Crossing the chasm: from Academia to Industry

Janet Bastiman, Venture Partner at MMC Ventures and Chief Science Officer at StoryStream

Abstract: Adoption of AI techniques by industry has risen rapidly in the past few years. Established companies are waking to the possibilities that AI techniques can give them and Europe is a breeding ground for academic researchers to make a difference as entrepreneurs. Whether the motivation for this is money, the challenge of running your own company and/or AI for good, making the step from research to being key in your own company brings a host of challenges for which many academics have little preparation.

In her talk, Bastiman will cover the main challenges faced in transitioning to industry that are just not present within the research community: world wide legislation and data provenance, explanability, AI operations, and the impact of accuracy and bias in real world decision making. She’ll outline the skills you need to build a successful industrial project, how you can get them, or what it might cost to hire them. Finally, she’ll go through how to get your business ready for funding.

Janet Bastiman in the AI Venture Partner at MMC Ventures Ltd, a London based venture capital company, and is also Chief Science Officer at StoryStream Ltd, an automotive-focussed company. She studied Cellular and Molecular Biochemistry at Oxford University with a scholarship from the Landau Foundation, and received her PhD from Sussex University in the Centre for Computational Neuroscience and Robotics, supported by the BBSRC with a CASE scholarship. Her career has focussed in industry where she has worked for large multinationals and smaller enterprises as Chief Technical Officer and continues to champion new technologies. She co-founded the Tech Women London meetup group and is treasurer of the IEEE UK STEM committee. Since 2018 she has also been a judge for Awards.AI, showcasing the best UK AI start-ups. Her interests are predominantly in combinatorial techniques in computer vision and the intersection of machine learning with biological inspiration.