This podcast interview focuses on product innovation focuses on the blended power of humans and AI to get clear answers to how their business or even the world might evolve years ahead of us. My guest is Bulent Ozel, Co-founder and CEO of LucidMinds
Bulent is data science consultant, researcher and lecturer. He likes building bridges between business, technology, science, and policy making. He is a hands-on software architect and enjoys coding. He has provided data-driven business consultancy and services for over 10 years, and these experiences have lead the foundation of Lucidminds.ai.
Bulent has published more than 50 peer-reviewed and citation indexed articles.
The thing that triggered me to invite Bulent for my podcast is their creation of an agent-based simulation engine - presumably the most advanced and complex simulation model for macroeconomic systems. It enables researchers and policymakers to collaborate on creating insights and clear answers on complex policy questions like green finance, housing market regulations, and exit or entry policies for economic unions similar to Eurozone. We explore the challenges around creating solutions that are required to look 10, 20, or even 30 years into the future and the ways to overcome such challenges
Here are some of his quotes:
There's a possibility to solve or approach the way we use emerging technology, which means we can have control over AI. It's just a technology, nothing different than a wheel. How we are going to use the wheel depends on the choices we make.
If we don't look into how things are suggested, how AI has been developed, that can go out of control.
We want to be able to work on limited incomplete data sets. More importantly: if you don't have complete information […] you need the input of human and usually human domain experts are the best decision makers.
The problem is how can get humans in involved in the process.
Humans can always have be better choice maker, they have more insights, the algorithm can have certain bias that the human can overcome, or for other practical reasons, there might not be enough data set that is using to train the algorithm.
During this interview, you will learn three things:
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