Uh, tell me why that's useful and kind of what you did.

Yeah, sure.

So you can think about i-i-- remember, we're trying to develop a technology that reduces the number of measurements that you need to understand this, this power amplifier's performance.

And so i-if you're using this, uh, load pull extrapolation technique in 2D, you still may have to take many extrapolations because there's a lot of other dimensions in this.

You can kind of imagine this higher dimensional space.

Uh, you still are gonna have to take a lot of measurements.

And so even though you've reduced the number of measurements to get one of these charts completed, one image, there's actually many, many images in this higher dimensional space that describe the device's performance.

What is agentic AI, and why, why are people so excited about it?

Tell me why that's useful and kind of what you did.

Yeah, sure.

So you can think about, remember, we're trying to develop a technology that reduces the number of measurements that you need to understand this power amplifier's performance.

And so if you're using this load pool extrapolation technique in 2D, you still may have to take many extrapolations because there's a lot of other dimensions in this you can kind of imagine this higher dimensional space, you still are going to have to take a lot of measurements.

And so even though you've reduced the number of measurements to get one of these charts completed, one image, there's actually many, many images in this higher dimensional space that describe the device's performance.

And one of the main things we care about, like Dr. Bayless was explaining earlier with power amplifiers, is the relationship between input power and output power.

And so one of the things that I've done is take the technique that Dr. Egbert has developed and then look at ways to generalize this machine learning architecture to something support three dimensions or a Smith tube.

What is agentic AI and why are people so excited about it?