Why can’t we see that we have a problem?

Recently, friend and neighbor Joe Russo sent me a pointer to a YouTube video clip about the exponential progression of information technology. At one point, the 2008 video notes, “There are 31 billion searches on Google every month.” And then it asks, “To whom were these questions addressed B.G.? (Before Google)”

The video also stated, “The amount of new technical information is doubling every 2 years …” and follows that with, “For students starting a 4-year technical degree, this means that half of what they learn in their first year of study will be outdated by their third year of study.”

I will add: The information we are dealing with — including videos like this — is almost universally unvetted and hardly trustworthy. The rate of growth of misinformation is even higher than the rate of growth of information. (Sorry, I can’t quote a source on that claim, but you can believe your own eyes and ears in this case.)

We are all at least dimly aware of these new realities, but no one is asking the inevitable question: If the amount of information is increasing exponentially — and is less trustworthy than it used to be — how is it even possible to use conventional methods of turning information into useful knowledge?

I tried reading 100 books and 200 newspapers a day, but I just couldn’t keep up. I was falling behind … still. Every time I blinked, several hundred thousand web pages were updated.

My point: Conventional literacy has reached an asymptotic dead end. You can’t read widely enough or fast enough to become knowledgeable in the same sense that we did just 20 years ago. You may be able to interact with thousands of people online … but not in intelligent ways.  So we get trapped in an unperceived process of adopting a desperate knowledge-acquisition strategy in which arbitrary purpose-specific abstractions, brute-force slogans, and re-purposing of existing words take the place of the thoughtful, recursive conversion of words into knowledge.

This new post-literacy form of communication is demonstrably ineffective, even counter-productive. There are simply too many words and pictures to consume, so we succumb to the temptation to adopt emotional stances. The war of words in American politics, society and economy, for example, has only one winner: Those who know how to “game” this new form of ignorance and the complexity that masks that ignorance.

We are not seeing this victory as a problem. Instead, we honor those who are successfully gaming this system for profit and power — the companies and individuals that are mining the surface characteristics of information (including personal information) with powerful computer tools. They have successfully convinced most of us that this is the only way to extract value from information.

They are wrong. Demonstrably wrong. The distortion of opinion and sentiment in social media is unmistakable. Long diatribes and treatises on every imaginable issue carry the same weight in search engines as thoughtful research. But to analytical tools scouring every inch of the Web, the two extremes are indistinguishable.

Wikipedia, by comparison, is a monument to intellectual honesty.

Demonstrating that “they” are wrong will not be easy. The way in which we convert information into meaning and knowledge has not changed. The meaning behind words has to be interpreted correctly. Assertions about reality need to be evaluated for relevance and “truth.” Chains of reasoning must be constructed thoughtfully and recorded … and cherished in the same way that the ancients cherished the scrolls in the Library of Alexandria and with the same care that the monasteries of the Middle Ages preserved and reproduced precious manuscripts.

The growing abundance of information will not abate. So we will need new models and tools to support the processes of interpreting, evaluating, and constructing chains of reasoning that we once performed as individuals, supported by interactions among small sets of people with shared interests and goals.

Online tools like “argumentation systems” (See, for example, Debategraph.org.) are heading in the right direction. But much more is needed.

This is, as usual, both a challenge and an opportunity. A really big opportunity.

© Copyright 2017 Philip C. Murray

 

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2 Responses to Why can’t we see that we have a problem?

  1. I think the problems you describe cannot be solved directly by we, humans. We need to be empowered by computing to achieve a coherent, collective and always-evolving knowledge “cloud”. I believe that the key is going beyond plain text (or image based) representation of knowledge, and beyond explicit knowledge registration (a la Wikipedia) to a discovery based “relative weighted” KB (e.g. what a ‘Black Hole’ is depend more on Stephen Hawking and Neil DeGrasse Tyson opinions that on those from John Doe) as a practical semantic web. I think big-data AI tools applied over massive sources of diverse info (from scientific papers to Facebook comments) will solve the problem. Then another set of tools, to take advantage of that KB will emerge.

    • Phil Murray says:

      Hi, Néstor —

      You raise so many big issues that I will reply in the form of a separate post and send you a link to the post. I would also like to express the evolving meaning of this exchange of ideas in your own application, ThinkComposer, using the model I recommend. (I will create a link to a PDF version of a ThinkComposer View.)

      My response will be delayed a few days because I have family activities to attend to. In the meantime, you may want to read my brief previous post, “We need the computer as an intermediary for taming complexity.”

      BTW, I just added a WordPress plug-in that allows commenters to be notified of replies to their own comments. You should see a message about this below the comment box.

      Thanks for taking the time to comment.

      FYI, I revised the right side of the header graphic of this blog to incorporate a very simple “map” that I created in ThinkComposer.

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