Kathryn Schulz for The New Yorker on Fantastic Beasts and How to Rank Them:
Consider the yeti. Reputed to live in the mountainous regions of Tibet, Bhutan, and Nepal. Also known by the alias Abominable Snowman. Overgrown, in both senses: eight or ten or twelve feet tall; shaggy. Shy. Possibly a remnant of an otherwise extinct species. More possibly an elaborate hoax, or an inextinguishable hope. […]
One of the strangest things about the human mind is that it can reason about unreasonable things. It is possible, for example, to calculate the speed at which the sleigh would have to travel for Santa Claus to deliver all those gifts on Christmas Eve. It is possible to assess the ratio of a dragon's wings to its body to determine if it could fly. And it is possible to decide that a yeti is more likely to exist than a leprechaun, even if you think that the likelihood of either of them existing is precisely zero.
In fact, it is not only possible; it is fun. […]
Matt Webb has posted some thoughts he's pulled together to introduce a discussion he's going to be taking part in on security and privacy. I was particularly tickled by this anecdote:
Hoxton Analytics supply, for their clients, pedestrian footfall intelligence. They count the number of people walking in and out of your shop.
Note the precise thing they're being asked to count: footfall. That's going to be important in a bit…
[Various reasons why facial recognition won't fly, and other techniques have their shortcomings too.]
Hoxton takes a different approach. They have cameras right down low on the floor, and they use machine learning - on the device - to recognise shoes.
It's crazy accurate. 95% accurate. It can also count group sizes, and whether people are going in or out. So it can do capacity.
Bingo! All you need to be measuring is feet, falling.
It also doesn't store personally identifiable information so it's good in Europe.
But get this. Because they've built this solution, it means they can also use it in public places. So you can point the camera out of the window and see how many people are walking past, versus how many people are walking in. This is the holy grail, like a conversion funnel, like Google Analytics, but for physical retail. And they've got there by considering privacy not as a product constraint, but as a product feature.
There's a little part of me that wonders whether the roots of this approach came when someone in the team was sitting racking their brain over how to solve this problem they'd been presented with and suddenly the notion "If all they want us to count is footfall, all we need to worry about is counting feet!" popped into their mind. AI for counting shoes: why not? No need to build a world of data about whose feet, or build up a deep, detailed picture of what else the owners of those feet bought in the last 30 days or whatever. Just count (potential) customers moving around your premises, be grateful it's more accurate than the data you currently have, and work from there.
In the town of Ísafjörður in Iceland, an experiment in getting the attention of motorists by painting pedestrian crossings in 3D is under way:
My suspicion would be that any impact the shock of seeing 'raised' obstacles in the middle of a road might have on drivers will soon fade unless there's a program of repeatedly changing the type of optical illusion being applied in a particular spot so as to prevent them becoming just another part of a familiar street's layout that drivers have mentally determined that they don't need to pay attention to. I'd love to be proved wrong on that.
James Bridle on the past, and future, of self-driving cars:
This timeline of the self-driving car begins and ends – for now – with a crash. The second, unlike the first, is fatal.
From a reading he gave recently at his friends' wedding, All Neil Gaiman knows about love.
[Too long to quote in full, too good for me to cite an excerpt. Go read the whole thing]
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