Can Computers Develop Intuition?
Intuition refers to our ability to know something without having to think about it. Other terms such as “hunch,” “gut feeling;” and “sense” imply that one arrives at a conclusion or knowing something without reasoning it out. You cannot explain how you know something, but you simply know. Everyone has experienced the feeling that something is not right when walking down a dark road in an unfamiliar part of town. Or you have a good feeling about someone you just met at a party. Our intuition guides and accompanies us all the time.
Why is intuition important?
Shouldn’t we just trust in our thinking minds and treat intuition as a lower, instinctual part of our mental capabilities?
Culturally speaking, in the West logic and the reasoning mind have been given top billing for hundreds of years. Instinct has been more closely connected to the animal world. However, recent studies in psychology and neuroscience have taken a much closer look at intuition and discovered some pretty fantastic things. Our intuition is often right and we would be well served to listen to it. In fact, the US military has allocated several million dollars for the study of intuition. The program is called “Enhancing Intuitive Decision Making Through Implicit Learning”. Citing examples from Iraq and Afghanistan where the soldiers reported a sixth-sense or feeling that something was going wrong, such as an ambush or road side bomb, the program wants to train soldiers to make better use of their intuition in battle. Our intuition operating below our conscious mind apparently detects patterns and abnormalities and processes them faster than our conscious mind can. The research will look into what signals and patterns are triggering that gut feeling that something is not right. Clearly, improving soldier effectiveness and safety is the primary focus the military’s research; however, computer scientists have also applied for funding from the grant hoping to learn about the nature of human intuition and apply these learnings to developing computer intuition.
Imagine if your smart phone or driverless car could augment your intuition. You could be planning to go jogging in an unknown park while on vacation and your smart phone could use information beyond your senses to plan the safest route to run. The question is how could a computer gain intuition if we really don’t understand how intuition works in the first place. The answer lies in the emerging fields of artificial intelligence and machine learning. Machine learning uses probability and repetition to do things that come naturally to humans. For example, facial recognition computers use machine learning to identify faces in photographs or on security monitors. The computer learns how to recognize faces by looking at thousands and thousands of faces to learn what a face looks like. This kind of machine learning is called supervised machine learning because people train the machines. Every time you see Netflix or Amazon recommend a movie or product for you, you are participating in supervised machine learning. The machine takes note of what you pick and begins to learn what you like. It also learns what others like you are selecting. In essence, you are training your movie selecting assistant. Similar advances in voice recognition and natural language processing are allowing computers to acquire skills that it was thought only people can do. But this is not intuition as we think of it.
Researchers suggest that another type of machine learning will be needed to better approximate human intuition—unsupervised machine learning. Unlike supervised machine learning where the machine is being trained by people, unsupervised machine learning will take information such as locations where road side bombs have been set or where ambushes have occurred and the computer will look on its own for patterns in the information. No one is telling the computer what to look for. Perhaps, certain patterns in geography, traffic, cell phone activity, or other data might be discovered that can alert soldiers on patrol of an imminent attack. This computer intuition combined with their human intuition may help soldier avoid or at least prepare for dangerous situations. The applications for artificial intuition go far beyond the battle field. Many safety applications spring to mind such as monitoring all the attributes of an aircraft in flight for dangerous situations or a driverless car navigating through a dangerous neighborhood. Artificial intuition may also help robots to operate safely among people by detecting and avoiding potentially harmful situations for people and the robot. The possibilities are limitless. Computer intuition may one day be a helpful companion to our own amazing intuition.
Dr. Smith’s career in scientific and information research spans the areas of bioinformatics, artificial intelligence, toxicology, and chemistry. He has published a number of peer-reviewed scientific papers. He has worked over the past seventeen years developing advanced analytics, machine learning, and knowledge management tools to enable research and support high-level decision making. Tim completed his Ph.D. in Toxicology at Cornell University and a Bachelor of Science in chemistry from the University of Washington.
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