Pict from Fruitnet
Many yeas ago, in 1990, I happened to be inching along toward the end of my 8th semester at the Technical University Budapest when I recognized that I hadn’t found any aha experience during those years with the academia.
Then, even to my surprise, I chose a strange, maybe „outlier” course: economic psychology. It covered a wide range of interesting topics and offered us a short immersion into the world of neural networks too. In 1990!
The neural networks topic presented one of the strangest questions that in its simplicity caught my attention while studying: what makes an apple an apple, what makes a pear a pear?
Simple enough, isn’t it?
We all know that it is not a question that can be answered by defining descriptors and value ranges of them. Color, shape, taste, surface… you can continue with the listing of different dimensions trying to depict an apple. Even if you were able to define which dimensions count (and which don’t) the number of appropriate combinations of the suitable values would exceed human capacities. And we would always find a counterexample.
Somehow, we all have the feeling that there should be another way to answer the question: what makes an apple an apple, what makes a pear a pear.
And indeed. As little children we also learned by examples what is an apple, what is a pear.
We were taught to be able to recognize the apples contra the pears. It took us a while but eventually we reached – almost unperceived – a point where we were able to decide whether a given fruit was an apple or a pear.
Having recognized, that there are problems where teaching and not the descriptor-based, rule-driven process is the appropriate solution, was a revelation to me.
And that is what draws me to AI, machine learning and neural networks.
Why are you interested in the world of AI?
Write me about it, please.
G.
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