Category: Computers

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.

I, Robot and Us

What happens if artificial intelligence (AI) technologies become significant economic players? The topic has come up in various ways for the past thirty years, perhaps longer. One model, the so-called technological singularity, posits that self-improving machines may be capable of a level of knowledge generation and disruption that will eliminate humans from economic participation. How far out this singularity might be is a matter of speculation, but I have my doubts that we really understand intelligence enough to start worrying about the impacts of such radical change.

Barring something essentially unknowable because we lack sufficient priors to make an informed guess, we can use evidence of the impact of mechanization on certain economic sectors, like agribusiness or transportation manufacturing, to try to plot out how mechanization might impact other sectors. Aghion, Jones, and Jones’ Artificial Intelligence and Economic Growth, takes a deep dive into the topic. The math is not particularly hard, though the reasons for many of the equations are tied up in macro and microeconomic theory that requires a specialist’s understanding to fully grok.

Of special interest are the potential limiting role of inputs and organizational competition. For instance, automation speed-ups may be limited by human limitations within the economic activity. This may extend even further due to fundamental limitations of physics for a given activity. The pointed example is that power plants are limited by thermodynamics; no amount of additional mechanization can change that. Other factors related to inputs or the complexity of a certain stage of production may also drag economic growth to a capped, limiting level.

Organizational competition and intellectual property considerations come into play, as well. While the authors suggest that corporations will remain relevant, they should become more horizontal by eliminating much of the middle tier of management and outsourcing components of their productivity. The labor consequences are less dire than some singularity speculations: certain low knowledge workers may achieve more influence in the economic activities because they remain essential to the production chain and their value and salaries rise. They become more fluid, as well, because they can operate as free lancers and thus have a broader impact.

This kind of specialization and out-sized knowledge influence, whether by low or high-knowledge workers, is a kind of singularity in itself. Consider the influence of the printing press in disseminating knowledge or the impact of radio and television. The economic costs of moving humans around to convey ideas or to entertain evaporate or minimize, but the influence is limited then to highly-regarded specialists who are competing to get a slice of the public’s attention. Similarly, the knowledge worker who is not easily replaceable by machine becomes the star of the new, AI economy. This may be happening already, with rumors of astronomical compensation for certain AI experts percolating out of Silicon Valley.

Ambiguously Slobbering Dogs

I was initially dismissive of this note from Google Research on improving machine translation via Deep Learning Networks by adding in a sentence-level network. My goodness, they’ve rediscovered anaphora and co-reference resolution! Next thing they will try is some kind of network-based slot-filler ontology to carry gender metadata. But their goal was to add a framework to their existing recurrent neural network architecture that would support a weak, sentence-level resolution of translational ambiguities while still allowing the TPU/GPU accelerators they have created to function efficiently. It’s a hack, but one that potentially solves yet another corner of the translation problem and might result in a few percent further improvements in the quality of the translation.

But consider the following sentences:

The dog had the ball. It was covered with slobber.

The dog had the ball. It was thinking about lunch while it played.

In these cases, the anaphora gets resolved by semantics and the resolution seems largely an automatic and subconscious process to us as native speakers. If we had to translate these into a second language, however, we would be able to articulate that there are specific reasons for correctly assigning the “It” to the ball in the first two sentences. Well, it might be possible for the dog to be covered with slobber, but we would guess the sentence writer would intentionally avoid that ambiguity. The second set of sentences could conceivably be ambiguous if, in the broader context, the ball was some intelligent entity controlling the dog. Still, when our guesses are limited to the sentence pairs in isolation we would assign the obvious interpretations. Moreover, we can resolve giant, honking passage-level ambiguities with ease, where the author is showing off in not resolving the co-referents until obscenely late in the text.

In combination, we can see the obvious problem with sentence-level “attention” calculations. The context has to be moving and fairly long.

Zebras with Machine Guns

I was just rereading some of the literature on Plantinga’s Evolutionary Argument Against Naturalism (EAAN) as a distraction from trying to write too much on ¡Reconquista!, since it looks like I am on a much faster trajectory to finishing the book than I had thought. EAAN is a curious little argument that some have dismissed as a resurgent example of scholastic theology. It has some newer trappings that we see in modern historical method, however, especially in the use Bayes’ Theorem to establish the warrant of beliefs by trying to cast those warrants as probabilities.

A critical part of Plantinga’s argument hinges on the notion that evolutionary processes optimize against behavior and not necessarily belief. Therefore, it is plausible that an individual could hold false beliefs that are nonetheless adaptive. For instance, Plantinga gives the example of a man who desires to be eaten by tigers but always feels hopeless when confronted by a given tiger because he doesn’t feel worthy of that particular tiger, so he runs away and looks for another one. This may seem like a strange conjunction of beliefs and actions that happen to result in the man surviving, but we know from modern psychology that people can form elaborate justifications for perceived events and wild metaphysics to coordinate those justifications.

If that is the case, for Plantinga, the evolutionary consequence is that we should not trust our belief in our reasoning faculties because they are effectively arbitrary. There are dozens of responses to this argument that dissect it from many different dimensions. I’ve previously showcased Branden Fitelson and Elliot Sober’s Plantinga’s Probability Arguments Against Evolutionary Naturalism from 1997, which I think is one of the most complete examinations of the structure of the argument. There are two critical points that I think emerge from Fitelson and Sober. First, there is the sober reminder of the inherent frailty of scientific method that needs to be kept in mind. Science is an evolving work involving many minds operating, when at its best, in a social network that reduces biases and methodological overshoots. It should be seen as a tentative foothold against “global skepticism.”

The second, and critical take-away from that response is more nuanced, however. The notion that our beliefs can be arbitrarily disconnected from adaptive behavior in an evolutionary setting, like the tiger survivor, requires a very different kind of evolution than we theorize. Fitelson and Sober point out that if anything was possible, zebras might have developed machine guns to defend against lions rather than just cryptic stripes. Instead, the sieve of possible solutions to adaptive problems is built on the genetic and phenotypic variants that came before. This will limit the range of arbitrary, non-true beliefs that can be compatible with an adaptive solution. If the joint probability of true belief and adaptive behavior is much higher than the alternative, which we might guess is true, then there is a greater probability that our faculties are reliable. In fact, we could argue that using a parsimony argument that extends Bayesian analysis to the general case of optimal inductive models (Sober actually works on this issue extensively), that there are classes of inductive solutions that, through eliminating add-ons, outperform predictively those solutions that have extra assumptions and entities. So, P(not getting eaten | true belief that tigers are threats) >> P(not getting eaten | false beliefs about tigers), especially when updated over time. I would be remiss if I didn’t mention that William of Ockham of Ockham’s Razor-fame was a scholastic theologian, so if Plantinga’s argument is revisiting those old angels-head-pin-style arguments, it might be opposed by a fellow scholastic.

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.

The Ethics of Knowing

In the modern American political climate, I’m constantly finding myself at sea in trying to unravel the motivations and thought processes of the Republican Party. The best summation I can arrive at involves the obvious manipulation of the electorate—but that is not terrifically new—combined with a persistent avoidance of evidence and facts.

In my day job, I research a range of topics trying to get enough of a grasp on what we do and do not know such that I can form a plan that innovates from the known facts towards the unknown. Here are a few recent investigations:

  • What is the state of thinking about the origins of logic? Logical rules form into broad classes that range from the uncontroversial (modus tollens, propositional logic, predicate calculus) to the speculative (multivalued and fuzzy logic, or quantum logic, for instance). In most cases we make an assumption based on linguistic convention that they are true and then demonstrate their extension, despite the observation that they are tautological. Synthetic knowledge has no similar limitations but is assumed to be girded by the logical basics.
  • What were the early Christian heresies, how did they arise, and what was their influence? Marcion of Sinope is perhaps the most interesting one of these, in parallel with the Gnostics, asserting that the cruel tribal god of the Old Testament was distinct from the New Testament Father, and proclaiming perhaps (see various discussions) a docetic Jesus figure. The leading “mythicists” like Robert Price are invaluable in this analysis (ignore first 15 minutes of nonsense). The thin braid of early Christian history and the constant humanity that arises in morphing the faith before settling down after Nicaea (well, and then after Martin Luther) reminds us that abstractions and faith have a remarkable persistence in the face of cultural change.
  • How do mathematical machines take on so many forms while achieving the same abstract goals? Machine learning, as a reificiation of human-like learning processes, can imitate neural networks (or an extreme sketch and caricature of what we know about real neural systems), or can be just a parameter slicing machine like Support Vector Machines or ID3, or can be a Bayesian network or mixture model of parameters.  We call them generative or non-generative, we categorize them as to discrete or continuous decision surfaces, and we label them in a range of useful ways. But why should they all achieve similar outcomes with similar ranges of error? Indeed, Random Forests were the belles of the ball until Deep Learning took its tiara.

In each case, I try to work my way, as carefully as possible, through the thicket of historical and intellectual concerns that provide point and counterpoint to the ideas. It feels ethically wrong to make a short, fast judgment about any such topics. I can’t imagine doing anything less with a topic as fraught as the US health care system. It’s complex, indeed, Mr. President.

So, I tracked down a foundational paper on this idea of ethics and epistemology. It dates to 1877 and provides a grounding for why and when we should believe in anything. William Clifford’s paper, The Ethics of Belief, tracks multiple lines of argumentation and the consequences of believing without clarity. Even tentative clarity comes with moral risk, as Clifford shows in his thought experiments.

In summary, though, there is no more important statement than Clifford’s final assertion that it is wrong to believe without sufficient evidence. It’s that simple. And it’s even more wrong to act on those beliefs.

Twilight of the Artistic Mind

Deep Dream Generated Image:

Kristen Stewart, of Twilight fame, co-authored a paper on using deep learning neural networks in her new movie that she is directing. The basic idea is very old but the details and scale are more recent. If you take an artificial neural network and have it autoencode the input stream with bottlenecking, you can then submit any stimulus and will get some reflection of the training in the output. The output can be quite surreal, too, because the effect of bottlenecking combined with other optimizations results in an exaggeration of the features that define the input data set. If the input is images, the output will contain echoes of those images.

For Stewart’s effort, the goal was to transfer her highly stylized concept art into the movie scene. So they trained the network on her concept image and then submitted frames from the film to the network. The result reflected aspects of the original stylized image and the input image, not surprisingly.

There has been a long meditation on the unique status of art and music as a human phenomenon since the beginning of the modern era. The efforts at actively deconstructing the expectations of art play against a background of conceptual genius or divine inspiration. The abstract expressionists and the aleatoric composers show this as a radical 20th Century urge to re-imagine what art might be when freed from the strictures of formal ideas about subject, method, and content.

Is there any significance to the current paper? Not a great deal. The bottom line was that there was a great deal of tweaking to achieve a result that was subjectively pleasing and fit with the production goals of the film. That is a long way from automated art and perhaps mostly reflects the ability of artificial neural networks to encode complex transformations that are learned directly from examples. I was reminded of the Nadsat filters available for Unix in the 90s that transformed text into the fictional argot of A Clockwork Orange. Other examples were available, too. The difference was that these were hand-coded while the film example learned from examples. Not hard to do in the language case, though, and likely easier in certain computational aspects due to the smaller range of symbol values.

So it’s a curiosity at best, but plaudits to Stewart for trying new things in her film efforts.