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†©

W. Brian Arthur wrote a very interesting article for McKinsey Quarterly called The second economy (h/t to @justinspratt for the link). Registration is required to view the article but it’s worth it.

Much has been written about digitization and technology’s impact on society, but Arthur takes a fresh approach by looking at the digital economy as an unseen layer underneath the physical economy. He starts by defining communication for this (second) economy:

[Processes] are “speaking to” other processes in the digital economy, in a constant conversation among multiple servers and multiple semi-intelligent nodes that are updating things, querying things, checking things off, readjusting things, and eventually connecting back with processes and humans in the physical economy.

You know, like Siri does. In fact, notice how perfectly Siri fits into Arthur’s central thesis about the second economy:

If I were to look for adjectives to describe this second economy, I’d say it is vast, silent, connected, unseen, and autonomous (meaning that human beings may design it but are not directly involved in running it). It is remotely executing and global, always on, and endlessly configurable. It is concurrent””a great computer expression””which means that everything happens in parallel. It is self-configuring, meaning it constantly reconfigures itself on the fly, and increasingly it is also self-organizing, self-architecting, and self-healing.

These last descriptors sound biological””and they are. In fact, I’m beginning to think of this second economy, which is under the surface of the physical economy, as a huge interconnected root system, very much like the root system for aspen trees. For every acre of aspen trees above the ground, ther’s about ten miles of roots underneath, all interconnected with one another, “communicating” with each other.

Arthur makes it clear that he’s not interested in the realm of Sci-Fi and AI. He’s not sharing a completely improbable vision of the future (well, with the exception of driverless cars, depending on how much of a Google believer you are). And even though nothing he describes is brand new, this idea of a silent, interconnected layer underneath the physical one gives us a new lens through which to view the digitization of our lives.

I don’t want to get all “The End Is Near!” on you, but I’m currently reading Sherry Turkle’s Alone Together - Why We Expect More from Technology and Less from Each Other, and Arthur’s article reminded me of her words of caution:

Now demarcations blur as technology accompanies us everywhere, all the time. We are too quick to celebrate the continual presence of a technology that knows no respect for traditional and helpful lines in the sand.

[A] stream of messages makes it impossible to find moments of solitude, time when other people are showing us neither dependency nor affection. In solitude we don’t reject the world but have the space to think our own thoughts. But if your phone is always with you, seeking solitude can look suspiciously like hiding.

Hopefully there will still be places to hide once the second economy has fully established itself.

When Netflix first came out with their movie recommendations, I thought it was a great idea. I started rating movies I’d seen — good and bad — confident that the brain behind it all will do its magic and recommend some hidden movie gems that will, you know, change my life. Well, I’m still waiting for those movies. And to be honest, I’ve become a little bit frustrated with the whole thing.

Describing the latest example I encountered will reveal how much I liked a movie that I probably have no business liking, but I’m willing to sacrifice a little bit of my reputation in the name of science, or whatever this is…

The first problem I encountered is a pure UI issue, and has to do with how Netflix shows the star movie ratings on their pages. As an example, this is what I see for the movie August Rush in my queue:

You would assume that the customer average rating is just over the 3-mark, right? Well, looking at it closer, it turns out that Netflix shows you a rating they call “Our best guess” (3.4 in this case), instead of showing you the customer average (4.1 in this case):

Here’s the problem. I loved this movie. I’m giving it 4 stars. But since Netflix doesn’t know that I have a soft spot for modern musicals (despite how highly I rated the movie “Once”), the “Netflix brain” didn’t think I would like this movie as much as the average customer.

This is a problem you see often on sites where the UI does not give proper user feedback about what it’s showing you.  It took me a few weeks to realize they’re showing me “Our best guess” in search results, and not the true customer average. Now I have to mouse over to see the true average every time. Why? Because I don’t trust the brain any more. (By the way, this is just one example, but as I’ve looked into it more, I realized it’s a systemic problem for me — Netflix’s best guess is rarely in line with my tastes).

Incidentally, on Amazon.com, the average user rating is 4.5 out of 5 stars. Pretty good. So this is the problem then. There is such a wide range of tastes out there that it’s hard to know who to trust. This is the problem Netflix is trying to solve — let’s look at “users like you” and then show you that average instead of the overall average. You’re therefore initially more inclined to believe the “best guess” rating provided by Netflix, than the average consensus provided by all users. It’s a good idea, but the implementation doesn’t seem to be there yet.  (The discussion about the validity of 5-star ratings in general is a separate and very interesting discussion).

I say all this to make a simple point — it appears that the collective wisdom of all users does a better job of predicting if I will like a movie than the recommendation engine provided by Netflix. The question is whether it would ever be possible for recommendation engines to get to know you well enough based on your preferences. Maybe if it takes into account not only your movie interests, but also music, books, online activity, etc.? Yes it sounds creepy, but how else would Netflix know how much I like strange modern musicals?