take a number of observations, as he suggests, and let the machine learn the code by determining the coefficients A₀ etc. by simple curvilinear regression. The duration of previous and subsequent signals and the interpretation given to prev…I maintain that there is some merit in studying self-improvement systems and if I am going to do that I am going to study systems where I know what I want, rather than more difficult problems, however attractive they may be to mathematician…
Donald MacKay
scientist · 3 mentions across 2 readings
In this course
MacKay was a pioneering cyberneticist and information theorist who developed frameworks for understanding adaptive systems, learning, and self-regulation in both machines and biological organisms. His work on Bayesian inference and self-organizing systems appears in these readings as a foundational approach to machine learning and goal-directed behavior, particularly in how systems can estimate probabilities and improve through observation rather than predetermined programming. The excerpts suggest he was instrumental in establishing the theoretical bridge between information theory and practical systems design that becomes central to later cybernetic thought.
Mentioned in 2 readings
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