But just as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations of intellectual processes in machines should lead to a science, eventually.
Interpretation
Advancements in understanding technology and machines can lead to the development of a formal science.
Marvin Minsky suggests that, similar to how astronomy evolved from astrology through rational discoveries, the empirical studies of how machines process information will eventually culminate in a systematic and scientific understanding of intelligence in machines. This analogy highlights the importance of observing and analyzing complex systems to reveal underlying principles that govern their functioning.
In practice
In a lecture about the future of artificial intelligence, one might use this quote to illustrate the evolution of understanding in technology.
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.
You 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.
When you're in a spacecraft, you need to know what things you can touch and what things you shouldn't touch!
People make their own fates, and if enough of us make our fate to be space explorers, perhaps we can actually get some space exploration done.
It may be that our cosmic curiosity... is a genetically-encoded force that we illuminate when we look up and wonder.
It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material.
The history of acceptance of new theories frequently shows the following steps: At first the new idea is treated as pure nonsense, not worth looking at. Then comes a time when a multitude of contradictory objections are raised, such as: the new theory is too fancy, or merely a new terminology; it is not fruitful, or simply wrong. Finally a state is reached when everyone seems to claim that he had always followed this theory. This usually marks the last state before general acceptance.
When it comes to how neuroscience could help the wider public, the worst thing is when we make advances in, say, mindfulness, and then decide that everybody can potentially think their way to curing themselves or develop their own psycho-neuro-immune mechanisms for boosting cancer defenses.
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