Category: Cognitive

Brain Gibberish with a Convincing Heart

Elon Musk believes that direct brain interfaces will help people better transmit ideas to one another in addition to just allowing thought-to-text generation. But there is a fundamental problem with this idea. Let’s take Hubert Dreyfus’ conception of the way meaning works as being tied to a more holistic view of our social interactions with others. Hilary Putnam would probably agree with this perspective, though now I am speaking for two dead philosphers of mind. We can certainly conclude that my mental states when thinking about the statement “snow is white” are, borrowing from Putnam who borrows from Quine, different from a German person thinking “Schnee ist weiß.” The orthography, grammar, and pronunciation are different to begin with. Then there is what seems to transpire when I think about that statement: mild visualizations of white snow-laden rocks above a small stream for instance, or, just now, Joni Mitchell’s “As snow gathers like bolts of lace/Waltzing on a ballroom girl.” The centrality or some kind of logical ground that merely asserts that such a statement is a propositional truth that is shared in some kind of mind interlingua doesn’t bear much fruit to the complexities of what such a statement entails.

Religious and political terminology is notoriously elastic. Indeed, for the former, it hardly even seems coherent to talk about the concept of supernatural things or events. If they are detectable by any other sense than some kind of unverifiable gnosis, then they are at least natural in that they are manifesting in the observable world. So supernatural imposes a barrier that seems to preclude any kind of discussion using ordinary language. The only thing left is a collection of metaphysical assumptions that, in lacking any sort of reference, must merely conform to the patterns of synonymy, metonymy, and other language games that we ordinarily reserve for discernible events and things. And, of course, where unverifiable gnosis holds sway, it is not public knowledge and therefore seems to mainly serve as a social mechanism for attracting attention to oneself.

Politics takes on a similar quality, with it often said to be a virtue if a leader can translate complex policies into simple sound bites. But, as we see in modern American politics, what instead happens is that abstract fear signaling is the primary currency to try to motivate (and manipulate) the voter. The elasticity of a concept like “freedom” is used to polarize the sides of political negotiation that almost always involves the management of winners and losers and the dividing line between them. Fear mixes with complex nostalgia about times that never were, or were more nuanced than most recall, and jeremiads serve to poison the well of discourse.

So, if I were to have a brain interface, it might be trainable to write words for me by listening to the regular neural firing patterns that accompany my typing or speaking, but I doubt it would provide some kind of direct transmission or telepathy between people that would have any more content than those written or spoken forms. Instead, the inscrutable and non-referential abstractions about complex ideas would be tied together and be in contrast with the existing holistic meaning network. And that would just be gibberish to any other mind. Worst still, such a system might also be able to convey raw emotion from person to person, thus just amplifying the fear or joy component of the idea without being able to transmit the specifics of the thoughts. And that would be worse than mere gibberish, it would be gibberish with a convincing heart.

The Obsessive Dreyfus-Hawking Conundrum

I’ve been obsessed lately. I was up at 5 A.M. yesterday and drove to Ruidoso to do some hiking (trails T93 to T92, if interested). The San Augustin Pass was desolate as the sun began breaking over, so I inched up into triple digit speeds in the M6. Because that is what the machine is made for. Booming across White Sands Missile Range, I recalled watching base police work with National Park Rangers to chase oryx down the highway while early F117s practiced touch-and-gos at Holloman in the background, and then driving my carpool truck out to the high energy laser site or desert ship to deliver documents.

I settled into Starbucks an hour and a half later and started writing on ¡Reconquista!, cranking out thousands of words before trying to track down the trailhead and starting on my hike. (I would have run the thing but wanted to go to lunch later and didn’t have access to a shower. Neither restaurant nor diners deserve an après-run moi.) And then I was on the trail and I kept stopping and taking plot and dialogue notes, revisiting little vignettes and annotating enhancements that I would later salt in to the main text over lunch. And I kept rummaging through the development of characters, refining and sifting the facts of their lives through different sets of sieves until they took on both a greater valence within the story arc and, often, more comedic value.

I was obsessed and remain so. It is a joyous thing to be in this state, comparable only to working on large-scale software systems when the hours melt away and meals slip as one cranks through problem after problem, building and modulating the subsystems until the units begin to sing together like a chorus. In English, the syntax and semantics are less constrained and the pragmatics more pronounced, but the emotional high is much the same.

With the recent death of Hubert Dreyfus at Berkeley it seems an opportune time to consider the uniquely human capabilities that are involved in each of these creative ventures. Uniquely, I suggest, because we can’t yet imagine what it would be like for a machine to do the same kinds of intelligent tasks. Yet, from Stephen Hawking through to Elon Musk, influential minds are worried about what might happen if we develop machines that rise to the level of human consciousness. This might be considered a science fiction-like speculation since we have little basis for conjecture beyond the works of pure imagination. We know that mechanization displaces workers, for instance, and think it will continue, but what about conscious machines?

For Dreyfus, the human mind is too embodied and situational to be considered an encodable thing representable by rules and algorithms. Much like the trajectory of a species through an evolutionary landscape, the mind is, in some sense, an encoded reflection of the world in which it lives. Taken further, the evolutionary parallel becomes even more relevant in that it is embodied in a sensory and physical identity, a product of a social universe, and an outgrowth of some evolutionary ping pong through contingencies that led to greater intelligence and self-awareness.

Obsession with whatever cultivars, whatever traits and tendencies, lead to this riot of wordplay and software refinement is a fine example of how this moves away from the fears of Hawking and towards the impossibilities of Dreyfus. We might imagine that we can simulate our way to the kernel of instinct and emotion that makes such things possible. We might also claim that we can disconnect the product of the effort from these internal states and the qualia that defy easy description. The books and the new technologies have only desultory correspondence to the process by which they are created. But I doubt it. It’s more likely that getting from great automatic speech recognition or image classification to the general AI that makes us fearful is a longer hike than we currently imagine.

Tweak, Memory

Artificial Neural Networks (ANNs) were, from early on in their formulation as Threshold Logic Units (TLUs) or Perceptrons, mostly focused on non-sequential decision-making tasks. With the invention of back-propagation training methods, the application to static presentations of data became somewhat fixed as a methodology. During the 90s Support Vector Machines became the rage and then Random Forests and other ensemble approaches held significant mindshare. ANNs receded into the distance as a quaint, historical approach that was fairly computationally expensive and opaque when compared to the other methods.

But Deep Learning has brought the ANN back through a combination of improvements, both minor and major. The most important enhancements include pre-training of the networks as auto-encoders prior to pursuing error-based training using back-propagation or  Contrastive Divergence with Gibbs Sampling. The critical other enhancement derives from Schmidhuber and others work in the 90s on managing temporal presentations to ANNs so the can effectively process sequences of signals. This latter development is critical for processing speech, written language, grammar, changes in video state, etc. Back-propagation without some form of recurrent network structure or memory management washes out the error signal that is needed for adjusting the weights of the networks. And it should be noted that increased compute fire-power using GPUs and custom chips has accelerated training performance enough that experimental cycles are within the range of doable.

Note that these are what might be called “computer science” issues rather than “brain science” issues. Researchers are drawing rough analogies between some observed properties of real neuronal systems (neurons fire and connect together) but then are pursuing a more abstract question as to how a very simple computational model of such neural networks can learn. And there are further analogies that start building up: learning is due to changes in the strength of neural connections, for instance, and neurons fire after suitable activation. Then there are cognitive properties of human minds that might be modeled, as well, which leads us to a consideration of working memory in building these models.

It is this latter consideration of working memory that is critical to holding stimuli presentations long enough that neural connections can process them and learn from them. Schmidhuber et. al.’s methodology (LSTM) is as ad hoc as most CS approaches in that it observes a limitation with a computational architecture and the algorithms that operate within that architecture and then tries to remedy the limitation by architectural variations. There tends to be a tinkering and tweaking that goes on in the gradual evolution of these kinds of systems until something starts working. Theory walks hand-in-hand with practice in applied science.

Given that, however, it should be noted that there are researchers who are attempting to create a more biologically-plausible architecture that solves some of the issues with working memory and training neural networks. For instance, Frank, Loughry, and O’Reilly at University of Colorado have been developing a computational model that emulates the circuits that connect the frontal cortex and the basal ganglia. The model uses an elaborate series of activating and inhibiting connections to provide maintenance of perceptual stimuli in working memory. The model shows excellent performance on specific temporal presentation tasks. In its attempt to preserve a degree of fidelity to known brain science, it does lose some of the simplicity that purely CS-driven architectures provide, but I think it has a better chance of helping overcome another vexing problem for ANNs. Specifically, the slow learning properties of ANNs have only scant resemblance to much human learning. We don’t require many, many presentations of a given stimulus in order to learn it; often, one presentation is sufficient. Reconciling the slow tuning of ANN models, even recurrent ones, with this property of human-like intelligence remains an open issue, and more biology may be the key.

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.

New Behaviorism and New Cognitivism

lstm_memorycellDeep Learning now dominates discussions of intelligent systems in Silicon Valley. Jeff Dean’s discussion of its role in the Alphabet product lines and initiatives shows the dominance of the methodology. Pushing the limits of what Artificial Neural Networks have been able to do has been driven by certain algorithmic enhancements and the ability to process weight training algorithms at much higher speeds and over much larger data sets. Google even developed specialized hardware to assist.

Broadly, though, we see mostly pattern recognition problems like image classification and automatic speech recognition being impacted by these advances. Natural language parsing has also recently had some improvements from Fernando Pereira’s team. The incremental improvements using these methods should not be minimized but, at the same time, the methods don’t emulate key aspects of what we observe in human cognition. For instance, the networks train incrementally and lack the kinds of rapid transitions that we observe in human learning and thinking.

In a strong sense, the models that Deep Learning uses can be considered Behaviorist in that they rely almost exclusively on feature presentation with a reward signal. The internal details of how modularity or specialization arise within the network layers are interesting but secondary to the broad use of back-propagation or Gibb’s sampling combined with autoencoding. This is a critique that goes back to the early days of connectionism, of course, and why it was somewhat sidelined after an initial heyday in the late eighties. Then came statistical NLP, then came hybrid methods, then a resurgence of corpus methods, all the while with image processing getting more and more into the hand-crafted modular space.

But we can see some interesting developments that start to stir more Cognitivism into this stew. Recurrent Neural Networks provided interesting temporal behavior that might be lacking in some feedforward NNs, and Long-Short-Term Memory (LSTM) NNs help to overcome some specific limitations of  recurrent NNs like the disconnection between temporally-distant signals and the reward patterns.

Still, the modularity and rapid learning transitions elude us. While these methods are enhancing the ability to learn the contexts around specific events (and even the unique variability of contexts), that learning still requires many exposures to get right. We might consider our language or vision modules to be learned over evolutionary history and so not expect learning within a lifetime from scratch to result in similarly structured modules, but the differences remain not merely quantitative but significantly qualitative. A New Cognitivism requires more work to rise from this New Behaviorism.

Evolving Visions of Chaotic Futures

FlutterbysMost artificial intelligence researchers think unlikely the notion that a robot apocalypse or some kind of technological singularity is coming anytime soon. I’ve said as much, too. Guessing about the likelihood of distant futures is fraught with uncertainty; current trends are almost impossible to extrapolate.

But if we must, what are the best ways for guessing about the future? In the late 1950s the Delphi method was developed. Get a group of experts on a given topic and have them answer questions anonymously. Then iteratively publish back the group results and ask for feedback and revisions. Similar methods have been developed for face-to-face group decision making, like Kevin O’Connor’s approach to generating ideas in The Map of Innovation: generate ideas and give participants votes equaling a third of the number of unique ideas. Keep iterating until there is a consensus. More broadly, such methods are called “nominal group techniques.”

Most recently, the notion of prediction markets has been applied to internal and external decision making. In prediction markets,  a similar voting strategy is used but based on either fake or real money, forcing participants towards a risk-averse allocation of assets.

Interestingly, we know that optimal inference based on past experience can be codified using algorithmic information theory, but the fundamental problem with any kind of probabilistic argument is that much change that we observe in society is non-linear with respect to its underlying drivers and that the signals needed are imperfect. As the mildly misanthropic Nassim Taleb pointed out in The Black Swan, the only place where prediction takes on smooth statistical regularity is in Las Vegas, which is why one shouldn’t bother to gamble. Taleb’s approach is to look instead at minimizing the impact of shocks (or hedging them in financial markets).

But maybe we can learn something from philosophical circles. For instance, Evolutionary Epistemology (EE), as formulated by Donald Campbell, Sir Karl Popper, and others, posits that central to knowledge formation is blind variation and selective retention. Combined with optimal induction, this leads to random processes being injected into any kind of predictive optimization. We do this in evolutionary algorithms like Genetic Algorithms, Evolutionary Programming, Genetic Programming, and Evolutionary Strategies, as well as in related approaches like Simulated Annealing. But EE also suggests that there are several levels of learning by variation/retention, from the phylogenetic learning of species through to the mental processes of higher organisms. We speculate and trial-and-error continuously, repeating loops of what-ifs in our minds in an effort to optimize our responses in the future. It’s confounding as hell but we do remarkable things that machines can’t yet do like folding towels or learning to bake bread.

This noosgeny-recapitulates-ontogeny-recapitulates-phylogeny (just made that up) can be exploited in a variety of ways for abductive inference about the future. We can, for instance, use evolutionary optimization with a penalty for complexity that simulates the informational trade-off of AIT-style inductive optimality. Further, the noosgeny component (by which I mean the internalized mental trial-and-error) can reduce phylogenetic waste in simulations by providing speculative modeling that retains the “parental” position on the fitness landscape before committing to a next generation of potential solutions, allowing for further probing of complex adaptive landscapes.

On Woo-Woo and Schrödinger’s Cat

schrodingers-cat-walks-into-a-bar-memeMichael Shermer and Sam Harris got together with an audience at Caltech to beat up on Deepak Chopra and a “storyteller” named Jean Houston in The Future of God debate hosted by ABC News. And Deepak got uncharacteristically angry back behind his crystal-embellished eyewear, especially at Shermer’s assertion that Deepak is just talking “woo-woo.”

But is there any basis for the woo-woo that Deepak is weaving? As it turns out, he is building on some fairly impressive work by Stuart Hameroff, MD, of University of Arizona and Sir Roger Penrose of Oxford University. Under development for more than 25 years, this work has most recently been summed up in their 2014 paper, “Consciousness in the universe: A review of the ‘Orch OR’ theory” available for free (but not the commentaries, alas). Deepak was even invited to comment on the paper in Physics of Life Reviews, though the content of his commentary was challenged as being somewhat orthogonal or contradictory to the main argument.

To start somewhere near the beginning, Penrose became obsessed with the limits of computation in the late 80s. The Halting Problem sums up his concerns about the idea that human minds can possibly be isomorphic with computational devices. There seems to be something that allows for breaking free of the limits of “mere” Turing Complete computation to Penrose. Whatever that something is, it should be physical and reside within the structure of the brain itself. Hameroff and Penrose would also like that something to explain consciousness and all of its confusing manifestations, for surely consciousness is part of that brain operation.

Now, to get at some necessary and sufficient sorts of explanations for this new model requires looking at Hameroff’s medical speciality: anesthesiology. Anyone who has had surgery has had the experience of consciousness going away while body function continues on, still mediated by brain activities. So certain drugs like halothane erase consciousness through some very targeted action. Next, consider that certain prokaryotes have internally coordinated behaviors without the presence of a nervous system. Finally, consider that it looks like most neurons do not integrate and fire like the classic model (and the model that artificial neural networks emulate), but instead have some very strange and random activation behaviors in the presence of the same stimuli.

How do these relate? Hameroff has been very focused on one particular component to the internal architecture of neural cells: microtubules or MTs. These are very small (compared to cellular scale) and there are millions in neurons (10^9 or so). They are just cylindrical polymers with some specific chemical properties. They also are small enough (25nm in diameter) that it might be possible that quantum effects are present in their architecture. There is some very recent evidence to this effect based on strange reactions of MTs to tiny currents of varying frequencies used to probe them. Also, anesthetics appear to bind to MTs, Indeed, they could also provide a memory strata that is orders of magnitude greater than the traditional interneuron concept of how memories form.

But what does this have to do with consciousness beyond the idea that MTs get interfered with by anesthetics and therefore might be around or part of the machinery that we label conscious? They also appear to be related to Alzheimer’s disease, but this could be just related to the same machinery. Well, this is where we get woo-woo-ey. If consciousness is not just an epiphenomena arising from standard brain function as a molecular computer, and it is also not some kind of dualistic soul overlay, then maybe it is something that is there but is not a classical computer. Hence quantum effects.

So Sir Penrose has been promoting a rather wild conjecture called the Diósi-Penrose theory that puts an upper limit on the amount of time a quantum superposition can survive. It does this based on some arguments I don’t fully understand but that integrate gravity with quantum phenomena to suggest that the mass displaced by the superposed wave functions snaps the superposition into wave collapse. So Schrödinger’s cat dies or lives very quickly even without an observer because there are a lot of superposed quantum particles in a big old cat and therefore very rapid resolution of the wave function evolution (10^-24s). Single particles can live in superposition for much longer because the mass difference between their wave functions is very small.

Hence the OR in “Orch OR” stands for Objective Resolution: wave functions are subject to collapse by probing but they also collapse just because they are unstable in that state. The resolution is objective and not subjective. The “Orch” stands for “Orchestrated.” And there is the seat of consciousness in the Hameroff-Penrose theory. In MTs little wave function collapses are constantly occurring and the presence of superposition means quantum computing can occur. And the presence of quantum computing means that non-classical computation can take place and maybe even be more than Turing Complete.

Now the authors are careful to suggest that these are actually proto-conscious events and that only their large-scale orchestration leads to what we associate with consciousness per se. Otherwise they are just quantum superpositions that collapse, maybe with 1 qubit of resolution under the right circumstances.

At least we know the cat has a fate now. That fate is due to an objective event, too, and not some added woo-woo from the strange world of quantum phenomena. And the cat’s curiosity is part of the same conscious machinery.

Bayesianism and Properly Basic Belief

Kircher-Diagram_of_the_names_of_GodXu and Tenebaum, in Word Learning as Bayesian Inference (Psychological Review, 2007), develop a very simple Bayesian model of how children (and even adults) build semantic associations based on accumulated evidence. In short, they find contrastive elimination approaches as well as connectionist methods unable to explain the patterns that are observed. Specifically, the most salient problem with these other methods is that they lack the rapid transition that is seen when three exemplars are presented for a class of objects associated with a word versus one exemplar. Adults and kids (the former even more so) just get word meanings faster than those other models can easily show. Moreover, a space of contending hypotheses that are weighted according to their Bayesian statistics, provides an escape from the all-or-nothing of hypothesis elimination and some of the “soft” commitment properties that connectionist models provide.

The mathematical trick for the rapid transition is rather interesting. They formulate a “size principle” that weights the likelihood of a given hypothesis (this object is most similar to a “feb,” for instance, rather than the many other object sets that are available) according to a scaling that is exponential in the number of exposures. Hence the rapid transition:

Hypotheses with smaller extensions assign greater probability than do larger hypotheses to the same data, and they assign exponentially greater probability as the number of consistent examples increases.

It should be noted that they don’t claim that the psychological or brain machinery implements exactly this algorithm. As is usual in these matters, it is instead likely that whatever machinery is involved, it simply has at least these properties. It may very well be that connectionist architectures can do the same but that existing approaches to connectionism simply don’t do it quite the right way. So other methods may need to be tweaked to get closer to the observed learning of people in these word tasks.

So what can this tell us about epistemology and belief? Classical foundationalism might be formulated as something is a “basic” or “justified” belief if it is self-evident or evident to our senses. Other beliefs may therefore be grounded by those basic beliefs. And a more modern reformulation might substitute “incorrigible” for “justified” with the layered meaning of incorrigibility built on the necessity that given the proposition it is in fact true.

Here’s Alvin Plantinga laying out a case for why justified and incorrigibility have a range of problems, problems serious enough for Plantinga that he suspects that god belief could just as easily be a basic belief, allowing for the kinds of presuppositional Natural Theology (think: I look around me and the hand of God is obvious) that is at the heart of some of the loftier claims concerning the viability or non-irrationality of god belief. It even provides a kind of coherent interpretative framework for historical interpretation.

Plantinga positions the problem of properly basic belief then as an inductive problem:

And hence the proper way to arrive at such a criterion is, broadly speaking, inductive. We must assemble examples of beliefs and conditions such that the former are obviously properly basic in the latter, and examples of beliefs and conditions such that the former are obviously not properly basic in the latter. We must then frame hypotheses as to the necessary and sufficient conditions of proper basicality and test these hypothesis by reference to those examples. Under the right conditions, for example, it is clearly rational to believe that you see a human person before you: a being who has thoughts and feelings, who knows and believes things, who makes decisions and acts. It is clear, furthermore, that you are under no obligation to reason to this belief from others you hold; under those conditions that belief is properly basic for you.

He goes on to conclude that this opens up the god hypothesis as providing this kind of coherence mechanism:

By way of conclusion then: being self-evident, or incorrigible, or evident to the senses is not a necessary condition of proper basicality. Furthermore, one who holds that belief in God is properly basic is not thereby committed to the idea that belief in God is groundless or gratuitous or without justifying circumstances. And even if he lacks a general criterion of proper basicality, he is not obliged to suppose that just any or nearly any belief—belief in the Great Pumpkin, for example—is properly basic. Like everyone should, he begins with examples; and he may take belief in the Great Pumpkin as a paradigm of irrational basic belief.

So let’s assume that the word learning mechanism based on this Bayesian scaling is representative of our human inductive capacities. Now this may or may not be broadly true. It is possible that it is true of words but not other domains of perceptual phenomena. Nevertheless, given this scaling property, the relative inductive truth of a given proposition (a meaning hypothesis) is strictly Bayesian. Moreover, this doesn’t succumb to problems of verificationalism because it only claims relative truth. Properly basic or basic is then the scaled contending explanatory hypotheses and the god hypothesis has to compete with other explanations like evolutionary theory (for human origins), empirical evidence of materialism (for explanations contra supernatural ones), perceptual mistakes (ditto), myth scholarship, textual analysis, influence of parental belief exposure, the psychology of wish fulfillment, the pragmatic triumph of science, etc. etc.

And so we can stick to a relative scaling of hypotheses as to what constitutes basicality or justified true belief. That’s fine. We can continue to argue the previous points as to whether they support or override one hypothesis or another. But the question Plantinga raises as to what ethics to apply in making those decisions is important. He distinguishes different reasons why one might want to believe more true things than others (broadly) or maybe some things as properly basic rather than others, or, more correctly, why philosophers feel the need to pin god-belief as irrational. But we succumb to a kind of unsatisfying relativism insofar as the space of these hypotheses is not, in fact, weighted in a manner that most reflects the known facts. The relativism gets deeper when the weighting is washed out by wish fulfillment, pragmatism, aspirations, and personal insights that lack falsifiability. That is at least distasteful, maybe aretetically so (in Plantinga’s framework) but probably more teleologically so in that it influences other decision-making and the conflicts and real harms societies may cause.

A Soliloquy for Volcanoes and Nearest Neighbors

Tongariro National Park: Emerald Lake
Tongariro National Park: Emerald Lake

A German kid caught me talking to myself yesterday. It was my fault, really. I was trying to break a hypnotic trance-like repetition of exactly what I was going to say to the tramper’s hut warden about two hours away. OK, more specifically, I had left the Waihohonu camp site in Tongariro National Park at 7:30AM and was planning to walk out that day. To put this into perspective, it’s 28.8 km (17.9 miles) with elevation changes of around 900m, including a ridiculous final assault above red crater at something like 60 degrees along a stinking volcanic ridge line. And, to make things extra lovely, there was hail, then snow, then torrential downpours punctuated by hail again—a lovely tramp in the New Zealand summer—all in a full pack.

But anyway, enough bragging about my questionable judgement. I was driven by thoughts of a hot shower and the duck l’orange at Chateau Tongariro while my hands numbed to unfeeling arresting myself with trekking poles down through muddy canyons. I was talking to myself. I was trying to stop repeating to myself why I didn’t want my campsite for the night that I had reserved. This is the opposite of glorious runner’s high. This is when all the extra blood from one’s brain is obsessed with either making leg muscles go or watching how the feet will fall. I also had the hood of my rain fly up over my little Marmot ball cap. I was in full regalia, too, with the shifting rub of my Gortex rain pants a constant presence throughout the day.  I didn’t notice him easing up on me as I carried on about one-shot learning as some kind of trance-breaking ritual.

We exchanged pleasantries and he meandered on. With his tiny little day pack it was clear he had just come up from the car park at Mangatepopo for a little jaunt. Eurowimp. I caught up with him later slathering some kind of meat product on white bread trailside and pushed by, waiting on my own lunch of jerky, chili-tuna, crackers, and glorious spring water, gulp after gulp, an hour onward. He didn’t bring up the glossolalic soliloquy incident.

My mantra was simple: artificial neural networks, including deep learning approaches, require massive learning cycles and huge numbers of exemplars to learn. In a classic test, scores of handwritten digit images (0 to 9) are categorized as to which number they are. Deep learning systems have gotten to 99% accuracy on that problem, actually besting average human performance. Yet they require a huge training corpus to pull this off, combined with many CPU hours to optimize the models on that corpus. We humans can do much better than that with our neural systems.

So we get this recently lauded effort, One-Shot Learning of Visual Concepts, that uses an extremely complicated Bayesian mixture modeling approach that combines stroke exemplars together for trying to classify foreign and never-before-seen characters (like Bengali or Ethiopic) after only one exposure to the stimulus. In other words, if I show you some weird character with some curves and arcs and a vertical bar in it, you can find similar ones in a test set quite handily, but machines really can’t. A deep learning model could be trained on every possible example known in a long, laborious process, but when exposed to a new script like Amharic or a Cherokee syllabary, the generalizations break down. A simple comparison approach is to use a nearest neighbor match or vote. That is, simply create vectors of the image pixels starting at the top left and compare the distance between the new image vector and the example using an inner vector product. Similar things look the same and have similar pixel patterns, right? Well, except they are rotated. They are shifted. They are enlarged and shrunken.

And then it hit me that the crazy-complex stroke model could be simplified quite radically by simply building a similar collection of stroke primitives as splines and then looking at the K nearest neighbors in the stroke space. So a T is two strokes drawn from the primitives collection with a central junction and the horizontal laying atop the vertical. This builds on the stroke-based intuition of the paper’s authors (basically, all written scripts have strokes as a central feature and we as writers and readers understand the line-ness of them from experience with our own script).

I may have to try this out. I should note, also in critique of this antithesis of runner’s high (tramping doldrums?), that I was also deeply concerned that there were so many damn contending voices and thoughts racing around my head in the face of such incredible scenery. Why did I feel the need to distract my mind from it’s obsessions over something so humanly trivial? At least, I suppose, the distraction was interesting enough that it was worth the effort.

Lucifer on the Beach

glowwormsI picked up a whitebait pizza while stopped along the West Coast of New Zealand tonight. Whitebait are tiny little swarming immature fish that can be scooped out of estuarial river flows using big-mouthed nets. They run, they dart, and it is illegal to change river exit points to try to channel them for capture. Hence, whitebait is semi-precious, commanding NZD70-130/kg, which explains why there was a size limit on my pizza: only the small one was available.

By the time I was finished the sky had aged from cinereal to iron in a satire of the vivid, watch-me colors of CNN International flashing Donald Trump’s linguistic indirection across the television. I crept out, setting my headlamp to red LEDs designed to minimally interfere with night vision. Just up away from the coast, hidden in the impossible tangle of cold rainforest, there was a glow worm dell. A few tourists conjured with flashlights facing the ground to avoid upsetting the tiny arachnocampa luminosa that clung to the walls inside the dark garden. They were like faint stars composed into irrelevant constellations, with only the human mind to blame for any observed patterns.

And the light, what light, like white-light LEDs recently invented, but a light that doesn’t flicker or change, and is steady under the calmest observation. Driven by luciferin and luciferase, these tiny creatures lure a few scant light-seeking creatures to their doom and as food for absorption until they emerge to mate, briefly, lay eggs, and then die.

Lucifer again, named properly from the Latin as the light bringer, the chemical basis for bioluminescence was largely isolated in the middle of the 20th Century. Yet there is this biblical stigma hanging over the term—one that really makes no sense at all. The translation of morning star or some other such nonsense into Latin got corrupted into a proper name by a process of word conversion (this isn’t metonymy or something like that; I’m not sure there is a word for it other than “mistake”). So much for some kind of divine literalism tracking mechanism that preserves perfection. Even Jesus got rendered as lucifer in some passages.

But nothing new, here. Demon comes from the Greek daemon and Christianity tried to, well, demonize all the ancient spirits during the monolatry to monotheism transition. The spirits of the air that were in a constant flux for the Hellenists, then the Romans, needed to be suppressed and given an oppositional position to the Christian soteriology. Even “Satan” may have been borrowed from Persian court drama as a kind of spy or informant after the exile.

Oddly, we are left with a kind of naming magic for the truly devout who might look at those indifferent little glow worms with some kind of castigating eye, corrupted by a semantic chain that is as kinked as the popular culture epithets of Lucifer himself.