He wasn't too soon, he was sabotaged by Minsky and Papert[1] who were competitors for funding. His premature death left his ideas in limbo for far too long.
Those are all the hallmarks of a rumour of the kind that spread on the internet, based on nothing else than half-digested information and a thirst for controversies. It should be noted that Minsky, himself, was a connectionist who worked on neural networks, as well as other approaches to AI. For example:
https://en.wikipedia.org/wiki/Stochastic_neural_analog_reinf...
I don't think most people who repeat those claims about Perceptrons have even read the book and really know what it says, other than from second- or third-hand sources (i.e. something someone once posted on twitter).
The entire book, in pdf format, can be downloaded from here:
https://ieeexplore.ieee.org/book/8076704
It's a whole book and not a blog post so it takes some reading. Alternatively, there is the introduction to the 1988 reissue with a short foreword by Leon Botu, focusing on the history of the book and its repercussions here:
https://leon.bottou.org/publications/pdf/perceptrons-2017.pd...
And a review of the book with more technical information here:
https://www.sciencedirect.com/science/article/pii/S001999587...
Tappert, C. C. (2019). Who Is the Father of Deep Learning? 2019 International Conference on Computational Science and Computational Intelligence (CSCI). doi:10.1109/csci49370.2019.00067 :
> Frank Rosenblatt and Marvin Minsky debated at conferences the value of biologically inspired computation, Rosenblatt arguing that his neural networks could do almost anything and Minsky countering that they could do little. Minsky, wanting to direct government funding away from neural networks and towards his own areas of interest, collaborated with Seymour Papert to publish Perceptrons, where they asserted about perceptrons (page 4), "Most of this writing ... is without scientific value...” Minsky, although well aware that powerful perceptrons have multiple layers and even Rosenblatt's basic feed-forward perceptrons have three layers, defined a perceptron as a two-layer machine that can handle only linearly separable problems and, for example, cannot solve the exclusive-OR problem. The book, unfortunately, stopped government funding in neural networks and precipitated an “AI Winter” that lasted about 15 years. This lack of funding also ended Rosenblatt’s research in neural networks [when he was only 41 years old].