Part 5/12:
Building such a system would require a superintelligent AI capable of recognizing objects, understanding household layouts, and executing nuanced tasks like clothing folding or medication delivery—all with minimal human intervention. This parallels Tesla's approach to self-driving, which involves accumulating driving data through millions of miles with human-in-the-loop beta testing and software updates.
By pre-selling or showcasing semi-teleoperated versions, companies gain initial funding and market visibility, effectively turning early adopters into unofficial beta testers. This strategy accelerates data collection needed for AI learning, much like Tesla's use of consumer vehicles to gather real-world driving data.