We are so excited by the idea of machines that can write, and create art, and compose music, with seemingly little regard for how many wells of creativity sit untapped because many of us spend the best hours of our days toiling away, and even more can barely fulfill basic needs for food, shelter, and water. I can’t help but wonder how rich our lives could be if we focused a little more on creating conditions that enable all humans to exercise their creativity as much as we would like robots to be able to.
I like the split-screen animated format for explaining this topic.
A genuinely interesting (and droll) deep dive into derp learning …for typography!
Feel bad because your favourite artists aren’t getting any income from Spotify? Here’s a handy tool from Hype Machine that allows you to import Sportify playlists and see where you can support those artists on Bandcamp.
I am not a believer in the AI singularity — the rapture of the nerds — that is, in the possibility of building a brain-in-a-box that will self-improve its own capabilities until it outstrips our ability to keep up. What CS professor and fellow SF author Vernor Vinge described as “the last invention humans will ever need to make”. But I do think we’re going to keep building more and more complicated, systems that are opaque rather than transparent, and that launder our unspoken prejudices and encode them in our social environment. As our widely-deployed neural processors get more powerful, the decisions they take will become harder and harder to question or oppose. And that’s the real threat of AI — not killer robots, but “computer says no” without recourse to appeal.
After reading this account of a wonderfully surreal text adventure game, you’ll probably want to play AI Dungeon 2:
A PhD student named Nathan trained the neural net on classic dungeon crawling games, and playing it is strangely surreal, repetitive, and mesmerizing, like dreaming about playing one of the games it was trained on.
Decomputerization doesn’t mean no computers. It means that not all spheres of life should be rendered into data and computed upon. Ubiquitous “smartness” largely serves to enrich and empower the few at the expense of the many, while inflicting ecological harm that will threaten the survival and flourishing of billions of people.
See how an Enigma machine works …and interact with it.
Letters to be encrypted enter at the boundary, move through the wire matrix, and exit.
What would Wiener think of the current human use of human beings? He would be amazed by the power of computers and the internet. He would be happy that the early neural nets in which he played a role have spawned powerful deep-learning systems that exhibit the perceptual ability he demanded of them—although he might not be impressed that one of the most prominent examples of such computerized Gestalt is the ability to recognize photos of kittens on the World Wide Web.
A history of buttons …and the moral panic and outrage that accompanies them.
By looking at the subtexts behind complaints about buttons, whether historically or in the present moment, it becomes clear that manufacturers, designers and users alike must pay attention to why buttons persistently engender critiques. Such negativity tends to involve one of three primary themes: fears over deskilling; frustration about lack of user agency/control; or anger due to perceptions of unequal power relations.
This is a rather beautiful piece of writing by Tom (especially the William Gibson bit at the end). This got me right in the feels:
Web 2.0 really, truly, is over. The public APIs, feeds to be consumed in a platform of your choice, services that had value beyond their own walls, mashups that merged content and services into new things… have all been replaced with heavyweight websites to ensure a consistent, single experience, no out-of-context content, and maximising the views of advertising. That’s it: back to single-serving websites for single-serving use cases.
A shame. A thing I had always loved about the internet was its juxtapositions, the way it supported so many use-cases all at once. At its heart, a fundamental one: it was a medium which you could both read and write to. From that flow others: it’s not only work and play that coexisted on it, but the real and the fictional; the useful and the useless; the human and the machine.
An online museum of sounds—the recordings of analogue machines.
A near-future sci-fi short by Hannu Rajaniemi that’s right on the zeitgest money.
The app in her AR glasses showed the car icon crawling along the winding forest road. In a few minutes, it would reach the sharp right turn where the road met the lake. The turn was marked by a road sign she had carefully defaced the previous day, with tiny dabs of white paint. Nearly invisible to a human, they nevertheless fooled image recognition nets into classifying the sign as a tree.
From smart dust and spimes, through to online journaling and social media, to machine learning, big data and digital preservation…
Is the archive where information goes to live forever, or where data goes to die?
This strikes me as a sensible way of thinking about machine learning: it’s like when we got relational databases—suddenly we could do more, quicker, and easier …but it doesn’t require us to treat the technology like it’s magic.
An important parallel here is that though relational databases had economy of scale effects, there were limited network or ‘winner takes all’ effects. The database being used by company A doesn’t get better if company B buys the same database software from the same vendor: Safeway’s database doesn’t get better if Caterpillar buys the same one. Much the same actually applies to machine learning: machine learning is all about data, but data is highly specific to particular applications. More handwriting data will make a handwriting recognizer better, and more gas turbine data will make a system that predicts failures in gas turbines better, but the one doesn’t help with the other. Data isn’t fungible.
Prompted by his time at Clearleft’s AI gathering in Juvet, Chris has been delving deep into the stories we tell about artificial intelligence …and what stories are missing.
And here we are at the eponymous answer to the question that I first asked at Juvet around 7 months ago: What stories aren’t we telling ourselves about AI?
A really excellent piece from Derek on the history of community management online.
You have to decide what your platform is for and what it’s not for. And, yeah, that means deciding who it’s for and who it’s not for (hint: it’s not bots, nor nazis). That’s not a job you can outsource. The tech won’t do it for you. Not just because it’s your job, but because outsourcing it won’t work. It never does.
A terrific cautionary look at the history of machine learning and artificial intelligence from the new laugh-a-minute book by James.
An even-handed assessment of the benefits and dangers of machine learning.
There was a time, circa 2009, when no home design story could do without a reference to Mad Men. There is a time, circa 2018, when no personal tech story should do without a Black Mirror reference.
Black Mirror Home. It’s all fun and games until the screaming starts.
When these products go haywire—as they inevitably do—the Black Mirror tweets won’t seem so funny, just as Mad Men curdled, eventually, from ha-ha how far we’ve come to, oh-no we haven’t come far enough.