Professional pattern recognition ...

Medieval Chinese:起信論義疏

Google Translate:From the letter on the sparse

Actual Meaning:Commentary on the Awakening of Faith

Pattern recognition is a set of special techniques, each appropriate for a special problem (domain), each of which don't work on the other domains. There is no known general algorithm, not even a Turing Machine program ... which is the definition of what an algorithm is. Therefore, it requires something non-algorithmic to solve problems that are beyond an algorithmically defined pseudo-random number (aka a code of a solution). There are subsets of algorithms, all of which can be computed by a Turing Machine program, which defines particular classes of pseudo-random numbers. There is a way on paper, a Turing Machine + Oracle ... that allows one in principle, but not in practice to specify the outlines, but not the specifics, of a non-algorithmicly generated number. Professional cryptography uses this .. the key is generated by a random natural process, not by an algorithm. Aka ... CIA, NSA etc assume that ... natural processes are not the result of any digital quantum computer. An analog quantum computer is a different matter. In so far as the universe is ruled by quantum mechanics, then it is an analog quantum computer ... but that isn't equivalent to a digital one. A digital computer can only produce a subset (equivalence set technically, an infinite number of reals are mapped to each integer) of the output of an analog computer. But this is good enough for things like accounting. Again, this ties to number theory ... the integers (which can be algorithmically defined by finite processes (in the sense of a Turing Machine) are a proper subset of the real numbers. Integers can only be used to approximate real numbers, that is not equivalence! Approximation is an old engineering joke, involving the difference between a mathematician and an engineer, trapped in a room with a beautiful girl, and they can only approach her under certain rules. The mathematician defeats himself because he is limited by Zeno's Paradox, but the engineer knows he can get what he wants ... with a sufficiently good approximation.

Google Translate uses the most advance language recognition available ... but it is brittle as all AI programs are ... it works real well on toy sentences, but not on more obscure ones, because this particular Medieval Chinese was not in its training data set. It is much more successful on more recent language quotes (even in Chinese) that are more likely to be similar to its training data set. This is a real limitation, not imposed by the system that is being trained. So the technical question is, is the behavior of a totally no-human-in-the-loop transportation system, representable by an algorithmically defined pseudo-random number or not? This can't be cogitated one way or another ... it can only be empirically demonstrated.

If for example, you have no driver in the car, but it is receiving data from the human driven cars all around ... then in fact, the human traffic is providing indirect steering of the driverless car, as opposed to direct steering by a person in the car. But that isn't driverless driving, just a more hidden version of driver driving. I await the results of the empirical demonstration of bumper cars ... but not while I am in traffic thanks.