Desire and Other Matters

From the frothy mind of Jeff Koons
From the frothy mind of Jeff Koons

“What matters?” is a surprisingly interesting question. I think about it constantly since it weighs-in whenever plotting future choices, though often I seem to be more autopilot than consequentialist in these conceptions. It is an essential first consideration when trying to value one option versus another. I can narrow the question a bit to “what ideas matter?” This immediately externalizes the broad reality of actions that meaningfully improve lives, like helping others, but still leaves a solid core of concepts that are valued more abstractly. Does the traditional Western liberal tradition really matter? Do social theories? Are less intellectually-embellished virtues like consistency and trust more relevant and applicable than notions like, well, consequentialism?

Maybe it amounts to how to value certain intellectual systems against others?

Some are obviously more true than others. So “dowsing belief systems” are less effective in a certain sense than “planetary science belief systems.” Yet there are a broader range of issues at work.

But there are some areas of the liberal arts that have a vexing relationship with the modern mind. Take linguistics. The field ranges from catalogers of disappearing languages to theorists concerned with how to structure syntactic trees. Among the latter are the linguists who have followed Noam Chomsky’s paradigm that explains language using a hierarchy of formal syntactic systems, all of which feature recursion as a central feature. What is interesting is that there have been very few impacts of this theory. It is very simple at its surface: languages are all alike and involve phrasal groups that embed in deep hierarchies. The specific ways in which the phrases and their relative embeddings take place may differ among languages, but they are alike in this abstract way.

And likewise we have to ask what the impact is of scholarship like René Girard’s theory of mimesis. The theory has a Victorian feel about it: a Freudian/Jungian essential psychological tendency girds all that we know, experience, and see. Violence is the triangulation of wanton desire as we try to mimic one another. That triangulation was suppressed—sublimated, if you will—by sacrifice that refocused the urge to violence on the sacrificial object. It would be unusual for such a theory to rise above the speculative scholarship that only queasily embraces empiricism without some prodding.

But maybe it is enough that ideas are influential at some level. So we have Ayn Rand, liberally called-out by American economic conservatives, at least until they are reminded of Rand’s staunch atheism. And we have Peter Thiel, from PayPal mafia to recent Gawker lawsuits, justifying his Facebook angel round based on Girard’s theory of mimesis. So we are all slaves of our desires to like, indirectly, a bunch of crap on the internet. But at least it is theoretically sound.

Theories of Leisure, Past and Future

img_0028I am at leisure. Specifically—and many may not regard this as leisure—I just ran 17.71 miles in Yosemite Valley. I dropped the car along the road near the 41 junction and then just started running. I went south for a while, then circled back to Bridalveil Falls (lightly flowing), then up to the Glacier Point loop, then back down to El Capitan, then up to Yosemite Falls (not flowing). Lunch was at the Village and then I tracked down the car again.

Now, then, I am at leisure. The barman has set me up with a martini. I have a Fresno Fig flatbread on the way: goat cheese, bacon, arugula, and the critical figs. I am showered all the way down to between my toes. The late afternoon light is filtering through a mild haze onto the muddy belly of the lake. There must be bass out there somewhere. Let the bass live. Let them be at leisure.

A must-read on this topic is Derek Thompson’s Atlantic article, The Free-Time Paradox in America. I don’t agree with the thesis, though. It’s not really a paradox. It’s just an unknown. You should read Derek’s original, but I will comment briefly on some of his points. He argues that John Maynard Keynes forecast a reduction in work requirements by the 21st Century. Mechanization would take the drudgery out of most things and we would get to 15 hour work weeks with the management of our leisure time an increasing burden on us.

The present didn’t work out that way.

Instead, educated high-earners work ever harder. The only leisure class is the non-college-educated male youth who don’t work much these days but instead play video games (75% of their spare time) and are happier than when more of them worked. Derek rolls up several theories about why this might be the case. First, maybe it’s because the industry jobs disappeared and young men don’t like to work in retail and health care. Second, perhaps it’s because the wealthy workers are trying to keep up with the Joneses, though not exactly in the way that the Thorstein Veblen imagined it. Instead of conspicuous consumption, it is conspicuous activity. Finally, maybe it’s because work and leisure have blurred too much; entertaining ourselves on our smartphones is just too close to responding to an email from work.

I agree partly with the suggestion that economic productivity can be a very high level of creative action that is implicit in some of Derek’s commentary. Is there really much difference between landscape design and watercolor painting? Both require an understanding of materials and methods that result in an aesthetic outcome, though the former has more of a pragmatic impact than the latter. Is this a significant deviation from past economic efforts? Perhaps. The modern startup doesn’t have the dark satanic mills of the past, and is based, generally, on technological advances that are intellectually interesting. Sometimes this was the case historically, but not consistently.

Ultimately, what constitutes leisure activities rather than productive activities is inherently blurry. I suppose the golfing set might claim otherwise, but I do work-related thinking while running, and may intertwine writing efforts with other actions without harm to either of them. Leisure is fungible.

The future of leisure is similarly fungible. We can guess that virtual gaming will be even more compelling than existing gaming options, pulling young men and others even further away from engagement with the traditional economic sphere. Yet, even here there are opportunities: toolkits for virtual world design, the designs themselves, monetizing the experiences in compelling ways. Even my Yosemite experience can be virtualized. Fly drones around, mapping and imagining every square inch in ultra-4K resolution. Yes, drones are currently illegal in National Parks, but they could be used by licensed content producers, I’m guessing. Then everyone could fly, run, hike, walk, boat, and swim this little, leisurely experience.

I am at leisure.

Subtly Motivating Reasoning

larson-sheepContinuing on with the general theme of motivated reasoning, there are some rather interesting results reported in New Republic, here. Specifically, Ian Anson from University of Maryland, Baltimore County, found that political partisans reinforced their perspectives on the state of the U.S. economy more strongly when they were given “just the facts” rather than a strong partisan statement combined with the facts. Even when the partisan statements aligned with their own partisan perspectives, the effect held.

The author concludes that people, in constructing their views of the causal drivers of the economy, believe that they are unbiased in their understanding of the underlying mechanisms. The barefaced partisan statements interrupt that construction process, perhaps, or at least distract from it. Dr. Anson points out that subtly manufacturing consent therefore makes for better partisan fellow travelers.

There are a number of theories concerning how meanings must get incorporated into our semantic systems, and whether the idea of meaning itself is as good or worse than simply discussing reference. More, we can rate or gauge the uncertainty we must have concerning complex systems. They seem to form a hierarchy, with actors in our daily lives and the motivations of those we have long histories with in the mostly-predictable camp. Next we may have good knowledge about a field or area of interest that we have been trained in. When this framework has a scientific basis, we also rate our knowledge as largely reliable, but we also know the limits of that knowledge. It is in predictive futures and large-scale policy that we become subject to the difficulty of integrating complex signals into a cohesive framework. The partisans supply factoids and surround them with causal reasoning. We weigh those against alternatives and hold them as tentative. But then we have to exist in a political life, as well, and it’s not enough to just proclaim our man or woman or party as great and worthy of our vote and love, we must also justify that consideration.

I speculate now that it may be possible to wage war against partisan bias by employing the exact methods described as effective by Dr. Anson. Specifically, if in any given presentation of economic data there was one fact presented that appeared to undermine the partisan position otherwise described by the data, would it lead to a general weakening of the mental model in the reader’s head? For instance, compare the following two paragraphs:

The unemployment rate has decreased from a peak of 10% in 2009 to 4.7% in June of 2016. This rate doesn’t reflect the broader, U-6, rate of nearly 10% that includes the underemployed and others who are not seeking work. Wages have been down or stagnant over the same period.


The unemployment rate has decreased from a peak of 10% in 2009 to 4.7% in June of 2016. This rate doesn’t reflect the broader, U-6, rate of nearly 10% that includes the underemployed and others who are not seeking work. Wages have been down or stagnant over the same period even while consumer confidence and spending has risen to an 11-month high.

The second paragraph adds an accurate but upbeat and contradictory signal to the more subtle gloom of the first paragraph. Of course, partisan hacks will naturally avoid doing this kind of thing. Marketers and salespeople don’t let the negative signals creep in if they can avoid it, but I would guess that a subtle contradiction embedded in the signal would disrupt the conspiracy theorists and the bullshit artists alike.

Startup Next

I’m thrilled to announce my new startup, Like Human. The company is focused on making significant new advances to the state of the art in cognitive computing and artificial intelligence. We will remain a bit stealthy for another six months or so and then will open up shop for early adopters.

I’m also pleased to share with you Like Human’s logo that goes by the name Logo McLogoface, or LM for short. LM combines imagery from nuclear warning signs, Robby the Robot from Forbidden Planet, and Leonardo da Vinci’s Vitruvian Man. I think you will agree about Mr. McLogoface’s agreeability:


You can follow developments at @likehumancom on Twitter, and I will make a few announcements here as well.

Euhemerus and the Bullshit Artist

trump-minotaurSailing down through the Middle East, past the monuments of Egypt and the wild African coast, and then on into the Indian Ocean, past Arabia Felix, Euhemerus came upon an island. Maybe he came upon it. Maybe he sailed. He was perhaps—yes, perhaps; who can say?—sailing for Cassander in deconstructing the memory of Alexander the Great. And that island, Panchaea, held a temple of Zeus with a written history of the deeds of men who became the Greek gods.

They were elevated, they became fixed in the freckled amber of ancient history, their deeds escalated into myths and legends. And, likewise, the ancient tribes of the Levant brought their El and Yah-Wah, and Asherah and Baal, and then the Zoroastrians influenced the diaspora in refuge in Babylon, until they returned and had found dualism, elemental good and evil, and then reimagined their origins pantheon down through monolatry and into monotheism. These great men and women were reimagined into something transcendent and, ultimately, barely understandable.

Even the rational Yankee in Twain’s Connecticut Yankee in King Arthur’s Court realizes almost immediately why he would soon rule over the medieval world as he is declared a wild dragon when presented to the court. He waits for someone to point out that he doesn’t resemble a dragon, but the medieval mind does not seem to question the reasonableness of the mythic claims, even in the presence of evidence.

So it goes with the human mind.

And even today we have Fareed Zakaria justifying his use of the term “bullshit artist” for Donald Trump. Trump’s logorrhea is punctuated by so many incomprehensible and contradictory statements that it becomes a mythic whirlwind. He lets slip, now and again, that his method is deliberate:

DT: Therefore, he was the founder of ISIS.

HH: And that’s, I’d just use different language to communicate it, but let me close with this, because I know I’m keeping you long, and Hope’s going to kill me.

DT: But they wouldn’t talk about your language, and they do talk about my language, right?

Bullshit artist is the modern way of saying what Euhemerus was trying to say in his fictional “Sacred History.” Yet we keep getting entranced by these coordinated maelstroms of utter crap, from World Net Daily to Infowars to Fox News to Rush Limbaugh. Only the old Steven Colbert could contend with it through his own bullshit mythical inversion. Mockery seems the right approach, but it doesn’t seem to have a great deal of impact on the conspiratorial mind.

Motivation, Boredom, and Problem Solving

shatteredIn the New York Times Stone column, James Blachowicz of Loyola challenges the assumption that the scientific method is uniquely distinguishable from other ways of thinking and problem solving we regularly employ. In his example, he lays out how writing poetry involves some kind of alignment of words that conform to the requirements of the poem. Whether actively aware of the process or not, the poet is solving constraint satisfaction problems concerning formal requirements like meter and structure, linguistic problems like parts-of-speech and grammar, semantic problems concerning meaning, and pragmatic problems like referential extension and symbolism. Scientists do the same kinds of things in fitting a theory to data. And, in Blachowicz’s analysis, there is no special distinction between scientific method and other creative methods like the composition of poetry.

We can easily see how this extends to ideas like musical composition and, indeed, extends with even more constraints that range from formal through to possibly the neuropsychology of sound. I say “possibly” because there remains uncertainty on how much nurture versus nature is involved in the brain’s reaction to sounds and music.

In terms of a computational model of this creative process, if we presume that there is an objective function that governs possible fits to the given problem constraints, then we can clearly optimize towards a maximum fit. For many of the constraints there are, however, discrete parameterizations (which part of speech? which word?) that are not like curve fitting to scientific data. In fairness, discrete parameters occur there, too, especially in meta-analyses of broad theoretical possibilities (Quantum loop gravity vs. string theory? What will we tell the children?) The discrete parameterizations blow up the search space with their combinatorics, demonstrating on the one hand why we are so damned amazing, and on the other hand why a controlled randomization method like evolutionary epistemology’s blind search and selective retention gives us potential traction in the face of this curse of dimensionality. The blind search is likely weakened for active human engagement, though. Certainly the poet or the scientist would agree; they are using learned skills, maybe some intellectual talent of unknown origin, and experience on how to traverse the wells of improbability in finding the best fit for the problem. This certainly resembles pre-training in deep learning, though on a much more pervasive scale, including feedback from categorical model optimization into the generative basis model.

But does this extend outwards to other ways in which we form ideas? We certainly know that motivated reasoning is involved in key aspects of our belief formation, which plays strongly into how we solve these constraint problems. We tend to actively look for confirmations and avoid disconfirmations of fit. We positively bias recency of information, or repeated exposures, and tend to only reconsider in much slower cycles.

Also, as the constraints of certain problem domains become, in turn, extensions that can result in change—where there is a dynamic interplay between belief and success—the fixity of the search space itself is no longer guaranteed. Broad human goals like the search for meaning are an example of that. In come complex human factors, like how boredom correlates with motivation and ideological extremism (overview, here, journal article, here).

This latter data point concerning boredom crosses from mere bias that might preclude certain parts of a search space into motivation that focuses it, and that optimizes for novelty seeking and other behaviors.

Soul Optimization

Against SuperheroesI just did a victory lap around wooden columns in my kitchen and demanded high-fives all around: Against Superheroes is done. Well, technically it just topped the first hurdle.  Core writing is complete at 100,801 words. I will now do two editorial passes and then send it to my editor for clean-up. Finally, I’ll get some feedback from my wife before sending it out for independent review.

I try to write according to a daily schedule but I have historically been an inconsistent worker. I track everything using a spreadsheet and it doesn’t look pretty:


Note the long gaps. The gaps are problematic for several reasons, not the least of which is that I have to go back and read everything again to return to form. The gaps arrive with excuses, then get amplified by more excuses, then get massaged into to-do lists, and then always get resolved by unknown forces. Maybe they are unknowable.

The one consistency that I have found is that I always start strong and finish strong, bursts of enthusiasm for the project arriving with runner’s high on the trail, or while waiting in traffic. The plot thickets open to luxuriant fields. When I’m in the gap periods I distract myself too easily, finding the deep research topics an easy way to justify an additional pause of days, then weeks, sometimes months.

I guess I should resolve to find my triggers and work to overcome these tendencies, but I’m not certain that it matters. There is no rush, and those exuberant starts and ends are perhaps enough of a reward that no deeper optimization of my soul is needed.

Quantum Field Is-Oughts

teleologySean Carroll’s Oxford lecture on Poetic Naturalism is worth watching (below). In many ways it just reiterates several common themes. First, it reinforces the is-ought barrier between values and observations about the natural world. It does so with particular depth, though, by identifying how coarse-grained theories at different levels of explanation can be equally compatible with quantum field theory. Second, and related, he shows how entropy is an emergent property of atomic theory and the interactions of quantum fields (that we think of as particles much of the time) and, importantly, that we can project the same notion of boundary conditions that result in entropy into the future resulting in a kind of effective teleology. That is, there can be some boundary conditions for the evolution of large-scale particle systems that form into configurations that we can label purposeful or purposeful-like. I still like the term “teleonomy” to describe this alternative notion, but the language largely doesn’t matter except as an educational and distinguishing tool against the semantic embeddings of old scholastic monks.

Finally, the poetry aspect resolves in value theories of the world. Many are compatible with descriptive theories, and our resolution of them is through opinion, reason, communications, and, yes, violence and war. There is no monopoly of policy theories, religious claims, or idealizations that hold sway. Instead we have interests and collective movements, and the above, all working together to define our moral frontiers.


Local Minima and Coatimundi

CoatimundiEven given the basic conundrum of how deep learning neural networks might cope with temporal presentations or linear sequences, there is another oddity to deep learning that only seems obvious in hindsight. One of the main enhancements to traditional artificial neural networks is a phase of supervised pre-training that forces each layer to try to create a generative model of the input pattern. The deep learning networks then learn a discriminant model after the initial pre-training is done, focusing on the error relative to classification versus simply recognizing the phrase or image per se.

Why this makes a difference has been the subject of some investigation. In general, there is an interplay between the smoothness of the error function and the ability of the optimization algorithms to cope with local minima. Visualize it this way: for any machine learning problem that needs to be solved, there are answers and better answers. Take visual classification. If the system (or you) gets shown an image of a coatimundi and a label that says coatimundi (heh, I’m running in New Mexico right now…), learning that image-label association involves adjusting weights assigned to different pixels in the presentation image down through multiple layers of the network that provide increasing abstractions about the features that define a coatimundi. And, importantly, that define a coatimundi versus all the other animals and non-animals.,

These weight choices define an error function that is the optimization target for the network as a whole, and this error function can have many local minima. That is, by enhancing the weights supporting a coati versus a dog or a raccoon, the algorithm inadvertently leans towards a non-optimal assignment for all of them by focusing instead on a balance between them that is predestined by the previous dog and raccoon classifications (or, in general, the order of presentation).

Improvements require “escaping” these local optima in favor of a global solution that accords the best overall outcome to all the animals and a minimization of the global error. And pre-training seems to do that. It likely moves each discriminative category closer to the global possibilities because those global possibilities are initially encoded by the pre-training phase.

This has the added benefit of regularizing or smoothing out the noise that is inherent in any real data set. Indeed, the two approaches appear to be closely allied in their impact on the overall machine learning process.