Attention Machines and Future Politics Highlight

I am saying that, since the early 2000s, many machine learning systems were arguably, in their essence, attention machines: they either were composed, computationally, of attention operations, reminiscent of the attentional processes employed by biological systems; or they performed, functionally, the core operations of attention.

I think this has been true for a very long time, but it has only really been clear to us, average folks, experientially, since LLMs became commonplace. Only since then can we really have the basic experience where a machine pays attention on our behalf at near-human competence. So when I say to Claude, “Please read these new regulations in light of my company's bylaws and my responsibilities in my role,” and it returns with a report about how Rule 104b places new reporting requirements on us, and I should take this to the board — something genuinely remarkable has happened. A machine has re-patterned an informational field according to salience policies I defined and thereby surfaced what matters to me. It has paid attention for me.