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Artificial Intelligence: Thinking Slow and Fast
The human brain is fascinating, but it is also something that is not well understood and is an intense topic of study by both medical, social science, and computer science researchers.
Social scientists have identified two types of thinking that humans do: ad hoc and deep.
The first type of thinking is ad hoc. Ad hoc thinking lets humans make some decisions and respond to some situations very quickly. That type of thinking is based on intuition and our historical playbook of previous scenarios that we’ve experienced. It is a basic ‘gut’ reaction and provides more often than not a reasonably good or correct response.
The second type of thinking is ‘deep thinking’. This is when the brain becomes analytical. During deep thinking, humans need to write things down, consult with their calculators and computers, create spreadsheets, discuss with others, and review their notes. This is slow thinking.
Current AI algorithms skew towards modeling human use of ‘deep thinking’. But in order for AI of the future to interact more fluidly with humans, it needs to be able to be more ad hoc, ad hoc in the sense that the AI can intuitively remember or know how to adjust to the needs of a specific situation.
AI generally lacks metamemory, some internal understanding or awareness of what is appropriate for a given situation, a kind of understanding that is intuitive for humans but very difficult for AI.
Takaya Arita, professor at Nagoya University, said that “in order to elucidate the evolutionary basis of the human mind and consciousness, it is important to understand metamemory. A truly human-like artificial intelligence, which can be interacted with and enjoyed like a family member in a person’s home, is artificial intelligence that has a certain amount of metamemory, as it has the ability to remember things that it once heard or learned. The need for metamemory depends on the user’s environment. Therefore, it is important for artificial intelligence to have a metamemory that adapts to its environment by learning and evolving. The key point is that artificial intelligence needs to learn and evolve to create a metamemory that adapts to its environment.”
