The Future of Intellectual Attribution: Quantifying the Massive Idea Sea Requires Convergence

October 22 2008 / by Alvis Brigis
Category: Education   Year: 2018   Rating: 1

Intellectual attribution is far from perfect, but as we systematically quantify the nature of the vast Idea Sea in which we swim, we will also create a more effective and equitable market for new innovations.

Last week a pair of Nobel Prize winning scientists conceded that much of their research had been based on an earlier study by a geneticist who now drives a shuttle for $8/hour just to keep food on the table, but of course didn’t go so far as to offer him a share of the $1.5 million prize they’d been awarded. This example clearly brings into focus the limits of our current idea attribution economy, a system that clearly isn’t encouraging a Nobel-caliber scientist to continue innovating for broader social benefit.

But rather than jump on the IP- and patent-bashing bandwagon as many bloggers tend to do, I’d like to explore how our idea attribution system might evolve over the coming decade.

First, let me be clear about my definition of the term “idea”. Ideas can more specifically be broken down into memes – “ideas or behaviors that can pass from one person to another by learning or imitation”, memeplexes – “groups of religious, cultural, political, and idealogical doctrines and systems”, and temes – “information copied by books, phones, computers and the Internet”. These structures co-evolve with humans to ultimately form a massive sea of what we commonly refer to as ideas. Though individuals often combine memes into valuable new memeplexes, no one person can ever truly claim total ownership of a concept that is essentially an outgrowth of the idea sea.

(That being said, I believe it’s still important to have a patent and IP system in place so idea facilitators, innovators, and replicators can be incented to advance idea development.)

It occurs to me that as we move forward, the increasingly granular quantification of memes, memeplexes and temes will be essential to developing effective new idea attribution models. This will be accomplished through the convergence of knowledge from a variety of fields including neuro-psychology (isolating memes, learning more about their specific behavior), neuro-economics (discovering how neural nets estimate value and process problems), social media (establishing clearer records of idea flow), search (studying Google for clues about how minds link and rank memes), micro-payment economies (tracking and valuing smaller units of content) and semantic web technologies (developing a better context for meaning). Better visualization and computer/software systems that can rapidly process massive volumes of data will also be key to ongoing idea quantification.

Obviously, there are a great deal of moving parts to this model (seemingly because ideas depend on a huge network for their context), so rather than specifically attempt to predict how they will all come together to form a more efficient idea machine I’ll instead venture the following mini-scenario:

Homework 2018: Georgia sits on a park bench preparing her English “homework” via a contact-lens display system synched to her hand gestures. Her every gesture and input unit is instantly analyzed and cataloged for subsequent comparison. This behavior is compiled alongside physiological and mental inputs, allowing the software to ascertain her exact input, sorting and output modes, which are shown through her task mixer overlay.

As Georgia composes her original fiction she uses this visual feedback to maintain the appropriate modes for the specific tasks at hand. Thus her intelligence is optimized for the most efficient brainstorming, researching, structuring, writing, and editing. This also helps her to quickly realize when she’s accessing a memory, regurgitating an old thought, being creative, potentially plagiarizing, or performing at a sub-optimal level.

She is also connected to the Global Brain. With every new word, the body of her text is immediately cross-referenced against the bulk of worldwide information, especially traditional and current material related to her assignment. The results are displayed as a regular ticker above her task mixer and state display. These sporadically trigger suggestions, links to past stories, names of associations or professors with relevant expertise, visuals or vids of related content or maps, among a host of other combinatorial reactions. In accordance with her settings, the most important of these occupy a greater percentage of her visual array, demanding more of her valuable attention.

This powerful suite of technology, information and communication catalyzes a substantially more effective learning and production experience than was possible just 10 years earlier. Georgia is able to more effectively manage her mood and personality mode, while also intelligently drawing on, in real-time, from the knowledge and context of the emergent Global Brain. As she writes, she is able to better understand how her work relates to the Global Brain. When she enters into editing and reflection modes, she is able to surf the historical web of people who generated the ideas from which her work is derived. These are all displayed according to her preferences as colors, attribution webs, and percent values.

Best of all, as she progresses the system constantly runs tests on her work and displays probabilities for her ultimate project grade, pageviews the piece may receive if published to the web, income that such a piece could generate online and new neuron growth accomplished by the end of the assigned process. Though it seems very mechanical, Georgia views it as very efficient and enabling. As a scholar and producer with a healthy ego, she is happy to help pay for school through her assignments and contribute to the Global Brain in a highly quantifiable manner.

Of course, some neo-luddites argue that Georgia’s dependence on the technological system is a dangerous crutch, but most people her age view it as an intelligence amplifier and fundamental human right. These supporters also make the irrefutable case that such systems are contributing to broad economic growth, more equitable wealth distribution and additional acceleration in technology, info and knowledge.

No longer is it possible to ignore those that contribute critical pillar memes that later evolve into market-rewarded structures. Unless they choose to open-source their concepts, these critical brains are always given their due cash %, which is partially why the world has seen such an explosion in part-time and full-time pontificators.

Incidentally, the quality and originality of the writing that Georgia is able to produce is equal to that of what an average person 3.7 years older would have output in the year 2000.

So, is this a realistic scenario for 2018? How do you see the Idea Sea and memetic/temetic quantification evolving?

Comment Thread (2 Responses)

  1. I haven’t time right now for a considered response, so a superficial “first reaction” only for now.

    I agree this concept has potential as a learning tool, but …

    What if “Georgia” is having bad menstral cramps or ate something for breakfast that’s causing her bad gas? Somewhat less provocatively, as a practical matter, how does such a technology differentiate between a systemic state arising from previously unexpressed knowledge and a truly transient physiological occurance? Too much of what you describe relies upon situational-dependant physiological expression(s) as a guidance or operational mechanism, I think.

    Can such a construct be made sensitive enough to function as designed while not being subject to unfortunately common physiological fluctuations that would express as noise or interference to the mechanism itself?

    Posted by: Will   October 22, 2008
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  2. How does such a technology differentiate between a systemic state arising from previously unexpressed knowledge and a truly transient physiological occurance?

    By 2018 I expect that such systems will be crunching multiple simultaneous inputs (reduces over-reliance narrow sets of indicators), be aware of longitudinal health/behavior patterns, and respond to the guidance of the user (people will still be active top-sighted controllers of these systems).

    Posted by: Alvis Brigis   October 23, 2008
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