Competition among food trucks in Washington, D.C., is fierce—enough, it seems, that some competitors carry machetes and occasionally attack each other with screwdrivers. The district decriminalized unlicensed street vending a few years ago, and while that spares plenty of good-hearted vendors from punishment, it also means food-quality standards are dicey, prices are wildly inconsistent, and a few unnamed bosses are hell-bent on guarding their turf. Jessica Sidman explores it all, from the heavily fortified frozen dessert warehouses to the shell companies that mask food-truck ownership.

Of the $16.50 I was charged, my Square receipt shows $1.50 in DC sales tax. The DC Office of Tax and Revenue later tells me it has no record of Fusion Swirl LLC, the company named on my receipt. . . .

I also look up the Virginia license plate of the shiny blue truck for traffic violations. Since last October, it has racked up 27 tickets totaling $2,842, mostly for illegal parking and not displaying a front license plate.

The receipt also has a phone number. A few days later, I call. The person on the other end tells me he’s the owner and has multiple food trucks. I explain I’m a journalist and ask his name.

“Uh, yeah, my name is . . . .” The call ends. Subsequent calls go straight to voicemail.

The AI Compass

This political compass style quiz by bambamramfan is pretty neat - answer 29 questions about AI and AI ethics to see which of the 30 archetypes you best fit.

I'm impressed that my answers on my first time through the quiz categorized me as "The Garage Tinkerer", patron saint myself!

It's implemented as a single page React app using the <script type="text/babel"> trick to avoid the necessary build step. Here's the code.

Via @erisianrite.com

Tags: ai, generative-ai, llms, ai-ethics

Math is wonderful, and there are so many different ways to play and experience it. I enjoy having conversations with other math lovers and sharing ideas, puzzles, pedagogy, and questions.

In one of these conversations with Dr. Maria at Natural Math, I learned about a book called Modultown by Drs. Sasha Fradkin and Allison Bishop, and the artist, Mark Gonyea. The project also has an adjacent puzzle book with a delightful puzzle called Moduloku.

I made a prototype of a simplified digital version while at the Recurse Center on my website Inquiries.Link.

I look forward to working on some of these in my math sessions with learners and playing with some of the variations in the book.

Spoiler:

Here are my thoughts as I solve one:

The first thing is to look for blanks I can check right away against the remainders. I see that the third column has one blank and the sum has a remainder of one when divided by 10. Of the numbers available, only 7 gives a remainder of 1.

I can then use the same approach to find the numbers in the first column. First I find the 9, but then, the next one is a little tricky. I need a 2, but since that isn't available, I can use 12 to get the same remainder.

Now, the top row has a remainder of 3 and the sum is 3 with two blanks. That means that the sum of the two blanks must be divisible by 10. The only combination that works is 6 and 4. So, the last two blanks must be 5 and 8.

We can do the same for the columns. So, column 2 has a remainder of 5 and sum of 11. That means that we must sum the two blanks to a number that has a remainder of 4 when divided by 10. The only combination of 4,6 and 5,8 that works is 6 and 8, which sums to 14.

Which leaves only one possible value for each remaining blank – Solved!

Drinking one beer a night for a year is a lot less harmful than drinking 365 beers in one go. The same applies to radiation exposure, but regulation doesn’t agree.

You may well seen the below viral image of a weight stack over the years. It’s often shared as evidence that the normal distribution (i.e. bell curve) is everywhere in daily life.

There’s just one problem: it doesn’t show a normal distribution.

A normal distribution often appears when we sample repeatedly from the same population and take the average each time (this is thanks to the central limit theorem). For example, if we calculate the average height of members at a series of different gyms, we’d expect the distribution of these averages to follow a normal curve if we included lots of gyms in the analysis.

But that’s not what happens if we look at a weight stack.

The wear marks on a stack show the distribution of individual efforts over time. And like many measures of individual performance, it has a long tail.

Just look at the image. The wear is concentrated at weights around 40-50. But some people have lifted well over 100.

If the distribution was truly normal, it would be symmetrical. But the marks in the image are not symmetrical; they are skewed to the right.

If we fit a normal curve to the data (well, the approximate area of wear estimated by an AI vision model), we therefore end up predicting negative weights. Which, of course, isn’t possible:

Just because observations are distributed around an average value, it doesn’t mean they automatically follow a textbook normal distribution.

If we’re focusing on simple distributions, something like a Gamma distribution – which is asymmetric and only generates positive values – gives a better overall fit to the observed data. It’s not perfect, and a more complex shape would do better (Gamma, like normal, has only two parameters) but at least it doesn’t predict a bunch of implausible negative values:

This is the problem with telling people that normal distributions exist in places where they don’t: it creates a temptation to use distributions that are familiar rather than realistic.

We saw this happen a lot during the early stages of the COVID pandemic, when people would fit simplistic bell-shaped curves to outbreaks with very different causes and dynamics, then claim that one could predict another. For example, outbreaks of food poisoning produce a curve that goes up then comes down, but that doesn’t mean they can tell us much useful about a major respiratory epidemic.

We also saw this happen dramatically in the run up to the 2008 financial crisis, when banks used overly simple distributions to model correlations between mortgage risks. One leading hedge fund reportedly kept an abacus in one of its conference rooms; there was a label next to it that read ‘correlation model’.

From racking up weights to racking up debts, it’s a good reminder that we shouldn’t rely too much on inappropriate distributions that don’t capture what’s really generating the patterns we observe.

When you have a cold, there’s nothing quite like a bowl of soup to help you along the way. The warm, typically umami-heavy broth may not have actual healing effects, but the combination of hydration, warmth, and more can help alleviate some of the symptoms, temporarily, of your particular ailment. And in any event, it can’t hurt. taken together, soup has become a staple of flu and cold season.

And, if you were a Viking, it can also be used to save other people’s lives — at your expense.

From, roughly, the year 800 to 1050, Viking from Scandinavia raided and colonized areas of Europe and into North America. The Viking Age, as the period became known, is now mythologized by tales of epic battles featuring men wearing horned hats, stabbing each other with swords.

While the horned helmets aren’t based in history, the swords definitely are. Vikings conquests — like any other warfare — were often bloody. Many of those conquests are detailed in Icelandic and Norse sagas, and while the accuracy of those records are debatable — the sagas were often written generations after the events — there are many details generally believed to be true. For example, these texts, per a paper in the Journal of Military and Veterans’ Health, outline how Vikings used herbal remedies to help injured warriors stave off infection and similar illnesses.

But like any other medication, those herbs weren’t in infinite supply. Viking healers needed to dole them out with efficiency. Giving a treatment to someone who was beyond hope was, while humane, a waste of such resources. So, per the sagas, they came up with a way to triage the injured to determine who could benefit from the herbs — and who couldn’t. Their method: a broth made of onions, leeks, and other smelly vegetables. We Are The Mighty explains:

If a Viking warrior was wounded in the stomach during a battle, they were fed a strong, pungent onion soup. Afterward, the Vikings tending to the wounded would smell the belly wounds to look for the signature onion smell. If they could smell the onions through the man’s wound, then they knew the stomach wall was cut, and the man would not survive his wounds. It would be pointless to try to save the man and another with a better chance of survival would be treated.

As a result, a lot of Viking warriors had a last meal of onion soup — especially if they, literally, couldn’t stomach it.

Bonus fact: As noted above, Vikings didn’t actually wear horned helmets. The iconic horned headwear is almost entirely a 19th-century invention. It became popular after costume designer Carl Emil Doepler outfitted performers in horned helmets for an 1876 production of Richard Wagner’s Der Ring des Nibelungen, per Yale historian Roberta Frank, and the image has stuck ever since. The ones found in archeology efforts, per Smithsonian, date back around 3,000 years — well before the Viking Age.

From the Archives: The Great Minnesota Goose Scandal of 2017: Vikings, yes. Horns on helmets, yet. Onion soup and bloody conquests? Not so much.