Before joining Culture Amp last year as a Senior Machine Learning Engineer, Isabell Kiral has worked across research and engineering, biotech, data science analytics and software architecture for companies such as Amazon and IBM. We put these Fast Five questions to Isabell about AI.
What appealed to you most about a career in AI?
Rather than choosing to work in AI per se, it was introduced to me as a tool that use to analyse highly complex data in the medical field. Using AI offered the potential to discover relationships in the data that we wouldn’t have thought to look for. The skills this gave me have proven to be highly transferable and in a field that is in high demand, which is a great position to be in.
What aspect of AI are you most interested in or passionate about?
It’s hard to look past generative algorithms. Any time an AI algorithm is used to compose music, write movie scripts, or otherwise create art, it gives us a nicely twisted insight into patterns us humans create that we might not have picked up on ourselves.
In your daily life, what are the positive impacts of AI that you see playing out in our society?
There are a lot of ways in which we take AI for granted that make our daily lives that little bit better. Map navigation, reverse image search, or finding all photos of cats on my phone at once are just a few examples. What I find personally more exciting though is research in the medical field that has the potential to have a positive impact, however there are some obvious challenges to overcome in trusting AI with our health and health information.
Why do you think it is important to empower young Australians with access to AI education?
The world of AI is moving very quickly and is touching nearly all aspects of our lives. It’s really important for students to know about, and having a sophisticated understanding of, the role data and AI can play in health care, information dissemination, and politics.
What advice would you give young Australians who want to learn more about and eventually work in AI?
I would encourage students who are interested in AI to start looking at the impact they are seeing (or want to see) and then work their way backwards to understanding the technology or maths that sits behind it. There are jobs – and I expect there will be many more in the future – that will benefit from some knowledge of AI without requiring intricate knowledge of algorithms or statistics.
