New state laws are making states more different from each other

Shawn Hubler and Jill Cowan, Flurry of New Laws Move Blue and Red States Further Apart, NYTimes, April 3, 2022.

As Republican activists aggressively pursue conservative social policies in state legislatures across the country, liberal states are taking defensive actions. Spurred by a U.S. Supreme Court that is expected to soon upend an array of longstanding rights, including the constitutional right to abortion, left-leaning lawmakers from Washington to Vermont have begun to expand access to abortion, bolster voting rights and denounce laws in conservative states targeting L.G.B.T.Q. minors.

The flurry of action, particularly in the West, is intensifying already marked differences between life in liberal- and conservative-led parts of the country. And it’s a sign of the consequences when state governments are controlled increasingly by single parties. Control of legislative chambers is split between parties now in two states — Minnesota and Virginia — compared with 15 states 30 years ago.

“We’re further and further polarizing and fragmenting, so that blue states and red states are becoming not only a little different but radically different,” said Jon Michaels, a law professor who studies government at the University of California, Los Angeles.

Americans have been sorting into opposing partisan camps for at least a generation, choosing more and more to live among like-minded neighbors, while legislatures, through gerrymandering, are reinforcing their states’ political identities by solidifying one-party rule.

H/t Tyler Cowen.

Life in a strange place

Cortical connectivity

#brain stuff : An estimation of the absolute number of axons indicates that human cortical areas are sparsely connected https://t.co/Ci7ZBTYXcw pic.twitter.com/IGCmFmnPtv

— Alessandro Crimi 🧠🧬🔬🩺🧪 (@Dr_Alex_Crimi) April 3, 2022

Abstract of the linked paper:

The tracts between cortical areas are conceived as playing a central role in cortical information processing, but their actual numbers have never been determined in humans. Here, we estimate the absolute number of axons linking cortical areas from a whole-cortex diffusion MRI (dMRI) connectome, calibrated using the histologically measured callosal fiber density. Median connectivity is estimated as approximately 6,200 axons between cortical areas within hemisphere and approximately 1,300 axons interhemispherically, with axons connecting functionally related areas surprisingly sparse. For example, we estimate that < 5% of the axons in the trunk of the arcuate and superior longitudinal fasciculi connect Wernicke’s and Broca’s areas. These results suggest that detailed information is transmitted between cortical areas either via linkage of the dense local connections or via rare, extraordinarily privileged long-range connections.

Luminous leaves

Analog vs. Digital: How general is the contrast?

I've been thinking about computation and the brain recently and decided to bump this post from 2016 to the top because it discusses the distinction between digital and analog.  When I started reading about computers in the 1960s every introductory discussion would mention that distinction. But by the time personal computers had become widespread those introductory discussions no longer mentioned analog computing. See the graph below.

* * * * *

 

The distinction between analog and digital computers and, more generally, between analog and digital phenomena, has been an important one in contemporary thinking. It is central to David Golumbia’s The Cultural Logic of Computation, where Golumbia says (p. 21):

...a rough approximation of my thesis might be that most of the phenomena in each sphere [covered in this book], even if in part characterizable in computational terms, are nevertheless analog in nature. They are gradable and fuzzy; they are rarely if ever exact, even if they can achieve exactness. The brain is analog; language is analog; society and politics are analog. Such reasoning applies not merely to what we call the “human species” but to much of what we take to be life itself...

Mark Liberman took issue with that statement in a post at Language Log, Is language “analog”?, in which he argued that “crucial aspects of human speech and language are NOT "analog" — are not continuously variable physical (or for that matter spiritual) quantities.” While I agree with Liberman on that point, that is not my point here; if that interests you, by all means read Liberman’s post. Rather, I want to recount something that came up in the discussion, which, in some measure, depended on just what these two terms mean.

I went on to ask when analog and digital began to be used in opposition to one another. It is easy enough to think of slide rules as analog devices and the abacus as a digital device, but is that how they were thought of when they originated?

The Wikipedia entry on analog computer lists a bunch of mechanical, electrical and electronic devices in the late 19th and into the 20th century, but did the people who conceived and used them explicitly conceptualize them as specifically analog in kind? I've run an ngram query on “analog,digital”: 

 

 

The lines for both terms hug the X axis at the bottom of the chart until about 1950 and then both start up, with “digital” quickly outstripping “analog.” The McCulloch-Pitts neuron dates to the early 1940s and was digital in character. Von Neumann discusses analog and digital in his 1958 Computer and the Brain; indeed, that contrast is one of the central themes of the book, if not THE central theme. How much contrastive discussion was there before then?

There’s certainly been a lot of such discussion after then. FWIW, when I started reading elementary accounts of computing and computers in the 1960s, analog vs. digital was a standard topic. At some time during the personal computing era I began noticing that popular articles no longer mentioned analog computing.

 

What I'm getting at is that it may be a mistake to treat the terms as having a well-settled meaning that we can take as given. That may in fact be true for a substantial range of cases. But that need not imply that our sense of the meanings of these terms is fully settled. Are we still working on it?

Liberman responded:

The original sense-extension of analog, as in "analogy", was in the context of one signal (for instance sound as time functions of air pressure) being represented by another (in that case sound represented analogously by voltage in a wire). And the original sense-extension of digital was in the context of a continuous time-function being represented by sequence of numbers (= "digits"). I would have thought that in both cases, the origins were in engineering discussions of telephone technology, but Nyquist's 1928 paper [PDF] doesn't use either word in this way, nor does Claude Shannon in 1948 [PDF]. The OED's earliest citations to this sense of analog are in discussion of "analog" vs. "impulse-type" computers, e.g.

1941 J. W. Mauchly Diary 15 Aug. in Ann. Hist. Computing (1984) 6 131/2 Computing machines may be conveniently classified as either ‘analog’ or ‘impulse’ types. The analog devices use some sort of analogue or analogy, such as Ohm's Law.., to effect a solution of a given equation. [Note] I am indebted to Dr. J. V. Atanasoff of Iowa State College for the classification and terminology here explained.

Note that the “analogy” these is not between a continuous signal and a series of numbers, but between an equation to be solved in one (discrete or continuous) domain and the physics of some machine's internal operations.

Thus it seems that the contrast between analog and digital is a relatively recent one and was originally made in a relatively narrow technical domain. So when we're trying to figure out whether or not or in what way language is analog or digital we're extending the contrast from a situation where it was relatively well defined to a very different situation. In the case of language we don't really know what’s going on and we’re using the analog/digital contrast as a tool for helping us figure it out. And the same is certainly true for nervous systems.

In the case of Golumbia’s example of celluloid film we have a technology that predates that analog/digital contrast. In the context of that distinction I find it reasonable to think of the discrete presentation of frames as digital in character, but I don't off-hand see that the digital concept gives further insight into how the film technology functions. As for digital video or high resolution digital ‘film’ (whether printed to celluloid or digitally projected), the effect on the human nervous system is pretty much the same as that of celluloid film. The frame rate may be different, but in all cases it exceeds the flicker-fusion rate of the visual system so that what we see is continuous motion.

Liberman responded:

FWIW, the distinction in mathematics between “discrete” and “continuous” (in various senses of both) goes back quite a ways, as does the idea of mathematical concepts as symbolically-encoded propositions. But in the end I don't think it's helpful to try to decide on a single binary global classification of issues like whether a function is differentiable, or what it means to describe a band-limited time function as a Fourier series, or whether digitally-encoded music is the same as or different from an analog tape recording, or whether words are discretely encoded in the brain as sounds or as meanings or in whatever other ways. Though all such questions are conceptually inter-related in various ways, each has its own properties, and trying to find one simple metaphor to rule them all is a recipe for confusion.

And that’s where I think we are. The use of a global contrast between analog and digital may have some value in relatively informal discussions, but it’s problematic where precision is required. In those cases we should seek terms crafted to the properties of the domain under discussion.

Seinfeld gets psyched [like Horowitz]

From Daniel McGinn, Life’s Work: An Interview with Jerry Seinfeld, Harvard Business Review (2017):

How do you get psyched up before going onstage?

You don’t have to get psyched up—the audience will take care of that. You walk out in front of 3,000 people who have paid $75 or $100, they’re sitting there saying, “We want to laugh right now,” and you feel that. But every comedian, like every athlete, has a little routine. Mine is to look at my notes until five minutes before the show. When my tour producer says, “Five minutes,” I put on the jacket, and when the jacket goes on, it’s like my body knows, “OK, now we’ve got to do our trick.” And then I stand, and I like to just walk back and forth, and that’s it. That’s my little preshow routine. I never vary it. It just feels comfortable.

Compare this with Vladimir Horowitz, the classical pianist:

The moment that I feel that cutaway – the moment I am in uniform – it's like a horse before the races. You start to perspire. You feel already in you some electricity to do something.

From Helen Epstein. Music Talks: Conversations with Musicians. McGraw-Hill Book Company, 1987, p. 10.

Performance is performance. It's a specific kind of behavioral mode.

Sparkychan ponders the future of AI while sitting beside a railroad track

Chess as THE prototypical problem for AI [w/ a Rodney Brooks postscript on prediction sins]

Back in August of 2020 I had some thoughts about chess as a prototypical domain for AI. My point was that chess is, in fact, a very specialized conceptual domain and is in fact not characteristic of human thought at all. I’m now looking at Luke Muehlhauser’s useful survey, What should we learn from past AI forecasts? (2016). In discussing “The Peak of AI Hype” he observes:

For example, Moravec (1988) claims that John McCarthy founded the Stanford AI project in 1963 “with the then-plausible goal of building a fully intelligent machine in a decade” (p. 20).

In some cases, this optimism may have been partly encouraged by the hypothesis that solving computer chess might be roughly equivalent to solving AI in full generality. Feigenbaum & McCorduck (1983), p. 38, report:

These young [AI scientists of the 1950s and 60s] were explicit in their faith that if you could penetrate to the essence of great chess playing, you would have penetrated to the core of human intellectual behavior. No use to say from here that somebody should have paid attention to all the brilliant chess players who are otherwise not exceptional, or all the brilliant people who play mediocre chess. This first group of artificial intelligence researchers… was persuaded that certain great, underlying principles characterized all intelligent behavior and could be isolated in chess as easily as anyplace else, and then applied to other endeavors that required intelligence.

Another reason for early optimism might have been that some AI scientists thought it might be relatively easy to learn how the human mind worked.

Whoops! We’ve learned a lot since then, haven’t we? But not so much as to inoculate us against hype.

I note that Muehlhauser says nothing about the hype attending machine translation (MT) in the late 1950s and early 1960s and the attendant collapse of funding when those rosy predictions failed to materialize. On the one hand that’s understandable since the group of researchers working on MT was distinctly different from that working on AI. However, MT has been within the purview of AI for that last quarter of a century or more and has many conceptual and technical issues in common with AI.

Rodney Brooks: Seven Deadly sins of predicting AI

September of 2017 Rodney Brooks posted an essay, [FoR&AI] The Seven Deadly Sins of Predicting the Future of AI.

They are:

1. Over and underestimating
2. Imagining magic
3. Performance versus competence
4. Suitcase words
5. Exponentials
6. Hollywood scenarios
7. Speed of deployment

From the first, over and underesting:

Roy Amara was a futurist and the co-founder and President of the Institute For The Future in Palo Alto, home of Stanford University, countless venture capitalists, and the intellectual heart of Silicon Valley. He is best known for his adage, now referred to as Amara’s law:

We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.

There is actually a lot wrapped up in these 21 words which can easily fit into a tweet and allow room for attribution. An optimist can read it one way, and a pessimist can read it another. It should make the optimist somewhat pessimistic, and the pessimist somewhat optimistic, for a while at least, before each reverting to their norm.

A great example⁠1 of the two sides of Amara’s law that we have seen unfold over the last thirty years concerns the US Global Positioning System. Starting in 1978 a constellation of 24 satellites (30 including spares) were placed in orbit. A ground station that can see 4 of them at once can compute the the latitude, longitude, and height above a version of sea level. An operations center at Schriever Air Force Base in Colorado constantly monitors the precise orbits of the satellites and the accuracy of their onboard atomic clocks and uploads minor and continuous adjustments to them. If those updates were to stop GPS would fail to have you on the correct road as you drive around town after only a week or two, and would have you in the wrong town after a couple of months.

The goal of GPS was to allow precise placement of bombs by the US military. That was the expectation for it. The first operational use in that regard was in 1991 during Desert Storm, and it was promising. But during the nineties there was still much distrust of GPS as it was not delivering on its early promise, and it was not until the early 2000’s that its utility was generally accepted in the US military. It had a hard time delivering on its early expectations and the whole program was nearly cancelled again and again.

Today GPS is in the long term, and the ways it is used were unimagined when it was first placed in orbit. My Series 2 Apple Watch uses GPS while I am out running to record my location accurately enough to see which side of the street I ran along. The tiny size and tiny price of the receiver would have been incomprehensible to the early GPS engineers. GPS is now used for so many things that the designers never considered. It synchronizes physics experiments across the globe and is now an intimate component of synchronizing the US electrical grid and keeping it running, and it even allows the high frequency traders who really control the stock market to mostly not fall into disastrous timing errors. It is used by all our airplanes, large and small to navigate, it is used to track people out of jail on parole, and it determines which seed variant will be planted in which part of many fields across the globe. It tracks our fleets of trucks and reports on driver performance, and the bouncing signals on the ground are used to determine how much moisture there is in the ground, and so determine irrigation schedules.

GPS started out with one goal but it was a hard slog to get it working as well as was originally expected. Now it has seeped into so many aspects of our lives that we would not just be lost if it went away, but we would be cold, hungry, and quite possibly dead.

There's much more in the post.

John Dollard on "free-floating" aggression and racism

 [Note: I'm posting in this, in part, to keep John Dollard's name in my mind.]

Why do people need racism? John Dollard addressed this question in his classic 1937 study, Caste and Class in a Southern Town, which “redirected the study of southern race relations in general and lynching in particular.” The question implies that mistaken beliefs about others are symptoms of racism, not its cause. Racism has some useful function in the individual or collective lives of racists. What function could that be? Dollard’s answer was, in effect: to keep the peace.

Dollard observed that social life is often frustrating, generating aggressive impulses which cannot be always be satisfied. In Dollard’s view this leads to

a generalized or “free-floating” aggression . . . [that] can be thought of as a tendency to kick, hit, scorn or derogate someone or something if one could only find out what. A second necessity is that of a permissive social pattern. This must exist in order to lift the in-group taboos on hostility. The permissive pattern isolates a group within the society which may be disliked. Usually it is a defenseless group. . . The third essential in race prejudice is that the object must be uniformly identifiable. [pp. 445-446]

In other words, white racists are using blacks as scapegoats for the accumulated frustrations they experience in daily life. Aggressive impulses are being displaced from their real objects, which are appropriate targets, to substitute objects, toward whom one can act aggressively.

The idea has certain attractions. It is fairly simply and straightforward and seems applicable in other cases as well. Racism and ethnic scapegoating are certainly not uniquely American. Between the Japanese and the Koreans, the Hindus and Muslims of India, the English and the Irish, the Gypsies and half the world, the Jews and half the world, ethnic scapegoating is common. It almost seems that wherever you have three people, two will get together and blame their troubles on the third. With groups that think of themselves as a people, whether the Serbs or the Hutus or the Germans, it is easy to find other groups to blame and to hate. 

 

Thus in his study of nineteenth century European aggression, The Cultivation of Hatred, Peter Gay notes that “Ethnic pride and ethnic anxiety were, in many, indistinguishable” and suggests that ethnic scapegoating was the obverse side of the coin of nationalism. The identity a people creates for itself is, in part, the fact that we are most emphatically not them, who are primitive and inferior. He suggests that World War I was a tremendous release for a frustrated and repressed Europe for “the war released aggressive impulses of which people had been unconscious in calmer times.”

 

* * * * * 

 

I elaborate on this in a post that dates back to 2014, Blacks, Blues, and Soul Sickness: Lynching and Racism in the USofA.

Here's a post that discusses the Amazon series, The Underground Railroad, and then lists several posts about racial violence. I particularly recommend Empaths & Gangstas: On the psycho-sociology of race in America [Media notes, Star Trek S3 E12].

3D print of a rat neuron

learning how to 3D print! this is a Purkinje neuron from a rat cerebellum. pic.twitter.com/aMVpYov70x

— Natalie Schaworonkow (@nschawor) April 1, 2022

Jason Crawford: What happened to belief in progress?

Jason Crawford, The Lure of Technocracy, The Roots of Progress, March 31, 2022.

I’ve said that society was generally optimistic about progress until the early 20th century, and lost that confidence in the World Wars. By the late 20th century, from about the 1970s on, a deep skepticism and distrust of progress had come to prominence. But what happened in between?

I have a new theory about what characterized the attitude toward progress (in the US, at least) from about the 1930s through the ‘60s. It’s just a hypothesis at this stage, but it goes like this:

The 19th century was dominated by a belief in the power of human reason and its ability to advance science and technology for the betterment of life. But after World War I and the Great Depression, it got harder to believe in the rationality of humanity or in the predictability and controllability of the world.

The generation that went through these shocks, however, was not ready to give up on the idea of progress. They still wanted progress and still believed that reason could achieve it—but they worried that the masses could not be trusted to be rational, and that progress could not be left to the chaos of democracy and free markets. Instead, progress was to be achieved by a technical elite that would exercise top-down control.

The purest form of this, perhaps, found expression in early Communism, which valorized industrial production but sought to achieve it by subordinating the individual to totalitarian rule. The US was too individualistic for that—but it evolved its own flavor of the idea that I’m just starting to understand. Call it “technocracy.” [...]

In the early 1970s, a perfect storm of events conspired to discredit the technocratic idea, including Vietnam, Watergate, and the oil shocks. By 1973 it was clear that our leaders were unfit to govern, in terms of either competence or ethics: they could not handle affairs at home or abroad, neither the economy nor foreign policy, and they were plagued by scandal.

From the 1970s on, the conversation changed. The belief in progress was not totally dead. But the idea that it could be achieved centrally by the elites held much less sway, and there was a major new element of distrust and skepticism at the very idea of progress—an element that has not gone away, and indeed by today has gone mainstream.

There's more at the link.

Contrast with the somewhat different views of Tim Burke, which I've excerpted here.

Scott Alexander on the argument from low-hanging fruit [stagnation, redux]

Scott Alexander, The Low-Hanging Fruit Argument: Models And Predictions, Astral Codex Ten, April 1, 2022.

It begins:

Imagine scientists venturing off in some research direction. At the dawn of history, they don’t need to venture very far before discovering a new truth. As time goes on, they need to go further and further.

Actually, scratch that, nobody has good intuitions for truth-space. Imagine some foragers who have just set up a new camp. The first day, they forage in the immediate vicinity of the camp, leaving the ground bare. The next day, they go a little further, and so on. There’s no point in traveling miles and miles away when there are still tasty roots and grubs nearby. But as time goes on, the radius of denuded ground will get wider and wider. Eventually, the foragers will have to embark on long expeditions with skilled guides just to make it to the nearest productive land.

Let’s add intelligence to this model. Imagine there are fruit trees scattered around, and especially tall people can pick fruits that shorter people can’t reach. If you are the first person ever to be seven feet tall, then even if the usual foraging horizon is very far from camp, you can forage very close to camp, picking the seven-foot-high-up fruits that no previous forager could get. So there are actually many different horizons: a distant horizon for ordinary-height people, a nearer horizon for tallish people, and a horizon so close as to be almost irrelevant for giants.

Finally, let’s add the human lifespan. At night, the wolves come out and eat anyone who hasn’t returned to camp. So the the maximum distance anyone will ever be able to forage is a day’s walk from camp (technically half a day, so I guess let’s imagine that everyone can teleport back to camp whenever they want).

This model can explain some otherwise confusing observations about the history of science:

1. Early scientists should make more (and larger) discoveries than later scientists.
2. Early scientists should be relatively more likely to be amateurs; later scientists, professionals.
3. Early scientists should make discoveries younger (on average) than later scientists.
4. These trends should move more slowly for the most brilliant scientists.
5. These trends should fail to apply in fields of science that were impossible for previous generations to practice.

Scott then goes on to elaborate on each of those five.

I've presented a somewhat more abstract version of this argument that takes a 1992 article by Paul Romer, Two Strategies for Economic Development (gated), as its point of departure: Stagnation, Redux: It’s the way of the world [good ideas are not evenly distributed, no more so than diamonds]. That blog post makes up the second part of my working paper, What economic growth and statistical semantics tell us about the structure of the world, August 24, 2020, 19 pp, https://www.academia.edu/43938531/What_economic_growth_and_statistical_semantics_tell_us_about_the_structure_of_the_world.

Here's the abstract from that working paper:

The metaphysical structure of the world, as opposed to its physical structure, resides in the relationship between our cognitive capacities and the world itself. Because the world itself is "lumpy, rather than "smooth" (as developed herein, but akin to "simple" vs. "complex"), it is learnable and hence livable. Machine learning AI engines, such as GPT-3, are able to approximate the semantic structure of language, to the extent that that structure can be modeled in a high-dimensional space. That structure ultimately depends on the fact that the world is lumpy. It is the lumpiness that is captured in the statistics. Similarly, I argue, the American economy has entered a period of stagnation because the world is lumpy. In such a world good "ideas" become more and more difficult to find. Stagnation then reflects the increasing costs the learning required to develop economically useful ideas.

Maplewood prophesy [Friday Photos]

Click on photo to enlarge it.

What’s going on at my Academia page at the end of March 2022

This graph depicts activity on my Academia.edu page from March 2, 2022 to April 1, 2022 (click on the graph to enlarge it):

What happened at the end of March?

The upper green line depicts the number of views for individual documents while the lower black line depicts the number of document downloads. We see a dramatic increase in the number of views. Here’s the number of views at the end-of-day for March 25 through April 1.

March 25: 34
March 26: 61
March 27: 104
March 28: 875
March 29: 388
March 30: 184
March 31: 249
April 1:     44 (7:39 AM)

The total for March 28, 875, is by far the largest number of views I’ve had in a single day. The total for the previous day, 104, may well be the largest number of views up to that date (I started posting at Academia a decade or so ago). The number of views per day is generally between 10 and 50. An older post on activity at my Academia page gives a sense of my normal action at the site: How am I doing at regulating my action at Academia.edu?

Why the sudden increase? I don’t know. However, once I noticed that the increase from the 27th continued on the 28th, I began tweeting links to various papers. I noticed that almost immediately after I’d posted a tweet to my Twitter feed, views of that document would show up on my Academia page. Obviously someone – bots perhaps? – was watching my tweet stream. I note that the increased activity seems to be coming from a number of different geographic locations.

I must have tweeted 20 or 30 or more links on the 28th. The total for the 29th, 388, was down considerably, but still well above the usual. I did some tweeting of papers on the 29th, but not so many. The totals for the 30th and 31st are still well above usual, with some tweeting of papers on my part. Note that I captured the graph at 7:39 AM on April 1, when there were 44 views. I don’t know what the number will be for the end of the day.

Robots in Japan in 2022