The Abnormal Normal

Another day, another COVID-19 conspiracy theory making the rounds. First there was the Chinese bioweapons idea, then the 5G radiation theory that led to tower vandalism, and now the Plandemic video. Washington Post covers the latter while complaining that tech companies are incompetently ineffectual in stopping the spread of these mind viruses that accompany the biological ones. Meanwhile, a scientist who appears in the video is reviewed and debunked in AAAS Science based on materials she provided them. I’m still interested in these “sequences” in the Pacific Ocean. I’ve spent some time in there and may need to again.

The WaPo article ends with a suggestion that we all need to be more skeptical of dumb shit, though I’m guessing that that message will probably not reach the majority of believers or propagators of Plandemic-style conspiracy thinking. So it goes with all the other magical nonsense that percolates through our ordinary lives, confined as they are to only flights of fancy and hopeful aspirations for a better world.

Broadly, though, it does appear that susceptibility to conspiracy theories correlates with certain mental traits that linger at the edge of mental illnesses. Evita March and Jordan Springer got 230 mostly undergraduate students to answer online questionnaires that polled them on mental traits of schizotypy, Machiavellianism, trait narcissism, and trait psychopathy. They also evaluated their belief in odd/magical ideas. Their paper, Belief in conspiracy theories: The predictive role of schizotypy, Machiavellianism, and primary psychopathy, shows significant correlations with belief in conspiracies. Interestingly, they suggest that the urge to manipulate others in Machiavellianism and psychopathy may, in turn, lead to an innate fear of being manipulated oneself.

Mental illness and certain psychological traits have always been a bit of an evolutionary mystery.… Read the rest

The Retiring Mind, Part V: Listening and Ground Truth

Human hearing is limited in the range of frequencies that we can discern. Generally, at the high end, that limit is around 20kHz, which is a very high pitch indeed. But, as we age, our high frequency perception reduces as well, until we may very well have difficulty hearing 8kHz or understanding human utterances in old age. You can test your own approximate limits with a simple YouTube video that raises pitches quickly up through the spectrum. I’m capping out at just north of 13.5kHz using a cheap speaker attached to my monitor, and with normal but quiet ambient background noise.

The original design of the Compact Disc by Phillips and Sony used the 20kHz limit as guidance for the encoding of the digital information on the disks. Specifically, the input analog waveform was sampled at a resolution of 16 bits 44.1kHz, which gives a maximum volume range of 2^16 (96dB) and supports the Nyquist sampling theorem that requires double the maximum frequency of the input stream in order to reconstruct that stream.

And CDs were very good, exceeding the capabilities of vinyl or cassettes, and approaching the best magnetic tape capabilities of the time. They also had some interesting side-effects in terms of mastering by freeing bass frequencies that had to be shifted towards the central channel on vinyl in order to avoid shortening recordings unduly because of the larger groove sizes needed to render low frequencies.

But now, with streaming, we can increase our resolution still further. Qobuz and Tidal offer Hi-Res audio formats that can range up to 24 bit resolution at 192kHz sample rates. Tidal also promotes MQA (Master Quality Authenticated) format that may use lossy compression but preserves aspects of the original master recording.… Read the rest

Forever Uncanny

Quanta has a fair round up of recent advances in deep learning. Most interesting is the recent performance on natural language understanding tests that are close to or exceed mean human performance. Inevitably, John Searle’s Chinese Room argument is brought up, though the author of the Quanta article suggests that inferring the Chinese translational rule book from the data itself is slightly different from the original thought experiment. In the Chinese Room there is a person who knows no Chinese but has a collection of translational reference books. She receives texts through a slot and dutifully looks up the translation of the text and passes out the result. “Is this intelligence?” is the question and it serves as a challenge to the Strong AI hypothesis. With statistical machine translation methods (and their alternative mechanistic implementation, deep learning), the rule books have been inferred by looking at translated texts (“parallel” texts as we say in the field). By looking at a large enough corpus of parallel texts, greater coverage of translated variants is achieved as well as some inference of pragmatic issues in translation and corner cases.

As a practical matter, it should be noted that modern, professional translators often use translation memory systems that contain idiomatic—or just challenging—phrases that they can reference when translating new texts. The understanding resides in the original translator’s head, we suppose, and in the correct application of the rule to the new text by checking for applicability according to, well, some other criteria that the translator brings to bear on the task.

In the General Language Understand Evaluation (GLUE) tests described in the Quanta article, the systems are inferring how to answer Wh-style queries (who, what, where, when, and how) as well as identify similar texts.… Read the rest

Bereitschaftspotential and the Rehabilitation of Free Will

The question of whether we, as people, have free will or not is both abstract and occasionally deeply relevant. We certainly act as if we have something like libertarian free will, and we have built entire systems of justice around this idea, where people are responsible for choices they make that result in harms to others. But that may be somewhat illusory for several reasons. First, if we take a hard deterministic view of the universe as a clockwork-like collection of physical interactions, our wills are just a mindless outcome of a calculation of sorts, driven by a wetware calculator with a state completely determined by molecular history. Second, there has been, until very recently, some experimental evidence that our decision-making occurs before we achieve a conscious realization of the decision itself.

But this latter claim appears to be without merit, as reported in this Atlantic article. Instead, what was previously believed to be signals of brain activity that were related to choice (Bereitschaftspotential) may just be associated with general waves of neural activity. The new experimental evidence puts the timing of action in line with conscious awareness of the decision. More experimental work is needed—as always—but the tentative result suggests a more tightly coupled pairing of conscious awareness with decision making.

Indeed, the results of this newer experimental result gets closer to my suggested model of how modular systems combined with perceptual and environmental uncertainty can combine to produce what is effectively free will (or at least a functional model for a compatibilist position). Jettisoning the Chaitin-Kolmogorov complexity part of that argument and just focusing on the minimal requirements for decision making in the face of uncertainty, we know we need a thresholding apparatus that fires various responses given a multivariate statistical topology.… Read the rest

Bullshit, Metaphors, and Political Precision

Given this natural condition of uncertainty in the meaning of words, and their critical role in communication, to say the least, we can certainly expect that as we move away from the sciences towards other areas of human endeavor we have even greater vagueness in trying to express complex ideas. Politics is an easy example. America’s current American president is a babbling bullshitter, to use the explanatory framework of the essay, On Bullshit, and he is easy to characterize as an idiot, like when he conflates Western liberalism with something going on exclusively in modern California.

In this particular case, we have to track down what “liberal” means and meant at various times, then try to suss out how that meaning is working today. At one time, the term was simply expressive of freedom with minimal government interference. Libertarians still carry a version of that meaning forward, but liberalism also came to mean something akin to a political focus on government spending to right perceived economic and social disparities (to achieve “freedom from want and despair,” via FDR). And then it began to be used as a pejorative related to that same focus.

As linguist John McWhorter points out, abstract ideas—and perhaps especially political ones—are so freighted with their pragmatic and historical background that the best we can say is that we are actively working out what a given term means. McWhorter suggests that older terms like “socialist” are impossible to put to work effectively; a newer term like “progressive” is more desirable because it carries less baggage.

An even stronger case is made by George Lakoff where he claims central metaphors that look something like Freudian abstractions govern political perspectives.… Read the rest

Doubt at the Limit

I seem to have a central theme to many of the last posts that is related to the demarcation between science and non-science, and also to the limits of what rationality allows where we care about such limits. This is not purely abstract, though, as we can see in today’s anti-science movements, whether anti-vaccination, flat Earthers, climate change deniers, or intelligent design proponents. Just today, Ars Technica reports on the first of these. The speakers at the event, held in close proximity to a massive measles outbreak, ranged from a “disgraced former gastroenterologist” to an angry rabbi. Efforts to counter them, in the form of a letter from a county supervisor and another rabbi, may have had an impact on the broader community, but probably not the die-hards of the movement.

Meanwhile, Lee Mcyntire at Boston University suggests what we are missing in these engagements in a great piece in Newsweek. Mcyntire applies the same argument to flat Earthers that I have applied to climate change deniers: what we need to reinforce is the value and, importantly, the limits inherent in scientific reasoning. Insisting, for example, that climate change science is 100% squared away just fuels the micro-circuits in the so-called meta-cognitive strategies regions of the brains of climate change deniers. Instead, Mcyntire recommends science engages the public in thinking about the limits of science, showing how doubt and process lead us to useable conclusions about topics that are suddenly fashionably in dispute.

No one knows if this approach is superior to the alternatives like the letter-writing method by authorities in the vaccination seminar approach, and it certainly seems longer term in that it needs to build against entrenched ideas and opinions, but it at least argues for a new methodology.… Read the rest

Two Points on Penrose, and One On Motivated Reasoning

Sir Roger Penrose is, without doubt, one of the most interesting polymaths of recent history. Even where I find his ideas fantastical, they are most definitely worth reading and understanding. Sean Carroll’s Mindscape podcast interview with Penrose from early January of this year is a treat.

I’ve previously discussed the Penrose-Hameroff conjectures concerning wave function collapse and their implication of quantum operations in the micro-tubule structure of the brain. I also used the conjecture in a short story. But the core driver for Penrose’s original conjecture, namely that algorithmic processes can’t explain human consciousness, has always been a claim in search of support. Equally difficult is pushing consciousness into the sphere of quantum phenomena that tend to show random, rather than directed, behavior. Randomness doesn’t clearly relate to the “hard problem” of consciousness that is about the experience of being conscious.

But take the idea that since mathematicians can prove things that are blocked by Gödel incompleteness, our brains must be different from Turing machines or collections of them. Our brains are likely messy and not theorem proving machines per se, despite operating according to logico-causal processes. Indeed, throw in an active analog to biological evolution based on variation-and-retention of ideas and insights that might actually have a bit of pseudo-randomness associated with it, and there is no reason to doubt that we are capable of the kind of system transcendence that Penrose is looking for.

Note that this doesn’t in any way impact the other horn of Penrose-Hameroff concerning the measurement problem in quantum theory, but there is no reason to suspect that quantum collapse is necessary for consciousness. It might flow the other way, though, and Penrose has created the Penrose Institute to look experimentally for evidence about these effects.… Read the rest

Narcissism, Nonsense and Pseudo-Science

I recently began posting pictures of our home base in Sedona to Instagram (check it out in column to right). It’s been a strange trip. If you are not familiar with how Instagram works, it’s fairly simple: you post pictures and other Instagram members can “follow” you and you can follow them, meaning that you see their pictures and can tap a little heart icon to show you like their pictures. My goal, if I have one, is just that I like the Northern Arizona mountains and deserts and like thinking about the composition of photographs. I’m also interested in the gear and techniques involved in taking and processing pictures. I did, however, market my own books on the platform—briefly, and with apologies.

But Instagram, like Facebook, is a world unto itself.

Shortly after starting on the platform, I received follows from blond Russian beauties who appear to be marketing online sex services. I have received odd follows from variations on the same name who have no content on their pages and who disappear after a day or two if I don’t follow them back. Though I don’t have any definitive evidence, I suspect these might be bots. I have received follows from people who seemed to be marketing themselves as, well, people—including one who bait-and-switched with good landscape photography. They are typically attractive young people, often showing off their six-pack abs, and trying to build a following with the goal of making money off of Instagram. Maybe they plan to show off products or reference them, thus becoming “influencers” in the lingo of social media. Maybe they are trying to fund their travel experiences by reaping revenue from advertisers that co-exist with their popularity in their image feed.… Read the rest

Free Will and Algorithmic Information Theory (Part II)

Bad monkey

So we get some mild form of source determinism out of Algorithmic Information Complexity (AIC), but we haven’t addressed the form of free will that deals with moral culpability at all. That free will requires that we, as moral agents, are capable of making choices that have moral consequences. Another way of saying it is that given the same circumstances we could have done otherwise. After all, all we have is a series of if/then statements that must be implemented in wetware and they still respond to known stimuli in deterministic ways. Just responding in model-predictable ways to new stimuli doesn’t amount directly to making choices.

Let’s expand the problem a bit, however. Instead of a lock-and-key recognition of integer “foodstuffs” we have uncertain patterns of foodstuffs and fallible recognition systems. Suddenly we have a probability problem with P(food|n) [or even P(food|q(n)) where q is some perception function] governed by Bayesian statistics. Clearly we expect evolution to optimize towards better models, though we know that all kinds of historical and physical contingencies may derail perfect optimization. Still, if we did have perfect optimization, we know what that would look like for certain types of statistical patterns.

What is an optimal induction machine? AIC and variants have been used to define that machine. First, we have Solomonoff induction from around 1960. But we also have Jorma Rissanen’s Minimum Description Length (MDL) theory from 1978 that casts the problem more in terms of continuous distributions. Variants are available, too, from Minimum Message Length, to Akaike’s Information Criterion (AIC, confusingly again), Bayesian Information Criterion (BIC), and on to Structural Risk Minimization via Vapnik-Chervonenkis learning theory.

All of these theories involve some kind of trade-off between model parameters, the relative complexity of model parameters, and the success of the model on the trained exemplars.… Read the rest

Free Will and Algorithmic Information Theory

I was recently looking for examples of applications of algorithmic information theory, also commonly called algorithmic information complexity (AIC). After all, for a theory to be sound is one thing, but when it is sound and valuable it moves to another level. So, first, let’s review the broad outline of AIC. AIC begins with the problem of randomness, specifically random strings of 0s and 1s. We can readily see that given any sort of encoding in any base, strings of characters can be reduced to a binary sequence. Likewise integers.

Now, AIC states that there are often many Turing machines that could generate a given string and, since we can represent those machines also as a bit sequence, there is at least one machine that has the shortest bit sequence while still producing the target string. In fact, if the shortest machine is as long or a bit longer (given some machine encoding requirements), then the string is said to be AIC random. In other words, no compression of the string is possible.

Moreover, we can generalize this generator machine idea to claim that given some set of strings that represent the data of a given phenomena (let’s say natural occurrences), the smallest generator machine that covers all the data is a “theoretical model” of the data and the underlying phenomena. An interesting outcome of this theory is that it can be shown that there is, in fact, no algorithm (or meta-machine) that can find the smallest generator for any given sequence. This is related to Turing Incompleteness.

In terms of applications, Gregory Chaitin, who is one of the originators of the core ideas of AIC, has proposed that the theory sheds light on questions of meta-mathematics and specifically that it demonstrates that mathematics is a quasi-empirical pursuit capable of producing new methods rather than being idealistically derived from analytic first-principles.… Read the rest