I think the author's point rests on a subtle equivocation. It's true that realized intelligence requires an environment and a sufficient dataset. And so in this

I think the author's point rests on a subtle equivocation. It's true that realized intelligence requires an environment and a sufficient dataset. And so in this sense, the author's point that there will be no realized intelligence that is unspecific to training environment is probably correct. But there is another sense in which intelligence can be cashed out. It's the sense in which a single learning algorithm can be trained to "behave intelligently" in a wide array of environments. It is generally this kind of intelligence that people speak of when they talk about general AI. There is no reason to think this kind of general AI is inherently impossible. For it to be impossible would mean that different kinds of optimization/learning problems are completely independent, i.e. there is no similarity or underlying regularity to be exploited that cuts across the entire class of optimization/learning problems. I think this is very probably false.