Mfakeyi Makayi

But there's a problem.

for everything that we're looking at, there are going to be an infinite number of possibilities.

And that's because we're building three-dimensional models to fit two-dimensional data.

So if a body was smaller and closer to the surface or larger and further away, the measurement would be the same.

The incumbent industry deals with this problem by ignoring it.

They pick one possible answer and act like the other ones don't exist.

And as a result, we design suboptimal mines, make suboptimal decisions, often mining unnecessary material.

We've invented a different way.

We collect all the possibilities consistent with the data measured, and we do this by simulating the physical response of each of the arrangement of rocks.

We do this 10,000 times faster by training an AI to learn the relevant physics of the rock beneath in the time it takes the conventional method to test one.

That means we collect better data, we make better predictions of where to look next,

So if you had a rock body, and a rock body that's denser than the material around it, you might drill through the middle of it.

But if you have all the hundreds of thousands of possible solutions, the best thing you can do is to collect data where you're the most uncertain and rigorously eliminate as many possibilities as possible.

This enables us to maximize the information we get for every dollar we spend, and we do this repeatedly so we can quantify our uncertainties.

Even after we've made an ore body discovery, we still have to contend with this uncertainty.

We have to define the size and shape of this ore body.

Let me illustrate how difficult this is.

So now, a thousand meters below your feet, you drilled, you sampled the rock, and you determined that it has five percent copper.

So now you know, you've got your data point and your observation.

Now I ask you to make a prediction of the concentration of copper of the person sitting next to you.