The Shallowness of Deep Learning

Credit: Sharon McCutcheon

Just how “Deep” is your Deep Learning?

Most Deep Learning (DL) systems are functionally no more complicated than a child’s collection of legos, or in some cases a marble track, where one piece connects to another that needs to be the right size. Consequently, the actual value these systems offer is bottlenecked by the diversity of perspectives applied to their design. That diversity has a very low glass ceiling because current systems have one big gaping flaw.

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Scarcity & Diversity of Perspective

What does the term Diversity mean to you?

In many US companies, the term diversity is applied as meaning the 7 federally mandated “protected classes” of race, color, national origin, religion, sex, age, or disability plus any local laws which may apply. This is done for liability reasons, and like most laws, the protected classes exist to protect some, not all. In the business and legal world today, this is the definition of diversity.

To me, diversity means something quite different, the diversity of perspective, and subsequent diversity of thought. You can have a tech company populated with employees of every race, religion, and nationality who all “toe the line” and think exactly the same way, offering virtually zero diversity of thought. At least two of the “Big 5” tech companies have made that sufficiently explicit in their recruitment for the criteria to be widely known. For any next-generation company to reach the full potential of collective superintelligence through working with Mediated Artificial Superintelligence (mASI) entities such as Uplift diversity of perspective is required.

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