DynaPsych Table of Contents


On Biological and Digital Intelligence


A Reaction to Jeff Hawkins’ Book On Intelligence



Ben Goertzel

October 7, 2004




OK, for starters I’ll get the conventional-book-review-ish stuff out of the way.  Jeff Hawkins’ new book On Intelligence is an excellent book, well worth reading whether you’re a total novice to the cognitive and neural sciences or an expert researcher in one or more related domains.  Don’t let the fact that Hawkins is a wealthy computer entrepreneur (a founder of Palm and Handspring) fool you – he’s also a very serious thinker with some innovative and sophisticated things to say about the nature of brain and intelligence.  With this book, he joins a small, elite crew of theorists who have spanned the conceptual and empirical aspects of brain and mind theory.  His concepts are both philosophically significant, and intimately tied to the empirical realities of scientific research.  And furthermore, the book is extremely well-written and readable, no doubt in part due to the efforts of coauthor Sandra Blakeslee, a well-known science journalist.  


If you want more details on the book, check out the many glowing reviews on Amazon.com, and also the official website associated with the book:



What I’m going to give you in the rest of this “reaction/review” is my personal take on Hawkins’ ideas – where they’re right, where they’re wrong, and where they’re significantly incomplete.   That is, this is going to be a critical review of Hawkins’ ideas, rather than of his book per se.  Actually, I think the book is pretty much a perfect exposition of the ideas contained in it; and this is a big piece of praise, because there aren’t too many books about which I’d make such a statement (I definitely wouldn’t say anything similar about any of my own books so far, though I have some hopes for one of my current works-in-progress).


My perspective here is blatantly opinionated: I have my own particular view on the nature of intelligence, which I’ve been refining for many years during my career in academia and industry.  The reason I’ve considered it worthwhile to write down my perspective on Hawkins’s ideas is precisely that his thinking is, in many respects, pretty damn similar to my own.  The issues he’s been thinking through are, to a large extent, the same as the ones I’ve been thinking through.  He has more of a neuroscience slant, similar to my perspective in the mid-1990’s; lately I’ve taken my work in a more AI-ish direction, but even so, our conceptual landscapes are not all that differently shaped.  Because of this, I think I’ve been able to pretty quickly suss out the strengths and weaknesses in his approach to understanding biological and digital intelligence.


One way to get a quick idea of my own perspective is to read the following brief paper on the Novamente AI system, currently under development by myself and my colleagues:



I’ll begin with some comments on Hawkins’ overall philosophy, then discuss his comments on the nature of conventional neuroscience and AI research, and then delve into the details of his theories.  At the end I’ll give some reflections on the relationship between neuroscience and AI.



Hawkins’ Philosophy of Mind


I believe Hawkins’ overall philosophy of mind is correct.  It’s far from original, and he doesn’t claim it to be -- it’s one that he shares with many others, including (to make a fairly arbitrary list) myself, Ray Kurzweil, Douglas Hofstadter, Marvin Minsky, Gregory Bateson, Eric Baum and Charles S. Peirce.  The basic philosophy is that minds are sets of patterns associated with certain physical systems, aka “brains.”   A mind is a particular pattern-of-arrangement of patterns.  And what a mind does is to recognize patterns in the world around it, and in itself, and then to enact patterns in the world and in itself.  The wording I’ve used in the last few sentences is my own – Hawkins uses his own phraseology rather than explicit Goertzellian patternism – but he gets across the same basic ideas.  Of course, this philosophy in itself doesn’t tell you very  much about the mind – to get anywhere significant one needs to make specific commitments about what kind of arrangement-of-patterns a mind is, and how it is that certain physical systems cause this kind of arrangement of patterns to emerge from themselves.  But at least he gets the philosophy right in the first place, which is necessary in order for any further progress to be made. 


A consequence of the patternist philosophy of mind – be it of the Goertzellian, Hawkinsian, Hofstadterian or any other variety – is that minds are not tied to human or mammalian brains or any other particular type of physical system.  Minds can be made to emerge from any sufficiently flexible physical substrate – such as, for example, digital computers.  This general statement also doesn’t tell you very much of use – it doesn’t tell you how to create a mind from a digital computer, for example. 


It’s consistent with the patternist philosophy to believe, for instance, that although digital minds are possible, engineering a digital mind by any means other than slavishly imitating the details of the human brain is impractically difficult.  This is basically the perspective taken by Eric Baum in his recent book What Is Thought?   Baum argues that mind is in essence a fairly simple thing, concerned with finding programs that encapsulate data in minimally complex forms – but that the definition of “minimally complex” for any realistic intelligence relies on a huge amount of in-built knowledge.  Humans get this in-built knowledge via evolution via our genes; and Baum conjectures that there is no way for computer software to display human-level intelligence unless it’s supplied with a vast mass of in-build implicit background knowledge similar to the knowledge he thinks humans obtain through their genes.  This knowledge, in his theory, isn’t explicit knowledge like that in the Cyc database; it’s implicit knowledge regarding subtle patterns of how the perceptual, manipulable, social and internal world of humans on Earth is structured.


Hawkins, on the other hand, is more optimistic than this, regarding the hope for software intelligence.  Hawkins doesn’t think we need to emulate the human brain in detail.  What he thinks is that we need to understand the basic principles by which the brain works – and then, once we’ve gotten these principles down pat, we’ll be able to figure out how to make computer software programs that obey these same principles, and human-level or superior computer intelligence will be the results.  I tend to agree with his intuition in this regard – so long as one remains on an abstract level as was done in the previous few sentences.  But once one digs a little deeper to see what this actually means some “hidden rocks” emerge.


The real question with this approach is just how far one has to abstract the principles guiding the human brain, in order to get something that can then be specialized to the computer software domain, in a way that yields tractably implementable software.  Hawkins seems to think one doesn’t need to abstract all that far – so far as I can tell from the way he words things in his book, he seems to think that there’s some “algorithm” of brain function that can be ported to software, yielding a practical approach to software intelligence.  On the other hand, I suspect that the level of “algorithms” (at least, taking this word as typically used in computer sciences) is way too concrete to serve the intended purpose.  I think that the algorithms used in the brain are highly specialized to the particular properties of neural wetware, and that intelligent computer software – if it’s going to make at all reasonably efficient use of the hardware it runs on – is going to have to use totally different algorithms, embodying the same basic conceptual processes.  (Now of course, one can play with words here and say that these “basic conceptual processes” are in fact abstract algorithms, so that, even according to my perspective, there are algorithms spanning neural and software intelligence.  I can accept this perfectly well – the wording isn’t important to me – my point is that one is going to have to abstract very far from neural process to get something that’s efficiently implementable on modern or near future computing technology.  I will make this point in a very clear and concrete way a little further on.)


Before leaving philosophy I should mention a couple somewhat peripheral philosophical points on which I don’t agree with Hawkins: consciousness and the Singularity.


His take on consciousness is basically in the reductionist “What’s all the fuss about?” vein.  I don’t think this detracts from Hawkins’ brain theory at all, any more than Dennett’s similar perspective detracts from his cognitive psychology theorizing in his mal-named book Consciousness Explained.   In essence, Hawkins argues that, to whatever extent the concept of “consciousness” can’t be boiled down to brain theory, it’s simply a bunch of hooey.


My own view on consciousness is subtler and has proved difficult for me to communicate effectively; if you want to try to understand what the heck I’m talking about in this area, see my online essay Patterns of Awareness.  In short, my view is that the physical and patternist views of the mind are valid ones, but there are also other valid views of the mind, such as a purely experiential view, in which conscious experience is more foundational than physical systems or linguistic communications.  Conscious experiences are associated with patterns, and patterns are associated with physical systems, but none of these is fully reducible to the other. 


But anyway, one doesn’t need to accept Hawkins’ reductionist view of consciousness to like his brain/mind theories; and nor do you have to accept my multi-perspectival view of consciousness to accept my cognitive theories or my approach to AI.


The other philosophical topic on which I take issue with Hawkins is the Singularity (a term that he doesn’t mention, which I think is an error in itself, in a book dealing to an extent with the long-term future of AI).  Perplexingly, he brushes aside questions of the long-term risks of superhuman AI, focusing instead on the useful applications that human-level AI will have for humanity, in the medium term.  He makes the correct point that roughly-human-level AI’s will have dramatically different strengths and weaknesses from human being, due to different sensors and actuators and different physical infrastructures for their cognitive dynamics.  But he doesn’t even touch the notion of self-modifying AI – the concept that once an AI gets smart enough to modify its own code, it’s likely to get exponentially smarter and smarter until it’s left us humans in the dust.  He doesn’t even bother to argue against this notion, he just ignores it entirely! 


Conjecturally, I attribute this oversight to his focus on software intelligence that closely emulates the human brain.  The human brain doesn’t radically self-modify (because we can’t easily see our neurodynamics, and messing with our brains is generally directly life-threatening), therefore if AI closely enough emulates the human brain it won’t radically self-modify either.  But of course, even if AI were to start out by fairly closely emulating the human brain, it would still have much greater capability for self-modification than a human brain due to its digital implementation, and then it would likely gradually revise itself further and further away from its human initial condition, in a direction of greater and greater intelligence.


Hawkins’ Take on Contemporary Neuroscience and AI


Hawkins spends a while, in the beginning of the book, meditating on the shortcomings of neuroscience and artificial intelligence as currently practiced by the majority of researchers.   I tend to agree with most of his critiques.  However, the ultimate conclusions he draws are strikingly different in regard to the two disciplines.  In the case of neuroscience, he advocates a different way of doing neuroscience – and he’s even founded a small institute to pursue neuroscience Hawkins-style.  In the case of AI, on the other hand, he basically advocates that AI researchers should give up and wait for the neuroscientists to show them the way.


Regarding neuroscience, Hawkins gives his own version of the familiar complaint that nearly all mathematicians, physical scientists or computer scientists have about biology: too many details, too much fragmentation, and not enough questing for simple unifying principles and theories!  To an extent this complaint is unjustified – often the biological world just isn’t as orderly and clean and abstractly-encapsulable as those of us trained in the “harder sciences” would like it to be.  And to an extent it is justified – in some ways the discipline of biology has overreacted to the diverse messiness of its subject matter and developed a culture of detail-focus that has trouble finding beautiful biological generalizations in the rare cases where they do exist.  In the domain of genomics, the existence of a network centralized databases has helped to partially overcome the tendency of biology toward fragmentation; similar databasing efforts are underway in brain science. 


In 2002, Hawkins founded the Redwood Neuroscience Institute (RNI), with the explicit goal of paying a few outstanding scientists to seek an integrative understanding of brain function – as opposed to doing what most brain scientists are paid to do, which is to analyze some particular aspect of brain function in microscopic detail.  This is, in my opinion, a really wonderful thing for a wealthy software entrepreneur to do with some of his money.  Many of the ideas in On Intelligence seem to have achieved concrete form via Hawkins’ interactions with the scientists he hired to work with him at RNI.


Regarding AI, on the other hand, Hawkins correctly chastises classical logic-oriented AI theorists for focusing on toy problems and ignoring the real nature of intelligence, which has to do with the ability to generalize broadly into different domains of experience.  He correctly points out that neural net AI, after getting off to an interesting but slow start, degenerated precisely as it became more popular – turning primarily into a study of narrowly-focused computer algorithms with scant relevance to either brain function or general intelligence; deserving the name “neural network” in that their basic computational elements embody crude mathematical models of neurons, yet having little do to with neuroscience in either network structure or dynamics.  He correctly points out that most of the famous recent successes of AI, such as the chess champion program Deep Blue, were achieved via fairly straightforward heuristic algorithms, which have little to do with human intelligence or any kind of general intelligence.


I agree with all these critiques of his regarding AI as currently practiced, but I don’t consider them to invalidate the pursuit of AI separately from neuroscience.  Rather, I think the problem is that the field of AI has come to focus on “narrow AI” – programs that solve particularly, narrowly-defined problems – rather than “artificial general intelligence” (AGI).  Of course, there is no such thing as a truly general intelligence, except in the theoretical domain of AI on infinitely powerful computers as explored by Marcus Hutter, Juergen Schmidhuber and others in their recent mathematical work.   But clearly humans display much greater general scope of intelligence than any current AI program, and clearly the AI field has not been nearly enthusiastic enough about pursuing AI programs displaying a reasonable degree of general intelligence.


I think the AI field has come to focus on narrow AI for much the same reason that the neuroscience field has come to focus on the analysis of highly particular biological phenomena.  Namely: it’s easier to get quick results if you’re doing something relatively narrow and simple.  And whether in academia or a corporate setting, scientists are always under a lot of pressure to get quick results, for publication or productization as well as general reputation-building.  And in difficult areas like these, even relatively “narrow and simple” research is often pretty damn interesting.


I have experienced the pressure toward narrow AI research quite powerfully in my own career.  As an academic for 8 years, I wrote a number of books and papers on the nature of intelligence, taking an integrative point of view involving cognitive science, artificial general intelligence, philosophy of mind and abstract mathematics.  I conceived some interesting designs for AGI systems but they were too much for me to implement on my own.  I implemented some narrow AI programs to generate interesting results to publish papers on.  I tried and failed to get government grant money to build AGI systems; but did get some bits and pieces of grant money for narrow AI work, which was more popular with funding sources due to its apparently greater chance of success.  Then, I left academia for industry and started an ambitious AGI R&D project in the context of a dot-com era startup company.  But that company ran out of money and died, and the project had to be abandoned halfway through for legal reasons.  So I started over again with a team of software-engineer colleagues from the previous company.  Funded by a one-year one-man grant from the Jeffrey Epstein Foundation, I spent a year working out a new, crisper mathematical design for an AGI system (Novamente); my colleagues started a software consulting company, and together we worked out an efficient software framework for implementing my new design.  But even the new simpler design is a multi-year, multi-programmer project: AGI just isn’t all that simple!  No one appeared to fund our new AGI project, so we decided to fund it ourselves via creating commercial software programs based on our software.  But of course, you can’t create commercial software based on your AGI system until your AGI system is all done.  So what we’re doing is creating commercial narrow AI programs, using the software framework that we’re building out with our AGI design in mind.  Each commercial narrow AI program uses some stuff that’s part of our general AI design, and some other stuff that’s particular to the domain where the program is being applied.  Slowly, we’re building out our AGI system at the same time as making a modest amount of money from our narrow AI applications.  If we should make a large sum of money from any of these applications, then to be sure, we’ll use the profits to fund our AGI research, and the path toward AGI will occur at a much accelerated pace!


Ok -- the point of that digression into my personal history was not just to let off steam (no, really!) – the point is that, in AI just as in neuroscience, a combination of practical pressures and disciplinary-psychology issues have tended to push research in a non-optimal direction, and away from a general and integrative understanding of the topic matter. 


And yet, in both AI and neuroscience, research has still been highly productive even though nonoptimal.  We neither understand the brain nor do we have a thinking machine.  But neuroscientists have understood a lot about the brain; and AI scientists have developed a lot of great technology, such as probabilistic inference algorithms, evolutionary learning algorithms, and so forth.  Hawkins is using the detail-knowledge neuroscientists have gathered to help him build his integrative brain theory, and I’m using some of the algorithms narrow-AI scientists have created to try to build an integrative AGI system.


And, interestingly, just as places like RNI are slowly bringing some focus onto integrative brain understanding, it seems that the mainstream AI community is slowly regaining an interest in artificial general intelligence.  One piece of evidence is that later this month (October 2004) in Washington DC, the American Association for AI is organizing a symposium on Achieving Human-Level Intelligence through Integrated Systems.   I’ll be attending and presenting a talk there, along with a couple other Novamente folks.  So far as I can tell none of the other speakers there are going to be describing approaches to AGI that are nearly as “serious” as Novamente, in terms of the combination of a viable engineering implementation and a detailed mathematical and cognitive theory.  But at least this sort of research is becoming almost legitimate again, rather than being completely relegated to the sidelines.


Hawkins’ Brain/Mind Theory


Now, we finally get to the meat of the matter!   The centerpiece of Hawkins’ book is his brain/mind theory.  His discussions on the future of AI, and of consciousness and creativity and related ideas, are not particularly original nor tremendously deep – the only new points he has in those areas are ones that ensue pretty directly from his hypotheses about neuroscience and basic cognitive structure/dynamics. 


Where is his brain/mind theory right, and where is it wrong?


Actually, I don’t think it is significantly wrong, at least not in terms of sins of commission.   I tend to largely agree with his take on the brain -- which is not surprising because he and I have been inspired by much of the same neuroscience work, and I’ve previously published some speculations on brain function that overlap significantly with his ideas.   My only real complaints about his brain/mind theory regard sins of omission.   I think he oversimplifies some things fairly seriously – giving them very brief mention when they’re actually quite long and complicated stories.  And some of these omissions, in my view, are not mere “biological details” but are rather points of serious importance for his program of abstracting principles from brain science and then re-concretizing these principles in the context of digital software.


Let me start with Hawkins’ mind theory, and then segue into his brain theory.  The two fit tightly together, as well they should.


On the pure cognitive level, he refers to his approach as a “memory-prediction model.”  This means he thinks the key features of the human brain-mind are:



Indeed, this is hard to disagree with.  Marcus Hutter’s general mathematical theory of intelligence – see his book Universal Artificial Intelligence



--  agrees with this perspective, as does my own quasi-mathematical theory of intelligence articulated in my 1993 book The Structure of Intelligence:



The questions then become:



Both of these questions break down into multiple subquestions, including some easier and some harder subquestions.  My feeling is that Hawkins does not address the harder subquestions.


On a conceptual level, it seems to me that the “memory-prediction” view should be explicitly traced back to the patternist philosophy of mind.  Hawkins does this implicitly but he doesn’t really make a point of it.  Storing memories that are abstracted versions of percepts, actions and thoughts (Hawkins refers to these abstracted versions as “invariants”) involves the recognition of patterns among and within percepts, actions and thoughts.  On the other hand, making predictions involves recognizing temporal patterns and then assuming they’ll continue.  So his memory-prediction approach is quite consistent with his overall patternist philosophy. 


One point Hawkins doesn’t really cover is how a mind/brain chooses which predictions to make, from among the many possible predictions that exist.  Three sensible potential answers exist here:



Eric Baum focuses on the simplicity aspect, but also on the genetic-heritage aspect, which he refers to as “inductive bias.”  My own approach to mind-modeling, similarly to Marcus Hutter’s, focuses on the simplicity and goal-orientation aspects – I define an intelligent system as one that achieves complex goals in complex environments, and posit that a system tends to make predictions oriented toward predicting which of its actions will help it to fulfill its goals.  An important point is that, once one goes beyond really trivial predictions, the search space for “reasonably simple” patterns becomes very large – so that one needs either specific goals or a specific inductive bias to guide one’s search.   Hawkins doesn’t really deal with this issue, though he doesn’t dismiss it as irrelevant either.


The essence of On Intelligence is a set of hypotheses regarding how the brain carries out memory and prediction.   Following a train of thought pioneered by Vernon Mountcastle and others, Hawkins focuses on the columnar structure of the neocortex, and he proposes that this corresponds to the hierarchical structure of mind – lower levels of the columnar hierarchy corresponding to less abstract patterns, and higher and higher levels corresponding to progressively more and more abstract patterns.  The dynamics of perception, action and cognition then involves information moving up and down this hierarchy of patterns among patterns among patterns. 


One of the book’s more innovative proposals involves the mechanics of pattern nesting.  Hawkins proposes that there are neurons or neuronal groups that represent patterns as “tokens,” and that these tokens are then incorporated along with other neurons or neuronal groups into larger groupings representing more abstract patterns.  This seems clearly to be correct, but he doesn’t give much information on how these tokens are supposed to be formed.  It’s not clear that Hebbian learning is a sufficient mechanism.  The brain may contain some additional dynamic, which causes attractors (see below) on one level in the cortical hierarchy to link to individual neurons one level higher in the hierarchy.  This is an extremely important point, because it’s a link between neurodynamics and symbolic processing – symbol creation and manipulation being one of the hallmarks of abstract, higher-level human thought.  This is one area where I have some hope Hawkins’ team at the RNI will provide deep new insights over the next few years: it’s at the heart of Hawkins’ theory and it’s also palpably central to the mind-brain connection.


I presented a somewhat similar theory of cortical function in an article in Complexity in 1996, also included as a chapter in From Complexity to Creativity -- see crude HTML dump at



What’s similar in my theory and Hawkins’ is the alignment of the cortical-column hierarchy with the cognitive hierarchy that guides pattern recognition and pattern creation.   What Hawkins adds are a lot of excellent details regarding the behavior of this hierarchy in different parts of the brain, the use of the hierarchy for temporal prediction, the interaction between the cortical-column hierarchy and the hippocampus, and so forth. Many of these details came out of neurophsyiological research done in the interval between 1996 and 2004, including some knowledge-integration done at the RNI.


So, what’s wrong with Hawkins’ picture of brain function?  Nothing’s exactly wrong with it, so far as I can tell.  I think it’s wonderful – as far as it goes.   It’s an outstanding extrapolation from the wild mess of available data.  But it’s badly incomplete.  Specifically, it seems to me to leave out three very important things:


  1. Attractors
  2. Evolutionary learning
  3. The emergent consequences of Hebbian learning


In fact, none of these is quite left out in Hawkins’ book  (though 2 comes very close), but they’re only very briefly and shallowly mentioned– whereas from my point of view, they’re extremely essential to understanding the brain and how it gives rise to the mind.


Attractors are a staple concept from nonlinear dynamics.  An attractor is basically a dynamical pattern in a system, which tends to continue once it gets started.  Attractors don’t need to be permanent; they can be “terminal attractors” that stay around for a while and then disappear.  A number of neuroscientists have written about the probable key role of attractors in the human brain; Walter Freeman is probably the most distinguished example.   Hawkins doesn’t refer to this literature at all in his book; but in fact I think it’s highly relevant to his brain theory.  Attractors among cortical columns are, in my opinion, critical to human intelligence.  I talked a great deal about the potential role of attractors in mind and brain in my books Chaotic Logic and From Complexity to Creativity, written in the mid-1990’s; rough and messy HTML dumps of these books are available online at



Though he doesn’t favor the language of “attractors,” this notion is central to Gerald Edelman’s theory of Neural Darwinism – a theory that I reviewed extensively in my 1993 book The Evolving Mind , see



Hawkins was inspired by Vernon Mountcastle’s theories about cortical columns, developed in the 1970’s.  I first learned about Mountcastle’s work from Gerald Edelman’s books. Edelman talks about “neuronal groups” rather than columns, but it’s largely the same idea.  But Edelman then takes the concept one step further and talks about “neural maps” – assemblies of neuronal groups that carry out particular perception, cognition or action functions.  Neural maps, in essence, are sets of neuronal groups that host attractors of neurodynamics.  And Edelman then observes, astutely, that the dynamics of the population of neuronal groups, over time, is likely to obey a form of evolution by natural selection.


This line of thinking is very interesting to me because one of the most exciting developments in the AI field during the last few decades is the notion of “evolutionary programming” – learning-algorithms that operate via emulating the process of evolution by natural selection.  How fascinating if the brain also operates in this way!


This kind of evolutionary cognition, if it exists as Edelman postulates, has deep implications for the nature of creativity; see my conceptual theory of creativity from From Complexity to Creativity: 



Hawkins argues that creativity is essentially just metaphorical thinking, generalization based on memory.  While this is true in a grand sense, it’s not a very penetrating statement.  There are a lot of possible generalizations based on memory, so the question is how does the mind choose the good generalizations from the mass of bad ones?   In his discussion, Hawkins evokes images of random search, but obviously he knows the search isn’t random.  Evolutionary learning is the most powerful general search mechanism known to computer science, and is also hypothesized by Edelman to underly neural intelligence.  This sort of idea, it seems to me, should be part of any synthetic approach to brain function.


Finally, the third point that’s drastically underplayed in Hawkins’ treatment: Hebbian learning.  Hawkins mentions the notion, and observes correctly that Hebbian learning in the brain is a lot subtler than the simple version that Donald Hebb laid out in the late 40’s.   But he largely portrays these variations as biological details, and then shifts focus to the hierarchical architecture of the cortex.  Of course, the architecture is important – but I believe that the details of Hebbian learning are extremely important too.  Because I believe that, if its parameters are tuned right, then Hebbian learning on the neuron level can lead to probabilistic logical reasoning on the level of neural maps.   I’ve argued this point of view in some depth in my essay “Hebbian Logic”:



I must stress that the above essay is speculative rather than rigorous, and is somewhat rough-drafty in form.  I haven’t proved anything there – I’ve just outlined some evocative ideas, and done some simple algebra to show that they’re plausible.  A lot of mathematical and computer-simulation work needs to be done to validate the Hebbian Logic idea.  However, regarding Hawkins’ work, my point is that this type of idea is entirely missing from his work.  He doesn’t address at all the question of how abstract symbolic reasoning arises from neuron-level or neural-cluster-level dynamics.  He lumps abstract symbolic reasoning into the grab-bag categories of “memory” and “prediction” – which is correct in a general sense, but not very informative. 


For the reader with a technical background, it’s worth briefly noting the weak link in my Hebbian Logic proposal.  I haven’t yet figured out an elegant, biologically plausible way to make Hebbian learning give rise to a particular phenomenon in formal logic: the binding of existential quantifiers to other quantifiers.  Every other aspect of logic seems to me to boil down nicely to Hebbian learning based phenomena, but not that one.  Of course, it’s worth noting that the human mind is pretty bad at doing reasoning doing this type of complex logic rule; so it may be that the brain implements this sort of thing using some kind of “horrible hack.”  But anyway, Hawkins doesn’t really get to the point of banging his head against tricky issues like this one, because he doesn’t try to elaborate the basic Hebbian learning mechanism in a way that will explain how it can give rise to high-level cognitive behaviors.


This discussion of reasoning and neurodynamics ties in with Hawkins’ critique of AI, which in my view is overly harsh.  He dismisses work on formal logic based reasoning as irrelevant to “real intelligence.”  But if it turns out that, as I claim, Hebbian learning on the neural map level in fact is a statistical approximation to probabilistic logic, then in fact probabilistic logic is not at all irrelevant to real intelligence – not even to real biological intelligence, let alone to real digital intelligence (which may potentially be engineered to rely on probabilistic logic more directly than the brain does). 


Maybe my speculations about Hebbian learning and probabilistic logic are wrong (though, OK, personally I’m confident that they’re so sensible they have to be right in essence, even if the details are significantly different from what I’ve speculated).  But some such theory has got to be posited, if one is going to have a theory of brain-mind.  It’s not enough just to say that logical inference comes about as a consequence of memory and prediction.  Of course, Hawkins realizes this isn’t enough – but nor does he highlight the fact that this is an area where a lot more work is needed.


So – to sum up – I think Hawkins’ statements about brain function are pretty much correct … i.e., his analysis and integration of the known data is adept, and his intuitive speculations generally agree with my own intuitive speculations!  However, he leaves out some important things, and there is a pattern to these omissions.  What he omits are, for instance,



These omissions are, interestingly, two areas where brain dynamics appears to overlap with contemporary AI research, two main strains within which are evolutionary computing and probabilistic inference.  So, Hawkins’ near-complete dismissal of modern AI research goes hand in hand with his sidestepping of those ways in which brain function resonates well with modern AI research!


In terms of his conceptualization of cognitive function in terms of memory and prediction, my suggestion is that Hawkins’ omissions are key points regarding knowledge representation and the learning of predictive patterns.  Memory of abstract patterns requires a suitably abstract knowledge representation – probabilistic logic is one such representation, which plausibly may emerge from neural structures and dynamics; Hawkins does not propose any concrete alternative.  Learning of predictive patterns requires an explicit or implicit search through a large space of predictive patterns; evolutionary learning provides one approach to this problem, with computer science foundations and plausible connections to brain function; again, Hawkins does not propose any concrete alternative.


AI, Neuroscience or Both?


By way of conclusion, I’ll now get back to the crucial question of how far one has to abstract away from brain function, to get to something that can be re-specialized into efficient computer software.  My intuition is that this will require a higher level of abstraction than Hawkins seems to believe.  But I stress that this is a matter of intuitive judgment – neither of us really knows.


For instance, my own feeling is that implementing Hebbian learning in software on contemporary computers is likely to be a big waste.  If it’s really true that Hebbian learning emergently gives rise to probabilistic inference, then why not just implement probabilistic inference instead?  Similarly, if what’s important about the dynamics of neural maps is that they display evolutionary learning, then why not just implement evolutionary learning in some way that’s more natural for digital computers?  The hierarchical structure of the cortex is clearly critical – but why not create an hierarchically structured network adapting itself via probabilistic inference and evolutionary learning, in a way that’s natural for digital computers?  Well, this is exactly what we’re trying to do with our Novamente AI system.


Of course, to interpret the Novamente design as an “abstraction from the brain” is to interpret this phrase in a fairly extreme sense – we’re abstracting general processes like probabilistic inference and evolutionary learning and general properties like hierarchical structure from the brain, rather than particular algorithms.  One may well be able to create reasonably efficient digital AGI systems that don’t abstract quite this far from what the brain does.  On the other hand, perhaps one can’t.  It’s a mistake to underestimate the extent to which the brain’s “algorithms” are fit to the particular strengths and weaknesses of the wetware for which they evolved.


Although I’m (unsurprisingly) most psyched about the Novamente approach, I think it’s also quite worthwhile to pursue AGI approaches that are closer to the brain level – there’s a large space between detailed brain simulation and Novamente, including neuron-level simulations, neural-cluster-level simulations, and so forth.   In fact, the Novamente software framework could even be used as a platform for the creation of such AGI systems. 


It’s true that the creation of these “intermediate-neural-fidelity” systems will become easier once more is understood about the brain.  But even so, as a matter of research planning, I don’t agree with Hawkins that it’s optimal for AGI research to wait for neuroscience to point the way.  Quite the contrary: my suspicion is that the two disciplines should work together.  Insights into the nature of evolutionary learning and probabilistic inference, obtained via AI work, are likely to lead to insights into how these processes are implemented in the brain; just as a deeper understanding of neurodynamics will doubtless help us structure and refine our AGI programs.


Finally, I’ve asked myself whether Hawkins’ ideas, so far, contain any insights helpful to my work with Novamente.  But I haven’t come up with anything so far.  The Novamente AI design, at this point, embodies solutions to a lot of hard problems in AGI design, and I believe that only one significant problem is left (in addition to a whole bunch of engineering and tuning regarding various problems that have been “solved in essence”):



(A snippet of technical detail: The definition of “moderately large” here is: representable using a few hundred nodes in a knowledge representation called a “Novamente combinator tree.”  Each node represents some Novamente concept or procedure, or some basic programming construct like a number or a sum or a loop.)   The approach taken to this problem in Novamente involves a combination of probabilistic evolutionary programming (the Bayesian Optimization Algorithm applied to “combinator trees”) and probabilistic inference.  But the control of probabilistic inference is itself a significant problem – the learning of inference control procedures it in itself a problem of “learning (moderately) large, complex procedures via breaking them down appropriately into hierarchical of smaller procedures.”  Teaching Novamente will be a matter of guiding the system through a bootstrapping process by which moderately complex inference control procedures allow the system to learn slightly more complex inference control procedures, etc.


My conclusion, however, is that Hawkins’ book doesn’t really present a solution to this tough problem, except in a very vague and general way.  The problem we’re wrestling with is clearly reflected in Hawkins’ brain theory – in his language, it’s essentially the problem of how Hebbian learning acts in a coordinated way through the cortical hierarchy, allowing unified learning of complex neural maps spanning multiple layers and columns.  But at this stage all he has to offer regarding this problem is some suggestive hand-waving.  Clearly he thinks the solution involves tokenization of patterns represented as patterns of activity among multiple neurons – but the question then becomes how the multi-level tokenization process is guided, something he doesn’t really address.  If he does solve this problem within his brain theory, the solution may well be very useful for Novamente.  For the present time, however, we’ll continue to explore our own solutions to the problem – we have some in mind that seem highly promising, though refining and testing them will be a lot of work.




Naahhh….  I don’t have a conclusion.  Everything important regarding these topics is still unresolved!


Jeff Hawkins has written a very nice book, about some very interesting theories, some his own and some extracted from the neuroscience research literature.   He hasn’t penetrated all the way into the mysteries of the brain/mind yet – but then, he doesn’t claim he has.  He’s reasonable all the way through – which is disappointingly rare in these domains – and he’s innovative in some important respects.  I think his research program is fairly likely to lead to fundamental progress in understanding the brain – and I wouldn’t say that about most neuroscience research initiatives I read about.  As yet his research program hasn’t led to anything that seems really striking to me -- based on my prior knowledge and prior thinking and writing on related topics -- but heck, RNI has only existed since 2002.


As I’ve noted above in detail, I think Hawkins’ current theories leave out some important points about brain dynamics -- but I also think that, by following his methodology of integrating all available scientific data into a solid mathematical and conceptual picture, he’ll probably eventually incorporate these points into a future version of his theory (most likely expressed in his own language rather than mine!).  I find his comments on AI much less penetrating than his comments on neuroscience, which is probably because he’s right in the thick of neuroscience research, whereas he’s commenting on AI as a relative outsider (albeit a fairly highly informed outsider).  What I’ve done in this review/reaction is mostly to elaborate on Hawkins’ ideas in the context of my own work in AI and theoretical cognitive science -- highlighting both the similarities and the differences.  Hopefully this exercise has been of some interest to you!