Category: Cognitive

Brain Gibberish with a Convincing Heart

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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.… Read the rest

The Obsessive Dreyfus-Hawking Conundrum

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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.… Read the rest

Tweak, Memory

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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.… Read the rest

Motivation, Boredom, and Problem Solving

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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.… Read the rest

New Behaviorism and New Cognitivism

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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.… Read the rest

Evolving Visions of Chaotic Futures

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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.… Read the rest

On Woo-Woo and Schrödinger’s Cat

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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.… Read the rest

Bayesianism and Properly Basic Belief

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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.… Read the rest

A Soliloquy for Volcanoes and Nearest Neighbors

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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.… Read the rest

Lucifer on the Beach

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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.… Read the rest