Applying Neuroscience to Artificial Intelligence Systems for Situational Understanding. Principles of the nervous system may be applied to create better artificial intelligence systems for situational understanding.
“It’s hard for a computer to know what a cup is,” said my lab mate.
His friends laughed at the statement— how could something so trivial to us humans be so hard for computers to understand? A cup is so simple that humans can accurately identify them from infancy. Most adults hardly have to think to process what is and what is not a cup. Technology is coming closer and closer to being able to take over many cognitively-demanding tasks. If a cup is hard for a computer to classify, then what about more complex classifications, such as recognizing emotional mismatches between a person’s tone of voice and their body language? Why do computers lack this kind of intuition?
Throughout our lives, we observe the behaviors of others. We make sense of cause and effect relations, putting things into context in a holistic way that creates rich tapestries of stories, rather than simple vectors of numbers. We tell stories about what we have experienced, we are constantly creating narratives and explanations. The holistic approach gives humans an advantage over many artificial intelligence systems: they connect cause and effect relationships.
Humans can use the context beyond a short list of properties to determine why something happened as it did. They can see some things that are invisible to an artificial intelligence. While machines may need numerous examples to learn what a cup is, a human may only need one example. A major issue that top engineers, data scientists, and software developers need to work with is how to create artificial intelligences that are able to interpret situations as humans would.
Principles of the nervous system may be applied to create better artificial intelligence systems for situational understanding.
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