TECHNICALLY SPEAKING
Natural Intelligence Systems AI IS EVERYWHERE BY HAILEY MINTON
“Mary had a little ____.” “If you’re happy and you know it, clap your ____.” If you are familiar with these nursery rhymes, your brain automatically fills the gaps with “lamb” and “hands.” Brains are trained to recognize sequences, which helps us correctly predict missing information. You’re probably not aware of it, but our brains look for patterns and fill in gaps with predictions as we go about our days. We base decisions off of those patterns. Natural Intelligence Systems won the most innovative company in Idaho award for 2021 because they are building an AI system that emulates the pattern-based operating system of our brains. For an AI system to operate, you have to feed it a lot of information. Within that information are sequences of patterns. All AI systems, whether mathematics-based or pattern-based, have to feed on what is called training data. Once the system has seen the data, then it has learned to make predictions about any future information it might see. Your brain consumes about the equivalent of 20 watts of power as you are awake and reading this. That’s about as much energy as is needed to power a small light bulb. “[Brains] are tremendously more powerful than the best AI systems,” says Paul Dlugosch, CEO and founder of Natural Intelligence Systems. Mathematically-based systems provide only a fraction 34
PHOTO COURTESY PAUL DLUGOSCH
Natural Intelligence Systems software engineers Matt Adsitt (left) and Anthony Harris.
of the capability of a human brain at a much higher energy cost. These mathematical systems can take thousands of watts to operate. They require a hundred times more energy than pattern-based AI because of the vast number of mathematical calculations they compute. “They’re solving the problem in the wrong domain,” says Dlugosch. That is why instead of trying to build upon the mathematics-based AI system, Natural Intelligence Systems is taking an entirely new approach. The company is working on pattern recognition instead of converting data into a mathematical problem. “When you stay in the domain that the human brain works in, it changes how AI can be implemented, and frankly it results in a better AI… The first thing we do in our system is take all the information as fast as it comes in and change it to digital patterns,” says Dlu-
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gosch. Mathematical systems, on the other hand, turn it into a number. He adds, “You can’t see it. We could print [the pattern] out in a form. From that moment forward we’re working in a system that is much more like the brain.” The energy savings isn’t the only perk from this technology. The mathematical operations in AI used today act like a data blender that is hard to unravel. It doesn’t give the reasons why it came to a decision. Natural Intelligence’s pattern-based AI is intrinsically explainable. Dlugosch says that when it tells us why it reached a certain conclusion, it makes us smarter and increases confidence in the conclusion the system came to. For example, more and more loan applications are being sent to AI systems instead of loan officers. Dlugosch anticipates that AI is going to have tremendous societal
