If you are working in the life science research space and battling with image recognition issues, firstly, you are far from alone, and secondly, there is a solution! That solution comes in the form of KML Vision, an AI-powered start-up co-founded by today’s guest, Philipp Kainz. In this episode, Philipp explains how he became aware of the image analysis problem and the process that he and his team have gone through to develop machine learning models that provide a range of benefits to a diverse cohort of end users. There is still a large gap between what is technologically possible in a research or lab setting and what is actually out there and what people can use. Through their flagship product, IKOSA, Phillip is on a mission to change that. Listen to this episode to gain an understanding of how machine learning is being used to shape the future of life science research!

Key Points:

  • The motivation behind the founding of KML Vision.
  • The value that KML Vision’s cloud platform, IKOSA, brings to the life science research space.
  • The diversity of end users of IKOSA.
  • Benefits that IKOSA provides to its users.
  • Examples of some of the most common use cases for IKOSA.
  • The role that machine learning plays at KML Vision.
  • How KML Vision trains their models.
  • The challenges that the KML Vision team have run into when training their models.
  • Philipp explains KML Vision’s approach to developing the machine learning aspects of a new product or feature.
  • How KML Vision helps to solve the problem of reproducibility in the life science research space.
  • Valuable advice for leaders of AI-powered start-ups.

Quotes:

“We basically set out to help people overcome this barrier of using new technologies for image analysis.” — Philipp Kainz

“There is still a big gap between what is technologically possible in a research or lab setting and what is actually out there and what people can use. So, we are actually focusing on bridging that gap.” — Philipp Kainz

“Nobody [really] has time to go into the inner workings of deep learning. They want to use it like we use this smartphone today. This is where we want to be in three to five years.” — Philipp Kainz

Links:

Philipp Kainz on LinkedIn

KML Vision

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