Planning In Humans And Adaptability In Machines

Planning In Humans And Adaptability In Machines

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Planning ahead for a task involves both thinking and deciding how to achieve it. Although a machine can make predictions to determine how to react to potential tasks, yet the fact that it can’t become aware of a task unless it encounters it in reality in the form of a prompt makes it unable to make a decision prior to this task. Accordingly, machines can’t be described as being capable of planning for future tasks.

Yet, a machine’s ability to make predictions enabling it to create possible scenarios of how to react adequately to potential tasks provides it with an advanced feature, which is learning to adapt rather than learning to learn, or meta-learning. The difference lies in that adapting involves being able to respond adequately to new unknown tasks without further training while meta-learning involves being an efficient learner of a newly encountered task.

However, the fact that machines aren’t aware of that world which exists before they encounter a new task makes them lack human attributes such as motivation, ambition, and determination, which drive human actions in response to a future task or a challenge.