Building on the trends in language processing, domain-specific foundation models are unlocking new possibilities. In the realm of drug discovery, Jason Rolfe is spearheading innovation at the intersection of AI and pharmaceuticals. As the Co-Founder and CTO of Variational AI, Jason leads a platform designed to generate novel small molecule structures that accelerate drug development. In this episode, he delves into how Variational AI uses foundation models to predict and optimize small molecules, overcoming the immense complexity of drug discovery by leveraging vast datasets and sophisticated computational techniques. He also addresses the key challenges of modeling molecular potency and why traditional machine-learning approaches often fall short. For anyone curious about AI's impact on healthcare, this conversation offers a fascinating look into cutting-edge innovations set to reshape the pharmaceutical industry. Tune in to find out how the types of breakthroughs we discuss in this episode could revolutionize drug development, bring new therapeutics to market across disease areas, and positively impact lives!

Key Points:

• An overview of Jason’s background and how it led him to create Variational AI.
• What Variational AI does for the small molecule domain for drug discovery.
• How they use foundation models to predict and enhance the design of small molecules.
• Defining small molecules, their appeal, and an overview of Variational AI's data sets.
• What goes into training Variational AI's foundation model.
• The computational infrastructure and algorithms necessary to process this data.
• Challenges of predicting molecular potency against disease-related protein targets.
• Various ways that Variational AI’s foundation model underpins everything they do.
• Evaluating progress: balancing predictive success with experimental validation.
• Lessons from developing foundation models that could apply to other data types.
• Jason’s funding and research-focused advice for leaders of AI-powered startups.
• The transformative impact of Variational AI’s technology on drug development.

Quotes:

“Rather than forming individual models for specific drug targets, we're creating a joint model over hundreds, eventually thousands of drug targets.” — Jason Rolfe

“Data quality is essential. In particular, if you're drawing from multiple different data sources, frequently, those sources aren't commensurable.” — Jason Rolfe

“If you don't have a proven track record where people are already throwing money at you, it is very challenging to try to bring a new technology from the drawing board into commercial application using venture funding.” — Jason Rolfe

“Whenever you're developing a new technology or product, you need to test early and often. Some of your intuitions will be good. Most of your intuitions will be a waste of time – The more quickly you can distinguish between those two classes, the more efficiently you can move toward success.” — Jason Rolfe

Links:

Variational AI

Variational AI Blog

Jason Rolfe on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

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AI is unlocking the future of materials science and today’s guest Jonathan Godwin, co-founder and CEO of Orbital Materials, is at the forefront of this transformation. With a background in AI research and experience leading groundbreaking projects at Google-owned DeepMind, Jonathan is now applying machine learning to develop advanced materials that can drive decarbonization.

In this episode, he explains how Orbital Materials is using foundation models (like ChatGPT for language or MidJourney for images) to design new materials that capture carbon, store energy, and improve industrial efficiency. He also shares insights into the company’s mission, the challenges of simulating atomic-level interactions, and why open-sourcing their model, Orb, is crucial for innovation.

To discover how AI is revolutionizing the fight against climate change and learn how these cutting-edge materials could shape a more sustainable future, don’t miss this inspiring conversation with Jonathan Godwin!

Key Points:

• Insight into Jonathan’s diverse career path and how it led him to Orbital Materials.
• What types of advanced materials Orbital develops and their potential impact.
• The critical role AI plays in developing materials for decarbonization purposes.
• Defining foundation models and why they’re an essential part of leveraging AI.
• 3D atomic simulations and other types of data that go into Orbital’s foundation model.
• The computing infrastructure required to build a foundation model for materials.
• Engineering and other challenges encountered while building models at this scale.
• How AI enhances scientific discovery without replacing human expertise.
• Why open-sourcing Orbital’s foundation model, Orb, is key for innovation.
• Lessons from developing this model that could be applied to other data types.
• Jonathan’s detail-oriented advice for leaders of AI-powered startups.
• Orbital’s exciting mission to accelerate new materials development.

Quotes:

“We develop materials that can capture CO2 from specific gas streams – coming out of an industrial facility, new energy storage technologies that allow – [data centers] to operate behind the meter, or ways to improve the water efficiency of a data center or industrial facility.” — Jonathan Godwin

“Foundation models are the crux of how we're able to leverage AI in this day and age. If you want to [say], 'We're pushing the limits of what AI is able to do. We're leveraging the most recent breakthroughs,' – you've got to be building foundation models or using foundation models.” — Jonathan Godwin

“AI is a massively powerful creativity aid and accelerant. We’ve seen that in other areas of AI and we're bringing that to advanced materials.” — Jonathan Godwin

Links:

Orbital Materials

Orbital Materials on LinkedIn

Orbital Materials on X

Orbital Materials on GitHub

Jonathan Godwin on LinkedIn

Jonathan Godwin on X

Jonathan Godwin Substack

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.

What if we could understand brain activity in real-time to better diagnose neurological conditions? In this episode, part of a special mini-series on domain-specific foundation models, I sit down with Dimitris Sakellariou, the founder and CEO of Piramidal, to talk about their groundbreaking work in automating EEG interpretation. Piramidal is focused on democratizing brain health insights, making interpreting brainwave data more accessible and accurate. With a strong foundation in neuroscience and AI, Dimitris and his team are developing models that could revolutionize how we understand brain activity and diagnose neurological conditions.

In our conversation, Dimitris explains the challenges of building a foundation model for brain activity, the role of data diversity, and the future potential for personalized brain health monitoring. Discover the implications of Piramidal’s technology beyond healthcare and its application in cognitive enhancement and stress management. Tune in as we explore how Piramidal is paving the way for personalized brain health monitoring and why this could be a game-changer for the future of medicine!

Key Points:

• Dimitris discusses his journey from physics to a career in neuroscience.
• Explore Piramidal's mission to automate EEG interpretation.
• Learn about the complexity and variability of brainwave patterns
• Hear how machine learning can better analyze brain activity.
• Uncover the challenges of building a foundation model for EEG data.
• Why diverse data sets are vital for training the foundational model.
• Piramidal's plans for making EEG analysis more accessible.
• Future use cases for Piramidal’s model in healthcare and beyond.
• Discover why domain knowledge for model building is essential.
• He shares advice for AI startup founders.

Quotes:

“Piramidal is primarily focused at the moment in automating, or otherwise democratizing the interpretation of these tests, these brainwave recordings so that patients and people that have issues with their brain can get access to the diagnosis much, much, much faster.” — Dimitris Sakellariou

“It's very important to have discussions with neuroscientists and clinical experts in order to understand what is the end-to-end pipeline from receiving data all the way to inference.” — Dimitris Sakellariou

“Finding the right person. Someone that is very keen to build together with you and make important and difficult decisions can change massively a trajectory of your company.” — Dimitris Sakellariou

Links:

Dimitris Sakellariou on LinkedIn

Dimitris Sakellariou on X

Piramidal

Piramidal on LinkedIn

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.