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The Public Learning System (PLS)

Note: Click on any word on this page (and click it again) to experience OLSN, the Online Learning Support Net.

Proposal: Like the Moon Mission and the Human Genome Project, a project for the good of all.

THE PUBLIC LEARNING SYSTEM

In many ways similar to ‘PBS’, the PLS is a free public service, a non-commercial, not for profit, ‘channel’ people can trust. PBS is a network within our television system, the PLS is a network within our Internet system. The PLS is a domain, a parallel-universe, within the Internet.

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Instead of being designed around ‘broadcasting’ (pushing content at people), PLS is designed around ‘learning’ (resourcing people learning).

In less than a decade we will be experiencing “Watson” like AI in our everyday relationship with technology. Through the descendants of our smartphones*, tablets, computers, appliances, and automobiles we will live e-nabled lives in ways we can barely imagine. Rather than leaving the relationship between our population and this ‘power’ entirely to the ethics of the marketplace, we can create an artificially intelligent agency that can provide each of our people an avatar capable of personally-relevantly stewarding his or her learning into/about anything and everything he or she might be interested in or need to learn.

Our futures depend on how well we learn our way into them.

Note: this has its roots in a proposal to California Senator John Vasconcellos re the Bill Bradley 2000 Campaign It was published here 3-13-2013.

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WATSON: Way back in 1997 the world’s reining chess champion, Garry Kasparov, played 6 games of chess with IBM’s “Deep Blue” computer. Deep Blue won the series. It was a milestone accomplishment in the rise of intelligent machines. In February of 2011 IBM’s “WATSON”, a supercomputer, beat the all-time champions of the TV game show Jeopardy. Capable of natural language Q&A with humans about any subject that might come up in the course of a Jeopardy game (and eventually all human activities that can be digitally modeled and processed), Watson’s applications are endless. A major health-care provider is using Watson to assist doctors in diagnosing and suggesting treatments for their patients. IBM plans to apply Watson to other similarly information intense jobs in telecom, financial services, and government. Imagine the descendants of Watson, Google and Wikipedia available through the descendants of Iphones and Ipads.

SINGULARITY: Computers that once took up an entire room have shrunk to the size of cellphones. In just a few decades we’ve seen a million fold increase in processing power and a thousand fold decrease in cost - a billion fold improvement in the power-to-cost performance of computers. And the rate of improvement is accelerating: in November of 2011 Intel announced a single chip processor capable of matching the performance of the most expensive supercomputers of a decade ago. Considering the trends toward ever better miniaturization and ever more powerful processing, another billion fold increase in cost-power benefit in less time – in the next 25 years - is not unlikely [Kurzweil]. Even if we reduce that estimate to only a thousandth of such predictions, in the time it will take today’s primary school children to become adults there will be a million fold increase in the power-to-cost performance of these machines. What will the world be like when Watson’s descendants are a million fold more cost-efficient? What kind of jobs will exist for humans? In what kinds of ‘knowledge work’ jobs will humans perform more cost-efficiently than machines? Today’s children will compete with Watson’s descendants for tomorrow’s jobs. Like the PCs and robots before them, the cost vs performance advantages of Watson’s descendants, in all socially acceptable functions, will make employing them the better business choice. What need will there be for human ‘knowledge workers’? Wherever the knowledge is stable, not much. Wherever the knowledge is too fluid for the machines to learn, a lot. But then why call it knowledge work? The work required to stay up with changing knowledge is more accurately called learning. If we don’t educate children to out learn the machines they will compete with, what kind of futures will they have?