Words Join Numbers in the Data World

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The data world today spans science, marketing and military intelligence.  These are examples of three large domains which are more and more data dependent.  Going back to the dawn of civilization, numbers ruled this data world, and until very recently this remained true throughout human history.  Pythagoras, the Hebrew Gematria system, Leibniz, Descartes, Turing, Chomsky and many others conceived the possibility of how words and numbers could be integrated together in mathematical algorithms to draw accurate insights -- Lexical Analysis.  Originally, this early line of thinking led to cryptography -- the writing and breaking of secret codes -- many Millennia ago. When computers emerged the idea of computerizing language translation e.g. English to Russian and back was the original use case that drove software development leading to natural language processing (NLP).

In 1997, work begun much earlier reached fruition in my Next Century Media tests with the cable industry, leading to our discovery of DriverTags.TM  These are 265 words derived by rigorous scientific method that explain and predict why people watch the TV shows they watch.  In other words, the by-definition untappable non-conscious mind can be seen as if in a mirror, in the passive tracking of what a set top box aggregately "watches."

Many a genius has flooded into Lexical Analytics, resulting today in great advances everywhere based on similar approaches focused on words.  For example, since 2005 Affinity Answers by analyzing vast quantities of social media data continuously has helped marketers see the connections between brand affinities and affinities to other things -- TV programs, celebrities and so on -- and better understanding people and brands based on how affinities cluster together in the real world.

At NBCU just the other day Linda Yaccarino and Josh Feldman announced their own proprietary Machine Learning system for finding the right place in a TV program script in which to place an ad for a specific type of product.  This continues a line of inquiry tracing back to Turner's InContext system, which was not based on Machine Learning, yet worked by determining how to make ads more effective based on the words in scripts.

And on November 28, 2018, the Advertising Research Foundation (ARF) put on one of their great webinars in which Unilever Executive Vice President Consumer and Market Insights Stan Sthanunathan presented excerpts from his new book with A.K. Pradeep and Andrew Appel, AI for Marketing and Product Innovation: Powerful New Tools for Predicting Trends, Connecting with Customers and Closing Sales.

I was excited to hear Stan talking about "Mind Drivers," the "Non-Conscious Mind" and the extraction of metaphors which reveal the non-conscious mind.  Of course, these phrases reminded me of DriverTagsTM and I am eager to compare notes with Stan.  In his case he is extracting specific brand metaphors from social media and in the case of DriverTagsTM RMT is extracting non-conscious motivations from passive-viewing data.  As with Affinity Answers and NBCU, this is similar thinking across different use cases all related to marketing.

When I went to buy the book on Amazon, I looked through the table of contents and I was delighted to see that the various main types of algorithms would be explained at the level of the "Intuition Behind" things like Neural Networks, Deep Learning, etc.  Algorithms in my definition (similar to the one Stan gave in the webinar) are in fact optimization instructions, derived from and embodying a human intuition -- an intuition first stimulated in a human mind by the noticing of a data pattern.  It's scientifically thrilling to see the connections between mysterious functions of the mind and groups of words.

Bringing words into data is a way of infusing information with great richness and interacts with the mind in stimulating ways conducive to insight and creative strategic thought.  I can't wait to read this brilliantly conceived book.

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