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Reader: What’s Wrong with Godless AI Technological Salvation, the Singularity?

Posted By: RumorMail
Date: Wednesday, 22-Nov-2017 14:56:03

In Response To: Scariest Vid Clip I've Seen in Ages - Ominous Video Shows Swarm of A.I. Drones Carry Out Mass Murder Without Human Orders (RumorMail)

From Reader WJ:

Snip

By Michael Keas | November 13, 2017 | 2:46 PM EST
(Sreenshot)

In a recent article, I began to unpack Rodney Brooks’ October 2017 essay “The Seven Deadly Sins of AI Predictions.” Now I continue my analysis by looking into the faulty atheistic thinking that motivates the AI salvation preached by futurists such as Google’s Ray Kurzweil. Although Brooks does not address this worldview dimension, his critique of AI predictive sins provides a great opportunity for just that.

Brooks is a pioneer of robotic artificial intelligence (AI) and is MIT Panasonic Professor of Robotics Emeritus. He is also the founder and chief technology officer of Rethink Robotics, which makes cobots—robots designed to collaborate with humans in a shared industrial workspace.

Previously I discussed Brooks’ remark that “all the evidence that I see says we have no real idea yet how to build” the superintelligent devices that Kurzweil and like-minded singularity advocates imagine.

Singularity believers claim that soon we will reach a tipping point in which AI devices will begin an exponential increase in capabilities that exceed human intelligence. AI salvation would occur by uploading human minds to the newly awakened world of computer consciousness, or so the story goes.

What’s wrong with the reasoning behind this AI-tech salvation?

Brooks identifies the problem of “suitcase words.” Marvin Minsky, one of the founding fathers of artificial intelligence, used the term “suitcase words” to identify words that have an unusually large variety of meanings. “Learning” is one such word, as in the AI term “machine learning.”

Brooks observes:

“When people hear that machine learning is making great strides in some new domain, they tend to use as a mental model the way in which a person would learn that new domain. However, machine learning is very brittle, and it requires lots of preparation by human researchers or engineers, special-purpose coding, special-purpose sets of training data, and a custom learning structure for each new problem domain. Today’s machine learning is not at all the sponge-like learning that humans engage in, making rapid progress in a new domain without having to be surgically altered or purpose-built.”


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