"One of the great association copies in the history of artificial intelligence": First Edition of Frank Rosenblatt's Principles of Neurodynamics Perceptrons and the Theory of Brain Mechanisms; From the Library of Marvin Minsky
First edition of this landmark work, from the library of Marvin Minsky with his ownership signature to the front free endpaper. Octavo, original cloth. Near fine in the rare original dust jacket, which is in very good condition, with some chips.
A signed association copy of singular importance in the history of artificial intelligence, joining the two opposing poles of the field's first generation in a single volume. Rosenblatt's Principles of Neurodynamics is the foundational statement of the perceptron - a learning machine built on biological principles and the cornerstone of what is now called neural network research and deep learning. Marvin Minsky, Rosenblatt's principal intellectual antagonist, would in 1969 publish, with Seymour Papert, Perceptrons, the rigorous mathematical critique credited with redirecting the field away from connectionist approaches for more than a decade. This is the founding text of one tradition, held in the personal library of the man who authored its most consequential refutation. Provenance: from the library of Marvin Minsky
One of the great association copies in the history of artificial intelligence, and the connection between the two figures is essentially the central dramatic conflict of the field's first generation.
Frank Rosenblatt published Principles of Neurodynamics in 1962 through Spartan Books, presenting the perceptron not merely as a machine but as a theory of brain mechanisms - a learning device built on biological principles, and the foundation of what we now call neural networks and deep learning. Rosenblatt has since come to be regarded as a father of deep learning.
Marvin Minsky was Rosenblatt's principal intellectual antagonist. The two had been contemporaries, and Minsky was deeply skeptical of the perceptron's claims. In 1969 Minsky, with Seymour Papert, published Perceptrons, a rigorous mathematical critique demonstrating the fundamental limitations of single-layer perceptrons, most famously their inability to compute the XOR function. That book is widely credited with redirecting funding and attention away from connectionist approaches and helping precipitate the first "AI winter" for neural network research, a chill that lasted into the 1980s.
