So, what we want to show today is that Tesla is much more than an electric car company

that we have deep AI activity in hardware on the inference level, on the training level.

And basically, I think arguably the leaders in real world AI as it applies to real world.

And those of you who have seen the full self-driving beta, I can appreciate the rate at which

the Tesla neural net is learning to drive.

And this is a particular application of AI, but I think there are more applications down

the road that will make sense, and we'll talk about that later in the presentation.

But yeah, we basically want to encourage anyone who is interested in solving real world

AI problems at either the hardware or software level to join Tesla, or consider joining Tesla.

Okay, hi everyone, welcome.

My name is Andre, and I lead the vision team here at Tesla Autopilot, and I'm incredibly

excited to be here to kick off this section, giving you a technical deep dive into the autopilot

stack, and showing you all the under-the-hood components that go into making a car drive

all by itself.

So here I'm showing the video of the raw inputs that come into the stack, and then neural

processes that into the vector space.

And you are seeing parts of that vector space rendered in the instrument cluster on the

car.

What I find kind of fascinating about this is that we are effectively building a synthetic

animal from the ground up.

So the car can be thought of as an animal, it moves around, it senses the environment,

and acts autonomously and intelligently.

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