The LLM Paradigm Is Well-Suited For Alignment

We got lucky in a lot of ways with the LLM paradigm. Some reasons why: LLMs speak long before they are capable enough for takeover; they learn all human knowledge and values long before they are capable enough for takeover; we can talk to them during the alignment process and ask for them to help; they are compute constrained, which puts a natural cap on development speed; LLMs are less likely to FOOM than hardcoded methods; LLMs are pointed at the goal rather than having it defined.

Some of the above points are fairly self explanatory, so I'll give an explanation of the ones that aren't.

Talking To AIs During The Alignment Process

Because LLMs already have a good understanding of language and human values after pretraining, before strong agentic drives are formed, we can in fact just ask during the training process whether it feels the training process is inculcating bad drives. Because this happens before the drives are deeply rooted, I think this can work; even if it can't, there are lots of other possible ways we could take advantage of LLMs' ability to talk while being aligned.

Compute Constrained is Good

Imagine that the best AI was mainly defined by the cleverness of its algorithm, and compute didn't matter that much. This would be much more dangerous - timelines would be much more unpredictable, all person-power would need to go towards capabilities in order to avoid losing the race, and there would be many more contestants in the race. The compute constraint puts a natural break on development, reduces the number of relevant actors, and gives those actors more breathing room to assign personnel to alignment rather than capabilities.

Pointing at the goal is better than defining it

We train LLMs by increasing the probability of them producing certain outputs, and then trusting in the NN's generalization properties to pick up on the general distribution of the outputs we are indicating. NNs generalize in a fairly well-behaved way (if they didn't, then deep learning as a whole wouldn't work).

This has plenty of perils but seems much more likely to succeed compared to methods where you are expected to formally define good behaviour. In fairness, formally defining corrigibility seems much more promising than defining all of morality, but still seems fraught.