So can you talk about the different areas or categories of artificial intelligence? Now, there are lots of different fields that AI works in. But if I were to on a very very high level group some of the major areas where artificial intelligence is applied, I'd like to start off with natural language. Because natural language is, I'd say, the most complex data for machine learning to work with. If you see all sorts of data, whether that be a sequence to genome, whether that be audio, whether that be images. There's some sort of discernible pattern. There's some sort of yes, this is what a car sounds like or yes, this is what human voice sounds like. But natural language is fundamentally, a very human task. It's very human data source. We as humans invented it for humans to understand. If I were to, for example, give you a book title, there's actually a very very famous book, and the title of the book is there are two mistakes in the the title of this book. Now, there's actually only one mistake, the two the's. The human brain doesn't realize that. What's the second mistake? That there was only one mistake. So this is a sort of natural language complexity that's involved here. Humans we don't view natural language literally. We view it conceptually. If I were to write a three instead of an E, you will understand it because we don't mean the three in a literal sense. We mean that in a symbolic sense to represent the concept of E and you can contextualize that three to figure out that, "Yeah. It means in E" and not an actual three. These are things that computers aren't capable of. So natural languages that number one field that I'm most interested in when it comes to machine learning. Second, I'd say the most popular would be visual. Visual data understanding, computer vision. Because it enables us to do so many things. As humans, our primary sense is vision. In fact, a vast majority of your brain's processing power at any given moment, goes to understanding what it is that you're seeing. Whether it be a person's face, or whether it be a computer or some texts, or anything of that sort. Third, I would say audio-based data. So text-to-speech, speech-to-text these are very very complex. The reason it's complex is because it combines a lot of challenges into one. First of all, you've got to support many languages. You can't just support English and call it a day. You've got to support other languages. You've got to support other demographics. Another challenge is that even within languages, there are absolutely infinite number of ways that any human could represent a language. Everyone's going to have a different accent. Everyone's going to have a different way of pronouncing certain words. There's no standardized way that every human will pronounce ice cube exactly like ice cube. That doesn't exist. If you take a look at another challenge, it's that audio data is fundamentally very very difficult to work with. Because the thing is, audio data exists in the natural world. What is audio? It's vibrations of air molecules, and vibrations of air molecules are fast. Audio is recorded at overpay say 44 kilohertz. That's a lot of data, 44,000 data points every single second. There are usually only 44,000 data points in an individual low-resolution image. So of course, there are lots of challenges to work around when it comes to audio. But companies like IBM, Google, Microsoft have actually worked around these challenges and they're working towards creating different services to make it easier for developers. So again, on a very very high level, there's natural language understanding, there's computer vision, there's audio data and of course, there's the traditional set of tabular data understanding. Which is essentially, structured data understanding.
