Thread by @dmonett

In my opinion, the "I" in #AI has been vulgarly kidnapped and abused on many levels (including 'serious' research, sadly) for the sake of anticipated glory.

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This has resulted in a tremendous disparity between how #intelligence in #machines is both perceived and understood by the public, and what does it truly mean (or could) in current human-made 'intelligent' artefacts.
#AI
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A similar vein:
"By jumping over the long, slow process of cognitive development and instead focusing on solving specific tasks with high commercial or marketing value, we have robbed #AI of any ability to process information in an intelligent manner."
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blogs.scientificamerican.com/observations/a…

Kenyon puts emphasis on #analogies, like Douglas Hofstadter:

"The path ahead runs through systems that mimic biology. Like their biological counterparts, intelligent machines must learn by analogy to acquire an intuitive understanding of the physical phenomena around them."

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He continues:

"To go forward into this future, we must first go backward and grant our machines a period of infancy in which to stumble through the structure of the world and discover the intuitive physics upon which all intelligent inference depends."

#AI #intelligence

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What certainly is urgently needed, on top of all this, is a broad discussion both inside & outside the #AI community about how defining and understanding (#machine) #intelligence are as important as doing 'hard' research in the field.

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The "I" in #AI has been taken for granted and is excessively overestimated in the current public discourse.

If we continue to oversee this, then we should not be surprised by future #AI winters or guaranteed disbelief at least in the mind of the non-experts.

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