Narrator (TYPE III AUDIO)

The Pando Problem.

Improving intuitive understanding directly.

The model for why this is worth trying is that I believe we are more bottlenecked by people having good intuitions guiding their research than, for example, by the ability of people to code and run evals.

Quite a few ideas in AI safety implicitly use assumptions about individuality that ultimately derive from human experience.

When we talk about AI scheming, alignment faking or goal preservation, we imply there is something scheming or alignment faking or wanting to preserve its goals or escape the data center.

If the system in question were human, it would be quite clear what that individual system is.

When you read about Reinhold Messner reaching the summit of Everest, you would be curious about the climb, but you would not ask if it was his body there, or his mind, or his motivations to climb like that spirit of mountaineering or some combination thereof.

In humans, all the answers are so strongly correlated that it does not make sense to ask.

But as we'll see, these human-centric assumptions often don't really fit the reality of AI systems.

We will start looking at a different domain.

Biology.

Then, we'll look at AI systems, drawing on concepts from the three-layer model of LLM psychology.

At the end, we'll see how confusions about AI individuality can have significant safety implications.

Heading Individuality in biology Individuality, even in relatively simple biological systems, turns out to be complex.

Consider the quaking aspen, Populus tremuloids.

Pando, a famous aspen clone in Utah, is a grove of around 47,000 trees, all genetically identical, all growing from a single massive underground root system.

In a sense, each tree is an individual.

Try chopping one down and the others won't fall.

But in another sense, Pando is one individual, by some estimates the largest single organism on Earth, weighing in at 6,600 tons.

There's an image here in the text.