If you understand something in only one way, then you don't really understand it at all. The secret of what anything means to us depends on how we've connected it to all other things we know. Well-connected representations let you turn ideas around in your mind, to envision things from many perspectives until you find one that works for you. And that's what we mean by thinking!
There was a failure to recognize the deep problems in AI; for instance, those captured in Blocks World. The people building physical robots learned nothing.
Interpretation
What this quote means
The quote highlights the shortcomings in understanding the complexities of AI during its development.
Marvin Minsky's quote emphasizes the failure of developers and researchers in the field of artificial intelligence to grasp and address the underlying challenges that come with it, particularly exemplified by the 'Blocks World' problem. Minsky argues that despite the technological advancements and the building of physical robots, there was a lack of awareness and learning from the fundamental issues that AI faces, suggesting that understanding those deep, inherent problems is crucial to the future of AI development.
Themes
In practice
Example use cases
In a tech conference, one might reference this quote to discuss the barriers faced in AI development.
More from Marvin Minsky
All quotes βYou don't understand anything unless you understand there are at least 3 ways.
I think every person either inherits or eventually makes up their own idea of what they are and who they are and what caused the world to be, and it seems to me that these stories of creation myth, adopted by different cultures - most of them are less insightful than the stories made up by individual poets and writers.
I believe that everyone has to construct a mental model of what they are and where they came from and why they are as they are, and the word soul in each person is the name for that particular mish-mash of those fully formed ideas of one's nature.
If you just have a single problem to solve, then fine, go ahead and use a neural network. But if you want to do science and understand how to choose architectures, or how to go to a new problem, you have to understand what different architectures can and cannot do.
You don't understand anything until you learn it more than one way.
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