"Once we have a set of weights that seem to work"
That's the fun bit. You harden that up and then another outlier arrives...
If there's a poster child for machine learning, it's neural networks. We gave a practical introduction to the topic here, but this time I'll take a different approach and explain the background to how neural networks, er, work. To demonstrate this, we'll show how a neural net can be used to classify different species of iris …
And for good measure:
Look, I love me a neural network as much as the next lazy AI creator. I love them so much that I build them on GPUs, so you can hide quite how much buggering around you did to get to a sensible answering system.
But like all AI problems, if there are clear goal states and easily classifiable middle states, it's a "very easy" to solve problems. Hence why we'll keep finding AIs that can play at an advanced adult level in games (which are artificially constrained) but can't manage child like tasks.
An AI usually needs to have already seen something in order to classify it. So a child/AI knows what a fire engine is, because it's seen lots of pictures of them, seen one going past, had it explained to them what it is. If you then say "at the airport, fire engines are yellow" you can be pretty sure the kid will figure out without more explanation, but the AI classification may have decided that something bein red meant fire engine ore than any other input.
I think you mean great-great-grandson. Unless you wanted to say great, great great-great-grandson, which sounds sycophantic.
And by the way : imagine you are a cyborg programmed with machine learning and ask yourself "why?" such as "why do I want to get out of bed in the morning?" or even "Who gives a toss about these stupid flowers?"
Crikey with a semblance of humanity most days I (with certain physiological needs) do not want to get out of bed in the morning.
Biting the hand that feeds IT © 1998–2021