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

Another major requirement for the success of a GA is a valid method of evaluating each possible solution. This evaluation needs to be conducted quickly, because it must take account of many thousands of possible solutions for each generation of simulated evolution. GAs are adept at handling problems with too many variables for which to compute precise analytic solutions. The design of an engine, for example, may involve more than a hundred variables and requires satisfying dozens of constraints; GAs used by researchers at General Electric were able to come up with jet engine designs that met the constraints more precisely than conventional methods.

When using GAs you must, however, be careful what you ask for. A genetic algorithm was used to solve a block-stacking problem, and it came up with a perfect solution…except that it had thousands of steps. The human programmers forgot to include minimizing the number of steps in their evaluation function.

Scott Drave’s Electric Sheep project is a GA that produces art. The evaluation function uses human evaluators in an open-source collaboration involving many thousands of people. The art moves through time and you can view it at electricsheep.org.

For speech recognition, the combination of genetic algorithms and hidden Markov models worked extremely well. Simulating evolution with a GA was able to substantially improve the performance of the HHMM networks. What evolution came up with was far superior to our original design, which was based on our intuition.

We then experimented with introducing a series of small variations in the overall system. For example, we would make perturbations (minor random changes) to the input. Another such change was to have adjacent Markov models “leak” into one another by causing the results of one Markov model to influence models that are “nearby.” Although we did not realize it at the time, the sorts of adjustments we were experimenting with are very similar to the types of modifications that occur in biological cortical structures.

At first, such changes hurt performance (as measured by accuracy of recognition). But if we reran evolution (that is, reran the GA) with these alterations in place, it would adapt the system accordingly, optimizing it for these introduced modifications. In general, this would restore performance. If we then removed the changes we had introduced, performance would be again degraded, because the system had been evolved to compensate for the changes. The adapted system became dependent on the changes.

One type of alteration that actually helped performance (after rerunning the GA) was to introduce small random changes to the input. The reason for this is the well-known “overfitting” problem in self-organizing systems. There is a danger that such a system will overgeneralize to the specific examples contained in the training sample. By making random adjustments to the input, the more invariant patterns in the data survive, and the system thereby learns these deeper patterns. This helped only if we reran the GA with the randomization feature on.

This introduces a dilemma in our understanding of our biological cortical circuits. It had been noticed, for example, that there might indeed be a small amount of leakage from one cortical connection to another, resulting from the way that biological connections are formed: The electrochemistry of the axons and dendrites is apparently subject to the electromagnetic effects of nearby connections. Suppose we were able to run an experiment where we removed this effect in an actual brain. That would be difficult to actually carry out, but not necessarily impossible. Suppose we conducted such an experiment and found that the cortical circuits worked less effectively without this neural leakage. We might then conclude that this phenomenon was a very clever design by evolution and was critical to the cortex’s achieving its level of performance. We might further point out that such a result shows that the orderly model of the flow of patterns up the conceptual hierarchy and the flow of predictions down the hierarchy was in fact much more complicated because of this intricate influence of connections on one another.

But that would not necessarily be an accurate conclusion. Consider our experience with a simulated cortex based on HHMMs, in which we implemented a modification very similar to interneuronal cross talk. If we then ran evolution with that phenomenon in place, performance would be restored (because the evolutionary process adapted to it). If we then removed the cross talk, performance would be compromised again. In the biological case, evolution (that is, biological evolution) was indeed “run” with this phenomenon in place. The detailed parameters of the system have thereby been set by biological evolution to be dependent on these factors, so that changing them will negatively affect performance unless we run evolution again. Doing so is feasible in the simulated world, where evolution only takes days or weeks, but in the biological world it would require tens of thousands of years.

So how can we tell whether a particular design feature of the biological neocortex is a vital innovation introduced by biological evolution—that is, one that is instrumental to our level of intelligence—or merely an artifact that the design of the system is now dependent on but could have evolved without? We can answer that question simply by running simulated evolution with and without these particular variations to the details of the design (for example, with and without connection cross talk). We can even do so with biological evolution if we’re examining the evolution of a colony of microorganisms where generations are measured in hours, but it is not practical for complex organisms such as humans. This is another one of the many disadvantages of biology.

Getting back to our work in speech recognition, we found that if we ran evolution (that is, a GA) separately on the initial design of (1) the hierarchical hidden Markov models that were modeling the internal structure of phonemes and (2) the HHMMs’ modeling of the structures of words and phrases, we got even better results. Both levels of the system were using HHMMs, but the GA would evolve design variations between these different levels. This approach still allowed the modeling of phenomena that occurs in between the two levels, such as the smearing of phonemes that often happens when we string certain words together (for example, “How are you all doing?” might become “How’re y’all doing?”).

It is likely that a similar phenomenon took place in different biological cortical regions, in that they have evolved small differences based on the types of patterns they deal with. Whereas all of these regions use the same essential neocortical algorithm, biological evolution has had enough time to fine-tune the design of each of them to be optimal for their particular patterns. However, as I discussed earlier, neuroscientists and neurologists have noticed substantial plasticity in these areas, which supports the idea of a general neocortical algorithm. If the fundamental methods in each region were radically different, then such interchangeability among cortical regions would not be possible.

The systems we created in our research using this combination of self-organizing methods were very successful. In speech recognition, they were able for the first time to handle fully continuous speech and relatively unrestricted vocabularies. We were able to achieve a high accuracy rate on a wide variety of speakers, accents, and dialects. The current state of the art as this book is being written is represented by a product called Dragon Naturally Speaking (Version 11.5) for the PC from Nuance (formerly Kurzweil Computer Products). I suggest that people try it if they are skeptical about the performance of contemporary speech recognition—accuracies are often 99 percent or higher after a few minutes of training on your voice on continuous speech and relatively unrestricted vocabularies. Dragon Dictation is a simpler but still impressive free app for the iPhone that requires no voice training. Siri, the personal assistant on contemporary Apple iPhones, uses the same speech recognition technology with extensions to handle natural-language understanding.