Category: Cognitive

Instrumentality and Terror in the Uncanny Valley

I got an Apple HomePod the other day. I have several Airplay speakers already, two in one house and a third in my separate office. The latter, a Naim Mu-So, combines Airplay with internet radio and bluetooth, but I mostly use it for the streaming radio features (KMozart, KUSC, Capital Public Radio, etc.). The HomePod’s Siri implementation combined with Apple Music allows me to voice control playlists and experiment with music that I wouldn’t generally have bothered to buy and own. I can now sample at my leisure without needing to broadcast via a phone or tablet or computer. Steve Reich, Bill Evans, Theolonius Monk, Bach organ mixes, variations of Tristan and Isolde, and, yesterday, when I asked for “workout music” I was gifted with Springsteen’s Born to Run, which I would never have associated with working out, but now I have dying on the mean streets of New Jersey with Wendy in some absurd drag race conflagration replaying over and over again in my head.

Right after setup, I had a strange experience. I was shooting random play thoughts to Siri, then refining them and testing the limits. There are many, as reviewers have noted. Items easily found in Apple Music are occasionally fails for Siri in HomePod, but simple requests and control of a few HomeKit devices work acceptably. The strange experience was my own trepidation over barking commands at the device, especially when I was repeating myself: “Hey Siri. Stop. Play Bill Evans. Stop. Play Bill Evans’ Peace Piece.” (Oh my, homophony, what will happen? It works.) I found myself treating Siri as a bit of a human being in that I didn’t want to tell her to do a trivial task that I had just asked her to perform. A person would become irritated and we naturally avoid that kind of repetitious behavior when asking others to perform tasks for us. Unless there is an employer-employee relationship where that kind of repetition is part of the work duties, it is not something we do among friends, family, acquaintances, or co-workers. It is rude. It is demanding. It treats people insensitively as instruments for fulfilling one’s trivial goals.

I found this odd. I occasionally play video games with lifelike visual and auditory representations of characters, but I rarely ask them to do things that involve selecting from an open-ended collection of possibilities, since most interactions with non-player entities are channeled by the needs of the storyline. They are never “eerie” as the research on uncanny valley effects refers to them. This is likely mediated by context and expectations. I’m playing a game and it is on a flat projection. My expectations are never violated by the actions within the storyline.

But why then did Siri as a repetitious slave elicit such a concern?

There are only a handful of studies designed to better understand the nature of the uncanny valley eeriness effect. One of the more interesting studies investigates the relationship between our thoughts of death and the appearance of uncanny wax figures or androids. Karl MacDorman’s Androids as an Experimental Apparatus: Why Is There an Uncanny Valley and Can We Exploit It? investigates the relationship between Terror Management Theory and our reactions to eeriness in objects. Specifically, the work builds on the idea that we have developed different cultural and psychological mechanisms to distract ourselves from the anxiety of death concerns. These take different forms in their details but largely involve:

… a dual-component cultural anxiety buffer consisting of (a) a cultural world-view — a humanly constructed symbolic conception of reality that imbues life with order, permanence, and stability; a set of standards through which individuals can attain a sense of personal value; and some hope of either literally or symbolically transcending death for those who live up to these standards of value; and (b) self-esteem, which is acquired by believing that one is living up to the standards of value inherent in one’s cultural worldview.

This theory has been tested in various ways, largely through priming with death-related terms (coffin, buried, murder, etc.) and looking at the the impact of exposure to these terms have on other decision-making after short delays. In this particular study, an android face and a similar human face were shown to study participants and their reactions were examined for evidence that the android affected their subsequent choices. For instance, a charismatic speech versus a “relationship-oriented” speech by politician. Our terror response hypothetically causes us to prefer the charismatic leader more when we are under threat. Another form of testing involved doing word completion puzzles that were ambiguous. For instance, the subject is presented with COFF_ _ and asked to choose an E or an I for the next letter (COFFEE versus COFFIN), or MUR _ _ R (MURMUR or MURDER). Other “uncanny” word sets (_ _ REEPY) (CREEPY, SLEEPY) and ST _ _ _ GE (STRANGE, STORAGE) were also included as were controls that had no such associations. The android presentation resulted in statistically significant increases in the uncanny word set as well as combined uncanny/death word presentations, though the death words alone were not statistically significant.

And what about my fear of treating others instrumentally? It may fall in a similar category, but due to the “set of standards through which individuals can attain a sense of personal value.” I am sensitive to mistreating others as both a barely-conscious recognition of their humanity and as a set of heuristic guidelines that fire automatically as a form of nagging conscience. I will note, however, that after a few days I appear to have become desensitized to the concern. Siri, please turn off the damn noise.

Black and Gray Boxes with Autonomous Meta-Cognition

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.

How can the fears of radical failure be reduced? In medicine we expect automated decision making to be backed up by a doctor who serves as a kind of meta-supervisor. When the diagnosis or prognosis looks dubious, the doctor will order more tests or countermand the machine. When the parole board sees the parole recommendation, they always have the option of ignoring it based on special circumstances. In each case, it is the presence of some anomaly in the recommendation or the input data that would lead to reconsideration. Similarly, it is certainly possible to automate that scrutiny at a meta-level. In machine learning, statistical regularization is used to reduce or eliminate outliers in data sets in an effort to prevent overtraining or overfitting on noisy data elements. In much the same way, the regularization process can provide warnings and clues about data viability. And, in turn, unusual outputs that are statistically unlikely given the history of the machine’s decisions can trigger warnings about anomalous results.

Deep Simulation in the Southern Hemisphere

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. We can no longer expect to discover some hauntingly elegant deep structure to our minds, language, or personalities. Instead, we are just about as deep as we are shallow, nothing more.

I’m alright with this, but think it is premature to draw the more radical conclusion. In the same vein that there are tactical advances—small steps—that improve simple systems in solving more complex problems, the harder questions of self-reflectivity and “general AI” still have had limited traction. It’s perfectly reasonable that upon further development, the right approaches and architectures that do reveal insights into deep cognitive “simulation” will, in turn, uncover some of that deep structure.

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.