Many data scientists dream of using their skills to develop ground-breaking AI technology. Yet, few manage to translate their dreams into commercially viable products – or even know where to begin.
In this episode, start-up founder Dr Jeroen Vendrig joins Dr Genevieve Hayes to discuss his experiences in developing AI-driven products, both in an academic setting and in a variety of organisations within the commercial world.
This is the first part of a three-part special focussing on the use of data science in start-ups.
Guest Bio
Dr Jeroen Vendrig is the Chief Technology Officer of ProofTec, an Australian technology start-up specialising in the development of AI-driven software for damage detection and assessment of high value assets. He has over 20 years’ experience in video analytics with world leading R&D labs and has over 25 patents in force.
Talking Points
- The key differences between doing data science/AI in an academic setting and doing it in the commercial world.
- How to go about translating academic research into commercially viable AI-based products.
- What makes for a successful university/commercial collaboration?
- The challenges of building AI products from scratch, including lack of data and how to tell if a project has the potential to be commercially viable.
- Protecting IP for AI systems.
- The impact of having real end users on AI product development.
- The most valuable skills data scientists can develop for building commercial AI technologies.
Links
- Connect with Genevieve on LinkedIn
- Be among the first to hear about the release of each new podcast episode by signing up HERE
