This review is a bit of a window into the past itself... after all, more time has passed since this review was written than elapsed between GEB’s publication and this review.
In 2021 the conventional wisdom is basically the opposite of the sentiment expressed here. Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction.
I loved GEB when I first read it in high school, and when I first reread it years later, but I don’t think its fundamental view of the relation between minds and machines has stood the test of time. It underestimated what behaviors could be emergent from running simple algorithms on large datasets. It is one of the most beautiful expressions of the ideas of the “classic” age of 1970’s AI, awesome to read but in the end somewhat incorrect about the future.
Perhaps one day the pendulum will swing back, and we will discover that large datasets are in some ways overrated, and clever aesthetic senses of pattern are necessary for progress. On that day it will be quite interesting to reread GEB.
In 2021 the conventional wisdom is basically the opposite of the sentiment expressed here. Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction.
I loved GEB when I first read it in high school, and when I first reread it years later, but I don’t think its fundamental view of the relation between minds and machines has stood the test of time. It underestimated what behaviors could be emergent from running simple algorithms on large datasets. It is one of the most beautiful expressions of the ideas of the “classic” age of 1970’s AI, awesome to read but in the end somewhat incorrect about the future.
Perhaps one day the pendulum will swing back, and we will discover that large datasets are in some ways overrated, and clever aesthetic senses of pattern are necessary for progress. On that day it will be quite interesting to reread GEB.