Rick recently sent me an article for my thoughts from “Wired” magazine in which a leading authority on AI discusses the need for reasoning in AI and machine learning. He wants his systems to ask “why.”
It is a valid point. Andrew Ng, in his great Coursera courses (highly recommended), suggested that machine learning was the new electricity. Well, now, it seems like it is the new steam, and actual AI is the new electricity. We have maybe gone about as far as we can standing on this foundation, and essentially the ground needs to shift. This might be confusing if you have heard machine learning and AI used interchangeably. I have mentioned this in other entries, but I have yet to describe the distinction in detail.
Some would make the distinction by identifying machine learning as a subset or genre of AI. Here is how I do it: My 10-month-old son gets put on the bed. He takes off crawling as fast as he can toward the end of the bed and face-plants on the ground (It should be noted here that this is not an actual experiment I performed). He does this maybe two or three times and then learns to flip around and put his feet down. He is now trained . . . yay! If I move him to another bed, and he flips around to put his feet down, that is machine learning. If I put him on any higher surface, and he flips around, that is AI. It can contextualize and does not need to see that specific situation to apply what it has learned.
When applying machine learning, we need a lot of good data, and the algorithm will cut the most convenient path, which—without proper precautions—can lead to bias and poor modeling. In the science fiction book “The Three Body Problem,” (spoiler alert, I guess) an alien species is en route to Earth to take over our resources, but they are about 100 years away, and Google Maps always underestimates the time to get anywhere due to stopping for space gas, etc. They are concerned that we may surpass them technologically by the time they arrive so they halt our progress by disrupting particle research. Stopping the foundation from growing stops anything from being built on it.
Once Geoffrey Hinton among others started to figuratively run four-minute miles in machine learning, the flood gates opened, and ideas built on ideas with industrial booms and talking speakers in every living room. Now things seem to be tapering off, and the capabilities of machine learning are being over sold. We need to teach our algorithms to put their feet down to allow for more growth and to fully realize the potential.
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