade effect. College graduates are forced to take jobs beneath their level of educational training, moving into clerical and service positions instead of into finance and high tech.

This cascade eliminates opportunities for those without college degrees who would otherwise fill those service and clerical jobs. These displaced workers are then forced to take even less demanding, less well-paying jobs, in a process that pushes everyone down. At the bottom, the unskilled are pushed out of the job market altogether.

So, college graduates are pushed into mid-paying jobs, and those jobs are being replaced by technology. Not good.

Meanwhile, in opposite world, Louise Aronson writes about The Future of Robot Caregivers (if you’re counting, that’s three for three on the New York Times):

We do not have anywhere near enough human caregivers for the growing number of older Americans.

Zeynep Tufekci’s excessively titled Failing the Third Machine Age: When Robots Come for Grandma is a good critique of that piece:

Let me explain. When people confidently announce that once robots come for our jobs, we’ll find something else to do like we always did, they are drawing from a very short history. The truth is, there’s only been one-and-a-three-quarters of a machine age—we are close to concluding the second one—we are moving into the third one.

And there is probably no fourth one.

Humans have only so many “irreplaceable” skills, and the idea that we’ll just keep outrunning the machines, skill-wise, is a folly.

Put all these pieces together and you get a very scary vision of the future of jobs. The good news — I think — is that job != work.

The future of jobs might be bleak, but the future of work certainly isn’t. Technology might be taking our jobs, but it’s also giving us new ways to be creative. To be entrepreneurs. To work. As programs like Girls Who Code continue to grow, I’m increasingly optimistic about my daughters’ futures. They might not get a “regular” job one day. But my role as a parent is not to prepare them for a job anyway. It’s to foster in them the tenacity and grit to learn how to think big and make things. I’m excited about that.

Gideon Lewis-Kraus discusses The Fasinatng … Frustrating … Fascinating History of Autocorrect. Turns out there’s more to it than meets the eye:

A handful of factors are taken into account to weight the variables: keyboard proximity, phonetic similarity, linguistic context. But it’s essentially a big popularity contest. A Microsoft engineer showed me a slide where somebody was trying to search for the long-named Austrian action star who became governor of California. Schwarzenegger, he explained, “is about 10,000 times more popular in the world than its variants”—Shwaranegar or Scuzzynectar or what have you. Autocorrect has become an index of the most popular way to spell and order certain words.

This article also taught me that swear words are complicated. And I really like the cartoons of various autocorrect errors, especially this one:

The AP is increasingly starting to use software with no human intervention to write basic news stories, but Kevin Roose says that we shouldn’t be alarmed about it. From his article Why Robot Journalism Is Great for Journalists:

Robot assistance may even spur human reporters to do our jobs better. With software producing the equivalent of old-school “clip files” for us, we’ll essentially have full-time research assistants. The information in our stories will be more accurate, since it will come directly from data feeds and not from human copying and pasting, and we’ll have to issue fewer corrections for messing things up. Plus, with our nuts-and-bolts reporting out of the way, we’ll be able to focus on the kinds of stories that educate and entertain readers in a deep way, rather than just dragging simple information from Point A to Point B.

Conor Friedersdorf talks about the differences between recommendations provided by people and algorithms in Would You Rather Get Tips from an Expert or an Algorithm?

The Amazon.com algorithm is very good at using what you’ve just bought to recommend things that you’ll want to buy, [David Weinberger, a senior researcher at the Berkman Center for Internet and Society] observed, but it can be hard to tell why. Perhaps you’ll be attracted to the content of the recommendation — or perhaps it’s the fact that the cover is also green, or that the print is in Helvetica font. 

In contrast, a skilled librarian is usually going to recommend a book solely because of its intellectual value, without any lurking, contentless variables. The librarian is therefore likelier to send a person in a direction they wouldn’t otherwise have gone in a way that will advance their thinking, education, or aesthetic taste, because they’re not just meeting needs that have already been expressed.

We’re seeing this divide come out in products as well, and some are starting to use their “humanness” as a differentiator. Whereas most music recommendation systems like Pandora, Spotify, and Rdio use algorithmic approaches, Beats touts the power of human curation on their product.

Go Book Yourself is a Tumblr site that publishes curated recommendations for books you might like based on other books you read and liked. Their tag line is Book recommendations by humans, because algorithms are so 1984.

The humans are coming.

In What If Doctors Could Finally Prescribe Behavior Change? Sean Duffy explains why behavior change is so difficult, particularly in healthcare:

Whether it’s for weight loss, smoking cessation, diabetes, or otherwise, the best research shows that meaningful behavior change outcomes require not just guidance from a trusted health professional, but also positive social support, easy-to-digest insights about their condition, a carefully orchestrated timeline, and a process that follows validated behavioral science protocols. That’s hard to squeeze into a phone call. Or a doctor’s visit, for that matter.

The good news is that this research is resulting in a new field called Digital Therapeutics, and despite quite a bit of snake oil out there, some apps are having success:

Another example is Jenna Tregarthen, a PhD candidate in clinical psychology and eating disorder specialist. She rallied a team of engineers, entrepreneurs, and fellow psychologists to develop Recovery Record, a digital therapy that helps patients gain control over their eating disorder by enabling them to self-monitor for destructive thoughts or actions, follow meal plans, achieve behavior goals, and message a therapist instantly when they need support.

In The Dawn of the Age of Artificial Intelligence Erik Brynjolfsson and Andrew McAfee talk about some of the good things that are coming out of the Artificial Intelligence community:

A user of the OrCam system, which was introduced in 2013, clips onto her glasses a combination of a tiny digital camera and speaker that works by conducting sound waves through the bones of the head. If she points her finger at a source of text such as a billboard, package of food, or newspaper article, the computer immediately analyzes the images the camera sends to it, then reads the text to her via the speaker.

There are a few more interesting, feel-good examples in the article.

John Foreman digs into Disney’s MagicBands in his article You don’t want your privacy — Disney and the meat space data race:

Disney World is like a petri dish for advanced analytic techniques because the hotels and parks are all tied together in one large, heavily controlled environment. If you ever wanted to star in The Truman Show, a trip to Disney is the next best thing — it feels like a centrally planned North Korea only with more fun, less torture and the same amount of artifice.

From the mundane to the magical, the fact is there’s probably an engineer behind the scenes at Disney who has thought through it. Disney has industrial engineers that work on everything from optimal food-and-beverage pricing and laundry facility optimization, to attraction performance and wait-time minimization (the vaunted FASTPASS system).

The article is largely a negative look at (legitimate) privacy issues with programs like these, but in Disney’s case, I just think it sounds awesome.

It took me a while to get through Michael Pepi’s The Postmodernity of Big Data. It’s dense, and the premise seemed so far-fetched that I wasn’t sure it would be worth the time investment:

But beyond economic motivations for Big Data’s rise, are there also epistemological ones? Has Big Data come to try to fill the vacuum of certainty left by postmodernism? Does data science address the insecurities of the postmodern thought?

Yes, I know, that sounds like a bit of a stretch. But I’m glad I stuck with it. The essay brings up some really interesting thoughts around the certainty promised by Big Data (even though some view it as nothing more than a clever marketing campaign for something that has been around a long time), and how that might be a response to the relativism of postmodernism:

Though both are projects that address positions about empiricism and meaning making, postmodernism and Big Data are in some senses opposites: Big Data is an empirically grounded quest for truth writ large, accelerated by exponentially expanding computing power. Postmodernism casts doubt on the very idea that reason can unearth an inalienable truth. Whereas Big Data sees a plurality of data points contributing to a singular definition of the individual, postmodernism negates the idea that a single definition of any entity could outweigh its contingent relations. Big Data aims for certainties — sometimes called “analytic insights” — that fly in the face of postmodernist doubt about knowledge. Postmodernism was confined to the faculty lounge and the academic conference, but Big Data has the ability to dictate new rules of behavior and commerce. An e-commerce outfit is almost foolish not to analyze browsing data and algorithmically determine likely future purchases, or as Jaron Lanier put it in Who Owns the Future, “your lack of privacy is someone else’s wealth.”

Consider this your difficult but satisfying weekend reading project.

Alexis Madrigal — who is turning into one of the most interesting journalists of our time — goes deep on Netflix’s 76,897 (often bizarre) micro-genres in How Netflix Reverse Engineered Hollywood:

Netflix has meticulously analyzed and tagged every movie and TV show imaginable. They possess a stockpile of data about Hollywood entertainment that is absolutely unprecedented.

Netflix is putting in a staggering amount of effort on the structured data of their TV shows and movies. And of course, it’s all for one reason — to get to know you better:

They capture dozens of different movie attributes. They even rate the moral status of characters. When these tags are combined with millions of users’ viewing habits, they become Netflix’s competitive advantage. The company’s main goal as a business is to gain and retain subscribers. And the genres that it displays to people are a key part of that strategy. “Members connect with these [genre] rows so well that we measure an increase in member retention by placing the most tailored rows higher on the page instead of lower,” the company revealed in a 2012 blog post. The better Netflix shows that it knows you, the likelier you are to stick around.

And now, they have a terrific advantage in their efforts to produce their own content: Netflix has created a database of American cinematic predilections. The data can’t tell them how to make a TV show, but it can tell them what they should be making. When they create a show like House of Cards, they aren’t guessing at what people want.

What’s interesting is that similar things are happening in other forms of media as well. Spotify and Rdio’s knowledge of our listening data can be used to inform record labels what type of albums they should invest in. And as David Streitfeld reports in As New Services Track Habits, the E-Books Are Reading You, a new crop of companies are helping authors figure out what type of books they should write:

The move to exploit reading data is one aspect of how consumer analytics is making its way into every corner of the culture. Amazon and Barnes & Noble already collect vast amounts of information from their e-readers but keep it proprietary. Now the start-ups — which also include Entitle, a North Carolina-based company — are hoping to profit by telling all.

“We’re going to be pretty open about sharing this data so people can use it to publish better books,” said Trip Adler, Scribd’s chief executive. […]

Scribd is just beginning to analyze the data from its subscribers. Some general insights: The longer a mystery novel is, the more likely readers are to jump to the end to see who done it. People are more likely to finish biographies than business titles, but a chapter of a yoga book is all they need. They speed through romances faster than religious titles, and erotica fastest of all.

All of this raises familiar questions about the loss of serendipity — finding interesting things we’re not looking for. But I still think this is an unnecessary fear.