Jagjit Singh wrote this book in 1966 as a guided tour of ideas that were, at the time, barely two decades old. Shannon's information theory, McCulloch-Pitts neural networks, von Neumann's reliability theorems, Turing's universal machines, Rosenblatt's perceptrons — all of it still fresh enough to smell like solder. Singh was not an original contributor to any of these fields. He was something rarer and, in retrospect, more useful: a patient explainer who understood that these ideas formed a single constellation, not a scatter of unrelated stars. The book's great structural achievement is its insistence that information theory, cybernetics, neural modeling, and the study of natural language are not separate disciplines but facets of one problem — the problem of how intelligence, natural or synthetic, processes signals into meaning and action. Sixty years later, that integrative instinct looks prophetic. The siloed academic departments Singh was implicitly arguing against have spent the intervening decades slowly, painfully re-converging under banners like "cognitive science" and "machine learning." He saw the shape of the river before it carved the canyon.

The prescience is real but uneven. Singh's discussion of perceptrons and adjustable synaptic weights in Chapter XV is, in essence, a description of the core principle behind modern deep learning — networks that modify their internal structure through experience rather than explicit programming. He even identifies the key distinction between algorithmic and heuristic problem-solving that would define the AI research agenda for decades. His chapter on game-playing machines anticipates, with startling clarity, the logic behind programs like AlphaGo: the necessity of pruning search trees, the insufficiency of brute-force enumeration, the role of evaluation functions that approximate human judgment. And his treatment of machine translation in Chapter XVII names, with almost clinical precision, the exact problems that stymied rule-based MT systems for forty years — the non-algorithmic nature of natural language, the failure of word-by-word substitution, the deep entanglement of syntax and semantics. What he could not imagine was the solution: that statistical methods operating on enormous corpora, and eventually transformer architectures trained on the near-totality of human text, would sidestep the linguistic analysis he assumed was prerequisite. He thought you had to understand language to translate it. Large language models suggest — or at least perform the suggestion — that you do not.