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…per 'kick' (to use Dr. Selfridge's metaphor) is very small unless the probability of success and failure are equal. If you have a system where there are a vast number of possibilities to be eliminated by rather feckless trial and error, the…
Oliver Selfridge
scientist · 2 mentions across 1 reading
In this course
Oliver Selfridge appears as an early pioneer of machine learning and adaptive systems, invoked here through his work on trial-and-error learning mechanisms and the concept of "kicks" as probabilistic feedback in self-organizing systems. The readings use his metaphor of machine learning through iterative feedback to ground discussions of how systems can discover patterns through regression and successive elimination of possibilities rather than explicit programming. His thinking is foundational to understanding how machines might autonomously adapt and learn, a central concern in cybernetics and early AI theory.
Mentioned in 1 reading
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