There’s no definitive or academically correct definition of artificial intelligence. The common ones usually define AI as computer models that perform tasks in a human like manner and simulate intelligent behaviour.
The truth is that for each expert on the field of AI you ask about the definition, you will get some new variation of the definition. In some cases you might even get a very religious schooling on how it’s only AI if the system contains deep learning models. Do you ask many startup founders if their systems contain AI even though it’s just a very simple regression algorithm like we have had in Excel for ages. In fact a VC fund recently found that less than half of startups claiming to be AI-startups actually have any kind of AI.
So when is something AI? My definition is pretty pragmatic. For me AI has a set of common features that if present then I would define it as AI. Especially when working with AI these features will be very much in your face. In other words, if it looks like a duck, and it swims like a duck, and quacks like a duck, then it probably is a duck. The same goes for AI.
The common features I see:
– The system simulates a human act or task.
– The learning comes from examples instead of instructions. The entails the need for data.
– Before the system is ready you cannot predict that input X will give you exactly output Y. Only just about.
So to me it’s not really important what underlying algorithm does the magic. If the common features are present then it will feel the same to use and to develop and the challenges you will face, will be the same.
It might seem like I’m with this definition taking AI to a simpler and less impactful place but this is just a tiny scratch in the surface. The underlying human, technical and organizational challenges are enormous when working with AI.
