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Mark Heyer

Sorry to come so late to this discussion. What you say is manifestly true and important. What has been theoretical for so many years is now staring us in the face. If a self driving car can be more than twice as safe as a human driver, then how much better will AI be at almost everything else that we routinely screw up in the name of "work." Let's face it, people would rather be doing any number of things in a car besides driving it. The question is what is meaningful "work" or maybe better, occupation? And what motivates it?

Traveling the world and studying some of the major monuments of human creation, I am left wondering if we can find examples of the present transition in the past. Half baked speculation at this point, but possibly very interesting.

In short, there were times in the past when there were far more people available than required to sustain the civilization. Some of these people were occupied with creating the almost incomprehensible works of art that are preserved in architecture. Let's take Angkor Wat as a single example.

The Khmer climax civilization emerged right around the Medieval Warm Period. Their histories speak of a tripling of rice production and great wealth. In a short period, thousands of temples similar to, but smaller than, Angkor Wat were erected. Every single exposed stone surface was carved with figures of some sort. Tens of thousands of artists and artisans had to undergo "adaptive learning" with a very high tacit knowledge content. How did this happen? Today, everyone in Cambodia is busy growing things or selling souvenirs to tourists. They could't rebuild Angkor Wat if they wanted to. Which comes to the question of motivation.

At the time of the Khmer climax civilization, the Buddhist Church owned 80% of all the land. They and the nobility whom they tolerated, made the plans and set the agenda - to create great art. As you point out, corporations today are focused on sheer efficiency to an almost soviet degree. Who will set the agenda for the "new" occupations? Can we predict their emergence? All good questions.


Scalable learning sounds more appealing (and humane) than scalable efficiency, but as machines become more capable learners, I wonder how much scalable learning will (also) be done by AI / robots.

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