October 24 2023, 20:48

Interestingly, the latest iPhone, even in terms of the location of production of most of its key components, is hardly Chinese anymore. Look, 29% of them, based on cost, are supplied from South Korea. LG Group provides parts for the camera, Samsung Group supplies the display.

The USA has provided the largest share of key components, 33%, with Qualcomm and Broadcom supplying communication chips. Japan’s share remains unchanged at 10%. Apple manufactures its own chips in Taiwan (TSMC). China’s share is 2.5%. A significant Chinese contribution is the titanium case.

October 24 2023, 20:18

I am currently reading Sapolsky, who describes an experiment showing that people who experienced disgust (for example, by holding their hand in a vomit simulation) tend to recommend harsher punishments for violations related to cleanliness.

This is explained by the fact that disgust is associated with activity in a part of the brain called the insula. This brain region is activated by repulsive smells or sensations. For the last hundred million years, this helped in survival by choosing what to put in one’s mouth and what not to. Later, when people encountered the need to assess acts and with moral judgment in general, evolution just added this role to the insula, as developing a specialized area of the brain simply takes more time and it’s a big question if it’s really necessary. Actually, the reverse pattern also works – a good smell and taste facilitate people being more agreeable and more often positively assessing artworks. Another study showed that hunger makes us less tolerant. Analyses of judges’ decisions were correlated with the times they had eaten.

Source: Implicit effects of sweet tastes: M.Schaefer et al. “Sweet Taste Experience Improves Prosocial Intentions and Attractive Ratings” Psychological Research 85 (2021): 1724. B.Meier et al., “Sweet Taste Preferences and Experiences Predict Prosocial Inferences, Personalities, and Behaviors,” Psychological Sciences 102 (2012): 163.

Do you now understand why it is good to keep small treats handy during negotiations?

October 22 2023, 13:01

I’ve just started reading “Determined” by Robert Sapolsky. I noticed that one of the chapters ahead (haven’t finished it yet) references Cellular automata. And I realized I forgot to share an interesting find—a book “A New Kind of Science” by Stephen Wolfram. I bought it earlier this year and have read about 60% of it.

“A New Kind of Science” is a voluminous tome of over a thousand pages that covers everything from physics to biology and neuroscience. Its author, Stephen Wolfram, a doctor of physical and mathematical sciences, is the creator and main developer of the scientific software Mathematica and also Wolfram Alpha. Having become incredibly wealthy, he dedicated his intellectual career to exploring what happens when simple rules are applied to cells on a plane or in space.

He discovered that even simple rules like “if the square to the right is black, be black; if it’s white, be white,” when repeated a million times, can generate surprisingly complex structures. To a human, this complexity does not fit well with the simplicity of the rules because everywhere in our world, it seems that simple rules generate simplicity, and complex rules generate complexity. Hence, his main idea is that often simple rules can generate very complex outcomes.

Essentially, this is how DNA works. After all, DNA is a program that receives a) the actual software code—a sequence of amino acids and b) the result of the previous execution (enzymes floating in the cell suppressing some fragments of the software code, as well as some initial state of enzymes floating in the mother’s egg cell). As a result, after 10 iterations, the same software code generates structures that simply did not exist before ten iterations. And at, say, the hundredth iteration, something else gets activated. And calculating what will activate if something initially goes slightly wrong—like if an enzyme isn’t present at a needed moment in the cell—is virtually impossible. This makes genetics a very complex field.

Wolfram has a whole system for such things. For example, for a simple algorithm – we choose the color of a cell in the next iteration based on the color of the cell in the current iteration and the colors of the immediate neighboring cells. Such rules are formulated as 8 transformations of three bits into one, which essentially gives a program that only needs 8 bits to fully describe its logic. These 8 bits define the program. For example, Rule 22, which can be formulated as: each cell in the next generation will be black if exactly one of its two neighboring cells or itself is black in the current generation. So, if you initiate this rule against some initial state, a random pattern of large and small triangles, which are simultaneously random and not, will form over millions and billions of generations. The most interesting part is that it’s unpredictable from the initial condition and by the rule—at least, science doesn’t know how, and observations can only be made by running simulations and examining the outcomes. For instance, it might be that somewhere at the billionth generation, the pattern begins to repeat. Or it doesn’t. Or an intricate trapezoid might appear in the pattern that hadn’t appeared before. Or after a certain step, everything might collapse into a completely white or black background.

But what’s interesting is that this complexity is determined from the very beginning. Essentially, the number (rule) + initial combination determines the infinite terabytes of the resulting pattern. Slightly change the rule or the initial combination, and everything changes to something else, but also determined.

By the way, almost 100% of Wolfram’s discoveries would have been understood even in ancient Greece, and nothing prevented them from being formulated and passed down to the next generations in ancient Greece. But for some reason, there hasn’t been a single attempt over thousands of years. Only in the 20th century did they gradually begin to dig into the topic. Well, try to think of any other branch of science that theoretically could have been invented in ancient Greece but was only conceived now.

In the book, he analyzes the biology of these resulting creatures, and even classifies them reasonably well. He identifies some common characteristics—the same triangles in the pattern, although neither he nor others can explain where they come from.

In general, the book is very entertaining.

Now about the oddities.

Wolfram claims that this is a new way of doing science: instead of mathematical modeling of physical processes, scientists can simply observe computer-generated patterns that accurately model phenomena. And that in many cases, this opens up new horizons. Well, that’s debatable. Well okay.

I’m also somewhat irritated by the speaking style “one of the most important discoveries that I made.” It’s practically in every chapter. It must be admitted that the topic Wolfram launched with this book around 2002 was already in circulation, but no one had systematized it quite like he has. And it must be admitted that since 2002, it has also not been particularly popular among scientists. Maybe there’s not much more to delve into than Wolfram did, or maybe they just don’t like him so much that no one wants to touch it 🙂

So, no doubt, this book must be incredibly irritating to industry practitioners who, reading it, seem to practically not exist compared to the self-proclaimed brilliant mind of Mr. Wolfram. Sometimes it almost crosses into parody. I remember from my school days programming Conway’s “Game of Life,” and I was amazed then, where the heck does the information to describe all this come from. But according to Mr. Wolfram, he was the first to think this way. That is, he of course refers to Conway, but unwillingly, cloaking all the references with self-praising statements. Possibly, Wolfram did indeed make many discoveries independently and helped revive the subject, but even if so, he needs to recognize the merits of others more in his texts and not boast about how smart he is.

The book, by the way, is available in public access—Wolfram has put it up on his website, well adapted for the web. In its printed version, it is also surprisingly affordable. So if you’re interested, I advise you to take a look and form your own opinion.

October 20 2023, 10:40

I’m currently in Atlanta, where this week a large group of us planned work for the next three months. And to wake people up in the morning for fun, we organized a hall to play rock-paper-scissors to find the absolute winner of the hall. Where I realized, I cannot play the American version of rock-paper-scissors. In the USA, they play it like this: they beat their fist on the palm on “rock”, then beat on “scissors”, then beat on “paper”, and then they say “shoot” and show their hand. Generally, we also have four counts (“rock, scissors, onion, mage”), but it’s not quickly apparent because there are exactly three words and no “shoot”. (Also, we have a longer version “rock, scissors, onion, mage, tsu, ye, fa”; however, in the USA, a version without “shoot” is also common)

Obviously, against a random number generator any strategy you take, the probability is 30%. But there are interesting studies on how to play against real people. Here are its findings:

1) winners repeat: people tend to repeat what helped them win last time. If you showed rock and won against scissors, you are more likely (than 1/3) to show rock again.

2) losers change: when losing, people tend to change strategy (showed rock, lost, why show it again)

From this, some hints on how to win:

1) if you lost, in the next round show what was not shown this time (if rock and scissors were shown, show paper)

2) if you won, then in the next round show what your opponent lost with the previous time

3) if it’s a tie, choose randomly

By the way, there’s a robot that wins at “Rock-Paper-Scissors” 100% of the time. But it cheats – it watches the micro-movements of your hand and anticipates by showing the opposite. Google “Janken Robot”