So can you talk about advice for people who want to get into AI, especially the teenagers like you or other students were fascinated by technology and AI. So really what I believe, first of all, is I mean I've seen a lot of misconceptions about AI really wherever I go. Whenever people ask me, "How can I get into the field of machine learning or AI?" Then a lots of misconceptions, they feel like machine learning has its own independent, isolated subject, and if I learn it, I can go ahead and implement machine learning algorithms with these things. But really if you think about it, machine learning is just another algorithm in the toolbox of algorithms when it comes to programming. Albeit in some cases it can be much more powerful than other algorithms, and then on a very fundamental level, it is another algorithm. So before you get into the field of machine learning, before you get into the field of AI, it's very important that you understand how to code. Since machine learning is relatively complex technology, having a very advanced knowledge of how computers work? How exactly coding works? Then even sometimes a back-ends behind compilers, really does help you on your journey of learning how to code machine learning algorithms. The reason I say this is because machine learning isn't just a regular algorithm that you implement, its not just path finding or search or something of that sort. It's special because it requires intense hardware acceleration. It requires you to understand at least a little bit of the calculus that goes behind backpropagation. All of this is necessary to understand the fundamental workings of machine learning and AI. So really to summarize what I'd recommend is first of all, you should be passionate about technology itself. If you're not passionate about technology, machine learning technology, it really isn't something that you should go and work towards. Then from there, once you know that you're passionate about it, you want to do it. Then go ahead and start off by learning concepts in the field of technology. Learn how to code in languages like Python, Julia as a new language, SWIFT because of the new SWIFT for TensorFlow project. Then from there, go ahead and learn a little bit at least of the actual math behind neural networks. I mean, I remember when I stumbled upon Watson, that was the first time I ever heard of machine learning. When I went from Watson to custom neural networks, the first thing I did is I actually drew a small neural network on paper, and in the backpropagation, manually to understand how exactly weights are updated, how a loss values work, all these sorts of things. Once you have that fundamental understanding, then you go ahead to implement those neural networks from scratch and your languages. Then once you have a good idea of how it works, once you practice. Once you learn by example, then you're finally ready to go ahead and use different libraries, use different toolkits to enable you to rapidly prototype and build applications in the field of machine learning. So that's a very very high level view point, that I'd say to get into machine learning, but of course, there are better ways and more intuitive ways to get into machine learning as well. For example, Watson, if you take a look at how it provides its APIs on the cloud, you don't need to understand any of the workings behind machine learning to use Watson, and at the same time, you can still leverage all those powerful capabilities, the language translator. Lets you use practically state of the art, neural machine translation techniques, and even train your own models without having to understand a single clue behind what goes behind the models. So being able to start off with a toolkit like Watson, getting a good idea of both programming and machine learning at once, what machine learning is and isn't capable of? Then getting to the custom math, is another great way.