Journal of No. 118

 It's fantastic seeing the MAGA crowd on GETTR questioning Trump over his support of Kevin McCarthy.

 

Trump created this monster out of bits of pieces of deplorables. Briefly it was tamed and voted along with the mainstream GOP. But now the monster is unleashed and will not obey its master!

 

On one level it's tragedy. On another, it's "A GIANT & EMBARRASSING DEFEAT".

 

 You Look Like A Thing And I Love You, by Janelle Shane

The book seems to be a reworking of material Shane has shared in her AI weirdness blog. On the one hand, it shows, on the other hand, it makes for light entertaining reading.

I was a little disappointed that the scope of the book focuses on various flavors of machine learning AI (as opposed to symbolic or strong AI). Of course, the latter is a super hard problem, but it's of infinitely more interest.

But the book does reinforce the idea that machine learning AI is terrible and terrifying. If you set it the task of winning at chess and other games, it does an amazing job, because it's easy to 'reward' the program with victory or points. But when the goal is to teach an AI to, for example, act as a customer service agent, or identify what's in a picture, or drive a car down the street, it's harder to train them when the victory condition is 'act like a human would act'.

So the book is a compilation of more or less hilarious and horrifying failures of AI. The basic idea is to train your AI to absorb training data and make a bajillion connections so that it can spit out more of the same. The problem seems to be that we're bad at setting tasks, bad at giving clean data, and bad at making the original judgments the AI is trying to emulate. One of the common examples is text generating AIs, where you train your AI on recipes or Harry Potter novels and let it make up its own once it gets the hang of it. Another common thing of the sort is the autocomplete or Google's help in finishing your search request, which is how "Why won't my parakeet eat my diarrhea?" became a thing. Once the AI randomly latched onto that as a cromulent phrase, people making google searches also latched onto it. That's some serious click bait there. And thus the problem. The Google AI takes those clicks as 'rewards' that it is very accurately predicting what people were going to ask.

But mostly they're just odd, like the motto hearts Shane recently posted:



Other examples look at trying to decipher what's in a picture. For training data, the AI used people-generated descriptions of pictures and then went number crunching away. Now ask yourself, honestly, how many times have you described a picture by saying, "It has zero giraffes in it." probably never. But if there were a giraffe in the picture, you'd probably be likely to mention it. So the AI has seen a few pictures that are described as containing giraffes, but it has never seen a picture described as having zero giraffes in it. The result is that the AI often declares that there are giraffes in pictures that do not contain giraffes.

This kind of bias in the sample has real-world consequences as well. An AI trained to make hiring or loan application decisions turns out to be very good at modelling the human-generated data and discriminating against the same kind of people the employers and banks do. Since the AIs are rather opaque black boxes, it's hard to root out such bias, since we don't really know what the AI is paying attention to.

A related example from driving. The researchers thought they were teaching the AI to keep the car in the middle of the road, and not driving off the sides. Instead, they seems to have taught the AI to keep the green grass at fixed locations on either side of the field of view. When the car went onto an overpass, the green disappeared and the car was flummoxed.


Great, amusing read that shines a light on some important issues as inevitably medical and financial data gets churned through AIs like this.

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The Witchfinder's Sister, by Beth Underdown

I found this a disappointment. Matthew Hopkins was an English witch-finder who had a brief, but very nasty career. I was ready to get my hate on, but the Hopkins of this novel does not seem to much resemble the original. As the title suggests, the book follows Hopkins' [entirely fictional] sister Alice as she returns to join his household after she is widowed. There are dim family secrets that slowly get winkled out, but the action stays on Alice, so much of the witchfinding occurs offstage. It does provide a slow excruciating crescendo as our narrator gets ever more closely involved with the witchfinding and the deplorable details emerge.

But the motivations provided by the backstory conflict with what we know of the real Hopkins, and the author toys with witchcraft being real, which seems like a bizarre step. If people really can and do magically murder people, it's worth finding them out. The tragedy of the witchhunts is that it was all bullshit.

You can see the PTSD building and building



'That rabbit smiled at me.'

A difficult inmate plaintiff.

Plaintiff began filing frivolous motions on a weekly basis and, in that relatively simple civil rights lawsuit, he ended up filing more than seventy-five pleadings, all of which required the considered attention of this Court and Judge Bowen. These motions included "Motion to Behoove an Inquisition" and "Motion for Judex Delegatus" and "Motion for Restoration of Sanity" and "Motion for Deinstitutionalization".
...
The motions ranged from the mundane, such as "Motion for Change of Venue", to the arcane, such as "Motion for Cesset pro Cessus" and "Motion for Judex Delegatus", to the curious, such as "Motion for Nunc pro Tunc" and "Motion for Psychoanalysis", to the outlandish, such as "Motion to Impeach Judge Alaimo" and "Motion to Renounce Citizenship" and "Motion to Exhume Body of Alex Hodgson".
...
In the instant case, Plaintiff has sued all of the judges and one magistrate judge from this District as well as one judge and one magistrate judge from the Middle District of Georgia. Plaintiff also unsuccessfully tried to join Judge Michael Karpf of the Superior Court of Chatham County and United States Senator Sam Nunn. His five motions to amend are overshadowed by the "Motion to Kiss My Ass" which Plaintiff filed [in which he moved "all Americans at large and one corrupt Judge Smith [to] kiss my got [sic] damn ass sorry mother fucker you."]

North Korean scientists have verified "a lair of the unicorn rode by King Tongmyong, founder of the Koguryo Kingdom (B.C. 277-A.D. 668)," confirming historical reports.

This also must be a shot in the arm for Biblical literalists, who have been confounded by smart alecky comments about unicorns in the Bible. Of course King Tongmyong's unicorn was a little Asian looking, but then again the Bible doesn't say much about the appearance of the unicorn anyway.

Improbable Research pointed to Lowering the Bar's Case Law Hall of Fame.

This one is quite a winner:

"Before proceeding further, the Court notes that this case involves two extremely likable lawyers, who have together delivered some of the most amateurish pleadings ever to cross the hallowed causeway into Galveston, an effort which leads the Court to surmise but one plausible explanation. Both attorneys have obviously entered into a secret pact--complete with hats, handshakes and cryptic words--to draft their pleadings entirely in crayon on the back sides of gravy-stained paper place mats, in the hope that the Court would be so charmed by their child-like efforts that their utter dearth of legal authorities in their briefing would go unnoticed. Whatever actually occurred, the Court is now faced with the daunting task of deciphering their submissions. With Big Chief tablet readied, thick black pencil in hand, and a devil-may-care laugh in the face of death, life on the razor's edge sense of exhilaration, the Court begins."

True to word, that is just the beginning of the merriment. Or at least such merriment as can be derived from a legal document.

Italian economist's laws of stupidity skirt the line between amusing and thought-provoking.

The economic take on things is kind of interesting. While a 'bandit' personality causes harm to others in order to benefit himself, "A stupid person is a person who causes losses to another person or to a group of persons while himself deriving no gain and even possibly incurring losses."

Essentially stupid people are dangerous and damaging because reasonable people find it difficult to imagine and understand unreasonable behaviour. An intelligent person may understand the logic of a bandit. The bandit's actions follow a pattern of rationality: nasty rationality, if you like, but still rationality. The bandit wants a plus on his account. Since he is not intelligent enough to devise ways of obtaining the plus as well as providing you with a plus, he will produce his plus by causing a minus to appear on your account. All this is bad, but it is rational and if you are rational you can predict it. You can foresee a bandit's actions, his nasty manoeuvres and ugly aspirations and often can build up your defenses.
With a stupid person all this is absolutely impossible as explained by the Third Basic Law. A stupid creature will harass you for no reason, for no advantage, without any plan or scheme and at the most improbable times and places. You have no rational way of telling if and when and how and why the stupid creature attacks. When confronted with a stupid individual you are completely at his mercy.

The 2011 Ig Nobels have been awarded. Perhaps the most wonderful thing about the Igs as they have evolved is that nearly all of the awardees attend the ceremony nowadays. If you studied beetles mating with beer bottles, or used a tank to smash illegally parked cars, you damn well have earned your spot in the limelight.

The Fermilab Tevatron is shutting down. For good. Today.

For twenty years and more it was the smashingest smasher in the world. Its major trophies were the detection of the top quark and the tau neutrino, filling out the leptons required by the Standard Model. The dingus has now been superseded by the LHC, but it served us well.