Can it connect with other agents, understand them, come to empathise with them and find a way to work with them better?
The answer is no to all of these, and there are other problems as well. Yes, this model is trained to use a domain specific language to reason and plan over puzzle problems, and so it's programmers have cracked arc-agi-3 and that's a great achievement, but there is an asymmetry here. The arc team are well funded but are charged with providing a target for the vast ocean of funding, compute and talent everywhere else.
Most importantly, arc-agi-3 and the other benchmarks are all verifiable. The model can check if it's succeeded or not. They are not A* of course, but long horizon problems where you have to overcome minima to get the solution are not alien to AI either.
Why does it need to empathise with something that doesn't have feelings in the first place? It clearly can learn from context. And experience? Again I don't see why it needs to feel anything.
I don't think it can learn from context, otherwise we could write Jane Eyre into it and it would be the novel? It's not learning as we conceive of it - it's another thing that we have labelled as "in context learning".
I have feelings, it needs to be able to empathise with me, or another driver, or a client...
1. It literally learns on data and gives outputs based on the context. I'm sure real time updating of weights will happen sooner than later.
2. What does intelligence have to do with feelings? It clearly doesn't learn like a human. Nor does it have to. Our world is filled with intelligence which behaves nothing like humans. Its a tool not another life form.
It can use new information when performing new tasks. It can store experience based on mistakes it made. It can store knowledge in several ways (context, files) and use it when needed.
There is a chance it will forget something, but the same can be said for humans.
Does it experience?
Can it connect with other agents, understand them, come to empathise with them and find a way to work with them better?
The answer is no to all of these, and there are other problems as well. Yes, this model is trained to use a domain specific language to reason and plan over puzzle problems, and so it's programmers have cracked arc-agi-3 and that's a great achievement, but there is an asymmetry here. The arc team are well funded but are charged with providing a target for the vast ocean of funding, compute and talent everywhere else.
Most importantly, arc-agi-3 and the other benchmarks are all verifiable. The model can check if it's succeeded or not. They are not A* of course, but long horizon problems where you have to overcome minima to get the solution are not alien to AI either.