Eden - Ancestors and Civilization
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Up to this point the songs were not of any particular value to the apes. In a biological sense they were parasites, taking advantage of the apes' tendency to imitate. Once the songs began to specialize, however, it became advantageous for an ape to pay attention to the songs of others and to differentiate between them. By listening to songs, a clever ape could gain useful information. For example, an ape could infer that another ape had found food, or that it was likely to attack. Once the apes began to take advantage of the songs, a mutually beneficial symbiosis developed.
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Songs enhanced their survival by conveying useful information. Apes enhanced their survival by improving their capacity to remember, replicate, and understand songs. The blind forces of evolution created a partnership between the songs and the apes that thrived on the basis of mutual self-interest. Eventually this partnership evolved into one of the world's most successful symbionts: us. Unfortunately, songs do not leave fossils, so unless some natural process has left a phonographic trace, we may never know if this is what really happened.
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But if the story is true, the apes and the songs became the two components of human intelligence. The songs evolved into the knowledge, mores, and mechanism of thought that together are the symbolic portion of human intelligence. The apes became apes with bigger brains, perhaps optimized for late maturity so that they could learn more songs.
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It is not unusual in nature for two species to live together so interdependently that they appear to be a single organism. Lichens are symbionts of a fungus and an alga living so closely intertwined that they can only be separated under a microscope. Bean plants need living bacteria in their roots to fix the nitrogen from the soil, and in return the bacteria need nutrients from the bean plants.
Even the single celled "Paramecium Bursarra"" uses green algae living inside itself to synthesize food.
There may be an example even closer to the songs and the apes, where two entirely different forms of "life" form a symbiosis. In "The Origins of Life", Freeman Dyson suggests that biological life is a symbiotic combination of two different self-reproducing entities with very different forms of replication. Dyson suggests that life originated in two stages.
While most theories of the origin of life start with nucleotides replicating in some "primeval soup," Dyson's theory starts with metabolizing drops of oil. In the beginning, these hypothetical replicating oil drops had no genetic material, but were self-perpetuating chemical systems that absorbed raw materials from their surroundings. When a drop reached a certain size it would split, with about half of the constituents going to each part.
Such drops evolved efficient metabolic systems even though their rules of replication were very different from the Mendelian rules of modern life. Once the oil drops became good at metabolizing, they were infected by another form of replicators, which, like the songs, have no metabolism of their own.
These were parasitic molecules of DNA which, like modern viruses, took advantage of the existing machinery of the cells to reproduce. The metabolizers and the DNA eventually co-evolved into a mutually beneficial symbiosis that we know today as life. This two-part theory of life is not conceptually far from the two-part story of intelligence.
Both suggest that a pre-existing homeostatic mechanism was infected by an opportunistic parasite. The two parts reproduced according to different set of rules, but were able to co-evolve so successfully that the resulting symbiont appears to be a single entity.
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Viewed in this light, choosing between emergence and symbolic computation in the study of intelligence would be like choosing between metabolism and genetic replication in the study of life. Just as the metabolic system provides a substrate in which the genetic system can work, so an emergent system may provide a substrate in which the symbolic system can operate.
Currently, the metabolic system of life is far too complex for us to fully understand or reproduce. By comparison, the Mendelian rules of genetic replication are almost trivial, and it is possible to study them as a system unto themselves without worrying about the details of metabolism which supports them. In the same sense, it seems likely that symbolic thought can be fruitfully studied and perhaps even recreated without worrying about the details of the emergent system which supports it.
So far this has been the dominant approach in artificial intelligence and the approach that has yielded the most progress. The other approach is to build a model of the emergent substrate of intelligence. This artificial substrate for thought would not need to mimic in detail the mechanisms of the biological system, but it would need to exhibit those emergent properties which are necessary to support the operations of thought. What is the minimum that we would need to understand in order to construct such a system? For one thing, we would need to know how big a system to build.
How many bits are required to store the acquired portion of human knowledge of a typical human?
We need to know an approximate answer in order to construct an emergent intelligence with human-like performance. Currently the amount of information stored by a human is not known to within even two orders of magnitude, but it can in principle be determined by experiment.
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There are at least three ways the question might be answered. One way to estimate the storage requirements for emergent intelligence would be from an understanding of the physical mechanisms of memory in the human brain. If that information is stored primarily by modifications of synapses, then it would be possible to measure the information storage capacity of the brain by counting the number of synapses. Elsewhere in this issue, Schwartz shows that this method leads to an upper bound on the storage capacity of the brain of 10 to the 15th bits.
Even knowing the exact amount of physical storage in the brain would not completely answer the question of storage requirement, since much of the potential storage might be unused or used inefficiently. But at least this method can help establish an upper bound on the requirements. A second method for estimating the information in symbolic knowledge would be to measure it by some form of statistical sampling. For instance, it is possible to estimate the size of an individual's vocabulary by testing specific words randomly sampled from a dictionary. The fraction of words known by the individual is a good estimate of the fraction of words known in the complete dictionary.
The estimated vocabulary size is this fraction times the number of words in the dictionary. The experiment depends on having a predetermined body of knowledge against which to measure. For example, it would be possible to estimate how many facts in the "Encyclopedia Britannica"" were known by a given individual, but this would give no measure of facts not contained within the encyclopedia. The method is useful only in establishing a lower bound. A related experiment is the game of "20 questions" in which one player identifies an object chosen by the other by asking a series of 20 yes-or-no questions.
Since each answer provides no more than a single bit of information, and since skillful players generally require almost all of the 20 questions to choose correctly, we can estimate that the number of allowable choices is on the order of 2 to the 20th, or about one million. This gives an estimated number of allowable objects known in common by the two players. Of course, the measure is inaccurate since the questions are not perfect and the choices of objects are not random. It is possible that a refined version of the game could be developed and used to provide another lower bound.
A third approach to measuring the amount of information of the symbolic portion of human knowledge is to estimate the rate of acquisition and to integrate over time.
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For example, experiments on memorizing random sequences of syllables indicate that the maximum memorization rate of this type of knowledge is about one "chunk" per second. A "chunk" in this context can be safely assumed to contain less than bits of information, so the results suggest that the maximum rate that a human is able to commit information to long-term memory is significantly less than bits per second. If this is true, a year-old human learning at the maximum rate for 16 hours a day would know less than 50 gigabits of information. I find this number surprisingly small.
A difficulty with this estimate of the rate of acquisition is that the experiment measures only information coming through one sensory channel under one particular set of circumstances. The visual system sends more than a million times this rate of information to the optic nerve, and it is conceivable that all of this information is committed to memory. If it turns out that images are stored directly, it will be necessary to significantly increase the bit per second limit, but there is no current evidence that this is the case.
In experiments measuring the ability of exceptional individuals to store "eidetic" images of random dot stereograms, the subjects are given about 5 minutes to "memorize" a x image. Memorizing only a few hundred of these bits is probably sufficient to pass the test. I am aware of no evidence that suggests more than a few bits per second of any type of information can be committed to long term memory. Even if we accept at face value reports of extraordinary feats of memory, such as those of Luria's showman in "Mind of the Mnemonist"", the average rate of commitment to memory never seems to exceed a few bits per second.
Experiments should be able to refine this estimate, but even if we knew the maximum rate exactly, the rate averaged over a lifetime would probably be very much less. Knowing the maximum rate would establish an upper bound on the requirements of storage. The sketchy data cited above would suggest that an intelligent machine would require 10 to the 9th bits of storage, plus or minus two orders of magnitude. This assumes that the information is encoded in such a way that it requires a minimum amount of storage, which for the purpose of processing information would probably not be the most practical representation.
As a would-be builder of thinking machines, I find this number encouragingly small, since it is well within the range of current electronic computers. As a human with an ego, I find it distressing. I do not like to think that my entire lifetime of memories could be placed on a reel of magnetic tape. Hopefully experimental evidence will clear this up one way or the other.