In this episode, I'm joined by Ron Alfa, Co-Founder and CEO of Noetik, to discuss the groundbreaking role of foundation models in advancing cancer immunotherapy. Together, we explore why these models are essential to his work, what it takes to build a model that understands biology, and how Noetik is creating and sourcing their datasets. Ron also shares insights on scaling and training these models, the challenges his team has faced, and how effective analysis helps determine a model’s quality. To learn more about Noetik’s innovative achievements, Ron’s advice for leaders in AI-powered startups, and much more, be sure to tune in!

Key Points:

• Ron shares his background and how his journey led to Noetik.
• Why a foundation model is important in their work.
• What goes into building a foundation model that understands biology.
• Building the dataset: where does the data come from?
• The types of data they generate from the samples they use in their models.
• He further explains the components necessary to build a foundation model.
• The scale and what it takes to train these models. 
• Ron sheds light on the challenges they’ve encountered in building their foundation model.
• How to determine if your foundation model is good. 
• Utilizing analysis to help identify ways to improve your model. 
• The current purpose for their foundation model and how they plan to use it in the future.
• Key insights gained from developing foundation models and how these can be adapted to other types of data.
• His advice to other leaders of AI-powered startups.
• Ron digs deeper into their goal to impact patient care by developing new therapeutics.

Quotes:

“Our thesis for Noetik is that one of the biggest problems we can impact if we want to make and bring new drugs to patients is predicting clinical success; so-called translation — that's where we focus Noetik, how can we train foundation models of biology so that we can better translate therapeutics from early discovery and preclinical models to patients.” — Ron Alfa

“We think the most important thing for any application of machine learning is the data.” — Ron Alfa

“The goal here is to train models that can do what humans cannot do, that can understand biology that we haven't discovered yet.” — Ron Alfa

“The big aim of Noetik is to develop these [foundational] models for therapeutics discovery.” — Ron Alfa

Links:

Ron Alfa on LinkedIn

Ron Alfa on X

Noetik

Noetik Octo Virtual Cell (OTCO)

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.