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Daily Archives: March 18, 2022

Chuckles of the Day

Age Is A Funny Thing

Have you ever been guilty of looking at others your own age and thinking, “Surely I can’t look that old?” Well, You’ll love this one!

I was sitting in the waiting room for my first appointment with a new dentist. I noticed his, DDS, which bore his full name. Suddenly, I remembered a tall, handsome, dark-haired boy with the same name had been in my high school class some 40-odd years ago. Could he be the same guy that I had a secret crush on, way back then?

Upon seeing him, however, I quickly discarded any such thought. This balding, gray-haired man with the deeply lined face was way too old to have been my classmate. Hmmm … Or could he?

After he examined my teeth, I asked him if he had attended Morgan Park High School.

“Yes. Yes, I did. I’m a Mustang,” he gleamed with pride.

“When did you graduate?” I asked.

He answered, “In 1959. Why do you ask?”

“You were in my class!” I exclaimed.

He looked at me closely. Then, that ugly, old wrinkled SOB asked, “What did you teach?”

* * * * * * *

The Senior Special

We went to breakfast at a restaurant where the “seniors’ special” was two eggs, bacon, hash browns and toast for $2.99.

“Sounds good,” my wife said. “But I don’t want the eggs.”

“Then, I’ll have to charge you three dollars and forty-nine cents because you’re ordering a la carte,” the waitress warned her.

“You mean I’d have to pay for not taking the eggs?” my wife asked incredulously.

“YES!!” stated the waitress.

“I’ll take the special then,” my wife said.

“How do you want your eggs?” the waitress asked.

“Raw and in the shell,” my wife replied. She took the two eggs home and baked a cake.

Infographic: The Science of Nuclear Weapon

See large image . . . . . .

Source : Visual Capitalist

Is Chinese Math Education as Good as It Seems?

Tang Lu wrote . . . . . . . . .

In 2015, Nature published a cover story about how the artificial intelligence firm DeepMind was training machine learning models to play old arcade games. Unlike dedicated chess supercomputers like IBM’s Deep Blue, DeepMind — a Google subsidiary and the developer of AlphaGo — wanted its algorithm to be able to master any game from scratch. Instead, the algorithm would explore each game’s world slowly, triggering rewards and punishments and gradually accumulating information about the game until it could compete against, and even beat, top human players, all without help from the company’s engineers.

DeepMind’s algorithm quickly mastered dozens of Atari classics — but a handful of titles withstood the onslaught of the machines. In Montezuma’s Revenge, for example, the algorithm failed to score a single point. The game, which requires players to navigate an Aztec temple filled with ropes, ladders, and other deadly traps, proved immune to DeepMind’s programming, in part because, unlike games such as Video Pinball, players in Montezuma’s Revenge do not score points until they collect the last item in a level. Left to its own devices, DeepMind’s machine learning algorithm, which was designed to jump from one point-scoring opportunity to the next, was stumped.

As an AI researcher, the Nature article had obvious appeal, but the story caught my attention for another reason as well. The story of DeepMind and Montezuma’s Revenge seemed a perfect metaphor for another field I care deeply about: education.

Since the advent of “reform and opening-up” in the late 1970s, China’s economic rise has been meteoric. At first, Chinese firms focussed on manufacturing, but over the past decade, much of the country’s GDP growth has been driven by the emergence of a vibrant tech start-up scene. Yet years of dazzling GDP figures have masked a fundamental problem: The lack of a solid basic research infrastructure means China’s tech industry is built on shaky foundations, with many of the country’s most innovative products involving the application of existing technologies, rather than the pioneering of new ones.

China’s poor track record in basic research is closely tied to long-term problems with the country’s basic education system, especially in fields like science and math, where an entrenched belief in the power of tests and scores to determine and reward performance continues to get in the way of key national goals like fostering innovation and creativity.

In 2000, a total of 3.75 million students signed up to take the gaokao — China’s national college entrance examination and by far the most important factor in university admissions decisions. By 2007, amid a rapid expansion of the country’s higher education sector, this figure topped 10 million. It has remained at that level ever since.

But as college enrollment and acceptance rates rose, educational resources failed to keep up. Now, as with the economy finally showing signs of slowing down, the chasm between the educational haves and have-nots is widening, and competition for admittance into a handful of top schools has become fierce.

This battle is primarily waged through the gaokao, with a secondary student’s test scores potentially determining their entire future.

This points-oriented teaching model shares a lot in common with the DeepMind algorithm that struggled to beat Montezuma’s Revenge. Both are based on well-known learning patterns. As early as the 1940s, psychologist B.F. Skinner proved that a wide variety of animals could be trained to perform seemingly complex tasks by rewarding them for simple actions that were either right or nearly right. To translate this into the educational context, the rewards are points on tests and exams.

Such rewards-based training methods exploit extrinsic motivation to incentivize learning. They may look complex, but they ultimately boil down into the process of shaping children into adults capable of performing complex tasks by stimulating dopamine secretion.

Needless to say, there are obvious problems with this approach. The more psychologists explore the mechanisms behind learning, the clearer it becomes that intrinsic motivators play an important role in the learning process. To give just one example, hungry rats will forgo food or even put up with electric shocks for the opportunity to explore new spaces.

Intrinsic motivation is innate, guided by novelty and wonder, and directed toward free exploration and creativity. And indeed, as researchers at OpenAI and elsewhere have gradually puzzled out how to reward “curiosity” in their models, machine learning programs are increasingly able to handle challenges like those posed by Montezuma’s Revenge.

Extrinsic motivation, on the other hand, has always been easier to game. To borrow an example from my own field, researchers once tried training a robot to ride a bicycle by rewarding it for moving closer to its intended destination. However, they neglected to institute punishments for moving away from the destination. The robot started biking in circles to rack up points as quickly as possible. Clearly, it was focused on the reward rather than the researchers’ desired outcome.

The potential lessons of AI for education aren’t limited to how schools reward students. Another common machine learning problem, “overfitting,” is also applicable to China’s current education system. In the context of machine learning, overfitting refers to the common mistake of training an AI model on data sets so precisely that it loses track of the underlying structures it is meant to learn, meaning it can no longer analyze new or emerging datasets.

For a real-world example of this problem, we need to look no further than the way schools prepare students for the gaokao. Rather than teach fundamentals that can be used to solve a wide array of problems, teachers emphasize rote memorization of practice exams. This improves students’ scores, but it doesn’t teach them how to apply the underlying theorems or basic mathematical principles to new problems.

The system of rewards and training that produces such fearsome mathletes at the primary and secondary levels quickly breaks down when students reach university. There are no quick rewards in advanced mathematics; all math students must begin by slowly grasping basic axioms and definitions, then using them to logically deduce various theorems, before slowly constructing stable theoretical systems.

That’s very different from the formulaic, step-by-step process used to drill students for the gaokao. As in Montezuma’s Revenge, the prize is only reached at the end of a long journey, if ever, and many students raised on external motivation and rote memorization feel lost in their studies. This, in turn, results in frustration and self-doubt, and ultimately to them abandoning mathematics.

Until recently, the apotheosis of these two problems — the overreliance on extrinsic rewards and tendency toward overfitting — could be found in the countrywide craze for math Olympiads. The popularity of these contests was not grounded in any particular desire to see children become mathematicians; rather, for years, success at an Olympiad was a potential shortcut to a spot at an elite university. An entire industry of cram schools and tutoring classes soon sprung up to drill students for success in the competition, and China’s dominance at the international level — the country has taken home 22 team titles at the International Mathematical Olympiad (IMO) since 1989 — became a source of public pride. Proud Chinese parents like to joke that 10 days of math tutoring in China is equivalent to a year’s worth of math classes in the United States.

That may be the case, but China’s success at the IMO over the past three decades has not translated into theoretical breakthroughs. Many participants are more focused on the external motivation of winning the prize — admission into a top school — than the joy of mathematics, and only a fraction have any interest in building a career in the field.

That is not to say the situation is hopeless. The government has cracked down on for-profit tutoring over the past year, and some provinces are trying to ease the pressure of competition that defines the gaokao. Exam designers, for their part, are working to produce more novel question topics to discourage rote memorization of practice exams and give students more room for experimentation.

It’s still too early to tell whether these measures will have their intended effect. In the meantime, China’s deficiencies in basic research loom as a crisis. If there’s an irony here, it’s that, as AI researchers become increasingly adept at finding ways to mimic intrinsic motivation in their algorithms, our schools seem content to turn students into machines.

Source : Sixth Tone


作者: 鄧希煒, 林康聖 . . . . . . . .




俄羅斯不但是世界最大天然氣生產國、第三大石油生產國, 以及各種原材料、稀有金屬及氣體(如氖和鈀是製造半導體的必要成份)的供應國,與烏克蘭合共出口全球30%的小麥,烏克蘭本身是全球第四大玉米出口國,所以西方的制裁和俄羅斯的反制裁勢必在短中期內推高能源、原材料、金屬和糧食價格,進一步增加全球的通脹壓力,延緩經濟復甦。自2月24日開戰以來,原油價格已升近三成至周三的每桶129美元,為2008年後新高;小麥及玉米的期貨價格分別升超過40%及18%;天然氣價格在各國亦創歷史新高,在歐洲曾一度飆漲超過79%。

觀乎歐盟國家近40%的天然氣和25%的石油來自俄羅斯(匈牙利、捷克進口俄羅斯天然氣更逾九成),原油及天然氣價格飆升對她們的影響自然最大。歐洲央行預計,俄烏戰爭將令歐羅區通脹在年底徘徊在4%以上水平。天然氣配給若減少10% ,或會使歐羅區本地生產總值(GDP)下降0.7%;若40%來自俄羅斯的供應悉數中斷,歐羅區將失去近3%的GDP。有鑑於此,筆者認為歐洲央行應會把年初預計的加息推遲。
















Source : HKU

Lots of Napping Could Raise a Senior’s Odds for Alzheimer’s

Denise Mann wrote . . . . . . . . .

Taking longer or more frequent naps during the day may sound enticing, but it may be a harbinger of Alzheimer’s disease.

Older adults who nap throughout the day may be more likely to develop Alzheimer’s, while napping may also be a consequence of advancing Alzheimer’s, a new study suggests.

“Daytime napping and Alzheimer’s disease seem to be driving each other’s changes in a bi-directional way,” said study author Dr. Yue Leng. She is an assistant professor of psychiatry at the University of California, San Francisco.

The bottom line? “Older adults, and especially those with Alzheimer’s disease, should pay more attention to their daytime napping behaviors,” Leng said.

There are several potential ways that daytime napping and Alzheimer’s may be linked.

“It could be a reflection of underlying Alzheimer’s pathology at the preclinical stage that affects the wake-promoting network and contributes to increased daytime sleepiness,” she said. “Excessive daytime napping might also impact and interact with nighttime sleep, resulting in altered 24-hour circadian rhythms, which has also been linked to an increased risk of Alzheimer’s.”

For the study, more than 1,400 older Americans, average age 81, wore a watch-like activity monitor for two weeks every year. Any prolonged period of no activity from 9 a.m. to 7 p.m. was considered a nap. Participants also underwent a battery of neurological tests each year.

When the study started, more than three-quarters of participants showed no signs of any cognitive impairment, 19.5% had mild cognitive impairment, and slightly more than 4% had Alzheimer’s disease.

Daily napping increased by about 11 minutes per year among folks who didn’t develop cognitive impairment during roughly 14 years of follow-up. The greater the increase in naps, the more quickly memory and thinking skills declined, the findings showed.

The rate of increase in naps doubled after a diagnosis of mild cognitive impairment and nearly tripled after a diagnosis of Alzheimer’s disease, according to the report published in the journal Alzheimer’s & Dementia.

Another part of the study sought to determine if napping is a risk for developing Alzheimer’s disease. To answer this question, the researchers compared participants who had normal memories and thinking skills at the start of the study but developed Alzheimer’s disease to their counterparts whose thinking remained stable during the study. They found that older people who napped more than an hour a day had a 40% higher risk of developing Alzheimer’s.

Alzheimer’s disease affects more than just memory and thinking skills, said study author Dr. Aron Buchman. He’s a professor of neurology at Rush University Medical Center and a neurologist at Rush Alzheimer’s Disease Center, in Chicago.

In some, this disease may steal memories, but in others, it may result in sleep issues. In others, it could affect motor function, Buchman said.

“More studies are needed to better understand the relationship between napping and Alzheimer’s disease. But it’s possible that improving sleep may be a way of modifying the course of Alzheimer’s disease and its manifold manifestations,” he added.

Experts who were not involved with the study caution that it is way too early to say napping increases the risk for Alzheimer’s disease.

Study patients could have already had pre-clinical signs of Alzheimer’s disease in their brains, said Ricardo Osorio, director of the Center for Sleep and Brain Health at NYU Langone in New York City. “At age 80, even with no symptoms, it is quite common to have Alzheimer’s pathology in the brain,” he explained.

In the future, research should look at napping patterns in younger people and follow them to see who develops Alzheimer’s disease and who doesn’t, Osorio suggested.

People can nap for reasons that have nothing to do with Alzheimer’s disease, he said. Daytime naps may be a result of sleep apnea, overexertion during the day, or even depression and loneliness. “We need to tease out the other things that may cause people to nap more before drawing conclusions,” Osorio added.

There are other caveats as well, said Dr. Derek Chong, vice chair of neurology at Lenox Hill Hospital in New York City.

“This study was performed in Chicago, where the society tends to have only one sleep period at night,” Chong said. “Many other cultures and societies have a siesta or midday nap that is often longer than one hour long, and some of these cultures are known to have slow aging, so these results may not be applicable worldwide.”

Still, this study does call attention to the health consequences of poor sleep, he noted.

“Even though the study does not tell us the cause for why people need to nap more, it should remind us the importance of daytime stimulation, seeking help with depression, and high-quality sleep, and checking with your doctor for things like sleep apnea, especially when we are sleepy during the day,” Chong said.

Source: HealthDay