eresting feature, the entropy of the 1982 English symbol-character source is computed as H(1982) = 4.185 bit/symbol. This is larger than the 1956 data (H(1956) = 4.147 bit/symbol) and the 1942 data (H(1942) = 4.140 bit/symbol). If we were to attribute equal reference value to these data, a rapid calculation shows that the English source entropy grew by 0.17% in 1942-1956, and by approximately 0.9% in 1956-1982. This growth also corresponds to 1.1% over 40 years, which can be linearly extrapolated to 2.75% in a century, say a growth rate of 3%/century. Applying this rate up to the year of this book writing (2005), the entropy should be at least H(2005) = 4.20 bit/ symbol. One century later (2105), it should be at least H(2105) = 4.32 bit/symbol. In reference to the absolute A-Z source limit (Hmax = 4.700 bit/symbol), this would rep- resent an efficiency of alphabet use of H(2105)/Hmax = 92%, to compare with today's efficiency, i.e., H(2005)/Hmax = 89%. We can only speculate that a 100% efficiency may never be reached by any language unless cultural influence and mixing makes it eventually lose its peculiar linguistic and root structures. Maximum entropy (discrete source) We have seen that the information related to any eventx having probability p(x) is defined as I(x) = - log p(x). Thus, information increases as the probability decreases or as the event becomes less likely. The information eventually becomes infinite (I(x) 00) in the limit where the event becomes "impossible" (p(x) 0). Let y be the complementary event of X, with probability p(y) = 1 - p(x). In the previous limit, the event y becomes "absolutely certain" (p(y) 1), and consistently, its information vanishes (I(y) = logp(y) 0). The above shows that information is unbounded, but its infinite limit is reached only for impossible events that cannot be observed. What about entropy? We know that entropy is the measure of the average information concerning a set of events (or a system described by these events). Is this average information bounded? Can it 