An update on Empiricism vs Rationalism debates

All papers involve at least two levels of discourse. The first comprises the thesis: what is being argued? What evidence is there for what is being argued? What’s the point of what is being argued? What follows from what is being argued? The second is less a matter of content than of style: how does the paper say what it says? This latter dimension most often reveals the (often more obscure) political dimensions of the discipline, and relates to topics like the following: What parts of one’s butt is it necessary to cover so as to avoid annoying the powers that be? Which powers is it necessary to flatter in order to get a hearing or to avoid the ruinous “high standards of scrutiny” that can always be deployed to block publication? Whose work must get cited and whose can be safely ignored? What are the parameters of the discipline’s Overton Window? If you’ve ever been lucky enough to promote an unfashionable position, you have developed a sensitivity to these “howish” concerns. And if you have not, you should. They are very important. Nothing reveals the core shibboleths (and concomitant power structures) of a discipline more usefully than a forensic inquiry into the nature of the eggshells upon which a paper is treading.  In what follows I would like to do a little eggshell inspection in discussing a pretty good paper from a (for me) unexpected source (a bunch of Bayesians). The authors are Lake, Ullman, Tenenbaum and Gersham (LUTG) all from the BCS group at MIT. The paper (here) is about the next steps forward for an AI that aspires to be cognitively interesting (like the AI of old), and maybe even technologically cutting edge, (though LUTG makes this second point very gingerly).

But that is not all the paper is about. LUTG is also a useful addition to the discussions about Empiricism (E) vs Rationalism (R) in the study of mind, though LUTG does not put matters in quite this way (IMO, for quasi-political reasons, recall “howish” concerns!). To locate the LUTG on this axis will require some work on my part. Here goes.

As I’ve noted before, E and R have both a metaphysical and an epistemological side. Epistemologically, E takes minds to be initially (relatively) unstructured, with mental contour forged over time as a by-product of environmental input as sampled by the senses.  Minds on this view come to represent their environments more or less by copying their structures as manifest in the sensory input.  Successful minds are ones that effectively (faithfully and efficiently) copy the sensory input. As Gallistel & Matzel put it, for Es minds are “recapitulative,” their function being to reliably take “an input that is part of the training input, or similar to it, [to] evoke[s] the trained output, or an output similar to it” (see here for discussion and links). Another way of putting this point is that E minds are pattern matchers, whose job is to track the patternings evident in the input (see here). Pattern matching is recapitulative and E relies on the idea that cognition amounts to tracking the patternings in the sensory input to yield reliable patterns.

Coupled with this E theory of mind comes a metaphysics. Reality has a relatively flat causal structure. There is no rich hierarchy of causal mechanisms whose complex interactions lie behind what we experience. Rather, what you sense is all there is. Therefore, effectively tracking and cataloguing this experience and setting up the right I/O associations suffices to get at a decent representation of what exists. There is no hidden causal iceberg of which this is the sensory/perceptual tip. The I/O relations are all there is. Not surprisingly (this is what philosophers are paid for), the epistemology and the metaphysics fit snugly together.

R combines its epistemology and its metaphysics differently. Epistemologically, R regards sensory innocent minds to be highly structured. The structure is there to allow minds to use sensory/perceptual information to suss out the (largely) hidden causal structures that produce these sensations/perceptions. As Gallistel & Matzel put it R minds are more like information processing systems structured to enable minds to extract information about the unperceived causal structures of the environment that generate the observed patterns of sensation/perception. On the R view, minds sample the available sensory input to construct causal models of the world which generate the perceived sensory patterns. And in order to do this, minds come richly stocked with the cognitive wherewithal necessary to build such models. R epistemology takes it for granted that what one perceives vastly underdetermines what there is and takes it to be impossible to generate models of what there is from sensory perception without a big boost from given (i.e. unlearned) mental structures that make possible the relevant induction to the underlying causal mechanisms/models.

R metaphysics complements this epistemological view by assuming that the world is richly structured causally and that what we sense/perceive is a complex interaction effect of these more basic complexly interacting causal mechanisms. There are hidden powers etc. that lie behind what we have sensory access to and that in no way “resembles” (or “recapitulates”) the observables (see here for discussion).

Why do I rehearse these points yet again? Because I want to highlight a key feature of the E/R dynamic: what distinguishes E from R is not whether pattern matching is a legit mental operation (both views agree that it is (or at least, can be)). What distinguishes them is the Es think that this is all there is (and all there needs to be), while Rs reserve an important place for another kind of mental process, one that builds models of the underlying complex non visible causal systems that generate these patterns. In other words, what distinguishes E from R is the belief that there is more to mental life than tracking the statistical patterns of the inputs. R doesn’t deny that this is operative. R denies that this suffices. There needs to me more, a lot more.[1]

Given this backdrop, it is clear that GG is very much in the R tradition. The basic observation is that human linguistic facility requires knowledge a G (a set of recursive rules). Gs are not perceptually visible (though their products often are). Further, cursory inspection of natural languages indicates that human Gs are quite complex and cannot be acquired solely by induction (even sophisticate discovery procedures that allowed for inductions over inductions over inductions…). Acquiring Gs requires some mental pre-packaging (aka, UG) that enables an LAD to construct a G on the basis of simple haphazard bits of G output (aka, PLD). Once acquired, Gs can be used to execute many different kinds of linguistic behaviors, including parsing and producing novel sentences and phrases. That’s the GG conceit in a small nutshell: human linguistic facility implicates Gs which implicates UGs, both G and UG being systems of knowledge that can be put to multiple uses, including G acquisition, and the production and comprehension of an unbounded number of very different novel linguistic expressions within a given natural language. G, UG, competence vs performance: the hallmarks of a R theory of linguistic minds.

LUTG makes effectively these points but without much discussing the language case (it does at the end but mainly to say that it won’t discuss it).  LUTG’s central critical claim is that human cognition relies on more than pattern matching. There is, in addition, model building, which relies on two kinds of given mental contents to guide learning.  The first kind, what LUTG calls “core” (and an R would call “given”) involves substantive specific content (e.g. naïve physics and psychology). The second involves formal properties (e.g. compositionality and certain kinds of formal analogy (what LUTG calls “learning-to-learn”).[2] LUTG notes that these two features pre-condition further learning and are indispensible. LUTG further notes that the two mental powers (i.e. model building and pattern recognition) can likely be fruitfully combined, though it (rightly, IMO) insists that model building is the central cognitive operation. In other words, LUTG recapitulates the observations above that R theories can incorporate E mechanisms and that E mechanisms alone are insufficient to model human cognition and that without the part left out, E pattern matching models are poor fits with what we have tons of evidence to be central features of cognitive life. Here’s how the abstract puts it:

We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

The paper usefully goes over these points in some detail.  It further notes that this conception of the learning (I prefer the term “acquisition” myself) problem in cognition challenges associationist assumptions which LUTG observes (again rightly IMO) is characteristic of most work in connectionism, machine learning and deep learning. LUTG also points to ways that “model free methods” (aka pattern matching algorithms) might usefully supplement the model building cognitive basics to improve performance and implementation of model based knowledge (see 1.2 and 4.3.2).[3]

Section 3 is the heart of the critique of contemporary AI, which largely ignores the (R) model building ethos that LUTG champions. As it observes, the main fact about humans when compared with current AI models is that humans “learn a lot more from a lot less” (6). How much more? Well, human learning is very flexible and rarely tied to the specifics of the learning situation. LUTG provides a nice discussion of this in the domain of learning written characters. Further, human learning generally requires very little “input.” Often, a couple of minutes or exposures to the relevant stimulus is more than enough. In fact, in contrast to current machine learning or DL systems humans do not need to have the input curated and organized (or, as Geoff Hinton recently put it (here): humans, as opposed to DLers, “clearly don’t need all the labeled data.”). And why not? Because unlike what DLers and connectionists and associationists have forever assumed, human minds (and hence brains) are not E devices but R ones. Or as LUTG puts it (9):

People never start completely from scratch, or even close to “from scratch,” and that is the secret to their success. The challenge of building models of human learning and thinking then becomes: How do we bring to bear rich prior knowledge to learn new tasks and solve new problems so quickly? What form does that prior knowledge take, and how is it constructed, from some combination of inbuilt capacities and previous experience?

In short, the blank tablet assumption endemic to E conceptions of mind is fundamentally misguided and a (the?) central problem of cognitive psychology is to figure out what is given and how this given stuff is used. Amen (and about time)!
Section 4 goes over what LUTG takes to be some core powers of human minds. This includes naïve theories of how the physical world functions and how animate agents operate. In addition, with a nod to Fodor, it outlines the critical role of compositionality in allowing for human cognitive productivity.[4] It is a nice discussion and makes useful comments on causal models and their relation to generative ones (section 4.2.2).
Now let’s note a few eggshells. A central recurring feature of the LUTG discussion is the observation that it is unclear how or whether current DL approaches might integrate these necessary mechanisms. The paper does not come right out and say that DL models will have a hard time with these without radically changing its sub-symbolic associationist pattern matching monomania, but it strongly suggests this. Here’s a taste of this recurring theme (and please note the R overtones).

As in the case of intuitive physics, the success that generic networks will have in capturing intuitive psychological reasoning will depend in part on the representations humans use. Although deep networks have not yet been applied to scenarios involving theory of mind and intuitive psychology, they could probably learn visual cues, heuristics, and summary statistics of a scene that happens to involve agents. If that is all that underlies human psychological reasoning, a data-driven deep learning approach can likely find success in this domain.

However, it seems to us that any full formal account of intuitive psychological reasoning needs to include representations of agency, goals, efficiency, and reciprocal relations. As with objects and forces, it is unclear whether a complete representation of these concepts (agents, goals, etc.) could emerge from deep neural networks trained in a purely predictive capacity. Similar to the intuitive physics domain, it is possible that with a tremendous number of training trajectories in a variety of scenarios, deep learning techniques could approximate the reasoning found in infancy even without learning anything about goal- directed or socially directed behavior more generally. But this is also unlikely to resemble how humans learn, understand, and apply intuitive psychology unless the concepts are genuine. In the same way that altering the setting of a scene or the target of inference in a physics-related task may be difficult to generalize without an understanding of objects, altering the setting of an agent or their goals and beliefs is difficult to reason about without understanding intuitive psychology.

Yup. Right on. But why so tentative? “It is unclear?” Nope it is very clear, and has been for about 300 years since these issues were first extensively discussed. And we all know why. Because concepts like “agent,” “goal,” “cause,” “force,” are not observables and not reducible to observables. So, if they play central roles in our cognitive models then pattern matching algorithms won’t suffice. But as this is all that E DL systems countenance, then DL is not enough. This is the correct point, and LUTG makes it. But note the hesitancy with which it does so. Is it too much to think that this likely reflects the issue mooted at the outset about the implicit politics that one’s dance over the eggshells reveals.
It is not hard to see from how LUTG makes its very reasonable case that it is a bit nervous about DL (the current star of AI). LUTG is rhetorically covering its posterior while (correctly) noting that unreconstructed DL will never make the grade. The same wariness makes it impossible for LUTG to acknowledge its great debt to R predecessors.[5] As LUTG states, its “goal is to build on their [neural networks, NH] successes rather than dwell on their shortcomings” (2). But those that always look forward and never back won’t move forward particularly well either (think Obama and the financial crisis). Understanding that E is deeply inadequate is a prerequisite for moving forward. It is no service to be mealy-mouthed about this. One does not finesse one’s way around veery influential bad ideas.
Ok, so I have a few reservations about how LUTG makes its basic points. That said, this is a very useful paper. It is nice to see this coming out of the very influential Bayesian group at MIT and in a prominent place like B&BS. I am hoping that it indicates that the pendulum is swinging away from E and towards a more reasonable R conception of minds. As I’ve noted the analogies with standard GG practice is hard to miss. In addition LUTG rightly points to the shortcomings with connectionist/deep learning/neural net approaches to mental life. This is good. It may not be news to many of us, but if this signals a return to R conceptions of mind, it is a very positive step in the right direction.[6]

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[1] R often goes farther: that even tracking the relevant perceptual regularities requires lots of given mental baggage. The world does not come pre-labeled. So zeroing in on the relevant dimensions for inductive generalization itself requires lots of pre-packaged “knowledge.” Geoff Hinton (the daddy of deep learning) seems to have come to a similar view of late concerning how hard it is to get things off the ground without curated data. See below for a reference.

[2] GGers should find this reminiscent of Chomsky’s discussion in Aspects of substantive and formal universals and how these interact to create Gs (models) of the ambient degenerate and deficient linguistic input available to the child (PLD).

[3] Or to put this in GG friendly terms; LUTG resurrects something very like a competence/performance distinction, model building being analogous to the former and model free methods applied to these models being analogous to the latter. An idea I found interesting is that given models of competence provides a useful domain for performance models wherein sophisticated pattern matching algorithms do their work. Conceptually, this idea seems very similar to what Charles Yang has been advocating as well (see here).

[4] Actually, though compositionality is critical to productivity, it does not suffice for generating an “infinite number of thoughts” or using an “infinite number of sentences”  “from a finite set if primitives” (14). For this we need more than compositionality, we need recursion as well.

[5] It is amazing, IMO, how small a role Chomsky plays in LUTG’s discussion. So far as I can tell, all of its major points were developed in the modern period by him. I am pretty sure that some of the authors know this but that highlighting this fact would hurt the paper politically by offending the relevant leading DL lights.

[6] BTW, LUTG has a nice discussion of the biological “plausibility” of neural net models of the brain. The short point is that short of being pictorially suggestive, there is no real reason for thinking that brains are like connectionist nets. As LUTG puts it (20):

Many seemingly well-accepted ideas concerning neural computation are in fact biologically dubious, or uncertain at best…

For example, most neural networks use some form of gradient based (e.g. back propogation) or Hebbian learning. It has long been argued, however, that

backpropagation is not biologically plausible. As Crick (1989) famously pointed out, backpropagation seems to rquire that information be transmitted backward along the axon, which does not fit with realistic models of neuronal function…

As LUTG observes, this should not in and of itself stop people from investigation neural nets as possible models of brain computation, but it should put an end to the prejudice that brains are nets because they look net-like. Sheesh! 

Explanation, prediction and control

As I never get tired of warning; Empiricism (E) is back and that ain’t good. And it is back in both its guises (see here and here). Let’s review. Oh yes, first an apology. This post is a bit long. It got away from me. Sorry. Ok, some content.

Epistemologically, E endorses Associationist conceptions of mind. The E conception of learning is “recapitulative.” This is Gallistel and Matzel’s (G&M) felicitous phrase (see here). What’s it mean? Recapitulative minds are pattern detection devices (viz.: “An input that is part of the training input, or similar to it, evokes the trained output, or an output similar to it” (see here for additional discussion). G&M contrasts this with (Rish) information processing approaches to learning where minds innately code considerable structure of the relevant learning domain.

Metaphysically, E shies away from the idea that deep casual mechanisms/structures underlie the physical world. The unifying E theme is what you see is what you get: minds have no significant structure beyond what is required to register observations (usually perceptions) and the world has no causal depth that undergirds the observables we have immediate access to.

What unifies these two conceptions is that Eism resists two central Rish ideas (i) that there is significant structure to the mind (and brain) that goes beyond the capacity to record and (ii) that explanation amounts to identifying the hidden simple interacting causal mechanisms that underlie our observations. In other words, for Rs minds are more than experiential sponges (with a wee bit of inductive bias) and the world is replete with abstract (i.e. non observed), simple, casual structures and mechanisms that are the ultimate scaffolding of reality.

Why do I mention this (again!)? It has to do with some observations I made in a previous post (here). There I observed that a recent call to arms (henceforth C&C)[1], despite its apparent self-regard as iconoclastic, was, read one way, quite unremarkable, indeed pedestrian. On this reading the manifesto amounts to the observation that language in the wild is an interaction effect (i.e. the product of myriad interacting factors) and so has to be studied using an “integrated approach” (i.e. by specifying the properties of the interacting parts and adumbrating how these parts interact). This I claimed (and still claim) is a truism. Moreover, it is a universally acknowledged truism, with a long pedigree.

This fact, I suggested, raises a question: Given that what C&C appears to be vigorously defending is a trivial widely acknowledged truism why the passionate defense?[2] My suggestion was that C&C’s real dissatisfaction with GG inclined linguistic investigations had two sources: (i) GGers conviction that grammaticality is a real autonomous factor underlying utterance acceptability and (ii) GGers commitment to an analytic approach to the problems of linguistic performance.

The meaning of (i) is relatively clear. It amounts to a commitment to some version of the autonomy of syntax (grammar) thesis, a weak assumption which denies that syntax is reducible to non-grammatical factors such as parsability, pragmatic suitability, semantic coherence, some probabilistic proxy, or any other non syntactic factor. Put simply, syntax is real and contributes to utterance acceptability. C&C appears to want to deny this weak thesis.

The meaning of (ii) is more fluffy, or, more specifically, it’s denial is. I suggested that C&C seemed attracted to a holistic view that appears to be fashionable in some Machine Learning environs (especially of the Deep Learning (DL) variety). The idea (also endemic to early PDP/connectionist theory) takes the unit of analysis to be the whole computational system and resists the idea that one can understand complex systems by the method of analysis and synthesis. It is only the whole machine/web that computes. This rejects the idea that one can fruitfully analyze a complex interaction effect by breaking it down into interacting parts. Indeed, on this view, there are no parts (and, hence no interacting parts) just one big complex gooey unstructured tamale that does whatever it does.

Here’s a version of this view by Noah Smith (the economist) (3-4):

…deep learning, the technique that's blowing everything else away in a huge array of applications, tends to be the least interpretable of all - the blackest of all black boxes. Deep learning is just so damned deep - to use Efron's term, it just has so many knobs on it. Even compared to other machine learning techniques, it looks like a magic spell…

Deep learning seems like the outer frontier of atheoretical, purely data-based analysis. It might even classify as a new type of scientific revolution - a whole new way for humans to understand and control their world. Deep learning might finally be the realization of the old dream of holistic science or complexity science- a way to step beyond reductionism by abandoning the need to understand what you're predicting and controlling.

This view, is sympathetically discussed here in a Wired article under the title “Alien Knowledge” (AK). In what follows I want to say a little about the picture of science that AK endorses and see what it implies when applied to cog-neuro work. My claim is that whatever attractions it might hold for practically oriented research (I will say what this is anon, but think technology), it is entirely inappropriate when applied to cognitive-neuroscience. More specifically, if one aims for “control” then this picture has its virtues. However, if “understanding” is the goal, then this view has virtually nothing to recommend it. This is especially so in the cog-neuro context. The argument is a simple one: we already have what this view offers if successful, and what it delivers when successful fails to give us what we really want. Put more prosaically, the attraction of the vision relies on confusing two different questions: whether something is the case with how something is the case. Here’s the spiel.

So, first, what's the general idea? AK puts the following forth as the main thesis (1):

The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

There are versions of this claim that are unobjectionable. For many technological ends, we really don’t need to understand mechanism.[3] We once had plenty of evidence that aspirin could work to counteract headaches even if we had no idea how aspirin did this.[4] Ditto, with many other useful artifacts. As Dylan once put it (see here): “you don’t need a weatherman to know which way the wind blows.”  Even less do you need decent meteorological theory. We all know that quite often, for many useful purposes, correlation is more than enough to get you what you want/need. Thus, for many useful practical considerations (even very important ones), though understanding is nice, it is not all that necessary (though see note 4).

Now, Big Data (BD)/Deep Learning (DL) allows for correlations in spades. Big readily available data sets plus DL allows one to find a lot of correlations, some of which are just what the technologist ordered. What makes the above AK quote interesting is that it goes beyond this point of common agreement and proposes that correlation is not merely useful for control and the technology that relies on control, but also for “understanding.” This is newish. We are all familiar with the trope that correlation is not causation. The view here agrees, but proposes that we revamp our conception of understanding by decoupling it from causal mechanism. The suggested revision is that understanding can be had without “models” without “mechanistic explanation,” without “unified theories,” and without any insight into the causal powers that make things tick.  In other words, the proposal here is that we discard the Galilean (and Rish) world view that takes scientific understanding to piggy back on limning the simple underlying causal substructures that complex surface phenomena are manifestations of. Thus, what makes the thesis above interesting is not so much its insistence that correlation (often) suffices for control and that BD/DL together make it possible to correlate very effectively (maybe more so than was ever possible before), but that this “correlation suffices” conception of explanation should replace the old Galilean understanding of the goals of scientific inquiry. Put more baldly, it suggests that the Galilean view is somewhat quaint given the power to correlate that the new BD/DL tools afford.

This vision is clearly driven by a techno imperative. As AK puts it, it’s the standard method of “any self-respecting tech company” and its success in this world challenges the idea that “knowledge [is] about finding the hidden order in chaos [aka: the Galilean world view, NH],” that involves “simplifying the world.” Nope, this is “wrong.” Here’s the money idea: “Knowing the world may require giving up on understanding it” (2).[5]

This idea actually has an old pedigree rooted in two different conceptions of idealization. The first takes it as necessary for finding the real underlying mechanism. This (ultimately Platonic/Rationalist) conception takes what we observe to be a distortion of the real underlying causal powers that are simple and which interact to create the apparent chaos that we see.  The second conception (which is at bottom Eish) takes idealization to distort. For Es, idealizations are the price we pay for our limitations. Were we able to finesse these, then we would not need to idealize and could describe reality directly, without first distorting it. In effect, for Rs, idealization clarifies, for Es it distorts by loosing information.

The two positions have contrasting methodological consequences. For Rs, the mathematization of the world is a step towards explaining it as it is the language best suited for describing and explaining the simple abstract properties/powers/forces that undergird reality.[6] For Es, it is a distortion as there are no simple underlying mechanisms for our models to describe.[7] Models are, at best, compact ways of describing what we see and like all such compactions (think maps, or statistical renderings of lists of facts) they are not as observationally accurate as the thing they compactly describe (see Borges here on maps).

The triumph of modern science is partially the triumph of the Platonic conception as reframed by Galileo and endorsed by Rs. RK challenges this conception, and with it the idea that a necessary step towards understanding is simplification via idealization. Indeed, on the Galilean conception the hardest scientific problem is finding the right idealization. Without it, understanding is impossible. For Platonists/Rs, the facts observed are often (usually) distorted pictures of an underlying reality, a reality that a condign idealization exposes. For Es, in contrast, idealizations distort as through simplifying they set aside the complexity of observed reality.

So why doesn’t idealization distort? Why isn’t simplifying just a mistake, perhaps the best that we have been able to do till now but something to be avoided if possible? The reason is that “good” idealizations don’t distort those features that count: the properties of the underlying mechanisms. What good idealizations abstract away from is the complexity of the observed world. But this is not problematic for the aim is not to “capture the data” (i.e. the observations), but to use some data (usually artificially contrived in artificial controlled settings) to describe and understand the underlying mechanisms that generate the data. And importantly, these mechanisms must be inferred as they are assumed to be abstract (i.e. remote from direct inspection).

A consequence of this is that not all observations are crated equal on the Galilean view (and so capturing the data (whatever that means (which is not at all clear)) is not obviously a scientifically reasonable or even useful project. Indeed, it is part of the view that some data are better at providing a path to the underlying mechanism than other data. That’s why experiments matter; they manufacture useful and relevant data. If this is so, then ignoring some of the data is exactly the way to proceed if one’s aim is to understand the underlying processes. More exactly, if the same forces, mechanisms and powers obtain in the “ideal” case as in the more everyday case and looking at the idealization simplifies the inquiry, then there is nothing wrong (and indeed everything right) with ignoring the more complex case because considering the complex case does not, by hypothesis, shed more light on the fundamentals than does the simpler ideal one.  In other words, to repeat, a good idealization does not distort our perception of the relevant mechanisms though it might only directly/cleanly apply to only a vanishingly small subset of the observable data. The Galilean/R conclusion is that aiming to “cover the data” and “capture the facts” is exactly the wrong thing to be doing when doing serious science.

These are common enough observations in the real sciences. So, for example, Steven Weinberg is happy to make analogous points (see here). He notes that in understanding fundamental features of the physical world everyday phenomena are a bad guide to what is “real.” This implies that successfully attaining broad coverage of commonly available correlations generally tells us nothing of interest concerning the fundamentals. Gravitational attraction works the same way regardless of the shapes of the interacting masses. However, it is much easier to fix the value of the Gravitational constant by looking at what happens when two perfectly spherical masses interact than when two arbitrarily shaped masses do so. The underlying physics is the same in both cases. But an idealization to the spherical case makes life a lot easier.

So too with ideal gasses and ideal speaker-hearers. Thus, in the latter case, abstracting away from various factors (spotty memory, “bad” PLD, non-linguistically uniform PLD, inattention) does not change the fundamental problem of how to project a G from limited “good” “uniform” PLD. So, yes, we humans are not ideal speaker-hearers but considering the LAD to have unbounded memory, perfect attention and learning only from perfect PLD reveals the underlying structure of the computational problem more adequately than does the more realistic actual case. Why? Because the mechanisms relevant to solving the projection problem in the ideal case will not be fundamentally different than what one finds in the actual one, though interactions with other relevant sub-systems will be more complex (and hence more confusing) in the less ideal situation.

So what is the upshot here? Just the standard Galilean/R one: if one aims to understand mechanism, then idealization will be necessary as a way of triangulating on the fundamental principles, which, to repeat, are the object of inquiry.[8]

Of course, understanding the basic principles may leave one wanting in other respects, but this simply indicates that given different aims different methods are appropriate. In fact, it may be quite difficult to de-simplify from the ideal case to make contact with more conventional observations/data. And it is conceivable that tracking surface correlations may make for better control of the observables than is adapting models built on more basic/fundamental/causal principles, as AK observes. Nor should the possibility be surprising. Nor should it lead us to given up the Galilean conception.

But that is not AK’s view. It argues that with the emergence of BD/DL we can now develop systems (programs) that fit he facts without detouring via idealized models. And that we should do so despite an evident cost. What cost? The systems we build will be opaque in a very strong sense. How strong? AK’s premise is that BD/DL will result in systems that are “ineffably complex” (4) (i.e. completely beyond our capacity to understand what causal powers the model is postulating to cover the data). AK stresses that the models that BD/DL deliver are (often) completely uninterpretable, while at the same time being terrifically able to correlate inputs and outputs as desired. AK urges that we ignore the opacity and take this to provide a new kind of “understanding.” AK’s suggestion, then, is that we revise our standards of explanation so that correlation done on a massive enough scale (backed by enough data, and setting up sufficiently robust correlations) just IS understanding and the simplifications needed for Galilean understanding amounts to distortion, rather than enlightenment.  In other words, what we have here is a vision of science as technology: whatever serves to advance the latter suffices to count as an exemplar of the former.

What are the consequences of taking AK’s recommendation seriously? I doubt that it would have a deleterious effect on the real sciences, where the Galilean conception is well ensconced (recall Weinberg’s discussion). My worry is that this kind of methodological nihilism will be taken up within cog-neuro where covering the data is already well fetishized. We have seen a hint of this in an earlier post I did on the cog-neuro of faces reviewing work by Chang and Tsao (see here). Tsao’s evaluation of her work on faces sees it as pushing back against the theoretical “pessimism” within much of neuroscience generated by the caliginous opacity of much of the machine learning-connectionist modeling. Here is Tsao in the NYT:

Dr. Tsao has been working on face cells for 15 years and views her new report, with Dr. Chang, as “the capstone of all these efforts.” She said she hoped her new finding will restore a sense of optimism to neuroscience.

Advances in machine learning have been made by training a computerized mimic of a neural network on a given task. Though the networks are successful, they are also a black box because it is hard to reconstruct how they achieve their result.

“This has given neuroscience a sense of pessimism that the brain is similarly a black box,” she said. “Our paper provides a counterexample. We’re recording from neurons at the highest stage of the visual system and can see that there’s no black box. My bet is that that will be true throughout the brain.”

C&C’s proposals for language study and AK’s metaphysics are part of the same problem that Tsao identifies.  And it clearly has some purchase in cog-neuro or Tsao would not have felt it worth explicitly resisting. So, while physics can likely take care of itself, cog-neuro is susceptible to this Eish nihilism.

Let me go further. IMO, it’s hard to see what a successful version of this atheoretical approach would get us. Let me illustrate using the language case.

Let’s say that I was able to build a perfectly reliable BD/DL system able to mimic a native speaker’s acceptability profile. So, it rated sentences in exactly the way a typical native speaker would. Let’s also assume that the inner workings of this program were completely uninterpretable (i.e. we have no idea how the program does what it does). What would be the scientific value of achieving this? So far as I can tell, nothing. Why so? Because we already have such programs, they are called native speakers. Native speakers are perfect models of, well, native speakers. And there are a lot of them. In fact, they can do much more than provide flawless acceptability profiles. They can often answer questions concerning interpretation, paraphrase, rhyming, felicity, parsing difficulty etc. They really are very good at doing all of this. The scientific problem is not whether this can be done, but how it is getting done. We know that people are very good at (e.g.) using language appropriately in novel settings. The creative aspect of language use is an evident fact (and don’t you forget it! (see here). The puzzle is how native speakers do it, not whether they do. Would writing a program that mimicked native speakers in this way be of any scientific use in explaining how it is done? Not if the program was uninterpretatble, as are those that AK (and IMO, C&C) are advocating.

Of course, such a program might be technologically very useful. It might be the front end of those endless menus we encounter daily when we phone some office up for information (Please listen carefully as our menu has changed (yea right, gotten longer and less useful). Or maybe it would be part of a system that would allow you to talk to your phone as if it were a person and fall hopelessly in love (as in Her). Maybe (though I am conceding this mainly for argument’s sake. I am quite skeptical of all of this techno-utopianism). But if the program really were radically uninterpretable, then its scientific utility would be nil precisely because its explanatory value would be zero.

Let me put this another way. Endorsing the AK vision in, for example, the cog-neuro of language is to confuse two very different questions: Are native speakers with their evident powers possible? versus How do native speakers with their evident powers manage to do what they do? We already know the answer to the first question. Yes, native speakers like us are possible. What we want is an answer to the second. But uninterpretable models don’t answer this question by assumption. Hence the scientific irrelevance of these kinds of models, those that AK and C&C and BD/DLers promote.

But saying this does not mean that the rise of such models might not have deleterious practical effects. Here is what I mean.

There are two ways that AK/C&C/DL models can have a bad effect. The first, as I have no doubt over discussed, is that they lead to conceptual confusions specifically by running together two entirely different questions. The second is that if DL can deliver on correlations the way its hype promises that it will then it will start to dissolve the link between science and technology that has lent popular prestige to basic research. Let me end with a word on this.

In the public mind, it often seems what makes science worthwhile is that it will give us fancy new toys, cure dreaded diseases, keep us forever young and vibrant and clear up our skin. Science is worthwhile because it has technological payoffs. This promise has been institutionalized in our funding practices in the larger impact statements that the NSF and NIH now require as part of any grant application. The message is clear: the work is worthwhile not in itself but to the degree that it cures cancer or solves world hunger (or clears up your skin). Say DL/BD programs could produce perfect correlation machines. These really could be of immense practical value. And given that they are deeply atheoretical, they would drive a large wedge between science and technology. And this would lessen the prestige of the pure sciences because it would lessen their technological relevance. In the limit, this would make theoretical scientific work aiming at understanding analogous to work in the humanities, with the same level of prestige as afforded the output of English and History professors (noooo, not that!!!).

So, the real threat of DL/BD is not that it offers a better answer to the old question of understanding but that it will sever the tie between knowledge and control. This AK gets right. This is already part of the tech start-up ethos (quite different from the old Bell Labs ethos, I might add). And doing this is likely to have an adverse effect on pure science funding (IMO, it has already). Not that I actually believe that technology can propser without fundamental research into basic mechanisms. At least not in the limit case. DL, like the forms of AI that came before, oversells. And like the AI that came before, I believe that it will also crash as a general program, even in technology. The atheoretical (like C&C) like to repeat the Fred Jelinek (FJ) quip that every time he fired a linguist his comp ling models improved. What this story fails to mention is that FJ recanted in later life. Success leveled off and knowing something about linguistics (e.g. sentence and phrasal structure in FJ’s case) really helped out.[9] It seems that you can go far using brute methods, but you then often run into a wall. Knowing what’s fundamentally up really does help the technology along.

One last point (really) and I end. We tend to link the ideas of explanation, (technological) control and prediction. Now, it seems clear that good explanations often license novel predictions and so asking for an explanation’s predictions is a reasonable way of understanding what it amounts to. However, it is also clear that one can have prediction without explanation (that’s what a good correlation will give you even if it explains nothing), and where one has reliable prediction one can also have (a modicum of) control.  In other words, just as AK calls into question the link between understanding and control (more accurately explanation and technology) it also challenges the idea that prediction is explanation, rather than a mark thereof. Explanation requires the right kind of predictions, one’s that follow from an understanding of the fundamental mechanisms. Predictions in and of themselves can be (and in DL models, appear to be) explanatorily idle.

Ok, that is enough. Clearly, this post has got away from me. Let me leave you with a warning: never underestimate the baleful influences of radical Eism, especially when coupled with techno optimism and large computing budgets. It is really deadly. And it is abroad in the land. Beware!

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[1] The authors were Christiansen and Chater, ‘C&C’ in what follows refers to the paper, not the authors.

[2] Of course, the authors might have intended this tongue in cheek or thought it a way of staying in shape by exercising in a tilting at windmills sort of way. Maybe. Naaahh!

[3] Note the qualifier ‘many.’ This might be too generous. I suspect that the enthusiasm for these systems lies most extensively in those technological domains where our scientific understanding is weakest.

[4] We now do know what it does (see here). And, interestingly, (as Paul noted in discussion) understanding how it worked allowed us to develop other pain relievers based on different chemical structures (e.g. Ibuprofen). This required getting beyond correlations to understanding mechanism.

[5] The ‘may’ is a weasel word. I assume that we are all grown up enough to ignore this CYA caveat.

[6] And given that mechanism is imperceptible, imagination (the power to conceive of possible unseen mechanisms) becomes an important part of the successful scientific mind.

[7] And hence, for Es imagination is suspect (recall there is no unseen mechanism to divine), the great scientific virtue residing in careful and unbiased observation.

[8] I don’t want to go into this here, but the real problem that Es have with idealization is the belief that there really is no underlying structure to problems. All there is are patterns and all patterns are important and relevant as there is nothing underneath. The aim of inquiry then is to “capture” these patterns and no fact is more relevant than any other as there is nothing but facts that need organizing. The Eish tendency to concentrate on coverage comes honestly, as data coverage is all that there is. So too is the tendency to be expansive regarding what to count as data: everything! If there is no underlying simpler reality responsible for the messiness we see, then the aim of science is to describe the messiness. All of it! This is a big difference with Rs.

[9] See Carl deMarken on the utility of headed phrases for statistical approaches to learning. Btw, Noah Smith (the CS wunderkind at U Wash told me about FJ’s reassessment many years ago)

AI now

Here are some papers on the current hype over AI. They are moderately skeptical about the current state of the art. Not so much about whether there are tech breakthroughs to be had. All agree that these are forthcoming. The skepticism concerns the implications of this. Let me a say a word or two about this.

There is a lot of PR concerning how Big Data will revolutionize our conceptions of how the mind works and how science should be conducted. Big Data is Empiricism on steroids. It is made possible because of hardware breakthroughs in memory and speed of computation. We can do more of what we have always done faster and this can make a difference. I doubt that this tells us much about human cognition. Or more accurately, what it does tell us is likely wrong. Big Data is often coupled with Deep Learning. And linguists have every reason to believe that Deep Learning is an incorrect model of human cognition. Why? Because it is a modern version of the old discovery procedure. Level1 generalizations are generalized again at level 2 and level two generalizations are generalized agains t level 3 and so on. As a model of cognition, this tells us that higher levels are just generalizations over lower ones (e.g. from phonemes we get morphemes and from morphemes we get we get phrase structure and from phrase structure we get...). GG started form the demonstration that this is an incorrect understanding of linguistic organization. Levels exist, but they are in no sense reducible to the ones lower down. Indeed, whether it makes sense to speak of 'higher' and 'lower' is quite dubious. The levels interact but don't reduce. And any theory of learning that supposes otherwise is wrong if this is right (and there is no reason to think that it is not right, or at least no argument has been presented arguing against level independence). So, Deep Learning and Big Data are. IMO, dead theories walking. We will see this very soon.

The interview with Gary Marcus (here) discusses these issues and notes that historically what we have here is more of the same that has in the past proven to be wildly oversold. He thinks (and I agree) that we are getting another snow job this time around too. The interview rambles somewhat (bad editing) but there is lots in here to provoke thought.

A second paper on a similar theme is here in Aeon. The fact that it is in Aeon should not be immediately held against it. True, there is reason to be suspicious given the track record, but the paper was not bad, IMO. It argues that there is no "coming of the machines."

Here is a third piece on programming and advice about how not to do it (the Masaya). It interestingly argues for a Marrian conception of programming. Understand the computational problem before you write code. Seems like reasonable advice.

Last point: I mentioned above that Big Data is not only influencing how we conceive of Minds and Brains but also on how we should do science. The idea seems to be that that with enough data, the search for basic causal architecture becomes quaint and unnecessary. We can just vacuum up the data and the generalizations of scientific utility will pop out. On this view, theories (and not only minds) are just compendia of data generalizations and given that we can now construct these compendia more efficiently and accurately by analyzing more and more data, theory construction becomes a quaint pastime. The only real problem with Empiricism is that we did not gather enough data fast enough. And now, Big Data can fix this. The dream of understanding without thinking is finally here. Oy vey!

Never thought I would say this

Never thought I would say this, but I found that I resonated positively to a recent small comment by Chris Manning on Deep Learning (DL) that Aaron White sent my way (here). It seems that the DL has computational linguistics (CL) of the Manning variety in its sights. Some DLers apparently believe that CL is just is nano-moments away from extinction. Here’s a great quote from one of the DL doyens:

NLP is kind of like a rabbit in the headlights of the Deep Learning machine, waiting to be flattened.

DL wise men like Geoff Hinton have already announced that they expect that machines will soon be able to watch videos and “tell a story about what happened” and be downsized onto an in-your-ear chip that can translate into English on the fly. Great things are clearly expected. Personally, I am skeptical as I’ve heard such hyperbole before. We have been five years away from this sort of stuff for a very long time.

Moreover, I am not alone. If I read Manning correctly, he is skeptical (though very politely so) as well.[1] But, like me, he sees an opportunity here, one I noted before (here and here). Of course we likely disagree about what kind of linguistics will be most useful for advancing these technological ends,[2] but when it comes to engineering projects I am very catholic in my tastes.

What’s the opportunity consist in? It relies on a bet: that generic machine learning (even of the DL variety) will not be able to solve the “domain problem.” The latter is the belief that how a domain of knowledge is structured matters a lot even if one’s aim is to solve an engineering problem.

An aside: shouldn’t those that think that the domain problem is a serious engineering hurdle also think that modularity is a good biological design feature? And shouldn’t these people therefore think that the domain specificity of FoL is a no-brainer? In other words, shouldn’t the idea that humans have domain specific knowledge that allows them to “solve” language problems (and support human facile acquisition and use) be the default position? Chris?  What think you? Dump general learning approaches and embrace domain specificity?

Back to the main point: The bet. So, if you think that using word contexts can only get you so far (and not interestingly far either), then you are ready to bet that knowing something about language will be useful in solving these engineering problems. And that provides linguists with an opportunity to ply their trade. In fact, Manning points to a couple of projects aimed at developing “a common syntactic dependency representation and POS (‘part of speech,’ NH) and feature label sets which can be used with reasonable linguistic fidelity and human usability across all human languages” (3).[3] He also advocates developing analogous representations for “Abstract Meaning.” This looks like the kind of thing that GGers could usefully contribute to. In other words, what we do directly fits into the Manning project.

Another aside: do not confuse this with investigating the structure of FL.  What matters for this project is a reasonable set of Greenberg “Universals.” Indeed, being too abstract might not be that useful practically, and being truly universal is not that important (what is important is finding those categories that best fit the particular languages of interest). This is not a bad thing. Engineering is not to be disparaged. It’s just not the same project as the one that GG has scientifically set for itself. Of course, should the Chomsky version of GG succeed, it is possible that it will contribute to the engineering problem. But then again, it might not. As I understand it, General Relativity has yet to make a big impact on land surveying. It really all depends (to fix ideas think birds and planes or fish and submarines. Last time I looked plane wings don’t flap and sub bodies don’t undulate).

Manning makes lots of useful comments about DL, many of which I didn’t understand. He makes some, however, that I did. For example, his the observation that DL has mainly proved useful in signal processing contexts (2) (i.e. where the problem is to get the generalization that is in the data, the pattern from (noisy) patternings). The language problem, as I’ve argued, is different from this (see here) so the limits of brute force DL will, I predict, become evident when the new wise men turn their attention to these. In fact, I make a more refined prediction: to “solve” this problem DLers will either (i) ignore it, (ii) restrict the domain of interest to finesse it or (iii) promise repeatedly that the solution is but 5 years away. This has happened before and will happen again unless the intricate structural constraints that characterize language are recognized and incorporated.

Manning also makes several points that I would take issue with. For example, IMO he (like many others) confuses squishy data for squishy underlying categories. See, in particular, Manning’s discussion of gerunds on p. 4. That the data does not exhibit sharp boundaries does not imply that the underlying structures are not sharp. In fact, at some level they must be for under every probabilistic theory there is a categorical algebra.  I leave it to you out there to come up with an alternative analysis of Manning’s observed data set. I give you a 30 second time limit to make it challenging.

At any rate, you will not be surprised to find out that I disagree with many of Manning’s comments. What might surprise you is that I think he is right in his reaction to DL hubris and he is right that there is an opportunity for what GGers know to be of practical value. There is no reason for DL (or Bayes or stats) to be inimical to GG. It’s just technology. What makes its practice often anathema is the hard-core empiricism gratuitously adopted by its practitioners. But this is not inherent to the technology. It is only a bias of the technologists. And there are some like Jordan and Manning and Reisinger who seem to get this. It looks like an opportunity for GGers to make a contribution? One, incidentally, that can have positive repercussions for the standing of GG. Scientific success does not require technological application. But having technological relevance does not hurt either.

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[1] I confess to a touch of schadenfreude given that this is the kind of thing that Manning and Co like to say about my kind of linguistics wrt to their CL approaches.

[2] Though I am not confident about this. I am pretty confident about what kind of linguistics one needs to advance the cognitive project. I am far less sure about what one needs to advance the engineering one. In fact, I suspect that a more “surfacy” syntax will fit the latter’s design requirements better than a more abstract one given its NLPish practical aims. See below for a little more discussion.

[3] I have it from a reliable source that this project is being funded by Google to the tune of millions. I have no idea how many millions, but given that billions are rounding errors to these guys, I suspect that there is real gold in them thar hills.