Manolis Kellis

So embrace it, use it as your partner and work with it

rather than sort of forbid it.

Because I think the productivity gains will actually lead to a better society.

And that's something that humans have been traditionally very bad at.

Every productivity gain has led to more inequality.

And I'm hoping that we can do better this time.

That basically right now, a democratization of these types of productivity gains will hopefully come with better sort of humanity level improvements in human condition.

So it's truly remarkable to be able to sort of be able to encapsulate this knowledge and sort of build these knowledge graphs and build representations of this knowledge in these sort of very high dimensional spaces.

being able to project them together jointly between say single cell data, genetics data, expression data, being able to sort of bring all these knowledge together allows us to truly dissect disease in a completely new kind of way.

And what we're doing now is using these models.

So we have this wonderful collaboration.

We call it drug GWAS with Brad Pintiluta in the chemistry department and Marin Kazitnik in Harvard Medical School.

And what we're trying to do is effectively connect all of the dots to effectively cure all of disease.

So it's no small challenge, but we're kind of starting with genetics.

We're looking at how genetic variants are impacting these molecular phenotypes.

how these are shifting from one space to another space, how we can kind of understand the same way that we're talking about language models, having personalities that are cross-cutting, being able to understand contextual learning.

So Ben Linger is one of my machine learning students.

It's basically looking at how we can learn cell specific networks across millions of cells, where you can have the context of the biological variables of each of the cells be encoded as an orthogonal component to the specific network of each cell type.

And being able to sort of project all of that into sort of a common knowledge space is transformative for the field.

And then large language models have also been extremely helpful for structure.