I disagree. It's not even clear from the paper exactly how much learning transfer is actually happening. I think it's fair not to be rolling out the red carpet and showering the authors with awards.

Lack of fanfare? Every techie news outlet is plastered with it, and I'd expect it to diffuse from there.

With "intelligence₂" we mean "the ability to produce a usable solution replacing the work of a professional in a specific task". It is a consolidated use with a pretty long history, and whether the task-generalist ANN offers a breakthrough for intelligence₂ has to be discussed.¹

With "intelligence₁" we remain referring to "the ability to reflect on world representations achieving (recursively) refined concepts and selectively producing founded conclusions". It seems that the research relevant to this submissions is still alien to the context of AGI.

A claim that "this is the Dawn of It" requires clarifications and demonstrations (meaning, explicitness in clarifying why).

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¹Some argument that it goes in that direction is in the Introduction: «Historically, generic models that are better at leveraging computation have also tended to overtake more specialized domain-specific approaches (Sutton, 2019), eventually».

This result is unsurprising. "Give a model a bunch of unique datasets and it can do a bunch of unique things." There's nothing showing any sort of generalized learning or capability here.

What is the achievement? It seems that the author has shown that this path is fruitful, but transfer learning is no where near being solved.

Every other answer to your comment was a bit pessimistic. I’m intrigued, we need to see more!

You're hanging out in the wrong (or right) circles if that's your perception.