I question what objective standards are here. Human intelligence sucks in terms of consistent long term memory, and possibly in terms of not being influenced by emotions/outside forces. It's pretty good at a number of things computers are terrible at however, including ability to generalize from very limited "training" examples, combining "models" i.e. general object recognition with physical movement/obstacle avoidance, lots of things having to do with natural language. What kinds of objective standards were you referring to?

For my part I have faith that computers are great at memorization, and will continue to improve on that front. However, I'm less convinced on their ability to "understand", which is admittedly poorly defined, but intuitive. It seems to me there's still a missing piece between machine learning (essentially all things that are called "AI" these days) and the kind of generalization that we expect out of even a human 1 year old or your average vertebrate.

AI agents need environments with a complexity similar to that of our own in order to understand, and a goal to optimise on (humans have 'survival' as the goal). The missing link is that intelligence is dependent on environment and AI agents don't have rich enough environments yet, or a long enough evolution. The level of understanding is related to the complexity of the environment.

But that can be fixed in simulation. That's why most RL research (RL being the closest branch to AGI) is centered on games. Games are simple environments we can provide to the AI agents today. In the future I don't see why a virtual environment could not be realistic, and AI agents able to 'understand'.

I agree with your first paragraph, but your second one is definitely wrong. We aren't anywhere near even understanding "what nature has wrought" the possibility of "catching up" within the foreseeable future is not realistic.