Bullshit, Metaphors, and Political Precision

July 12, 2019July 12, 2019 Mark Davis2 Comments

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

May 14, 2019May 15, 2019 Mark DavisLeave a comment

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

April 29, 2019April 29, 2019 Mark Davis1 Comment

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

April 18, 2019April 19, 2019 Mark Davis5 Comments

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)

March 28, 2019March 28, 2019 Mark DavisLeave a comment

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

March 5, 2019March 24, 2019 Mark Davis1 Comment

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

Hypersensitive Conspiracy Disorder

August 27, 2018August 27, 2018 Mark DavisLeave a comment

I was once cornered in a bar in Suva, Fiji by an Indian man who wanted to unburden himself and complain a bit. He was convinced that the United States had orchestrated the coups of 1987 in which the ethnically Fijian-dominated military took control of the country. The theory went like this: ethnic Indians had too much power for the Americans to bear as we were losing Subic Bay as a deep water naval base in the South Pacific. Suva was the best, nearest alternative but the Indians, with their cultural and political ties to New Delhi, were too socialist for the Americans. Hence the easy solution was to replace the elected government with a more pro-American authoritarian regime. Yet another Cold War dirty tricks effort, like Mossaddegh or Allende, far enough away that the American people just shrugged our collective shoulders. My drinking friend’s core evidence was an alleged sighting of Oliver North by someone, sometime, chatting with government officials. Ollie was the 4D chess grandmaster of the late 80s.

It didn’t work out that way, of course, and the coups continued into the 2000s. More amazing still was that the Berlin Wall came down within weeks of that bar meetup and the entire engagement model for world orders slid into a brief decade of deconstruction and confusion. Even the economic dominance of Japan ebbed and dissipated around the same time.

But our collective penchant for conspiracy theories never waned. And with the growth of the internet and then social media, the speed and ease of disseminating fringe and conspiratorial ideas has only increased. In the past week there were a number of news articles about the role of conspiracy theories, from a so-called “QAnon” advocate meeting with Trump to manipulation of the government by Israel’s Black Cube group.… Read the rest

Black and Gray Boxes with Autonomous Meta-Cognition

January 27, 2018January 28, 2018 Mark DavisLeave a comment

Vijay Pande of VC Andreessen Horowitz (who passed on my startups twice but, hey, it’s just business!) has a relevant article in New York Times concerning fears of the “black box” of deep learning and related methods: is the lack of explainability and limited capacity for interrogation of the underlying decision making a deal-breaker for applications to critical areas like medical diagnosis or parole decisions? His point is simple, and related to the previous post’s suggestion of the potential limitations of our capacity to truly understand many aspects of human cognition. Even the doctor may only be able to point to a nebulous collection of clinical experiences when it comes to certain observational aspects of their jobs, like in reading images for indicators of cancer. At least the algorithm has been trained on a significantly larger collection of data than the doctor could ever encounter in a professional lifetime.

So the human is almost as much a black box (maybe a gray box?) as the algorithm. One difference that needs to be considered, however, is that the deep learning algorithm might make unexpected errors when confronted with unexpected inputs. The classic example from the early history of artificial neural networks involved a DARPA test of detecting military tanks in photographs. The apocryphal to legendary formulation of the story is that there was a difference in the cloud cover between the tank images and the non-tank images. The end result was that the system performed spectacularly on the training and test data sets but then failed miserably on new data that lacked the cloud cover factor. I recalled this slightly differently recently and substituted film grain for the cloudiness. In any case, it became a discussion point about the limits of data-driven learning that showed how radically incorrect solutions could be created without careful understanding of how the systems work.… Read the rest

Deep Simulation in the Southern Hemisphere

December 27, 2017December 27, 2017 Mark DavisLeave a comment

I’m unusually behind in my postings due to travel. I’ve been prepping for and now deep inside a fresh pass through New Zealand after two years away. The complexity of the place seems to have a certain draw for me that has lured me back, yet again, to backcountry tramping amongst the volcanoes and glaciers, and to leasurely beachfront restaurants painted with eruptions of summer flowers fueled by the regular rains.

I recently wrote a technical proposal that rounded up a number of the most recent advances in deep learning neural networks. In each case, like with Google’s transformer architecture, there is a modest enhancement that is based on a realization of a deficit in the performance of one of two broad types of networks, recurrent and convolutional.

An old question is whether we learn anything about human cognition if we just simulate it using some kind of automatically learning mechanism. That is, if we use a model acquired through some kind of supervised or unsupervised learning, can we say we know anything about the original mind and its processes?

We can at least say that the learning methodology appears to be capable of achieving the technical result we were looking for. But it also might mean something a bit different: that there is not much more interesting going on in the original mind. In this radical corner sits the idea that cognitive processes in people are tactical responses left over from early human evolution. All you can learn from them is that they may be biased and tilted towards that early human condition, but beyond that things just are the way they turned out.

If we take this position, then, we might have to discard certain aspects of the social sciences.… Read the rest