Our digital brain will also accommodate substantial redundancy of each pattern, especially ones that occur frequently. This allows for robust recognition of common patterns and is also one of the key methods to achieving invariant recognition of different forms of a pattern. We will, however, need rules for how much redundancy to permit, as we don’t want to use up excessive amounts of memory on very common low-level patterns.
The rules regarding redundancy, recognition thresholds, and the effect on the threshold of a “this pattern is expected” indication are a few examples of key overall parameters that affect the performance of this type of self-organizing system. I would initially set these parameters based on my intuition, but we would then optimize them using a genetic algorithm.
A very important consideration is the education of a brain, whether a biological or a software one. As I discussed earlier, a hierarchical pattern recognition system (digital or biological) will only learn about two—preferably one—hierarchical levels at a time. To bootstrap the system I would start with previously trained hierarchical networks that have already learned their lessons in recognizing human speech, printed characters, and natural-language structures. Such a system would be capable of reading natural-language documents but would only be able to master approximately one conceptual level at a time. Previously learned levels would provide a relatively stable basis to learn the next level. The system can read the same documents over and over, gaining new conceptual levels with each subsequent reading, similar to the way people reread and achieve a deeper understanding of texts. Billions of pages of material are available on the Web. Wikipedia itself has about four million articles in the English version.
I would also provide a critical thinking module, which would perform a continual background scan of all of the existing patterns, reviewing their compatibility with the other patterns (ideas) in this software neocortex. We have no such facility in our biological brains, which is why people can hold completely inconsistent thoughts with equanimity. Upon identifying an inconsistent idea, the digital module would begin a search for a resolution, including its own cortical structures as well as all of the vast literature available to it. A resolution might simply mean determining that one of the inconsistent ideas is simply incorrect (if contraindicated by a preponderance of conflicting data). More constructively, it would find an idea at a higher conceptual level that resolves the apparent contradiction by providing a perspective that explains each idea. The system would add this resolution as a new pattern and link to the ideas that initially triggered the search for the resolution. This critical thinking module would run as a continual background task. It would be very beneficial if human brains did the same thing.
I would also provide a module that identifies open questions in every discipline. As another continual background task, it would search for solutions to them in other disparate areas of knowledge. As I noted, the knowledge in the neocortex consists of deeply nested patterns of patterns and is therefore entirely metaphorical. We can use one pattern to provide a solution or insight in an apparently disconnected field.
As an example, recall the metaphor I used in chapter 4 relating the random movements of molecules in a gas to the random movements of evolutionary change. Molecules in a gas move randomly with no apparent sense of direction. Despite this, virtually every molecule in a gas in a beaker, given sufficient time, will leave the beaker. I noted that this provides a perspective on an important question concerning the evolution of intelligence. Like molecules in a gas, evolutionary changes also move every which way with no apparent direction. Yet we nonetheless see a movement toward greater complexity and greater intelligence, indeed to evolution’s supreme achievement of evolving a neocortex capable of hierarchical thinking. So we are able to gain an insight into how an apparently purposeless and directionless process can achieve an apparently purposeful result in one field (biological evolution) by looking at another field (thermodynamics).
I mentioned earlier how Charles Lyell’s insight that minute changes to rock formations by streaming water could carve great valleys over time inspired Charles Darwin to make a similar observation about continual minute changes to the characteristics of organisms within a species. This metaphor search would be another continual background process.
We should provide a means of stepping through multiple lists simultaneously to provide the equivalent of structured thought. A list might be the statement of the constraints that a solution to a problem must satisfy. Each step can generate a recursive search through the existing hierarchy of ideas or a search through available literature. The human brain appears to be able to handle only four simultaneous lists at a time (without the aid of tools such as computers), but there is no reason for an artificial neocortex to have such a limitation.
We will also want to enhance our artificial brains with the kind of intelligence that computers have always excelled in, which is the ability to master vast databases accurately and implement known algorithms quickly and efficiently. Wolfram Alpha uniquely combines a great many known scientific methods and applies them to carefully collected data. This type of system is also going to continue to improve given Dr. Wolfram’s observation of an exponential decline in error rates.
Finally, our new brain needs a purpose. A purpose is expressed as a series of goals. In the case of our biological brains, our goals are established by the pleasure and fear centers that we have inherited from the old brain. These primitive drives were initially set by biological evolution to foster the survival of species, but the neocortex has enabled us to sublimate them. Watson’s goal was to respond to Jeopardy! queries. Another simply stated goal could be to pass the Turing test. To do so, a digital brain would need a human narrative of its own fictional story so that it can pretend to be a biological human. It would also have to dumb itself down considerably, for any system that displayed the knowledge of, say, Watson would be quickly unmasked as nonbiological.
More interestingly, we could give our new brain a more ambitious goal, such as contributing to a better world. A goal along these lines, of course, raises a lot of questions: Better for whom? Better in what way? For biological humans? For all conscious beings? If that is the case, who or what is conscious?
As nonbiological brains become as capable as biological ones of effecting changes in the world—indeed, ultimately far more capable than unenhanced biological ones—we will need to consider their moral education. A good place to start would be with one old idea from our religious traditions: the golden rule.
CHAPTER 8
THE MIND AS COMPUTER
Shaped a little like a loaf of French country bread, our brain is a crowded chemistry lab, bustling with nonstop neural conversations. Imagine the brain, that shiny mound of being, that mouse-gray parliament of cells, that dream factory, that petit tyrant inside a ball of bone, that huddle of neurons calling all the plays, that little everywhere, that fickle pleasuredome, that wrinkled wardrobe of selves stuffed into the skull like too many clothes into a gym bag.