Monthly Archives: September 2015

Sparse distributed representations compute similarity relations exponentially more efficiently than localist representations

If concepts are represented localistically, the first, the most straightforward thing to do is to place those representations in an external coordinate system (of however many dimensions as desired/needed).  The reason is that localist representations of concepts have the semantics of “point masses”.  They have no internal structure and no extension.  So it is natural to view them as points in an N-dimensional coordinate system.  You can then measure the similarity of two concepts (points) using for example, Euclidean distance.  But, note that placing a new point in that N-space does not automatically compute and store the distance between that new point and ALL points already stored in that N-space.  This requires explicit computation and therefore expenditure of time and power.

On the other hand, if concepts are represented using sparse distributed codes (SDCs), i.e., sets of co-active units chosen from a much larger total field of units, where the sets may intersect to arbitrary degrees, then it becomes possible to measure similarity (inverse distance) as the size of intersection between codes.  Note that in this case, the representations (the SDCs) fundamentally have extension…they are not formally equivalent to point masses.  Thus, there is no longer any need for an external coordinate system to hold these representations.  A similarity metric is automatically imposed on the set of represented concepts by the patterns of intersections of their codes.  I’ll call this an internal similarity metric.

Crucially, unlike the case for localist codes, creating a new SDC code (i.e., choosing a set of units to represent a new concept), DOES compute and store the similarities of the new concept to ALL stored concepts.  No explicit computation, and thus no additional computational time or power, is needed beyond the act of choosing/storing the SDC itself.

Consider the toy example below.  Here, the format is that all codes will consist of exactly 6 units chosen from the field.  Suppose the system has assigned the set of red cells to be the code for the concept, “Cat”.  If the system then assigns the yellow cells to be the code for “Dog”, then in the act of choosing those cells, the fact that three of the units (orange) are shared by the code for “Cat” implicitly represents (reifies in structure) a particular similarity measure of “cat” and “Dog”.  If the system later assigns the blue cells to represent “Fish”, then in so doing, it simultaneously reifies in structure particular measures of similarity to both “Cat” and “Dog”, or in general, to ALL concepts previously stored.  No additional computation was done, beyond the choosing of the codes themselves, in order to embed ALL similarity relations, not just the pairwise ones, but those of all orders (though this example is really too small to show that), in the memory.

SDC_reified_simialarities

This is why I talk about SDC as the coming revolution in computation. Computing the similarities of things is in some sense the essential operation that intelligent computers perform.  Twenty years ago, I demonstrated, in the form of the constructive proof that is my model TEMECOR, now Sparsey®, that choosing an SDC for a new input, which respects the similarity structure of the input space, can be done in fixed time (i.e., the number of steps, thus the compute time and power, remains constant as additional items are added).  In light of the above example, this implies that an SDC system computes an exponential number of similarity relations (of all orders) and reifies them in structure also in fixed-time.

Now, what about the possibility of using localist codes, but not simply placed in an N-space, but stored in a tree structure?  Yes.  This is, I would think, essentially how all modern databases are designed.  The underlying information, the fields of the records, are stored in localist fashion, and some number E of external tree indexes are constructed and point into the records.  Each individual tree index allows finding the best-matching item in the database in log time, but only with respect to the particular query represented by that index.  When a new item is added to the database all E indexes must execute their insertion operations independently.  In the terms used above, each index computes the similarity relations of a new item to ALL N stored items and reifies them using only logN comparisons.  However, the similarities are only those specific to the manifold (subspace) corresponding to index (query).  The total number of similarity relations computed is the sum across the E indexes, as opposed to the product. But it is not this sheer quantitative difference, but rather that having predefined indexes precludes reification of almost all of the similarity relations that in fact may exist and be relevant in the input space.

Thus I claim that SDC admits computing similarity relations exponentially more efficiently than localist coding, even localist codes augmented by external tree indexes.  And, that’s at the heart of why in the future, all intelligent computation will be physically realized via SDC….and why that computation will be able to be done as quickly and power-efficiently as in the brain.

Quantum Computing in the Offing

Huge money is being invested in quantum computing (QC), IBM, Intel, Microsoft, Google, China…  However, I propose that it will not arrive (or at least arrive first) by the path these giants are pursuing.  All the “mainstream” efforts to build QC are based on exotic physical mechanisms, e.g., extremely low temperature entities that can remain in superposition (i.e., do not decohere).  But this is not needed to realize QC.  As I’ve argued elsewhere, QC is physically realizable by a standard, single-processor Von Neumann computer, i.e., your desktop, your smart phone.  It’s all a matter of how information is represented in the system, not of the physical nature of the individual units (memory bits) that represent the information.  Specifically, the key to implementing QC lies in simply changing from representing information localistically (see below) to representing information using sparse distributed representations (SDR), a.k.a. sparse distributed coding (SDC).  We’ll use “SDC” throughout.  To avoid confusion, let me emphasize immediately that SDC is a completely different concept than the far more well-known and (correctly) widely-embraced concept of “sparse coding” (Olshausen & Field, 1996), though they are completely compatible.

A localist representation is one in which each item of information (“concept”) stored in the system, e.g., the concept, ‘my car’, is represented by a single, atomic unit, and that physical unit is disjoint from the representations of all other concepts in the system.  We can consider that atomic representational unit to be a word of memory, say 32 or 64 bits.   No other concept, of any scale, represented in the database can use that physical word (representational unit).  Consequently, that single representational unit can be considered the physical representation of my car (since all of the information stored in the database, which together constitutes the full concept of ‘my car’, is reachable via that single unit).  This meets the definition of a localist representation…the representations of the concepts are physically disjoint.

In contrast to localism, we could devise a scheme in which each concept is represented by a subset of the full set of physical units comprising the system, or more specifically, comprising the system’s memory.  For example, if the memory consisted of 1 billion physical bits, we could devise a scheme in which the concept, ‘my car’, might be represented by a particular subset of, say, 10,000 of those 1 billion bits.  In this case, if the concept ‘my car’ was active in that memory, that set of 10,000 bits, and only that particular subset, would be active.

What if some other concept, say, ‘my motorcycle’, needs to become active?  Would some other subset of 10,000 bits that is completely disjoint from the 10,000 bits representing my car, become active?  No.  If our system was designed this way, it would again be a localist representation (since we’d be requiring the representations of distinct concepts to be physically disjoint).  Instead, we could allow the 10,000 bits that represent my motorcycle to share perhaps 5,000 bits in common with my car’s representation.  The two representations are still unique.  After all, they each have 5,000 bits—half their overall representations—not in common with each other.  But the atomic representational units, bits, can now be shared by multiple concepts, i.e., representations can physically overlap.  Such a representation in which a) each concept is represented by a small subset of the total pool of representational units and b) those subsets can intersect, is called a sparse distributed code (SDC).

With these definitions in mind, it is crucial (for the computer industry) to realize  that to date, virtually all information stored electronically on earth, e.g., all information stored in fields of records of databases, is represented localistically.  Equivalently, to date there has been virtually no commercial use of SDC on earth.   Moreover, only a handful of scientists have thus far understood the importance of SDC, Kanerva (~1988), Rachkovskij & Kussul (late 90’s), myself (early 90’s, Thesis 1996), Hecht-Nielsen (~2000), Numenta (~2009), and a few others.  Only in the past year or so, have the first few attempts at  commercialization begun to appear, e.g., Numenta.  Thus, two things:

  1. The computer industry may want to at least consider (due diligence) that SDC may be the next major, i.e., once-in-a-century, paradigm shift
  2. it could be that SDC = QC

With SDC, it becomes possible for those 5,000 bits that the two representations (‘my car’ and ‘my motorcycle’) have in common to represent features (sub-concepts) that are common to both my car and my motorcycle.  In other words, similarity in the space of represented concepts can be represented by physical overlap of the representations of those concepts.  This is something that cannot be achieved with a localist representation (because localist representations don’t overlap).  And from one vantage point, it is the reason why SDC is so superior to localist coding, in fact, exponentially superior to localist coding.

But, the deep (in fact, identity) connection of SDC and QC is not that more similar concepts will have larger intersections.  Rather it is that if all representable (by a particular memory/system) concepts are represented by subsets of an overall pool of units and if those subsets can overlap, then any single concept. i.e., any single subset, can be viewed as, and can function as, a probability (or likelihood) distribution over ALL representable concepts.  We’ll just use “probability”.  That is, any given active representation represents all representable hypotheses in superposition.  And if the model has enforced that similar concepts are assigned to more highly overlapping codes, then the probability of any particular concept at a given moment is the fraction of that concept’s bits that are active in the currently (fully) active code (making the reasonable assumption that for natural worlds, the probabilities of two concepts should correlate with their similarities).

This has the following hugely important consequence.  If there exists an algorithm that updates the probability of the currently active single concept in fixed time, i.e., in computational time that remains constant over the life of the system (more specifically, remains constant as more and more concepts are stored in the memory), then that algorithm can also be viewed as updating the probabilities of all representable concepts in fixed time.  If the number of concepts representable is of exponential order (i.e. exponential in the number of representational units), then we have a system which updates an exponential number of concepts, more specifically, an exponential number of probabilities of concepts (hypotheses), in fixed time. Expressed at this level of generality, this meets the definition of QC.

All that remains to do in order to demonstrate QC is to show that the aforementioned fixed time operation that maps one active SDC into the  next—or equivalently, that maps one active probability distribution into the next—changes the probabilities of all representable concepts in a sensible way, i.e., in a way that accurately models the space of representable concepts (i.e., accurately models the semantics, or the dynamics, or the statistics, of that space).   In fact, such a fixed time operation has existed for some time (since about 1996).  It is the Sparsey® model (formerly TEMECOR, and see thesis at pubs).  And, in fact, the reason why the updates to the probability distribution (i.e., to the superposition) can be sensible is that is, as suggested above, that similarity of concepts can be represented by degree of intersection (overlap) of their SDCs.

I realize that this view, that SDC is identically QC, flies in the face of dogma, where dogma can be boiled down to the phrase “there is no classical analog of quantum superposition”.  But I’m quite sure that the mental block underlying this dogma for so long has simply been that that quantum scientists have been thinking in terms of localist representations.  I predict that it will become quite clear in the near future that SDC constitutes a completely plausible classical analog of quantum superposition.

..more to come, e.g., entanglement is easily and clearly explained in terms of SDC…