You know that guy, Germaine? He's sneaky and needs to be kept honest. One way to do that is to show, from time to time, how crazy, out of control and just generally QAnon-level nuts California is. Here is, no surprise, Faux News gleefully gloating over rampant crime in Oakland CA. The point Faux loves to make is that liberals, Democrats and non-Republicans generally are human scum and all of them are incompetent nincompoops or just like the thugs in Oakland who terrorize innocent Republicans and Christians in rural Alabama and everywhere else in America.
Faux writes, with an accompanying one minute, 21 second video showing carnage, mayhem and other awfulness:
CNN covers massive crime surge in Oakland as families flee the city:
'Everyone seems to be a target'
Residents of Oakland, California told CNN that they are scared to leave their homes because of rising crime as some families leave the city entirely.
Things are bad in Oakland. There, sneaky Germaine has been kept honest. Darned liberal California. I'm leaving ASAP! Texas, here I come!
There, Germaine has been kept honest, darn him anyway. Grumble, grumble . . . .
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- The average UPS driver could get $170,000 in pay and benefits in five years' time in a new contract.
- Tech workers said the boost could make UPS driver pay competitive with tech salaries.
- Some tech workers said they felt "underpaid" in comparison while others said UPS drivers had difficult jobs.
"This is disappointing, how is possible that a driver makes much more than average Engineer in R&D?" a worker at the autonomous-trucking company TuSimple wrote on Blind, an anonymous job-posting site that verifies users' employment using company emails. "To get a base salary of $170k you know you need to work hard as an Engineer, this sucks."
Woof!! $170,000 smackers/year, including healthcare and pension payments on top of that? Wowser bowser! The labor contract averted what Business Insider said would be a strike by the Teamsters Union that would be very costly to UPS. Some tech workers are in a fit of jealous snit.
One really must wonder if labor is starting to be re-evaluated and more valued and/or if artificial intelligence, and/or something else is going on and gnawing away at the tech worker standard of living. This is very interesting (IMHO). Keep eyes open for further developments:
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Keeping Germaine honest
In the spirit of keeping Darned Germaine honest, there are some things one ought to know about guns and gun violence research. Over at BNR, I was recently bluntly informed that both fully automatic and semi-automatic guns had been invented before the Bill of Rights was finally ratified and became part of the US Constitution on Dec. 15, 1791. That was a surprise. The (debatable) argument at BNR was that the Founders knew about and wrote the 2nd Amendment to protect private citizens' right to own machine guns and semi-automatic weapons.
Wikipedia writes about the almost fully automatic machine gun of 1718, the Puckle Gun:
The Puckle gun (also known as the defense gun) was a primitive
crew-served, manually-operated flintlock revolver patented in 1718 by James Puckle (1667–1724), a British inventor, lawyer and writer. It was one of the earliest weapons to be referred to as a "machine gun", being called such in a 1722 shipping manifest. .... Production was highly limited and may have been as few as two guns.
The Girardoni air rifle is an air gun designed by Italian inventor Bartolomeo Girardoni circa 1779. The weapon was also known as the Windbüchse ("wind rifle" in German). One of the rifle's more famous associations is its use on the Lewis and Clark Expedition to explore and map the Louisiana Purchase of 1803.
The Girardoni air rifle was in service with the Austrian army from 1780 to around 1815. Many references to the Girardoni air rifles mention lethal combat ranges of 125 to 150 yards [375-450 feet] and some extend that range considerably. The advantages of a high rate of fire, no smoke from propellants, and low muzzle report granted it acceptance.
The metal ball contains compressed air
the gun is reloaded by hand or machine pumping
to refill the air reservoir
There, Germaine has been kept honest. Sinking Germaine. Grumble, grumble . . . .
New gun violence data
The Quarterly Journal of Economics reports new gun violence data. This is from an experiment in Chicago with 2,456 participants. Researchers wanted to see if gun violence costs could be reduced by an experimental intervention, a job, behavior therapy and social support. Maybe it can, just maybe. For context, shooting of humans by guns in America is very expensive per person shot or killed.
Research from 2022 estimated that gun violence in the US costs about $557 billion annually, about 2.6% of US GDP (US GDP is $21.4 trillion?? Jeez, that's a lot).
Gun violence is the most pressing public safety problem in American cities. We report results from a randomized controlled trial (N = 2,456) of a community-researcher partnership called the Rapid Employment and Development Initiative (READI) Chicago. The program offered an 18-month job alongside cognitive behavioral therapy and other social support. Both algorithmic and human referral methods identified men with strikingly high scope for gun violence reduction: for every 100 people in the control group, there were 11 shooting and homicide victimizations during the 20-month outcome period. Fifty-five percent of the treatment group started programming, comparable to take-up rates in programs for people facing far lower mortality risk. After 20 months, there is no statistically significant change in an index combining three measures of serious violence, the study’s primary outcome. Yet there are signs that this program model has promise. One of the three measures, shooting and homicide arrests, declines 65 percent (p = .13 after multiple-testing adjustment). Because shootings are so costly, READI generates estimated social savings between $182,000 and $916,000 per participant (p = .03), implying a benefit-cost ratio between 4:1 and 20:1. Moreover, participants referred by outreach workers—a prespecified subgroup—show enormous declines in both arrests and victimizations for shootings and homicides (79 and 43 percent, respectively) which remain statistically significant even after multiple-testing adjustments. These declines are concentrated among outreach referrals with higher predicted risk, suggesting that human and algorithmic targeting may work better together.
So, to translate that, the main goal of the experiment failed (
p = 0.13 = a 13% chance the data is a statistical fluke = not statistically significant).
p means statistical probability. The main goal was to reduce three measures of serious violence. But when the data for one of the three measures, shootings and homicide arrests, was analyzed, that data was statistically significant (
p = 0.03 = a 3% chance the data is a statistical fluke = statistically significant). The cutoff point for statistical significance is
p = 0.05 = a 5% chance the data is a statistical fluke.
Note that because the data is weak but suggestive of something that might work better, some form of intervention like READI could be useful if the experimental protocol is tweaked. The researchers suggest the use of algorithmic targeting (or maybe artificial intelligence?**) could help researchers refine the READI protocol to get better results and/or better target people would have a higher chance of responding to READI intervention.
An algorithm is a set of instructions — a preset, rigid, coded recipe that gets executed when it encounters a trigger. AI on the other hand — which is an extremely broad term covering a myriad of AI specializations and subsets — is a group of algorithms that can modify its algorithms and create new algorithms in response to learned inputs and data as opposed to relying solely on the inputs it was designed to recognize as triggers. This ability to change, adapt and grow based on new data, is described as “intelligence.”
AI at maturity is like a gear system with three interlocking wheels: data processing, machine learning and action or decision. It operates in an automated mode without any human intervention. Data is created, transformed and moved without data engineers. Actions or decisions are implemented without any operators or agents. The system learns continuously from the accumulating data and actions or decisions and outcomes get better and better with time.
