Human + Machine: The Superintelligence Businesses Need
We’re in an age of infatuation with AI and its ever-evolving technological capabilities. Yet, it’s still humans—executives, managers, and others—who use the output of these algorithms to make mission-critical decisions. These decision-makers bring their human judgment, individual personalities, opinions, and biases to the decision-making process, determining how to use the analytically generated output.
Consider that for all the advancements in AI, it was humans—flesh-and-blood physicians and scientists—that were needed to predict viral propagation during the pandemic. As John Sicard, president and CEO of Kinaxis, a leader in supply chain software, told us:
“Every math-based model collapsed when COVID hit. Every machine learning algorithm collapsed. None of the assumptive parameters could be trusted.”
While we’re all fascinated with the apparent eloquence and “fluency” of chatbots and large language models like ChatGPT, it’s important that we don’t confuse these systems with intelligence. We’re far from systems that have a semantic understanding of what they’re saying. We’re also far from systems with reasoning capabilities—including common-sense reasoning—which remains elusive for machines and strictly in the domain of humans.
Man vs. Machine?
Humans and technology are often seen as competitors in the new world. But the reality is more nuanced and should be approached with cautious optimism rather than fear. The strengths of one are, in fact, the weaknesses of the other.
Automation technologies, such as machine learning and robotics, play an increasingly greater role in everyday life and have had a huge effect on the workplace. Today, automation has gone beyond repetitive manufacturing activities. Robots run factories, work side by side with physicians in operating rooms, read X-rays, and even render medical diagnoses. AI analytics improve fraud detection and guide autonomous vehicles on our highways.
Yes, machines are far superior at these types of tasks. They have precision and strength and don’t fatigue. In contrast, humans are imprecise, overconfident, and place too much faith in their intuitions. They’re highly biased.
Still, machines have their own flaws.
All machine intelligence is based on data and is only as good as the data it’s fed. Machines aren’t good at out-of-the-box thinking.
With generative AI, they may appear creative, but how can an algorithm develop a truly innovative strategy or a unique marketing campaign?
Sure, machines can identify photos of dogs, but they can also confuse a dachshund dog with a hot dog, and they can’t extrapolate that one is a pet while the other is a ballpark treat.
Context matters, and machines don’t have it.
AI’s Big Miss: The ‘Broken-Leg Problem’
Consider the “broken-leg problem.” Just ask an AI: “What are the chances of Person A going to the movies this week?” An actuarial formula based on historical data might be highly accurate in predicting the odds of this person catching a new release on the big screen. But that model would fail if the person has a broken femur.
The broken-leg problem demonstrates the importance of context. We might be able to use data to predict odds, but the forecast will fail if we don’t appreciate the context.
Obviously, without that information, an algorithm would be way off. All the data provided to the algorithm suddenly becomes irrelevant because of this change in context.
Today, companies operate in volatile markets and environments, and broken-leg problems are a fact of corporate life. Disruptions that require understanding context and providing interpretation are simply a part of doing business. This could be a storm delaying a shipment, a political event, a competitor launching a new product, or a union strike.
Algorithms could offer an answer if the future facts look exactly like the data upon which forecasts are based. But they can’t help when the environment and context change. The database lags reality; the map is not the actual terrain.
The Answer: The Humachine
Machines are computationally stronger, faster, and more precise. Humans are mentally more intuitive, creative, and understanding of context. Harnessing the strengths of both—and finding the right way for them to work together—is the recipe for success.
A number of inventions that enjoy widespread commercial applications were invented by sheer humanity. Accidental good fortune, fun, and humor are foreign notions to computers, but they sure drive a lot of inventions. Post-it® notes, the artificial sweetener saccharin, and Slinky toys were all born from purely human thinking.
Perhaps when our brightest workers are liberated from the repetitive and analytical tasks that now consume our work lives, we can discover that elusive miracle cure, develop renewable energy resources, and find ways to augment human happiness.
Technology creates a tremendous opportunity for business by freeing us to innovate.
A “humachine,” combining man’s unique traits with machines’ economies of scale and processing, is the path forward.
Nada R. Sanders, Ph.D., is an internationally recognized AI thought leader and expert in forecasting and global supply chain intelligence. Ranked in the world’s top 2% of scientists, she’s the author of 100-plus scholarly publications and seven books, including The Humachine: AI, Human Virtues, and the Superintelligent Enterprise. Learn more at nadasanders.com.