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This is the third in a sequence of posts taken from my recent report: Why Did Environmentalism Become Partisan?
Summary
Rising partisanship did not make environmentalism more popular or politically effective. Instead, it saw flat or falling overall public opinion, fewer major legislative achievements, and fluctuating executive actions.
Public Opinion...
I think right now EAs might be making a significant mistake by paying insufficient attention to the political realm. As EAs we tend to figure out what’s most impactful for us to work on and focus hard. That’s great! But there are various actions that are ‘non-delegatable’ - the extent to which an individual can do the action is limited (like voting, going to a protest, making hard money contributions to particular campaigns). It might be useful if we were all more in the habit of doing variou...
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New Video from AI in Context: The Fall and Rise of Sam Altman
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I appreciate you're sharing this as an alternative way of framing AI alignment for people who react badly to using anthropomorphic language to describe LLMs, and I can see it could be useful from that point of view. But I strongly disagree with the core argument being made in that blogpost.
The problem with saying that LLMs are just functions mapping between large vector spaces, is that functions mapping between large vector spaces can do an awful lot! If the brain is just a physical system operating according to known laws of physics, then its evolution in time can also be described as a mapping from R^n -> R^m for some huge n and m, because that's the form that the laws of physics take as well. If the evolution of the universe is described by Schrodinger's equation, then all time-evolution is just matrix multiplication!
There might be very good reasons to think that LLMs are a long way from having human-like intelligence, but saying this follows because they are just a mathematical function is a misleading rhetorical sleight of hand.
This comment prompted a lot of reflection, so thank you!
I don't think the blog post claims that LLMs are a long way from human-like intelligence. For what it's worth, I agree with your reasoning against that line of argument.
My main takeaway from this post's core about being mindful about the level of abstraction in language. Obviously for technical AI safety, the low-level, mechanistic view seems important. But it also seems like rhetorical sleight of hand to go with high-level anthropomorphic language to motivate people/make explanations easier. In good written up resources they lead with a fundamental understanding of e.g. how neural nets work (bluedot, global challenges project) but I personally think the movement could still bear this in mind more when introducing AI safety to newcomers. Needless to say hype language is also a problem in mainstream capabilities discussion.
Side note: On the analogy to physics itself, I'm not an expert, but I've also been told that the premise of the universe or brain being describable by purely linear maps is contested. Regardless of that, I'm not sure how pragmatically important the analogy is compared to the immediate choice of which abstraction to use for AI safety work.