Voice recognition oddities

Two words I can't get Apple's voice recognition software to recognize when I say them:

  1. Than: I always get "then," unless I really consciously stress the difference, in which case I get "van."
  2. Will: This comes out as "we'll." This one is especially puzzling to me: if I listen to myself say the word "will," my pronunciation of the vowel doesn't sound very much like a long-e to me.


I got some pushback on my piece on ethno-nationalism from people who said, "No, an ethnicity must be characterized by a common bloodline!" Oddly, this pushback came both from racists who wanted to exclude non-whites from being "true Americans" and from their critics.

First of all, racists define ethnicity as being identical (almost identical?) to bloodline. So what? We now have to turn to racists for our word definitions?

But more importantly, if we define things that way, there pretty much are no nations for the racial-nationalists to "preserve." Consider England: Far from all being descended from a common bloodline, the English people are descended from Picts, Celts, Romans, Angles, Saxons, Jutes, Danes, Norman French and more. Two of the most prominent Englishmen of the 19th century, Ricardo and Disraeli, were the descendants of Portuguese and Italian Jews, respectively, and yet both were clearly English. James Callaghan was of Irish and Jewish descent and like Disraeli became Prime Minister.
Idris Elba is pretty obviously English, in a way I never could be, despite my being genetically closer to the average resident of England than he is.

Similarly, the Spanish are Iberians, Lusitanians, Celts, Romans, Germans, Moors and more. The Italian people are made up of "bloodlines" of Celts, Etruscans, Greeks, Romans, Lombards, Moors, and so on. The idea that to be ethnically Italian means to be descended from some common ancestor along with all other Italians is stupid, unless we want to run that bloodline back to Adam and Eve.

If racial-nationalists are looking for some "pure bloodline" around which to found a nation, they are in for a long search.

Philosophy of Nature

I am currently reviewing Paul Feyerabend's Philosophy of Nature for The British Journal for the History of Philosophy. Feyerabend worked on this book in the 1970s, but it was only released this year.

This promises to be a wonderful review experience, since Feyerabend was a brilliant man, and in this work he reviews the "philosophy of nature" from the Stone Age to Bohm.

And here is my first quote of note from the work:

"The assumption that humans of the Stone or Bronze Age would have had only the most primitive knowledge of nature may be flattering to our progressivist self-image. But it has little plausibility since Stone Age humans were already fully developed members of the species Homo sapiens, and it is incompatible with recent research. The environmental and societal problems that the early Homo sapiens had to face were incomparably greater than the challenges facing our contemporary scientists. These problems has to be solved with the most primitive means, often without any division of labor or specialized skills, and the solutions arrived at indicate a level of intelligence and sensitivity that is clearly not inferior to ours." -- pp. 5-6

My book reviews

I've assembled a partial list.

Trump's Tremendous Trolling

Trump just riled up a bunch of his opponents with his tweet about "taking away the citizenship" of anyone who burned the flag.

Of course this is absurd: he's not going to do anything like that: he's trolling.

A friend recognized this, and said Trump's trolling is "Not nice."

This is an understanding of politics as a big kindergarten classroom: If you're just nice to Johnny and let him play with your truck, he will let you play with his.

Unfortunately, real politics is nothing like kindergarten: the new prince, as Machiavelli taught us, must consolidate his rule. If he is overly "nice," his foes will see it as a sign of weakness and oppose him all the more fiercely. And as Machiavelli noted, to be "nice" and fail to establish one's rule is really not nice at all, since civil unrest and ultimately civil war result, and they are very not nice.

So Trump trolled those claiming "Trump is not my president," and got just the reaction he wanted: televised shots of Trump's opponents burning the American flag. Across the nation, the image that will stick with people is: those opposed to Trump hate America. (I'm not saying this makes sense, I'm saying that's the emotional impact of the images.)

Machiavelli would have recommended rounding up the protesters and having them executed. By contrast, Trump's technique of tweet-trolling them into political oblivion is nice indeed.

Wisdom from Scott Adams

As anyone reading this blog consistently knows, I do not "worship" Scott Adams, or anything of the sort. As soon as he starts to talk philosophy, he talks nonsense. But in understanding persuasion, he is a true pro. And in discussing the "pizza-gate" "scandal", he notes:

"Here’s what I know that most of you do not: Confirmation bias looks EXACTLY LIKE a mountain of real evidence. And let me be super-clear here. When I say it looks exactly the same, I am not exaggerating. I mean there is no way to tell the difference."

And of great importance here: Adams is de-bunking an anti-Clinton instance of confirmation bias. He doesn't just see confirmation bias when he wants to see it, and deny its possibility when he likes its implications.

This is what is so hard to accept about what the "Godzilla" of influence, Robert Cialdini, describes in his book Pre-suasion. We are all susceptible to being primed, by pre-adopting a certain framework, to read "evidence" in a certain way. If people are shown an identical video of someone describing their behavior in some situation of violent conflict, but one group has previously read a biography of the narrator as a decorated war hero, and another group has read a biography of the narrator as a violent sociopath, the two groups will judge what is described in the exact same video radically differently. Furthermore, members of each group will mostly be certain that they reached their conclusions entirely based on the actual evidence of the video. If asked if the biography had any influence on them, most of them will answer, "Of course not: that person is clearly [a brave soldier / a sociopath] based only on what they said in the video."

That is what confirmation bias is like.

Let me offer you an example of how important "pre-suasion" can be, with a story I have related on this blog previously.

My last month at the London School of Economics, I was staying at the flat of a friend. He told me that when I arrived in London, I should call him, and we would meet, and he would bring me to my new residence. When I landed and called him, he told me he was at the laundromat. Without explicitly stating this to myself, I subconsciously concluded, "Oh well, there are no laundry facilities at the flat."

After I had been there a couple of weeks, my friend asked me why I kept doing my laundry in the bathtub. (I am not addicted to modern conveniences, and I'm perfectly willing to wash my dishes or my laundry by hand.)

I responded, "because there is no washing machine in the apartment."

My friend walked me to the kitchen, and asked, "Well, what is that?"

Clearly visible in the kitchen, which I had been in by that point dozens of times, was a washing machine. But my friend's statement that he was at the laundromat had "pre-suaded" me that there was no washing machine at our flat. (It just happened that he had been at the laundromat to wash some duvets, which would not fit in the flat's small washing machine.) Thus pre-suaded, I was literally unable to see a completely unhidden and undisguised washing machine.

In this case, no one had been intentionally trying to convince me that there was no washing machine in the flat. There was no team of master persuaders at work trying to hide the presence of the washing machine from me. And yet still I was unable to see it.

Now imagine that a team of master persuaders has been trying to convince you that something that is there, is not, or something that is not there, is. How much more likely are you to believe that there is a "mountain of evidence" that what they want you to believe really is (or isn't) there, and that you have reached your conclusion entirely on your own?

Algorithms and the concrete universal

(A follow-up to this post.)

Hegel's notion of the "concrete universal," later adopted by British idealists (like Bosanquet, Collingwood and Oakeshott) and Italian idealists (like Croce), and important to a modern philosopher such as Claes Ryn, is difficult to grasp. We are used to thinking of the concrete and the universal as opposites of some sort. So what on earth is a "concrete universal"?

This passage from R. G. Collingwood expresses the idea philosophically about as well as I have seen:

"The concept is not something outside the world of sensuous experience: it is the very structure in order of that world itself... This is the point of view of concrete thought... Too abstract is to consider separately things that are inseparable: to think of the universal, for instance, without reflecting that it is merely the universal of its particulars, and to assume that one can isolate it in thought and to study it in this isolation. This assumption is an error." -- Speculum Mentis (1924)

In shorter form, Bernard Bosanquet wrote: "the fullest universal of character and consciousness will embody itself in the finest and most specialized and unrepeatable responses to environment." -- The Principle of Individuality and Value: The Gifford Lectures for 1911 (1927)

Rather than a philosophical definition, what I would like to offer here is a concrete example of the concrete universal, that of algorithms. At first glance, nothing could be more abstract than an algorithm. But let us try to state what that "abstract" algorithm is: let us take, for instance, the algorithm for the Towers of Hanoi. We can describe the algorithm in words; but these will be particular, concrete words. We can picture the actual puzzle game, and even actually play it:

But this represents the algorithm with particular, concrete pieces of wood and particular instructions on how to play.

We can offer an implementation of the algorithm, in, for instance, Python:

def hanoi(n, source, helper, target):
    if n > 0:
        # move tower of size n - 1 to helper:
        hanoi(n - 1, source, target, helper)
        # move disk from source peg to target peg
        if source:
        # move tower of size n-1 from helper to target
        hanoi(n - 1, helper, source, target)
source = [4,3,2,1]
target = []
helper = []
hanoi(len(source), source, helper, target)

But this is a particular set of instructions in a particular programming language.

We might even provide some pseudo-code, but the pseudo-code will still consist of particular symbols written according to a particular pseudo-convention.

In short, the abstract algorithm is an airy nothing, a "we know not what" (as Berkeley described the abstract matter of Descartes and Locke), unless embodied in some concrete form. Or, as Collingwood said, "it is merely the universal of its particulars." We cannot "isolate it in thought and to study it in this isolation." We can only reach the universal through the concrete, which is its only reality.

Chipping away at the illusion


The other-worldliness of CLRS algorithms

I'm teaching algorithms from Cormen, Leiserson, Rivest, and Stein, which is the current standard for advanced algorithm courses. I'm working now on coding up their rod-cutting algorithm.

Supposedly we are solving a practical problem for a company, Serling Enterprises (Rod Serling pun), that buys long steel rods and wants to know how best to cut each rod to maximize revenues, given that different rod lengths sell at different prices.

CLR&S offer an algorithm that determines the best cuts, and then... returns the maximum revenue possible, using those cuts.

Can you imagine a manager at Serling actually using this code? She has a rod of 120 inches in length, and an list of prices for various rod lengths on the market. She feeds this items into the CLRS algorithm, and gets back the answer... $43.

Say what?! The manager wants to know how she should cut the rod. Yes, it is nice to know, also, what revenue she will get from those optimal cuts. But an algorithm that returns only the maximum revenue is useless to her! OK, if she makes optimal cuts, she can get $43 in revenue. But what are those optimal cuts?