Today, people often speak of neural networks as offering a promise of machines that do not need to be programmed. But speaking of those old machines in such a way stands history on its head, since the concept of programming had barely appea…Arthur Samuel’s two questions can help us see why perceptrons worked as well as they did. First, Rosenblatt’s credit-assignment method turned out to be as effective as any such method could be. When the answer is obtained, in effect, by add…
Arthur Samuel
scientist · 2 mentions across 1 reading
In this course
Arthur Samuel was a pioneer of machine learning who framed two foundational questions about how credit assignment works in learning systems, questions that help explain why early perceptrons succeeded in their training regimes. His work is invoked here to historicize the claim that "unprogrammed" machines represent something radically new—in fact, the very concept of programming was underdeveloped when Samuel's research challenged the assumption that machines needed explicit instruction. By resituating Samuel's questions alongside Rosenblatt's credit-assignment methods, the readings use him to complicate narratives of AI's novelty and to ground contemporary debates about machine learning in deeper cybernetic precedents.
Mentioned in 1 reading
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