What everybody is looking for is imagination and invention. Current AI systems can give best guess statistical answer from dataset the've been fed. It is always compression.

The problem and what most people intuitively understand is that this compression is not enough. There is something more going on because people can come up with novel ideas/solutions and whats more important they can judge and figure out if the solution will work. So even if the core of the idea is “compressed” or “mixed” from past knowledge there is some other process going on that leads to the important part of invention-progress.

That is why people hate the term AI because it is just partial capability of “inteligence” or it might even be complete illusion of inteligence that is nowhere close what people would expect.

▲

fl7305 5 hours ago | parent | next [-]

> Current AI systems can give best guess statistical answer from dataset the've been fed.

Counterpoint: ChatGPT came up with the new idiom "The confetti has left the cannon"

▲

in-silico 15 hours ago | parent | prev [-]

> Current AI systems can give best guess statistical answer from dataset the've been fed.

What about reinforcement learning? RL models don't train on an existing dataset, they try their own solutions and learn from feedback.

RL models can definitely "invent" new things. Here's an example where they design novel molecules that bind with a protein: https://academic.oup.com/bioinformatics/article/39/4/btad157...

	▲	omnimus 14 hours ago \| parent [-]
		Finding variations in constrained haystack with measurable defined results is what machine learning has always been good at. Tracing most efficient Trackmania route is impressive and the resulting route might be original as in human would never come up with it. But is it actually novel in creative, critical way? Isn't it simply computational brute force? How big that force would have to be in physical or less constrained world?