Alas, for several decades the computing power found in advanced Artificial Intelligence and Robotics systems has been stuck at insect brainpower of 1 MIPS. While computer power per dollar fell rapidly during this period, the money available fell just as fast. The earliest days of AI, in the mid 1960s, were fuelled by lavish post-Sputnik defense funding, which gave access to $10,000,000 supercomputers of the time. In the post Vietnam war days of the 1970s, funding declined and only $1,000,000 machines were available. By the early 1980s, AI research had to settle for $100,000 minicomputers. In the late 1980s, the available machines were $10,000 workstations. By the 1990s, much work was done on personal computers costing only a few thousand dollars. Since then AI and robot brainpower has risen with improvements in computer efficiency. By 1993 personal computers provided 10 MIPS, by 1995 it was 30 MIPS, and in 1997 it is over 100 MIPS. Suddenly machines are reading text, recognizing speech, and robots are driving themselves cross country.
The long stall: From 1960 to 1990 the cost of computers used in AI research declined, as their numbers increased greatly. The dilution absorbed computer efficiency gains during this period, and the power available to individual AI programs remained almost unchanged at 1 MIPS, barely insect power. AI computer cost bottomed in 1990, and since then power has risen instead, to several hundred MIPS by 1997. The major visible exception is computer chess, whose prestige has lured the resources of major computer companies, as well as the talents of special machine designers. Less visible exceptions probably exist in high value competitive applications, like petroleum exploration and intelligence gathering.
Agony to ecstasy: In forty years, computer chess progressed from the lowest depth to the highest peak of human chess performance. It took a handful of good ideas, culled by trial and error from a larger number of possibilities, an accumulation of previously evaluated game openings and endings, good adjustment of position scores, and especially a ten-million-fold increase in the number of alternative move sequences the machines can explore. Note that chess machines reached world champion performance as their (specialized) processing power reached about 1/30 human, by our brain to computer measure. Since it is plausible that Garry Kasparov (but hardly anyone else) can apply his brainpower to the problems of chess with an efficiency of 1/30, the result supports that retina-based extrapolation. In coming decades, s general-purpose computer power grows beyond Deep Blue's specialized strength, machines will begin to match humans in more common skills.