Where Will the Information and Computing Resources Asymmetry in AI Lead Us To?
Given that currently, the pace of allocating computing resources for AI is accelerating at a rate "7 times exceeding the Moore's Law," it seems, that the AI race is on a trajectory similar to that of cryptocurrencies a decade earlier. Except, in this race, there is a point, where machine coordination makes human labor unnecessary, and the rest of humanity unnecessary in creation of supply of all goods and services of value, making one wonder:
- How much compute power is currently allocated to deep learning AI models versus mining cryptocurrencies?
- Where is the point, when it is more profitable to learn an AI models than mine cryptocurrencies?
- Who owns most of the computing resources, specifically? What are their values?
- How can the expensively trained models be made into an asset to us all, rather than a continued expense for getting results we need through capped, conditional, transactional querying?
- When companies try to capitalize on every match due to information and computation power asymmetry, where is the Wikipedia's quest for having the "Sum of All Human Knowledge"? "Imagine a world in which every single person on the planet is given free access to the sum of all human knowledge." (Jimmy Wales)