So what advice would you give to someone who's looking to get into a career in AI. Yes absolutely. I had a bit of an unusual route becoming a data scientist or an AI specialists myself. I have a background in psychology so when I was studying, I was basically thinking of continuing to do academic research and not going to industry. But at some point in time, I had a realization that I didn't really want to become a professor. I didn't really want to teach or do research in an academic setting. I started looking for jobs and it was quite difficult because what we see today is sometimes what you'll learn in university doesn't translate directly into what companies want. So how do you bridge that gap exactly? For me, when I was at the time still in Tokyo, Japan, I didn't have a lot of resources around me or English speaking resources at the time where I could develop my skills. So I looked towards online courses and online courses helped me practice python, practice R, my data analysis skills through programming and I was able to develop a foundation in the skill set that was highly sought after by many different companies. Then from there, the question became, okay, now I have these skills, these newly developed skills through these courses that I've taken online, how do I translate that to something that I can show and demonstrate to potential employers? So then the next problem I had to deal with was, well, how do I demonstrate that some way? So I created a portfolio of different online assignment exercises projects to demonstrate that I could do the kinds of things that they were hoping that I could do to show my competency. I also when I came back to Toronto, Canada, I started doing meet ups to teach at these meet ups, teach R, teach Python, teach data science. Again, further demonstrate my competencies so that people could really see that I could do what I was claiming that I could do. Which was a little bit difficult because people would just look at my resume and you see, oh, you have a background psychology, what does that have to do with data science or AI? So it was hard in that sense that had to overcome that burden of proof. The final piece of advice that I would give is connect with fellow local AI specialists. Whether they are data scientists, specialists at particular field of AI, machine learning researchers, data engineers, just to get a better sense of what kind of AI specialists you might be interested in becoming because today there's so many different kinds of AI specialists. You could be very technical. You could be doing research on the latest state-of-the-art algorithms or you could be doing something a bit more business-oriented, like trying to optimize revenue, maybe some reporting as well. Trying to find ways to personalize a particular service to each and every kind of user that you might have. There's different aspects of AI and I think it's really important to have some clarity into what kind of role and what kind of job you want. The first step to do that is to be able to reach out and ask questions to people who are already in the field. It's a great way to get a sense and also maybe even form some connections that might be meaningful in your career as well. Those are the four things. Number one is, acquire skills wherever you can. Always be open to learning new things and new skills. Number two is demonstrate your capabilities through projects, exercises. Something that you can share online. Even blog posts and articles. Number three, if you can teach at meet ups or maybe even more ideally conferences so you can demonstrate your competencies in public. Then number four is going to be try to connect with local experts or local specialists who are already in the field so you can get a better sense of what that career kind of road map might look like for you.