A.I. Genius

The Founder and CEO of Landing AI, DeepLearning.AI, and Managing General Partner of AI Fund, Andrew Ng, delivers remarkable evidence of Artificial Intelligence (AI) genius.

“He says, and I quote, the real differentiator between businesses that are successful at AI and those that aren’t is data: What data is used to train the algorithm, how is it gathered and processed, and how is it governed?” I feel ESG for AI is coming up; human idiocracy multiplied.

Let’s break this implicit cyclical reasoning down. To be good at AI, you have to be good at AI. Wow, that kind of reasoning will reverberate well in Silicon Valley, future-selling another theory without verifiable proof of humanitarian benefit.

The more severe problem with this statement is the inference that more consequential data collected and regurgitated will lead to the identification of causation. That, by the asymmetry of cause and consequence, in physics, defined as moving from an organized state to an unorganized state, cannot be reversed. In other words, cause leads to consequence; consequence cannot lead to the identification of causation. Confounding cause and consequence, in the words of Nietzsche, leads to grave depravity of reason.

In data speak, data is hindsight derived from events that have already occurred. The regurgitation of hindsight does not extrapolate to foresight that breaks the norm. Hence, AI is a pacifier for those who cannot distinguish between hindsight and foresight. May I humbly suggest they Learn To Think?

Be forewarned. Do not let the love for technology I share usurp and disavow the laws of physics in seemingly innocuous and innocent speeches soiling nature’s truth.

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The sign of a vibrant, innovative nation is its willingness to pursue the ever-unfolding discovery of nature's truth and reinvent itself continually against those proven new normalizations upstream. Let’s inspire the world with new rigors of excellence we first and successfully apply to ourselves.

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