Understanding vs Interpretation

Machines and humans interact with information in different ways. Being able to distinguish between the two effectively will help you understand artificial intelligence capabilities more precisely.

Machines interpret information according to predefined rules.  Those rules are defined by things like schemas, ontologies,  theories, and rules that have been represented in some sort of machine-readable form.  Machines interpret, they don't actually understand what they are doing.

Humans understand information based on context, skills, experience, research, observation, and reasoning. Humans actually know the meaning of something. Humans may even comprehend something which is to understand something completely.  Humans bring meaning to their ability to understand beyond information that is actually provided in the form of intuition and common knowledge that machines simply don't have.  Humans can understand implications, "deeper meaning" from context, experience, skills, and knowledge that goes beyond the actual information itself.

Computers are dumb beasts.  Literally.  A machine is 100% purely computational.  Computation is a machine's way of interpreting information; not truly understanding that information. The complexity of a system determines if that system is computational. The power of logical reasoning determines the capabilities of the artificial intelligence used.

This distinction between machine interpretation and human understanding is critically important to ensures you are clear about the nature of a machine's capabilities versus real human intelligence.

Machines interpret information that is provided in a machine-readable form using algorithms and predefined logic (i.e. it must physically exist in advance in machine readable form). They do not understand the information and there ability to interpret is directly corelated to the algorithms and predefined logic available.  Full stop.

Whether artificial intelligence is rules-based or probability-based or a combination of the two; algorithms and predefined logic of some sort drives the machine-based process; not magic.

That said, the more useful machine-readable information provided to the machine, the more that the machine can do. Providing a rich, interconnected, enhanced, structured, classified set of information to a machine can enable the machine to do more complicated and sophisticated work including inferring information from other existing information. But there are limits.

Knowledge representation approach matters. To maximize the intelligence amplification that a machine can provide and the aggregate work capabilities of a human/machine team; how you represent the machine-readable knowledge matters a lot.


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