Michael Levin

Or do you want to find the high level modular controls that say, build an arm here?

You already know how to build an arm.

You did it before.

Do it again.

So that's I think it's both.

It's both difficult and it challenges us to develop new ways of engineering.

And it's hugely empowering.

Right now, it's largely in the lab because our simulations do not capture yet the most interesting and powerful things about biology.

So the simulation does.

What we're pretty good at simulating are feedforward emergent types of things.

So cellular automata, if you have simple rules and you sort of roll those forward for every agent or every cell in the simulation, then complex things happen, ant colony algorithms, things like that.

We're good at that, and that's fine.

The difficulty with all of that is that it's incredibly hard to reverse.

So this is a really hard inverse problem.

If you look at a bunch of termites and they make a thing with a single chimney and you say, well, I like it, but I'd like two chimneys.

How do you change the rules of behavior-free termites so they make two chimneys?

Or if you say...

Here are a bunch of cells that are creating this kind of organism.

I don't think that's optimal.

I'd like to repair that birth defect.