Pursuing the Future of Human Labor with AI
Michael G. Alcock is an author, speaker, moderator, and expert in the field of AI ethics.

If correct, his current theory of Perspective Economics, would be a robust solution to the puzzle of good human jobs in an increasingly automated world. This site showcases the theory, invites discourse, encourages investment, recruits brighter minds to the cause and celebrates all who also seek to secure a brighter 'future of work' for humanity. 
The future of human jobs with AI looks wonderful... if we can avoid killing everyone in-between here and there.

To understand, start with a simple question:
What is the one thing every AI needs that every human produces in abundance?
If you said, “Unique, time-bound perspectives,” you’d be right. Humans produce them in abundance, and AIs need them to function properly. AI technology is still young.  Right now it’s development is following the path of least resistance—that means automating existing work and that means Layoffs... Fewer people working means fewer “Unique, time-bound, high-stakes perspectives,” being produced, which creates a downward pressure cycle. Garbage in, garbage out… until the AIs, under the weight of their own synthetic feedback loops, trigger some form of systemic cascade failure.  This is why it is no exaggeration to say:
We (the market) are currently engineering the most sophisticated closed-loop failure in economic history.

We cannot stop.
We cannot change course.
The only way forward is to create more road ahead.

Remember the invention of the Internet, GPS, HDTV, and Bluetooth? Those are all examples of technologies that had to exist before the markets they enable could emerge.  The enabling technology we need most right now is a General-Purpose Standard for Human-to-AI Feedback Loops — or H2AI.  H2AI answers the engineering question: How do billions of humans work to reliably produce consistent high quality data feeds to keep our AI's capabilities well ahead of the competition? 


Human beings generate the best data when they feel pressure to compete, see clear incentives and know that the system they are relying upon algorithmically discourages cheating and manipulation.  A working H2AI equals a general standard structured for paid relevant data generation at scale.  That enables a marketplace for any company to tap into the right feedback data stream for the right price. 


I have spent the past 15 years working on Human-to-AI Feedback Loops and the foundations of Perspective Economics.  This is a nascent but necessary technology.  It requires industry partners to form a consortium, fund the research, and ratify a standard.   

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