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

For each of these descriptions of specific thought processes, we also need to consider the issue of redundancy. As I mentioned, we don’t have a single pattern representing the important entities in our lives, whether those entities constitute sensory categories, language concepts, or memories of events. Every important pattern—at every level—is repeated many times. Some of these recurrences represent simple repetitions, whereas many represent different perspectives and vantage points. This is a principal reason why we can recognize a familiar face from various orientations and under a range of lighting conditions. Each level up the hierarchy has substantial redundancy, allowing sufficient variability that is consistent with that concept.

So if we were to imagine examining your neocortex when you were looking at a particular loved one, we would see a great many firings of the axons of the pattern recognizers at every level, from the basic level of primitive sensory patterns up to many different patterns representing that loved one’s image. We would also see massive numbers of firings representing other aspects of the situation, such as that person’s movements, what she is saying, and so on. So if the experience seems much richer than just an orderly trip up a hierarchy of features, it is.

A computer simulation of the firings of many simultaneous pattern recognizers in the neocortex.

But the basic mechanism of going up a hierarchy of pattern recognizers in which each higher conceptual level represents a more abstract and more integrated concept remains valid. The flow of information downward is even greater, as each activated level of recognized pattern sends predictions to the next lower-level pattern recognizer of what it is likely to be encountering next. The apparent lushness of human experience is a result of the fact that all of the hundreds of millions of pattern recognizers in our neocortex are considering their inputs simultaneously.

In chapter 5 I’ll discuss the flow of information from touch, vision, hearing, and other sensory organs into the neocortex. These early inputs are processed by cortical regions that are devoted to relevant types of sensory input (although there is enormous plasticity in the assignment of these regions, reflecting the basic uniformity of function in the neocortex). The conceptual hierarchy continues above the highest concepts in each sensory region of the neocortex. The cortical association areas integrate input from the different sensory inputs. When we hear something that perhaps sounds like our spouse’s voice, and then see something that is perhaps indicative of her presence, we don’t engage in an elaborate process of logical deduction; rather, we instantly perceive that our spouse is present from the combination of these sensory recognitions. We integrate all of the germane sensory and perceptual cues—perhaps even the smell of her perfume or his cologne—as one multilevel perception.

At a conceptual level above the cortical sensory association areas, we are capable of dealing with—perceiving, remembering, and thinking about—even more abstract concepts. At the highest level we recognize patterns such as that’s funny , or she’s pretty , or that’s ironic , and so on. Our memories include these abstract recognition patterns as well. For example, we might recall that we were taking a walk with someone and that she said something funny, and we laughed, though we may not remember the actual joke itself. The memory sequence for that recollection has simply recorded the perception of humor but not the precise content of what was funny.

In the previous chapter I noted that we can often recognize a pattern even though we don’t recognize it well enough to be able to describe it. For example, I believe I could pick out a picture of the woman with the baby carriage whom I saw earlier today from among a group of pictures of other women, despite the fact that I am unable to actually visualize her and cannot describe much specific about her. In this case my memory of her is a list of certain high-level features. These features do not have language or image labels attached to them, and they are not pixel images, so while I am able to think about her, I am unable to describe her. However, if I am presented with a picture of her, I can process the image, which results in the recognition of the same high-level features that were recognized the first time I saw her. I would be able to thereby determine that the features match and thus confidently pick out her picture.

Even though I saw this woman only once on my walk, there are probably already multiple copies of her pattern in my neocortex. However, if I don’t think about her for a given period of time, then these pattern recognizers will become reassigned to other patterns. That is why memories grow dimmer with time: The amount of redundancy becomes reduced until certain memories become extinct. However, now that I have memorialized this particular woman by writing about her here, I probably won’t forget her so easily.

In the previous chapter I discussed how we can recognize a pattern even if the entire pattern is not present, and also if it is distorted. The first capability is called autoassociation: the ability to associate a pattern with a part of itself. The structure of each pattern recognizer inherently supports this capability.

As each input from a lower-level pattern recognizer flows up to a higher-level one, the connection can have a “weight,” indicating how important that particular element in the pattern is. Thus the more significant elements of a pattern are more heavily weighted in considering whether that pattern should trigger as “recognized.” Lincoln’s beard, Elvis’s sideburns, and Einstein’s famous tongue gesture are likely to have high weights in the patterns we’ve learned about the appearance of these iconic figures. The pattern recognizer computes a probability that takes the importance parameters into account. Thus the overall probability is lower if one or more of the elements is missing, though the threshold of recognition may nonetheless be met. As I pointed out, the computation of the overall probability (that the pattern is present) is more complicated than a simple weighted sum in that the size parameters also need to be considered.

If the pattern recognizer has received a signal from a higher-level recognizer that its pattern is “expected,” then the threshold is effectively lowered (that is, made easier to achieve). Alternatively, such a signal may simply add to the total of the weighted inputs, thereby compensating for a missing element. This happens at every level, so that a pattern such as a face that is several levels up from the bottom may be recognized even with multiple missing features.

The ability to recognize patterns even when aspects of them are transformed is called feature invariance, and is dealt with in four ways. First, there are global transformations that are accomplished before the neocortex receives sensory data. We will discuss the voyage of sensory data from the eyes, ears, and skin in the section “The Sensory Pathway” on page 94.

The second method takes advantage of the redundancy in our cortical pattern memory. Especially for important items, we have learned many different perspectives and vantage points for each pattern. Thus many variations are separately stored and processed.

The third and most powerful method is the ability to combine two lists. One list can have a set of transformations that we have learned may apply to a certain category of pattern; the cortex will apply this same list of possible changes to another pattern. That is how we understand such language phenomena as metaphors and similes.