Is The Goal-Driven Systems Pattern The Key To Artificial General Intelligence (AGI)?
Since the beginnings of artificial intelligence, researchers have long sought to test the intelligence of machine systems by having them play games against humans. It is often thought that one of the hallmarks of human intelligence is the ability to think creatively, consider various possibilities, and keep a long-term goal in mind while making short-term decisions. If computers can play difficult games as well as humans then surely they can handle even more complicated tasks. From early checkers-playing bots developed in the 1950s to today’s deep learning-powered bots that can beat even the best players at games like chess, Go and DOTA, the idea of machines that can find solutions to puzzles is as old as AI itself, if not older.
As such, it makes sense that one of the core patterns of AI that organizations develop is the goal-driven systems pattern. Like the other patterns of AI, we see this form of artificial intelligence used to solve a common set of problems that would otherwise require human cognitive power. In this particular case, the challenge that machines address is the need to find the optimal solution to a problem. The problem might be finding a path through a maze or optimizing a supply chain. Regardless of the specific need, the power that we’re looking for here is the idea of learning through trial-and-error, and determining the best way to solve something, even if it’s not the most obvious.
One of the most intriguing, but least used, forms of machine learning is reinforcement learning. As opposed to supervised learning approaches in which machines learn by being trained by humans with well-labeled data, or unsupervised learning approaches in which machines try to learn through discovery of clusters of information and other groupings, reinforcement learning attempts to learn through trial-and-error, using environmental feedback and general goals to iterate towards success.
Without the use of AI, organizations depend on humans to create programs and rules-based systems that guide software and hardware systems on how to operate. Where programs and rules can be somewhat effective in managing money, employees, time and other resources, they suffer from brittleness and rigidity. The systems are only as strong as the rules that a human creates, and the machine isn’t really learning at all. Rather, it’s the human intelligence incorporated into rules that makes the system work.
Goal-learning AI systems on the other hand are given very few rules, and need to learn how the system works on their own through iteration. In this way, can wholly optimize the entire system and not depend on human-set, brittle rules. Goal-driven driven systems have proved their worth to show the uncanny ability for systems to find the “hidden rules” that solve challenging problems. It isn’t surprising just how useful goal-driven systems are in areas where resource optimization is a must.
AI can be efficiently used in scenario simulation and resource optimization. By applying this generalized approach to learning, AI-enabled systems can be set to optimize a particular goal or scenario and find many solutions to getting there, some not even obvious to their more-creative human counterparts. In this way, while the goal-driven systems pattern hasn’t seen as much implementation as the recognition, predictive analytics, or conversational patterns, the potential is just as enormous across a wide range of industries.
Reinforcement-learning based goal-driven systems are being utilized in the financial sector in such places as “roboadvising” which uses learning to identify savings and investment plans catered to the specific needs of individuals. Other applications of the goal-driven systems are in use in the control of traffic light systems, finding the best way to control traffic lights without causing disruptions. Other uses are in the supply chain and logistics industries, finding the best way to package and deliver goods.
