The name, AI, implies a replication of human intelligence in silicon form. Yet, it’s easy to lose sight of the hidden brain that brings AI to useful life. Let’s explore the neuroscience as a metaphor to understand this premise.
The typical adult brain weighs about 3 lbs. and consumes 20 WATTs of power. It is a remarkably efficient machine. Nobel prize-winning psychologist, Daniel Kahneman alludes to this efficiency-seeking function when he describes System 1 and System 2 thinking. He proved that we have a subconscious, and thus low-powered, method of processing information. It operates more often than the higher-powered, executive function.
Neuro-anatomy experts believe that memories are encoded with emotions, but those emotions are not individually stored. They are essentially references built and stored in the limbic system. Basically, we remember an event and then there is a lookup table for how we felt about it. It is also a powerful influence on how we subconsciously make choices.
This limbic system, located in the middle brain, influences future decisions because it uses emotional memory as a framework for what might serve us or what might slay us. Without it, we make sub-optimal choices because we lose context for risk or reward.
Similarly, AI analysis without all the right data leads to a faulty future. Therefore, it is worthwhile to talk about how organizing and presenting “all the right data” is critical. Management of the messy, high-volume, unstructured data should be considered as important to AI as the limbic system is to the predictive function of the human brain.
Yet, there are other factors to automatic decision-making beyond the emotional memory system. Let us explore the brain metaphor further. Kevin Simler and Robin Hanson argue in their book, The Elephant in the Brain: Hidden Motives in Everyday Life, how unconscious we are about the nature of our own behaviors. They make the case that we are like our primate “cousins” in acting according to social motivations. Whether you consider this evolutionary biology or learned in the family of origin, matters less than understanding that there is something else hidden in our human brains.
This blind spot might also explain why technologists often oversee data management as phenomenon of culture. Typically, pundits only write about data management in two dimensions. The first is technology focused. It begins with byte sizes, throughput, and access patterns. This is a platform mindset that affords the procurement, storage, and availability of data. It has a strong bias to metadata (data about data) because this is the steering wheel with which to drive the car.
The second dimension commonly exploited is process. This systems-level view comprehends the entire pipeline, from acquisition at the source, to sorting and shuffling, to cataloging, to presenting, and finally to archiving. It is the farm-to-table point of view. Or rather, farm-to-Tupperware point of view. It concerns itself with the “how,” while technology takes a “what” perspective.
