One of the first lessons that we learned was to use AI accelerators. Use computers that are good at machine learning to do machine learning. For a while we've been really successful at building CPUs. CPUs are really good at running my operating system, powering the internet, running the apps on my smartphone, but they're not very good at linear algebra. Some of the applications of linear algebra that we've known about in the past are things like scientific computing, computer graphics. This leads to things like GPUs. One of the things that we learned recently is that linear algebra is also really good at AI. Neural networks are built on top of linear algebra. And so if we have computers that are really good at linear algebra, like we built for computer graphics, like GPUs, and we use them to run neural networks, they can be a lot faster than CPUs. CPUs exploit technologies like branch prediction, heavily pipeline, high clock frequencies, out of order execution. Those things are really great for sequential programs like the apps on my smartphone, but linear algebra is very different than that. It's very computationally intensive, it's very regular, it has a lot of data locality, and this fits in perfectly with things like massive parallelism and local data access. And so when you put all these things together, you can build AI accelerators like GPUs that are way faster than CPUs at running neural networks. So whereas CPUs might be able to run neural networks at single teraflops, like single trillions of operations per second, single AI accelerators like GPUs can be a hundred times faster so they can run at things like a hundred teraflops. If you get together a collection of GPUs into a cluster, like you would in a super computer, you can be even bigger than this, or even faster than this. That's possible to group together thousands of GPUs and have them solve a single neural network trading problem. And this can boost your performance to maybe tens of peteflops. So you can be about 10,000 times faster than a CPU using systems that you can put together today. So you know, if you're one of the things that we learned when we're building AI algorithms that are really compute intensive, they need to train really big models on really big data sets and the computational requirements of this are really big, you should use AI accelerators. So you should use computers that are really well optimized at this problem.