John Pavlus in Your Body Does Not Want to Be an Interface:

The assumption driving these kinds of design speculations is that if you embed the interface — the control surface for a technology — into our own bodily envelope, that interface will “disappear”: the technology will cease to be a separate “thing” and simply become part of that envelope.

The trouble is that unlike technology, your body isn’t something you “interface” with in the first place. You’re not a little homunculus “in” your body, “driving” it around, looking out Terminator-style “through” your eyes. Your body isn’t a tool for delivering your experience: it is your experience. Merging the body with a technological control surface doesn’t magically transform the act of manipulating that surface into bodily experience. I’m not a cyborg (yet) so I can’t be sure, but I suspect the effect is more the opposite: alienating you from the direct bodily experiences you already have by turning them into technological interfaces to be manipulated. 

It’s an excellent essay. I especially like the distinction between Ready-at-hand and Present-at-hand technologies, and how our bodies shouldn’t become marionettes to technology.

In his post Why we fear Facebook and why we shouldn’t Paul Jacobson makes an interesting counterpoint to the common refrain that it’s bad to share our personal data with companies:

Conventional wisdom is that if you are not paying for a product, you are the product. That may be true, as a generalisation. I prefer to think it isn’t so much we who are the products on Facebook but rather our preferences and attention. What does that buy us? For starters, it buys us Facebook, Twitter, Google services and more. It also buys us slightly less annoying ads that can be remarkably relevant. It buys advertisers a better chance that we may want to buy their products and services because those products and services may just be what we are looking for at that point in time.

It’s a good question. Is it really that bad to get highly targeted ads in our news feeds? The more targeted the ads are, the more useful they are to us, right? So why is there such pushback against this trend in companies like Google and Facebook to try to find out everything they can about us?

I think there are three main reasons why we need to be wary of letting ad-driven companies know too much about our preferences, even if they just use it to serve us more targeted information and ads.

1. It makes the web smaller

If we only see stuff we’re already interested in, we run the risk of becoming sucked into the Internet’s “filter bubble”, where it’s much harder to discover new information beyond our current knowledge. Maria Popova puts it like this in Are We Becoming Cyborgs?:

The Web by and large is really well designed to help people find more of what they already know they’re looking for, and really poorly designed to help us discover that which we don’t yet know will interest us and hopefully even change the way we understand the world.

When an algorithmic constraint is placed on the information we see, and that constraint is based solely on our current preferences, we will remain safely locked into the world we know. That means that we become less likely to broaden our horizons with new discoveries.

2. It results in heightened confirmation bias

When we’re steeped in information that confirms our existing beliefs (regardless of whether those beliefs are true or not) we not only seek out more of the same information everywhere we go, but we also become incapable of changing our minds even if we eventually are presented with the truth (the denial of Global Warming is a good example of this…). This is called confirmation bias, and Clay Johnson writes about it in the context of media and the Internet in his book The Information Diet:

It’s too high of a cognitive and ego burden to surround ourselves with people that we disagree with. If you’re a Facebook user, try counting up the number of friends you have who share your political beliefs. Unless you’re working hard to do otherwise, it’s likely that you’ve surrounded yourself with people who skew towards your beliefs. Now look beyond political beliefs—how many of your friends share the same economic class as you? […]

Those algorithms are everywhere: our web searches, our online purchases, our advertisements. This network of predictions is what Pariser calls the Filter Bubble in his book by the same name—the network of personalization technology that figures out what you want and keeps feeding you that at the expense of what you don’t want.

So, for example, through its EdgeRank algorithm Facebook figures out what we like and what we believe in, and then shows us stories and ads that confirm those beliefs. It doesn’t care about truth, it cares about engagement — even if that engagement comes at the expense of what is right.

3. It designs our lives for us

This is true for all advertising, but even more so for hyper-targeted advertising: it tries to sell us stuff we don’t necessarily need. Yes, I know we’re tired of hearing how we should all live with less stuff blah blah blah. That’s not necessarily what I’m saying. What I’m saying is that we need to be careful that we don’t become a society built around the needs of corporations. David Cain talks about this in his chilling essay called Your Lifestyle Has Already Been Designed:

We’ve been led into a culture that has been engineered to leave us tired, hungry for indulgence, willing to pay a lot for convenience and entertainment, and most importantly, vaguely dissatisfied with our lives so that we continue wanting things we don’t have. We buy so much because it always seems like something is still missing. […]

The perfect customer is dissatisfied but hopeful, uninterested in serious personal development, highly habituated to the television, working full-time, earning a fair amount, indulging during their free time, and somehow just getting by.

There’s nothing wrong with stuff, of course. But there is something scarily wrong about the way we let our desires be dictated by advertising — especially targeted advertising by companies that know us so well.

What it means…

I don’t think our biggest fears about the data that companies collect about us should revolve around identity theft or the government coming to get us (although, in some regions, that’s certainly legitimate concerns). Our biggest fear should be what Huxley points to in the future he paints in Brave New World: that we will be ruled by what he calls “man’s almost infinite appetite for distractions”. Huxley believed we should fear companies who aim to control us by inflicting pleasure on us, and I think he might have been on to something.

I know that sounds really alarmist. But still, I can’t look at my Facebook news feed and not think about this possible future. That’s why I think we should hold our personal data and preferences just a little bit closer to our hearts.

In How the internet is making us poor Christopher Mims asks a chilling question about what he calls the “hollowing out of the middle class” — the phenomenon where knowledge workers are being replaced by computers:

Like farming and factory work before it, the labors of the mind are being colonized by devices and systems. In the early 1800′s, nine out of ten Americans worked in agriculture—now it’s around 2%. At its peak, about a third of the US population was employed in manufacturing—now it’s less than 10%. How many decades until the figures are similar for the information-processing tasks that typify rich countries’ post-industrial economies?

The article also quotes this thought-provoking statement from Marc Andreessen:

The spread of computers and the Internet will put jobs in two categories: People who tell computers what to do, and people who are told by computers what to do.

It might seem like the usual doom-and-gloom “technology will kill as all” refrain, but the article reviews some very interesting historical (and current) data, so it’s worth checking out.

Figuring out what to read (and what to believe) about Big Data is becoming a Big Data problem in and of itself1. I wrote The hype, benefits, and dangers of Big Data a while ago to give an overview of what’s out there, but there are two more interesting articles from the last week that I’d like to highlight as well.

First, on the HBR blog Jake Porway talks about Big Data and social entrepreneurship and makes the point that You Can’t Just Hack Your Way to Social Change:

Any data scientist worth their salary will tell you that you should start with a question, NOT the data. Unfortunately, data hackathons often lack clear problem definitions. Most companies think that if you can just get hackers, pizza, and data together in a room, magic will happen. This is the same as if Habitat for Humanity gathered its volunteers around a pile of wood and said, “Have at it!” By the end of the day you’d be left with half of a sunroom with 14 outlets in it.

And on Wired, Does ‘Big Data’ Mean the Demise of the Expert — And Intuition? is a very interesting excerpt from Viktor Mayer-Schönberger and Kenneth Cukier’s new book on the topic:

In the same spirit, the biggest impact of big data will be that data-driven decisions are poised to augment or overrule human judgment.

The subject-area expert, the substantive specialist, will lose some of his or her luster compared with the statistician and data analyst, who are unfettered by the old ways of doing things and let the data speak. This new cadre will rely on correlations without prejudgments and prejudice. To be sure, subject-area experts won’t die out, but their supremacy will ebb. From now on, they must share the podium with the big-data geeks, just as princely causation must share the limelight with humble correlation.

It seems like an obvious conclusion, but everything I’ve read so far about Big Data confirms that if we think cutting the “messiness” of human decision-making out of data analysis will result in better decisions, we’re sorely mistaken.

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1. Sorry, I didn’t get much sleep last night, so even though I know this isn’t a particularly funny joke, I just can’t help myself. ↩

A Readlist of all the articles referenced in this post is available here. Readlists allow you to send all the articles to your Kindle, read them on your iOS device, or download it as an e-book.

Despite the overly alarmist title, Andrew Leonard’s How Netflix is turning viewers into puppets1 is a fascinating article on how Netflix uses Big Data in their programming decisions:

“House of Cards” is one of the first major test cases of this Big Data-driven creative strategy. For almost a year, Netflix executives have told us that their detailed knowledge of Netflix subscriber viewing preferences clinched their decision to license a remake of the popular and critically well regarded 1990 BBC miniseries. Netflix’s data indicated that the same subscribers who loved the original BBC production also gobbled down movies starring Kevin Spacey or directed by David Fincher. Therefore, concluded Netflix executives, a remake of the BBC drama with Spacey and Fincher attached was a no-brainer, to the point that the company committed $100 million for two 13-episode seasons.

The article also asks what this approach means for the creative process, something I’ve written about before in The unnecessary fear of digital perfection, so I won’t rehash that argument here.

What’s interesting to me about the rise in Big Data approaches to decision-making is the high levels of inaccuracy inherent to the analysis process. Of course, this is something we don’t hear about often, but Nassim N. Taleb recently wrote a great opinion piece about it for Wired called Beware the Big Errors of ‘Big Data’, in which he states:

Big-data researchers have the option to stop doing their research once they have the right result. In options language: The researcher gets the “upside” and truth gets the “downside.” It makes him antifragile, that is, capable of benefiting from complexity and uncertainty — and at the expense of others.

But beyond that, big data means anyone can find fake statistical relationships, since the spurious rises to the surface. This is because in large data sets, large deviations are vastly more attributable to variance (or noise) than to information (or signal). It’s a property of sampling: In real life there is no cherry-picking, but on the researcher’s computer, there is. Large deviations are likely to be bogus.

He gets into more detail on the statistical problems with Big Data in the article, and his book Antifragile looks really interesting too.

Since I haven’t written about Big Data before, I also want to reference a few articles on the topic that I enjoyed. Sean Madden gives some interesting real world examples in How Companies Like Amazon Use Big Data To Make You Love Them2. But over on the skeptical side, Stephen Few argues in Big Data, Big Deal that “interest in big data today is a direct result of vendor marketing; it didn’t emerge naturally from the needs of users.” He also makes the point that data has always been big, and that by focusing on the “bigness” of it, we’re missing the point:

A little more and a little faster have always been on our wish list. While information technology has struggled to catch up, mostly by pumping itself up with steroids, it has lost sight of the objective: to better understand the world—at least one’s little part of it (e.g., one’s business)—so we can make it better. Our current fascination with big data has us looking for better steroids to increase our brawn rather than better skills to develop our brains. In the world of analytics, brawn will only get us so far; it is better thinking that will open the door to greater insight.

Alan Mitchell makes a similar point in Big Data, Big Dead End, a case for what he calls Small Data:

But if we look at the really big value gap faced by society nowadays, it’s not the ability to crunch together vast amounts of data, but quite the opposite. It’s the challenge of information logistics: of how to get exactly the right information to, and from, the right people in the right formats at the right time. This is about Very Small Data: discarding or leaving aside the 99.99% of information I don’t need right now so that I can use the 0.01% of information that I do need as quickly and efficiently as possible.

What I think we should take from all of this is that our ability to collect vast amounts of data comes with enormous predictive and analytical upside. But we’d be foolish to think that it makes decision-making easier. Because Big Data does not take away the biggest challenge of data analysis: figuring how to turn data into information, and information into knowledge. In fact, Big Data makes this harder. To quote Nassim again:

I am not saying here that there is no information in big data. There is plenty of information. The problem — the central issue — is that the needle comes in an increasingly larger haystack.

In other words: proceed with caution.

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1. Link via @mobivangelist ↩

2. It’s interesting that the phrasing of both this headline and the Netflix one implies that companies are using Big Data to persuade us to do things against our will. But I can’t figure out if that’s a real fear, or just clever linkbait. ↩

In Bridging the gap between humans and computers Heather Kelly takes a look at some recent ethnographic research on our relationship with technology. It’s full of interesting stories like this one:

In one experiment, Ju’s group rigged automatic doors to open in different ways: Some would open slowly, then pause before fully opening; others would immediately jerk all the way open. The people walking by the doors assigned them different levels of intelligence, and thought the doors that opened in two steps just seemed smarter.

It turned out that adding the pause gave illusion of forethought, even though it was just an extra programming step. People thought the door was more intelligent because it appeared to think before carrying out an action.

One of my favorite books on this topic is Sherry Turkle’s Alone Together: Why We Expect More from Technology and Less from Each Other. The first half of the book is all about our relationship with high tech “things” — what we find creepy vs. comforting, how different cultures behave differently, etc. Highly recommended.

In Living inside the Machine James Bridle writes about computers and data centres as aesthetic objects. It’s a very interesting idea and a great article. There’s one part in particular that stuck with me. James quotes William Gibson in an interview with the Paris Review from 2011, about his time in Vancouver in the late 70s/early 80s:

The only computers I’d ever seen in those days were things the size of the side of a barn. And then one day, I walked by a bus stop and there was an Apple poster. The poster was a photograph of a businessman’s jacketed, neatly cuffed arm holding a life-size representation of a real-life computer that was not much bigger than a laptop is today. Everyone is going to have one of these, I thought, and everyone is going to want to live inside them.

Everyone is going to have one of these, and everyone is going to want to live inside them. How prophetic…

James sums it up nicely in his article:

We used to posit this space, the network, the notional space, as being elsewhere, the other side of the screen. But increasingly we have these images of the machine as something that surrounds us, that we live inside, within. As something that enfolds us.

At first it’s hard to figure out what the title of Nicholas Carr’s A post on the occasion of Facebook’s billionth member has to do with Facebook. Especially since he hardly even mentions Facebook. It appears to be an essay about boredom and computer intelligence:

We’ll know that computers are really smart when computers start getting bored. If you assign a computer a profoundly tedious task like spotting potential house numbers in video images, and then you come back a couple of hours later and find that the computer is checking its Facebook feed or surfing porn, then you’ll know that artificial intelligence has truly arrived.

But stick with it. It all makes sense once you get to the end and reflect on the words for a couple of hours. Also, full marks to Parampreet Singh for a comment that references Sisyphus, and compares his plight (“to roll an immense boulder up a hill, only to watch it roll back down, and to repeat this action forever”) with our tendency to check our Facebook feeds constantly.

Nicholas Carr wrote an excellent, balanced article on the rise of massive open online courses (MOOCs1) like Coursera and Udacity, and the complex data mining required to make it work. From The Crisis in Higher Education:

The advances in tutoring programs promise to help many college, high-school, and even elementary students master basic concepts. One-on-one instruction has long been known to provide substantial educational benefits, but its high cost has constrained its use, particularly in public schools. It’s likely that if computers are used in place of teachers, many more students will be able to enjoy the benefits of tutoring. According to one recent study of undergraduates taking statistics courses at public universities, the latest of the online tutoring systems seem to produce roughly the same results as face-to-
> face instruction.

This is some really in-depth reporting, and it’s not all sunshine and roses. Nicholas went out of his way to seek out and report on legitimate counterarguments to this movement as well.

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1. Yes, really. ↩

Bret Victor wrote a very interesting rant a few days ago on the the problem with touch interfaces and the future of Interaction Design. The piece got a lot of attention, so today he followed up with some responses to the questions and comments he received.

I particularly enjoyed his thoughts on the limits of voice control. His argument is that voice is a good way to get information or issue commands (yes, like Siri), but that it’s not very good for creating and understanding:

I have a hard time imagining Monet saying to his canvas, “Give me some water lilies. Make ‘em impressionistic.” Or designing a building by telling all the walls where to go. Most artistic and engineering projects (at least, non-language-based ones) can’t just be described. They exist in space, and we manipulate space with our hands.

It’s obvious, yes, but I think we need to remind ourselves of this. Creating things requires “manipulating space with our hands”, even if that means manipulating words onto a page when they’re stubbornly stuck in space somewhere.[1]

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1. Sure, some people (like John Siracusa) are able to dictate the first drafts of stuff they write, but I’m pretty sure they’re not editing their work through voice control. Editing (which is the hardest part of writing) requires a keyboard and lots of banging your head on it.  â†©