Let’s invoke the boilerplate frontispiece disclaimer:

This is a work of fiction. Names, characters, businesses, places, events and incidents are either the products of the author’s imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.

In my story, a big high-tech company has developed a self-learning AI system that can carry on natural-language conversations online, by text or by voice, spanning a wide array of topics. Would the AI pass the Turing Test? The tech company arranges for a number of university professors to enroll the company’s AI in their online classes. At the beginning of the semester each participating professor announces to the students that one among them is an AI and that it’s their task, individually and collectively, to identify the android in their midst.

Already within the first week several students have come forward, confessing to their classmates that they are the androids. Pranksters no doubt, budding philosophers playing with the idea that humans aren’t all that different from machines. But what if the self-confessed androids are telling the truth? Maybe it’s a ruse, the AI system deploying reverse psychology in order to throw the humans off their scent. So now the Turing Test gets turned around: can an intelligent entity prove that it’s not an android?

 

 

A month or so ago I received a jury duty summons in the mail. Only once before had I been called — it was while we were living in Boulder — but one of the attorneys eliminated me during voir dire. A lot of people try to avoid jury duty, but I’d been looking forward to it. A couple of years earlier my wife Anne had been seated on a jury for a horrific alleged crime, and while finding it stressful to sit through the testimony and presentation of evidence Anne had been fascinated by the trial proceedings and the jury deliberation. When it was over she felt that she had contributed something to the greater civic good, and had received something meaningful in return. I’d wanted in, and was disappointed when they gave me the boot.

The summons, issued by the Office of the Sheriff, instructed me to report for duty in the Jury Assembly Room at the Durham County Courthouse at 8:30 this morning. However, the letter further informed me that “sometimes jurors may no longer be needed by the date for which you are summoned.” I was told to call a number after 5:30 on the evening before my service date to see if they still wanted me to come in. I called, and they didn’t. “You don’t need to report for jury service,” the recorded woman’s voice informed me. “The court’s juror requirements have been met. By being available you have fulfilled your service. Thank you.” Well, a little bit of a letdown. Maybe I’m not the only one who’s felt disappointed. I’d been available for future service, which would have become present service today. But the recorded message informed me that I had already fulfilled my service in the past merely by being available. Now I suppose I can hold my head high: I fulfilled my civic duty, received my honorable discharge, no need to thank me for my service, I count it a privilege. I wonder if the Courthouse sells “I Served” t-shirts. My Badge Number and Juror Number are printed in the upper-left heading of my summons– the t-shirt could leave blank lines where the purchaser can write in his own numbers.

With time on my hands and blueberries and buttermilk in the fridge I decided to make pancakes for breakfast. I cracked the egg and slid it into the bowl — two yolks! According to the Egg Industry this is a one-in-a-thousand occurrence. The dual-yolk eggs used to be pulled from the production lines during candling because they tended to freak out the cooks, presaging either good luck or pregnancy or twins or death. I guess when you’re making pancakes you don’t want to look much further ahead than breakfast. Nowadays though the double-yolk eggs just pass on through — it’s probably just a cost-effectiveness thing, though superstition seems to have lost potency to shape destinies over recent decades. In any event, the pancakes turned out nicely — I’d like to thank that hen for her service. Now we’ll see what the rest of the day holds in store.

Writing this stuff down it starts feeling like the beginning of a novel…

“You have fulfilled your service.”

John hung up the phone. He’d already mapped out the route and planned what to bring along to keep himself occupied while waiting to be called. Though he’d never entertained any inclination to study the law, though he had found himself gravely disappointed in his only brush with the system, small claims court having meted out its lazy injustice on what he deemed arbitrary and capricious grounds, not to mention the outright lying of his duplicitous adversary, John had long been intrigued by the legal process, by the all-rises and the swearing-ins, by the evidence bags and the depositions, by crime scene photos and forensic expert testimony, by the contrasting narratives spun by prosecution and defense, stories that to the jury would sound clear and convincing beyond a shadow of a doubt…

Here’s a summary of a couple of recent experiments demonstrating deep placebo/nocebo effect. It’s not too surprising that, when told that their DNA test results reveal a particular genetic predisposition, study participants demonstrated attitudes, feelings, and behaviors consistent with that genetic marker, even when the DNA results are fake news. But the participants also underwent physiological changes consistent with the fake genetic news.

This work comes out of the Stanford Mind & Body Lab, which “focuses on how subjective mindsets (e.g., thoughts, beliefs, and expectations) can alter objective reality through behavioral, psychological, and physiological mechanisms.” They’re tracing a causal cascade that runs opposite from but complementary to that of contemporary neuroscience, which explores how brain physiology shapes subjective mindsets. Then there’s this earlier study from Harvard demonstrating that placebo responsiveness might itself be genetic.

These sorts of findings tend to stimulate extravagant inferences.

Las Vegas oddsmakers have made Michigan State a 2½-point favorite over Purdue in this afternoon’s basketball game. How did they come up with that spread? Mostly it’s an algorithmic simulation, a multivariate statistical equation built up from analyzing massive amounts of data collected about all the teams’ past performances in all of their games. But the bookmakers make their money not by betting on the games but by collecting the vig — the house’s cut on each bet placed, typically 10% of the amount bet.

Suppose Purdue, in a mild upset, wins the game. Vegas will pay off on bets that went under the predicted 2½-point MSU margin of victory. But they’ll also collect from bets that went over the spread. As long as the total amount of money bet on the “under” doesn’t greatly exceed the “over,” Vegas breaks even on the betting money while still making a 10 percent profit on the vig.

The point-spread algorithm isn’t deterministic, cranking out a single prediction of which team will win and by how much. It’s probabilistic, generating a Bell curve’s worth of possible outcomes of varying degrees of likelihood. Each of the factors going into the oddsmaking algorithm has a plus-or-minus confidence interval associated with it.

E.g., in the 18 games it’s played so far this year MSU has scored an average of 83.8 points, while Purdue has yielded to its opponents an average of 68.2 points. So you’re first best guess might be that MSU will score halfway between its average score and Purdue’s average points allowed: (83.8 + 68.2)/2 = 76 points. But there’s also the variability to take into consideration: MSU has scored between 106 and 63 points, while Purdue has allowed between 89 and 46 points.

The same idea holds for pace of play, rebounding margin, shooting percentage, and all of the other relevant variables: an average score is surrounded by a plus-or-minus halo of variation. These probabilistic variables are combined and differentially weighted in an equation that, like its components, generates a probabilistic average and a range of variation.

Only one actual game between MSU and Purdue will be played this afternoon; when the buzzer sounds only one actual final score will be posted on the scoreboard; some betters will have won while others have lost. But from the algorithm’s standpoint the game that’s played this afternoon is only one of an infinite number of MSU-Purdue games that could be played — a practical application of multiverse theory. The algorithm runs a series of simulations on maybe a thousand hypothetical games, systematically tweaking the plus-or-minus variabilities in the model to generate what-if scenarios, then cranking out a final score for each simulated game. In some simulations MSU wins by a dozen; in others MSU loses by a dozen. What Vegas wants to know is the midpoint, where half of the simulated game results fall on left tail of the distribution, the other half on the right tail. That’s where Vegas wants to set the point spread.

But not so fast. Just because the algo cranked out the average point spread for its simulated games, that doesn’t mean that the betters are going to fall 50-50 on either side of that spread. People who bet on games might have a system and do analyses; they might even have an algorithm of their own that they’ve built or a service they’ve bought into to help them beat Vegas at its own game. But betters also play hunches, follow instincts, play favorites.

MSU has won 21 straight Big Ten games: aren’t they about due for a loss? And MSU has beat the Vegas point spread in 8 straight times: surely things are due to even out. That sort of thinking is called the gambler’s fallacy — that the longer somebody has been on a lucky streak, the greater the likelihood that the streak will come to an end. Still, just because it’s a fallacy from an empirical standpoint doesn’t mean that bettors stop believing it.

So there’s still a seat in the back room for the guy with the green eyeshade smoking a cigarette. Let’s say that the algo runs a thousand simulations of the game and the average prediction is MSU by 5 points. The savvy bookmaker might have reason to expect that gambler’s fallacy will play a role in the betting and consequently nudge the spread down a little bit. But the bookmaker needn’t rely solely on intuition and experience. There’s plenty of historic betting data to be mined; simulated bets can be placed online or in focus groups days before the actual odds are to be posted. Almost surely Vegas relies now on a self-learning AI to predict human betting patterns for each game, using its findings in tandem with those of the point-spread prediction algorithm.

The open-access college basketball algorithm website I pay attention to is Kenpom. According to that algo, MSU is about 9 points better than Purdue. However, the game is being played on Purdue’s home court, and according to Kenpom’s analysis the home court advantage is worth on average about 3½ points. So MSU should be expected to win by 9 – 3½ = 5½, covering the Las Vegas spread of 2½.

I have rooting interests in MSU, having graduated from there. On the other hand, I do tend to think that MSU has been playing over their heads lately and are due for a bit of a comeuppance. However, I acknowledge that there’s a certain amount of gambler’s fallacy bias creeping in there. So I’ll split the difference between Las Vegas and Kenpom.

My prediction: Michigan State by 4 — I’m taking the over.

——

UPDATE… Final score: Purdue 73, Michigan State 63. One of those wrong futures showed up in the present. Good thing I didn’t put my money where my mouth was.