By Dave Tress
It seems like everywhere you look, Artificial Intelligence (AI) emerges as the “secret sauce” for improving and streamlining our lives. Computers recognize human speech, play chess, and even drive cars. We are rapidly approaching the point where AI seems the norm, and actual human intervention is the surprise.
I must admit some of the hype leaves me scratching my head. For starters, AI is not a new technology…not even close. I first had the chance to attend a seminar on AI in 1987 as a young Air Force officer/engineer who wrote specifications for support equipment. Back then, the concept of computers performing human behaviors was “out there” and the idea that computer programs could identify questions, process information through deductive logic chains, and come back with a decision also loomed beyond the horizon. Instead, we speculated and toyed with fuzzy logic and confidence intervals.
Although robotics, neural networks, and natural language processing intrigued me, the potential of expert systems piqued my interest above all else. I saw an immediate and valuable way to tap into the power of expert systems and advance my day-to-day spec responsibilities. And yet, ES then—and now—stays in AI’s shadow.
Regardless, I studied expert systems and honed my skills—anticipating the day we could bring this technology mainstream. In the early 1990s, I had my chance. My employer at the time, Ingersoll-Rand needed to develop a quotation system. They wanted to pull the information traditionally bound in price books into a system that would allow their engineers and sales team to properly map specialty knowledge, heuristics, and general rules of thumb to create quotes. We were past the days of using fuzzy logic—it was just not accurate enough. Developing an expert system allowed us to achieve this goal by using computers to refine and perform traditional human behaviors at a new level.
In many ways, expert systems transformed the traditional concept of computer science. Those who were once known as computer scientists evolved into knowledge engineers, a term deeply rooted in the expert system realm. These engineers go beyond just capturing product knowledge by actually applying it within a computer system.
Expert systems, then, lay at the heart of what built Intelliquip’s software solutions. We take in data, run specific calculations, and use that information to produce potentially acceptable searches. But we go one step further. We have mechanisms to sort and find the best result from all available options. All of this integrates the very best of expert systems to date, to assist Intelliquip users in pragmatic and efficient ways.
That’s why I see my professional life over the past 30 years as being heavily influenced and shaped by expert systems. Tomorrow may be even more fascinating as this science continues to recognize repetitive sequences, capture their patterns, encapsulate data, and replicate it within a system.
There is room for technology to gather data and identify the areas where customers have been searching and coming up empty—this information is hugely beneficial.
The most exciting part for me is how expert systems will grow human potential. We have yet to reach the human limit, beyond logical repetition, to explore beyond what’s expected. With the advent of automation in expert systems, we no longer need people to do these logical, repetitive tasks. Where we do need people, and in my opinion always will, is to provide the human touch—the creative problem solving and personal feedback that we are all ultimately seeking. These skills are what will truly help us move more quickly in the long run.
Sure, I see major implications for AI, but I’m betting on expert systems’ continued domination of our work in the fluid handling industry.