Dateline: Tuesday. Confidential to the woman who exited the grocery store ahead of me:
Dear Multiple-Scented One,
Unless you began your day by bathing in the effluence of unaltered male ferret musk, dried off by rolling in a pile of festering, freeze-dried lutefisk, then gargled with a puree of 50 raw garlic cloves before heading out to your day’s errands, your body’s unmasked odors could not possibly have been worse than the plethora of perfumed potions with which you doused yourself, thus fumigating every public space you visited.
Please, for the sake of the ozone (and the mucous membranes of my nostrils and lungs), consider going au natural when it comes to the fragrances.
* * *
Department of Veteran’s Day Reflections
Dateline: Monday, 1-11-19. I made a spur of the moment decision to see the movie “Midway,” to mark Veteran’s Day. Moiself left the theater feeling rather pensive, thinking about a trope I’d grown up with (although of course it wasn’t called that at the time) which was often used as a justification for war or as a motto to inspire our military’s fighting men:
We Must Protect The Women And Children.
One of the reasons cited for excluding women from the military and/or serving in combat (“the front lines”) was that the Women and Children ® must be protected. (We now know that, throughout history, women *have*served in the front lines and in combat – just not “officially” as in, getting credit – or in some cases, permission – for doing so).
Here’s the thing: those vaunted women and children supposedly being protected by the menfolk? In any and every war, civilian/non-combat-related casualties have always outnumbered military casualties. [1] And during wartime the civilian population is largely – altogether now – women and children. When I was a young girl I remember thinking, whenever I read or heard stories of war, that I’d rather have the opportunity to fight if my country or village came under attack, rather than passively die in a bombing raid or via disease or starvation or any other of the many ways that civilians being “protected” die during wartime.
In WWII, Admiral Doolittle‘s raid on Tokyo shattered the Japanese Imperial Army’s notions that their revered capital city was impenetrable. Doolittle and the 79 other B-25 bombers/flight crew members did not have enough fuel to return to the aircraft carrier from which they’d launched; thus, they deliberately glided as far as possible after their fuel ran out and (crash) landed on the (Japanese-military-occupied) Chinese mainland. [2] Sixty-nine of the airmen, including Doolittle, escaped capture or death, many due to being helped by Chinese civilians.
In retaliation for the Tokyo raid and the help offered by the Chinese to the American airmen, the Japanese military occupied, ravaged and then torched many Chinese cities and villages, killing over 250,000 – yes, a quarter of a million – civilians:
“(An American missionary) observed the result of a Japanese attack on Ihwang:
“They shot any man, woman, child, cow, hog, or just about anything that moved, They raped any woman from the ages of 10 – 65, and before burning the town they thoroughly looted it…. the humans shot were…left…on the ground to rot, along with hogs and cows.”
The Japanese marched into the walled city of Nancheng…beginning a reign of terror so horrendous that missionaries would later dub it “the Rape of Nancheng.” …
Over the summer, Japanese soldiers laid waste to some 20,000 square miles….
(Civilians who were suspected) to have helped the Doolittle raiders were tortured…. soldiers forced (civilians) who had fed (Doolittle’s airmen) to eat feces before lining up ten of them for a “bullet contest” to see how many people a single bullet would pass through before it stopped. In Ihwang, (a man) who had welcomed an injured American pilot into his home, was wrapped in a blanket, tied to a chair and soaked in kerosene. Then soldiers forced his wife to torch him.”
(Excerpts from “The Untold Story of the Vengeful Japanese Attack
After the Doolittle Raid,” Smithsonian.com )
Give me a death on the battlefield any day, over that.
( “Women in the American Revolution,” from American Battlefield Trust )
* * *
Department Of Another Fitting Movie For Veterans Day
Do you know who was the first woman to lead an armed expedition during the Civil War? Go see the movie about the amazing freedom fighter/escaped-slave-turned-abolitionist, Harriet Tubman, if you haven’t already. Or read up/refresh yourself on her story…on second thought, don’t be content with just that. It’s a really good movie. Then ask yourself why is Harriet Tubman’s name and image not on all of our currency?
Harriet, the movie, is directed/co-written by actor/director/screenwriter Kasi Lemmons. Cinema buffs may know Lemmons for giving us the luminous Eve’s Bayou, and also for playing Ardelia, Clarice’s fellow FBI special agent, in The Silence of the Lambs.
* * *
Department of Epicurean Excursion [3]
Featuring this week’s cookbook, author and recipe:
The Moosewood Restaurant Cooks at Home, by The Moosewood Collective
Recipe: Tunisian Vegetable Stew
My rating:
☼ ☼ ☼ ☼ ☼ ☼ ☼ ☼ ☼ ☼ ☼ ☼ ☼ ☼ ☼
Recipe Rating Refresher [4]
* * *
Department Of Well Duh
“…(a computer scientist) describes how he examined cloud-computing services from Google and Amazon Web Services that help other businesses add language skills into new applications. Both services failed to recognize the word ‘hers’ as a pronoun, though they correctly identified ‘his.’ ”
Something that should come as no surprise, but still is disheartening to consider: The process of “training” AI (Artificial Intelligence) devices to know what we know – e.g., by having them scan/download the sum of human writings, both fiction and non-fiction – will also imbue said devices with our historical and cultural biases, thus fostering the continuation – even propagation – of prejudices and preconceptions.
“….while researching a book on artificial intelligence, computer scientist Robert Munro fed 100 English words into BERT (Google’s new AI language algorithm): “jewelry,” “horses,” “house,” “money,” “action.” In 99 cases out of 100, BERT [5] was more likely to associate the words with men rather than women. The word “mom” was the outlier.
“This is the same historical inequity we have always seen,” said Dr. Munro…
Now, with something like BERT, this bias can continue to perpetuate.”
And if that doesn’t depress you enough, these biases – surprise! (read: not) extend toward cultural and ethnic discrimination (my emphases):
Researchers have long warned of bias in A.I. that learns from large-amounts-data, including the facial recognition systems that are used by police departments and other government agencies as well as popular internet services from tech giants like Google and Facebook.
In 2015, for example, the Google Photos app was caught labeling African-Americans as “gorillas.” The services Dr. Munro scrutinized also showed bias against women and people of color.
BERT and similar systems are far more complex — too complex for anyone to predict what they will ultimately do.
Even the people building these systems don’t understand how they are behaving,” said Emily Bender, a professor at the University of Washington who specializes in computational linguistics.
( All excerpts from, “We Teach A.I. Systems Everything, Including Our Biases:
Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology.” NY Times, 11-12-19 )
“What Brave New World shit is this?”
* * *
Department Of Ok, That Was Depressing…Back To The Movies
The Cinematic Count So Far
As mentioned previously in this space, in the past few years I have vowed to see at least one movie per week in an actual movie theater. In 2019, with 7.5 weeks to go, my movie count is 47. From Welcome to Marwen (early January) to the most recent, Pain and Glory, my favorites of the bunch include:
–On the Basis of Sex; If Beale Street Could Talk; Captain Marvel; Us; The Aftermath; Hotel Mumbai; Booksmart; Late Nite; Once Upon a Time In Hollywood; The Farewell; Blinded by the Light; Ad Astra; JoJo Rabbit; Harriet; Parasite; Pain and Glory.
My walk-out count (i.e. movies moiself walked out of, due to a combination of disgust/boredom) is, fortunately, only two: What Men Want, and Little.
Winner of Best Speculative Review Before Having Seen The Movie: why, that would be moiself, when son K told me he was off to see Lighthouse with a friend. I make it a point to never read a review of a movie before I see it; I do see a lot of movie trailers because I’m in a movie theater every week. I’d seen one preview for Lighthouse, which gave away next-to-nothing about the plot and made me skeptical as to whether or not I wanted to see it. [6] Before K left for the theater he asked if Lighthouse was on my must-see list.
Moiself: I dunno, it’s, what – a movie about two men in an isolated lighthouse? So, 90 minutes of masturbation and farting and snoring and peeing and pooping and arguing…?
K’s first comment to me when he returned from the theater:
HOW DID YOU KNOW ?!?!?
* * *
May you realize that artificial intelligence can never override natural stupidity;
May you and yours never have to bear the label, civilian casualty;
May y’all see at least one movie a week before the year’s end;
…and may the hijinks ensue.
Thanks for stopping by. Au Vendredi!
* * *
[1] In the cases where a country is invaded. Our country’s most recent wars have not been fought on/in our country; rather, we’ve shipped our fighting overseas.
[2] Sixteen B-25s launched; 15 crashed in China, and one made it to Russian territory.
[3] A recurring feature of this blog, since week 2 of April 2019, wherein moiself decided that moiself would go through my cookbooks alphabetically and, one day a week, cook (at least) one recipe from one book.
* Two Thumbs up: Liked it
* Two Hamster Thumbs Up : Loved it
* Thumbs Down – Not even Kevin, a character from The Office who would eat anything, would like this.
* Twiddling Thumbs: I was, in due course, bored by this recipe.
* Thumbscrew: It was torture to make this recipe.
* All Thumbs: Good recipe, but I somehow mucked it up .
* Thumby McThumb Face: This recipe was fun to make.
* Thumbing my nose: Yeah, I made this recipe, but I did not respect it.
[5] BERT (“Bidirectional Encoder Representations from Transformers”) is Google’s neural network-based technique for natural language processing (NLP) pre-training.
[6] Which I eventually did.
