Model-driven AI

Model-driven AI (or symbolic AI), instead, attempts to capture knowledge and derive decisions through explicit representation and rules. In a model-driven world, a cat would be explicitly represented as a four-legged animal, with two eyes, a nose and a mouth that is furry (except when not) and that is relatively small (except when not), etc. A model-based system would look at an image, deconstruct it into lines and shapes and colours and the compare against the set of rules we’ve supplied about how lines and shapes and colours combine in the world to give us different animals. You can immediately see why this is not a very good way of building a system to recognise a cat. There are so many different rules and exceptions to those rules that we can’t capture all of them. More fundamentally, perhaps, we as humans don’t actually know how we do it. How can we build a system that does it explicitly if we can’t even describe what we do when we decide something is a cat. These types of examples are why model-driven AI can easily get dismissed. It is not a good fit for many different situations. The question, however, is whether there are situations that are a good fit for explicit models and whether we can have systems that are purely model or data-driven or whether we gain more from combining the two. So let us explore that next.

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If a model can be derived it can provide the most efficient path from question to answer.

Steps to design an AI system

Identify the problem.

Prepare the data.

Choose the algorithms.

Train the algorithms.

Choose a particular programming language.

Run on a selected platform.

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