What do you think has changed? The situation is still about as promising for AGI in a few years - if not more so. Papers like this are the academics mapping out where the engineering efforts need to be directed to get there and it seems to be a relatively small number of challenges that are easier as the ones already overcome - we know machine learning can solve Towers of Hanoi, for example. It isn't fundamentally complicated like Baduk is. The next wall to overcome is more of a low fence.
Besides, AI already passes the Turing test (or at least, is most likely to fail because it is too articulate and reasonable). There is a pretty good argument we've already achieved AGI and now we're working on achieving human- and superhuman-level intelligence in AGI.
> What do you think has changed? The situation is still about as promising for AGI in a few years - if not more so
It's better today. Hoping that LLMs can get us to AGI in one hop was naive. Depending on definition of AGI we might be already there. But for superhuman level in all possible tasks there are many steps to be done. The obvious way is to find a solution for each type of tasks. We have already for math calculations, it's using tools. Many other types can be solved the same way. After a while we'll gradually get to well rounded 'brain', or model(s) + support tools.
So, so far future looks bright, there is progress, problems, but not deadlocks.
PS: Turing test is a <beep> nobody seriously talks about today.
Besides, AI already passes the Turing test (or at least, is most likely to fail because it is too articulate and reasonable). There is a pretty good argument we've already achieved AGI and now we're working on achieving human- and superhuman-level intelligence in AGI.