Emilia Javorsky

speaker

1,653 appearances 2 recordings 1 series first heard Mar 2026 last heard 20 Mar

Emilia Javorsky’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 2 in all, peaking in Mar 2026 with 2.

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Future of Life Institute Podcast · What AI Companies Get Wrong About Curing Cancer (with Emilia Javorsky) · 20 Mar 2026 podcast

And we haven't actually measured biology enough to have a data set that we could actually train this intelligence, future intelligence on.

So I do think that is another fallacy that comes up is saying AI is going to accelerate science and medicine and physics and math are all put in the same bucket.

I think we need to disentangle that a little bit is where there's first principles.

increasing AI capabilities are going to have increasing gains, but when there's not, that feedback loop breaks down.

Yeah, so that I think is one of the biggest misconceptions around biology.

I think people see that there's all of these journals, there's all of this work happening.

So therefore, we must have tons of biological data that would be great to train this model with.

We get medical records, our doctors take all these notes, like, isn't there the data out there to train biology?

what we need and feed it to the AI to help deliver insights.

Now, there's a number of problems with that, starting sort of at the basic level and moving up.

So at the basic science level, there's actually very little data that is in a format or accessible to being trained by AI.

So most lab experiments that are done

That data is never actually brought into the public comments or into any sort of standardized, even private database.

Right.

It's siloed in academic institutions or within pharmaceutical companies.

If it is captured electronically at all, there are still many actual

leading academic institutions that still have paper lab notebooks and don't actually have mandatory electronic capture of experiments and data.

Second, when you're capturing experimental data, you're not capturing all the tacit knowledge and everything that's being observed.

You're capturing actually a very small portion of what it is that you're measuring.

Secondly, even if we were capturing all of that data, that data isn't necessarily comparable, right?

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