Ray Kurzweil - How to Create a Mind - The Secret of Human Thought Revealed

The performance of these systems is a testament to the power of mathematics. With them we are essentially computing what is going on in the neocortex of a speaker—even though we have no direct access to that person’s brain—as a vital step in recognizing what the person is saying and, in the case of systems like Siri, what those utterances mean. We might wonder, if we were to actually look inside the speaker’s neocortex, would we see connections and weights corresponding to the hierarchical hidden Markov models computed by the software? Almost certainly we would not find a precise match; the neuronal structures would invariably differ in many details compared with the models in the computer. However, I would maintain that there must be an essential mathematical equivalence to a high degree of precision between the actual biology and our attempt to emulate it; otherwise these systems would not work as well as they do.

LISP (LISt Processor) is a computer language, originally specified by AI pioneer John McCarthy (1927–2011) in 1958. As its name suggests, LISP deals with lists. Each LISP statement is a list of elements; each element is either another list or an “atom,” which is an irreducible item constituting either a number or a symbol. A list included in a list can be the list itself, hence LISP is capable of recursion. Another way that LISP statements can be recursive is if a list includes a list, and so on until the original list is specified. Because lists can include lists, LISP is also capable of hierarchical processing. A list can be a conditional such that it only “fires” if its elements are satisfied. In this way, hierarchies of such conditionals can be used to identify increasingly abstract qualities of a pattern.

LISP became the rage in the artificial intelligence community in the 1970s and early 1980s. The conceit of the LISP enthusiasts of the earlier decade was that the language mirrored the way the human brain worked—that any intelligent process could most easily and efficiently be coded in LISP. There followed a mini-boomlet in “artificial intelligence” companies that offered LISP interpreters and related LISP products, but when it became apparent in the mid-1980s that LISP itself was not a shortcut to creating intelligent processes, the investment balloon collapsed.

It turns out that the LISP enthusiasts were not entirely wrong. Essentially, each pattern recognizer in the neocortex can be regarded as a LISP statement—each one constitutes a list of elements, and each element can be another list. The neocortex is therefore indeed engaged in list processing of a symbolic nature very similar to that which takes place in a LISP program. Moreover, it processes all 300 million LISP-like “statements” simultaneously.

However, there were two important features missing from the world of LISP, one of which was learning. LISP programs had to be coded line by line by human programmers. There were attempts to automatically code LISP programs using a variety of methods, but these were not an integral part of the language’s concept. The neocortex, in contrast, programs itself, filling its “statements” (that is, the lists) with meaningful and actionable information from its own experience and from its own feedback loops. This is a key principle of how the neocortex works: Each one of its pattern recognizers (that is, each LISP-like statement) is capable of filling in its own list and connecting itself both up and down to other lists. The second difference is the size parameters. One could create a variant of LISP (coded in LISP) that would allow for handling such parameters, but these are not part of the basic language.

LISP is consistent with the original philosophy of the AI field, which was to find intelligent solutions to problems and to code them directly in computer languages. The first attempt at a self-organizing method that would teach itself from experience—neural nets—was not successful because it did not provide a means to modify the topology of the system in response to learning. The hierarchical hidden Markov model effectively provided that through its pruning mechanism. Today, the HHMM together with its mathematical cousins makes up a major portion of the world of AI.

A corollary of the observation of the similarity of LISP and the list structure of the neocortex is an argument made by those who insist that the brain is too complicated for us to understand. These critics point out that the brain has trillions of connections, and since each one must be there specifically by design, they constitute the equivalent of trillions of lines of code. As we’ve seen, I’ve estimated that there are on the order of 300 million pattern processors in the neocortex—or 300 million lists where each element in the list is pointing to another list (or, at the lowest conceptual level, to a basic irreducible pattern from outside the neocortex). But 300 million is still a reasonably big number of LISP statements and indeed is larger than any human-written program in existence.

However, we need to keep in mind that these lists are not actually specified in the initial design of the nervous system. The brain creates these lists itself and connects the levels automatically from its own experiences. This is the key secret of the neocortex. The processes that accomplish this self-organization are much simpler than the 300 million statements that constitute the capacity of the neocortex. Those processes are specified in the genome. As I will demonstrate in chapter 11, the amount of unique information in the genome (after lossless compression) as applied to the brain is about 25 million bytes, which is equivalent to less than a million lines of code. The actual algorithmic complexity is even less than that, as most of the 25 million bytes of genetic information pertain to the biological needs of the neurons, and not specifically to their information-processing capability. However, even 25 million bytes of design information is a level of complexity we can handle.

As I discussed in chapter 3, Jeff Hawkins and Dileep George in 2003 and 2004 developed a model of the neocortex incorporating hierarchical lists that was described in Hawkins and Blakeslee’s 2004 book On Intelligence . A more up-to-date and very elegant presentation of the hierarchical temporal memory method can be found in Dileep George’s 2008 doctoral dissertation. 12 Numenta has implemented it in a system called NuPIC (Numenta Platform for Intelligent Computing) and has developed pattern recognition and intelligent data-mining systems for such clients as Forbes and Power Analytics Corporation. After working at Numenta, George has started a new company called Vicarious Systems with funding from the Founder Fund (managed by Peter Thiel, the venture capitalist behind Facebook, and Sean Parker, the first president of Facebook) and from Good Ventures, led by Dustin Moskovitz, cofounder of Facebook. George reports significant progress in automatically modeling, learning, and recognizing information with a substantial number of hierarchies. He calls his system a “recursive cortical network” and plans applications for medical imaging and robotics, among other fields. The technique of hierarchical hidden Markov models is mathematically very similar to these hierarchical memory systems, especially if we allow the HHMM system to organize its own connections between pattern recognition modules. As mentioned earlier, HHMMs provide for an additional important element, which is modeling the expected distribution of the magnitude (on some continuum) of each input in computing the probability of the existence of the pattern under consideration. I have recently started a new company called Patterns, Inc., which intends to develop hierarchical self-organizing neocortical models that utilize HHMMs and related techniques for the purpose of understanding natural language. An important emphasis will be on the ability for the system to design its own hierarchies in a manner similar to a biological neocortex. Our envisioned system will continually read a wide range of material such as Wikipedia and other knowledge resources as well as listen to everything you say and watch everything you write (if you let it). The goal is for it to become a helpful friend answering your questions— before you even formulate them—and giving you useful information and tips as you go through your day.