faurroar

I don't think it's surprising, per say, but that's a consequence of the fact that I don't believe there is some sort of magic threshold at which a system becomes agential.

Like I don't necessarily disagree with any of your framing. The thrust of the alignment problem, as I see it, is that there is an intrinsic problem of aligning the goals of two distinct systems that poses catastrophic risks precisely when one of the systems is significantly more capable (in some sense or other, maybe not in a general/absolute sense) than the other.

"Why are we surprised? The model did exactly what it was told, albeit in an unintended, emergent strategy that's very different from what was intended."

So you managed to hit upon the exact problem, then slyly appended "exactly like the hundreds of such algorithms before". When has an algorithm ever been capable of developing an emergent strategy at this level of sophistication? This ~is~ the alignment problem, as another commenter pointed out. Impressive level of cognitive dissonance to lay this bare in your own words, then conclude that it's a non-issue.

Hinton and Bengio don't understand how LLMs work?

Architectures have evolved significantly since then. DeepSeek v4 =/= GPT-3. Even then, a great deal of complexity lies in everything surrounding the architectures e.g. how do you implement them performantly on modern accelerators, how do you distribute the model across a set of accelerators, how do you post-train, etc. And pre-training itself is a dark art. If you legitimately think that frontier labs are doing something equivalent to whatever you wrote on your whiteboard, you’re clueless.