Naturally Stupid Versus Artificially Intelligent (Part 2)

Doug Freud

In Part One of this blog, I examined the inherent flaws in human cognitive machinery, which lead to poor information processing and a flawed approach to knowledge discovery, making us all “naturally stupid.”  In Part Two, I’ll explore how we will overcome our natural stupidity and evolve via artificial intelligence.

“Doubt is not a pleasant condition, but certainty is an absurd one.” –Voltairealba

In January 2000, Artist Edward Kac, in conjunction with Louis-Marie Houdebine, a genetic researcher at France’s National Institute of Agronomic Research, embarked on a project to create a green-colored rabbit. Alba the rabbit, an albino, was genetically engineered by splicing the green fluorescent protein (GFP) of a jellyfish into her genome. If Alba’s hair was removed and exposed to light, we would see a green rabbit.

Ignoring the ethical concerns of this research, it illustrates an important concept and change in the evolution of humans. We are moving past random natural selection into an era in which we will intelligently design and control our own destiny.

We are becoming artificially intelligent despite the inherent limitations of the way the brain processes information.

Why are we evolving into the era of AI, and what are the implications?

From cognitive to the AI revolution

In the book Sapiens, Yuval Noah Harari examines the history of Homo sapiens and why we have become the most powerful animal on the planet. His argument is that Homo sapiens is the only animal that can flexibly collaborate in large numbers. While insects can collaborate in large numbers, they can do so only in fixed patterns (like searching for food). Other animals, like chimpanzees, can collaborate flexibly, but not in large numbers.

It is our ability to do both that sets us apart from all other animals.

In examining the last 30,000 years, Harari identifies the key “revolutions” that have enabled the age of artificial intelligence.

Harari defines these revolutions as:

  • Cognitive Revolution: Homo sapiens (e.g., wise man) through random variation acquires advanced cognitive capabilities. In addition to language, one of the key capabilities is creativity, and to believe in shared myths that satisfy a need for causal explanation.
  • Agricultural Revolution: The transition from hunter-gatherers to farmers as the main supply of food. While there were numerous negative consequences of this revolution, it was ultimately adaptive and allowed for a larger, more genetically diverse population to survive.
  • Scientific Revolution: This revolution began when we finally admitted that our shared myths could not adequately explain how the world worked. Admitting our ignorance ultimately led to the adoption of scientific method and an explosion of knowledge discovery. The industrial revolution is simply a continuation of the scientific revolution and was powered by the ideologies of capitalism and consumerism.

Harari argues that the ability of Homo sapiens to believe in “imaginary concepts” (like religion, ideologies, money, and perpetual economic growth) is what enables us to collaborate flexibly and in large numbers.

This capability, combined with the exponential growth of knowledge acquired via the Scientific and Industrial Revolutions, enabled humans to largely overcome our traditional challenges of famine, plagues, and war.

The evolution of knowledge discovery at an exponential rate will thrust us into a new age where we can control how or what we evolve into.

Implications of the AI revolution for organizations

As our process for knowledge discovery accelerates and authority moves from human to AI, we’ll need an agile approach for restructuring organizations.

Henry Minztberg, an internationally renowned academic and author on business and management, conceived of the organigraph to graphically represent an organization’s structure and processes. Here is an example of an organigraph:

Minztberg’s theory is that an organization is split into the following five structures:

  • Strategic apex (top management)
  • Middle line (middle management)
  • Operating core (operations, operational processes)
  • Techno structure (analysts that design systems, processes, etc.)
  • Support staff (support outside of operating workflow)

Each of these structures requires a coordination mechanism between tasks (like R&D and supply chain), and as we move into the age of AI and next-generation supply chains, they will need to evolve. Even white-collar jobs, where the coordination mechanism typically is standardization of worker skills and knowledge, will morph into standardization of work output and process. The techno structure will soon become the most important part of the organization and it will require a process for AI-powered innovation.

Organizations that want to grow and disrupt will have an organigraph that looks like this.

Organizational evolution via AI

Organizations, like humans, encounter complex evolutionary challenges and historically are subject to natural selection via market forces. Most big companies indicate it is their processes that lead to superior products and services and ultimately differentiate them in the market.

As machine learning and AI become mainstream, one possible outcome is that traditional advantages will devolve. Whatever its size and position in the market, organizations need to welcome the paradigm shift.

Ultimately, if an organization doesn’t recognize the new rate of knowledge discovery and evolve via AI, it is unlikely to survive the revolution.

Read these blog posts for more on artificial intelligence, machine learning, and predictive analytics.


Doug Freud

About Doug Freud

Doug Freud is a global Vice President of Data Science for the SAP Customer Innovation and Engagement Platform team. His academic background is Industrial Organizational psychology, and he has worked in both GTM and professional service roles. He is a proven leader with ability to manage cross-functional teams that implement innovative solutions. His passion is using data and machine learning to change business processes and create new systems of innovation.