Optimal parking strategy

https://arxiv.org/pdf/1904.06612v1.pdf

https://www.youtube.com/watch?v=6W7UrAKcvQw

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mfw@wyle.org | 1.425.249.3936

Understanding Cyber Conflict: 14 Analogies ed. by George Perkovich & Ariel Levite

Very interesting analysis, eye opening..  5/5 Stars.

Cauldron of Fire by Jay Allen

Interesting story twists and military tactics, 4/5 stars.

Accenture SOW wins coveted Hugo Award

A statement of work for eBay Marketing has won a science fiction award.

Ig Noble Prizes

https://arstechnica.com/science/2019/09/2019-ig-nobels-honor-cubed-wombat-poo-magnetic-roaches-and-more/

• temperature differences in left and right side of French postmen's private parts.
• Automatic diaper changer for humans
• more

Echos of Glory by Jay Allen

The adventures (and terrible celestial mechanics) continue! Fun story line, character arcs, politics, and mindless entertainment, 4/5 stars.

Jetsons then and now

if you are old enough to remember the Jetsons intro:

https://www.youtube.com/watch?v=FyinD6ZDqeg

 . . . then you will love this new Arconic ad:

https://www.youtube.com/watch?v=EdzVmRN2IfI

After The Galaxy series 1: The Unsung by Scott Bartlett

Interesting characters and setting but lazy, contrived, melodramatic writing and "too much story."  Really terrible science, yuck.  1/5 stars.

Erdenweh by Bo Balder

This one is not terrible, despite the bad science.  The concept is that we continuously under-estimate the microbiome in our ecosystem understanding, including the microbiota effects on human mood and suicide rates.  3/5 stars.

AI, human behaviors, bias, subtle unobserved data, & causality (long but worth it)

In my day job, I am now trying to measure and predict which software efforts from my teams will deliver how much incremental revenue for my company.  The inherent uncertainties (errors) of these estimations are larger than any forecast value.  70% (or more) of all software efforts fail across most industries.  The most frustrating part of my experience is that everyone lies and pretends their estimates are always perfect.  And there are no data or scholarly analysis for the justifications.  And, of course, the real "attribution" of any revenue to any specific effort in the complex ecosystem of a marketplace is very dodgy and is itself uncertain.

My colleagues in "analytics" data science claim to measure (perfectly, of course!) the exact percentage of people who "would have bought anyway" without my team's marketing effort / campaign / incentive.  The assumptions they make are (of course!) tested with biased assumptions in the test formulation and the predictive accuracy on unknown data are never tested.  But in general, they do great work and I agree with all of their reasoning if not all of their numerical methods.

But the main purpose of my rant here for why everyone in AI should pay more attention to the points Taleb raises in Incerto (uncertainty) is that many of the axioms and foundations upon which we are basing "AI" and "data science" are themselves very questionable:

• Judea Pearl has recently explained why our perceptions of "causality" are fundamentally wrong; current AI methods, and applications are exacerbating the consequences
• Megan Stevenson has recently shown why judges misperceptions and reliance on AI algorithms has not improved fairness in our legal proceeding

In this latter case there are very many reasons we need to be even more careful about AI in our judiciary and legal proceedings, not just those points raised by the author.   The origin data upon which judgments and the AI calculates are biased because of the humans who acted, recorded, selected, and encoded.  Judges, using ancient human unconscious perceptions of other humans' feelings, motivation, trustworthiness. 

Google is now monetizing those annoying "I'm not a robot" challenges

Google sells, and more frequently gives away the use of reCAPTCHA to protect your web site or web pages from bots.  When their algorithm suspects you are a bot or that you are abusing a site with a lot of traffic, they challenge you with a problem to solve that is difficult for a bot.

These challenges and the problems are usually image classification problems that help Google improve their image classification algorithms.  These human judgments the world provides to google are called "labels" in machine learning.

Now, Google is selling the use of those annoying reCAPTCHA challenges that billions of people perform for them for free. If you need your datasets labeled by people challenged by those annoying "I'm not a bot" prompts, you can sign up and pay Google for their free labor.

Brilliant!

The Day the Icicle Works Closed by Frederick Pohl (1960)

Holds up surprisingly well, great plot, 4/5 Stars.

Ruins of Empire by Jay Allan

Light, brainless military space opera with great melodrama, fun.  Terrible science 3/5 stars.