Wisdom Of The Week

• Consider the human mind to be like the skin of an onion, in each layer we find mechanical operations that can be explained in mechanical terms but we say these layers do not correspond to the real mind - if that is true then where is it to be found? Do we ever peel back an onion layer and find the real mind?
• The only really satisfactory support for thinking machines will be provided by waiting for the end of the century and then playing 'The Imitation Game'.
• The problem is mainly one of programming, rather than a engineering or data storage problem. Estimates of the storage capacity of the brain vary from 1010 to 1015 binary digits. Only a very small fraction is used for the higher types of thinking. Most of it likely used for the retention of visual impressions. It would be surprising if more than 109 was required for satisfactory playing of 'The Imitation Game'.
• Think about the process which has brought about the adult mind: The initial state of the mind (birth), the education to which it has been subjected & other experience, not to be described as education, to which it has been subjected
• Why not separate the problem into two, first create a child's brain, then educate it. Through experimentation of the machine and teaching methods you could emulate an evolutiontionary process. Opinions may vary on the complexity of this child machine, one might try to make it as simple as possible following the general principles, alternatively, one might have a complete system of logical inference "built in."
• Regulating the order in which the rules of the logical system are applied is important as at each stage there would be a very large number of valid alternative steps. The choices mean the difference between a brilliant and a poor reasoner.
• An important feature of a learning machine is that its teacher will often be largely ignorant of what is going on inside, although he may still be able to some extent to predict his pupil's behavior. This is in clear contrast with normal procedure when using a machine to do computations one's object is then to have a clear mental picture of the state of the machine at each moment in the computation. Intelligent behaviour presumably consists in a departure from the completely disciplined behaviour involved in computation
• It is probably wise to include a random element in a learning machine. A random element is rather useful when we are searching for a solution of some problem. A systematic method has the disadvantage that there may be an enormous block without any solutions in the region which has to be investigated first. The learning process may be regarded as a search for a form of behaviour which will satisfy the teacher (or some other criterion). Since there is probably a very large number of satisfactory solutions the random method seems to be better than the systematic. It should be noticed that it is used in the analogous process of evolution. But there the systematic method is not possible. How could one keep track of the different genetical combinations that had been tried, so as to avoid trying them again?

- Summary of 'Computing Machinery and Intelligence' (1950) by Alan Turing & the original paper here

Finally this week, I got around reading Warren and Charlie's Berkshire Hathaway 2014 annual report. Here's a small snippet where they brilliantly distinguish between risk and volatility:

Stock prices will always be far more volatile than cash-equivalent holdings. Over the long term, however, currency-denominated instruments are riskier investments – far riskier investments – than widely-diversified stock portfolios that are bought over time and that are owned in a manner invoking only token fees and commissions. That lesson has not customarily been taught in business schools, where volatility is almost universally used as a proxy for risk. Though this pedagogic assumption makes for easy teaching, it is dead wrong: Volatility is far from synonymous with risk. Popular formulas that equate the two terms lead students, investors and CEOs astray.