This is absolutely wonderful!
There’s deep dives and then there’s Marcin’s deeeeeeep dives. Sit back and enjoy this wholesome detective work, all beautifully presented with lovely interactive elements.
This is what the web is for!
The mathematics behind the halting problem is interesting enough, but what’s really fascinating is the community that coalesced. A republic of numbers.
Writing, both code and prose, for me, is both an end product and an end in itself. I don’t want to automate away the things that give me joy.
And that is something that I’m more and more aware of as I get older – sources of joy. It’s good to diversify them, to keep track of them, because it’s way too easy to run out. Or to end up with just one, and then lose it.
The thing about luddites is that they make good punchlines, but they were all people.
I feel like there’s a connection here between what Kevin Kelly is describing and what I wrote about guessing (though I think he might be conflating consciousness with intelligence).
This, by the way, is also true of immersive “virtual reality” environments. Instead of trying to accurately recreate real-world places like meeting rooms, we should be leaning into the hallucinatory power of a technology that can generate dream-like situations where the pleasure comes from relinquishing control.
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.
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?
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.
James talks about automation and understanding.
Just because a technology – whether it’s autonomous vehicles, satellite communications, or the internet – has been captured by capital and turned against the populace, doesn’t mean it does not retain a seed of utopian possibility.
The transcript of Josh’s fantastic talk on machine learning, voice, data, APIs, and all the other tools of algorithmic design:
The design and presentation of data is just as important as the underlying algorithm. Algorithmic interfaces are a huge part of our future, and getting their design right is critical—and very, very hard to do.
Josh put together ten design principles for conceiving, designing, and managing data-driven products. I’ve added them to my collection.
- Favor accuracy over speed
- Allow for ambiguity
- Add human judgment
- Advocate sunshine
- Embrace multiple systems
- Make it easy to contribute (accurate) data
- Root out bias and bad assumptions
- Give people control over their data
- Be loyal to the user
- Take responsibility
A profile of Norbert Wiener, and how his star was eclipsed by Claude Shannon.
Have you tried turning it off and on again?
This is a terrific write up of this year’s dConstruct, tying together all the emergent themes.
A really terrific piece by George Dyson taking a suitably long-zoom look at information warfare and the Entscheidungsproblem, tracing the lineage of PRISM from the Corona project of the Cold War.
What we have now is the crude equivalent of snatching snippets of film from the sky, in 1960, compared to the panopticon that was to come. The United States has established a coordinated system that links suspect individuals (only foreigners, of course, but that definition becomes fuzzy at times) to dangerous ideas, and, if the links and suspicions are strong enough, our drone fleet, deployed ever more widely, is authorized to execute a strike. This is only a primitive first step toward something else. Why kill possibly dangerous individuals (and the inevitable innocent bystanders) when it will soon become technically irresistible to exterminate the dangerous ideas themselves?
The proposed solution? That we abandon secrecy and conduct our information warfare in the open.
In which twelve drawings of historical drawing machines are drawn by a computer numerical controlled machine.
Wonderful musings from Matt on meeting the emerging machine intelligence halfway.