Juni

I like to cover AI in science, and we're seeing more and more applied AI in science.

The highlight for me is the moving from the simple applications, sifting through information or doing the mundane tests.

Because that's where we're at with implementation right now is, oh, we have this test pattern or sequence already.

So instead of just programming it by hand, so to speak, we implement AI to just keep running it.

There are scientists running all those mundane tests just in control of a robot arm.

the advancements in the research and the science, finding the new materials, looking back at our old stuff and moving forward, working in tandem to develop far-reaching applications.

This is the real hype to believe in or when it was presented to us about AI is, okay, cool, this is...

Energy problems, solved.

Cool.

That solves a lot of underscoring problems from that.

And we have always talked about in medicine and disease and genetics and all that kind of stuff.

So, yeah.

So seeing all of these components come together, it does look like we're ready to... There's a lot of parallel development, and it looks like we're ready to reach a certain point where it's like, okay, it can do this now and reliably.

So because we understand that AI moves so fast, we kind of need to be aware of and, you know,

develop the governance and control systems and thinking ahead.

I think a lot of the last three years has been people trying to catch up with the idea of how far ahead.

Even with developing rocketry, landing on the moon, developing supercomputers, the internet, huge giant logistic global systems, travel, all of that kind of stuff, and most of that's been in the last 50, 70 years.

And it was double that the last cycle of 100 years, and it took double that for the previous 100 years.

So technology, we expect to turn over every 50 years.

Well, now we need to expect it to completely turn over every five or every two.