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.

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.

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.

In a world where conventional drug discovery methods frequently fall short, today's guest addresses the critical challenge of fighting human diseases by drawing inspiration from nature’s most resilient creatures. Could the secret to overcoming our most stubborn illnesses lie in the extraordinary adaptability of extreme mammals? Veterinarian-scientist Ashley Zehnder, the Co-founder and CEO of AI-driven drug discovery company Fauna Bio, believes so.

By leveraging data from 100 million years of evolved disease resistance in mammals, Ashley sees a unique opportunity at the crossroads of genomics and emerging model species to improve health for all species, including humans. In this episode, she explores how harnessing the biological secrets of these animals using AI and machine learning could revolutionize medicine, leading to breakthroughs that benefit us all. Tune in to discover how Fauna Bio is pioneering a new frontier in drug discovery and how understanding the resilience of these creatures could reshape the future of healthcare!

Key Points:

• Insight into the diverse backgrounds of Fauna Bio’s founding members.
• Ways that Fauna Bio uses AI and genomics to identify key targets for new therapeutics.
• The role machine learning plays in analyzing and annotating large volumes of data.
• Gene expression and other data inputs that drive Fauna Bio’s discoveries.
• The collaborative effort required to collate datasets from 400+ mammals.
• Challenges of working with genomic data and training ML models on it.
• How Fauna Bio rigorously validates their AI-driven discoveries.
• Cooperation between ML developers and domain experts to advance this technology.
• Technological advancements that enable Fauna Bio’s innovations.
• Ashely’s advice on differentiation for leaders of AI-powered startups.
• Where she sees Fauna Bio making the biggest impact in the future.

Quotes:

“[Fauna Bio uses] AI and genomics as a way to identify the most impactful targets for new therapeutic programs across a broad number of diseases.” — Ashley Zehnder

“It’s certainly easier than it has been in the past to generate very high-quality single-cell RNA sequencing. We’re doing a lot of that. The challenges on the technical side are getting much easier. The challenges on the interpretation side are still there.” — Ashley Zehnder

“There are many points along the drug discovery path where AI companies can differentiate. But that story has to be clear because, otherwise, it's very hard to get out of the signal-to-noise that is the AI discovery landscape in biopharma” — Ashley Zehnder

Links:

Fauna Bio

Ashley Zehnder on LinkedIn

Ashley Zehnder on X

Ashley Zehnder Email

Zoonomia Project

Science Issue dedicated to the Zoonomia Project

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.

One of the biggest hurdles in medical research is the gap between animal studies and human trials, a disconnect that often leads to failed drug tests and wasted resources. But what if there was a way to bridge that gap and create treatments that are more effective for humans from the start?

Today, I am joined by Dr. Andrei Georgescu, Founder and CEO of Vivodyne, a groundbreaking biotechnology company that is transforming how scientists study human biology and develop new therapeutics. In this episode, he reveals how Vivodyne harnesses lab-grown tissue and advanced multimodal AI to create more effective therapeutics. We explore the challenges of gathering human tissue data, the collaboration between biologists, robotics engineers, and machine learning developers to build powerful machine learning models, and the profound impact that Vivodyne is poised to make in the fight against diseases. To discover how Vivodyne’s innovations can lead to more successful treatments and faster drug development, tune in today!

Key Points:

• Insight into Andrei’s background and how it led him to create Vivodyne.
• What Vivodyne does and why it’s so important for drug discovery.
• The role that AI and machine learning play in analyzing vast amounts of data.
• Different data inputs and outputs for Vivodyne’s advanced multimodal AI.
• The value of biased and unbiased AI outputs depending on the context.
• Why interpretability and explainability are crucial in fields like biotechnology.
• Challenges associated with collecting human tissue data to train Vivodyne’s models.
• What goes into validating Vivodyne’s machine learning models.
• Difficulties in integrating biology knowledge with robotics and machine learning.
• Andrei’s business-focused advice for technical founders.
• The profound impact that Vivodyne will have on drug discovery in the future.

Quotes:

“Vivodyne grows human tissues at a very large scale so that we can understand human physiology and we can test directly on it in order to discover and develop better drugs that are both safer and more efficacious.” — Andrei Georgescu

“We use machine learning and AI as a mechanism to understand the complexity of very deep data and to very efficiently apply that complexity and infer from what we've learned across the very large breadth of data that we collect.” — Andrei Georgescu

“To address [the problem of a] glaring lack of trainable data, we create that data by growing it at scale.” — Andrei Georgescu

“If you're a technical founder, do something that is incredibly hard because the ability for you to do that thing will grant you much more leverage than creating what is otherwise a much more simple and generic business.” — Andrei Georgescu

“[With Vivodyne], we will enter a world of plenty where the development of new drugs against diseases becomes a far more successful, reliable, and predictive process, and we're able to make much safer and much more effective drugs just by virtue of being able to optimize that therapeutic on human tissues before giving it to people for the first time in-clinic.” — Andrei Georgescu

Links:

Andrei Georgescu

Vivodyne

Andrei Georgescu 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.

Marie Coffin is the Vice President of Science and Modeling at CIBO Technologies, and she is with me today to discuss regenerative agriculture. Join us as we explore CIBO’s work to influence company carbon footprints across industries, and how machine learning supports this process through remote sensing. Delving deeper, Marie unpacks how satellite imagery integrates with their computer vision system for a more scalable solution. Next, we discuss obtaining and categorizing data in the US, exploring some of the obstacles that stem from privacy and data protection concerns. We touch on data quality and discuss the reason behind the geographical parameters they have applied to the work before Marie shares her approach to collaborating with external experts and agronomists. She offers her advice for startups in the tech space, emphasizing creating value for your clients over keeping up with trends, predicts the future endeavors that CIBO will focus on, and more. Thanks for listening!

Key Points:

• Introducing Marie Coffin and her background leading up to her role at CIBO Technologies.
• CIBO’s work to influence company carbon footprints to improve agricultural sustainability.
• The role of machine learning in this process: remote sensing.
• What remote sensing is used for at CIBO.
• How satellite imagery interacts with their computer vision system.
• Gathering, labeling, and annotating data with a focus on the boundary of the field.
• Obtaining this information through a farmer’s recording process.
• Why their work is largely limited to the US at the moment.
• Challenges related to privacy and data protection while working with training models.
• Managing data quality issues.
• Validating models within a geographical context.
• Collaborating with domain experts and external agronomists to understand and validate thier approaches.
• How the seasonal nature of agriculture impacts the timing of reports and outputs.
• Advice for tech startups; addressing trends, who to hire, and more.
• Qualities Marie seeks in new hires.
• Her prediction for CIBO’s growing impact in the next three to five years.

Quotes:

“It’s pretty straightforward to estimate the carbon footprint of a single farmer’s field or even the carbon footprint of a whole farm, but, to make an impact, we need to be able to scale that across the landscape.” — Marie Coffin

“That is really the biggest challenge; it’s just getting enough data.” — Marie Coffin

“When you’re working in a really cutting-edge area, it’s tempting to sort of get caught up in the buzz of the new technology and lose sight of what the customer needs.” — Marie Coffin

“We need to not always be following the latest, greatest advance. We need to be going in a direction that’s going to really provide value.” — Marie Coffin

Links:

CIBO Technologies

Marie Coffin 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.

What if technology could be the key to averting a biodiversity crisis? Today, I explore this possibility with Mads Fogtmann, Chief Data Officer of FaunaPhotonics, as we discuss their groundbreaking approach to biodiversity monitoring. I talk with Mads about the looming biodiversity crisis, the innovative solutions his team is developing to address the urgent need for scalable biodiversity monitoring, and the central role that humans have to play in all this. Find out how the FaunaPhotonics platform is employing advanced sensing technology and machine learning to protect ecosystems, why insects are such useful proxies for monitoring ecosystem health, and their successful partnerships with other domain experts and researchers. Our conversation also covers the broader implications of biodiversity loss, the role of public awareness in conservation, and the future of biodiversity monitoring. Join us for a comprehensive and insightful discussion on how technology can help safeguard our planet's future and ensure the stability of natural and human systems alike!

Key Points:

• Some background on Mads and his transition from academia to the private sector.
• The FaunaPhotonics platform and how it monitors biodiversity.
• An overview of the biodiversity crisis and the urgent need to address it.
• Understanding our connection to, and dependence on, nature.
• The risks that the biodiversity crisis poses for supply chains.
• FaunaPhotonics’ role in measuring the biodiversity crisis: why this protects ecosystems.
• Why insects are the best available proxy for measuring ecosystem health.
• How sensing technology and machine learning are utilized by FaunaPhotonics.
• Case studies showcasing the impact of FaunaPhotonics' technology.
• Future directions and innovations in biodiversity monitoring.
• Key challenges faced in developing and deploying biodiversity monitoring technology.
• FaunaPhotonics’ collaboration with other domain experts and researchers in the field.
• Why there is such an urgent need for scaleable biodiversity monitoring.
• The importance of public awareness and education in addressing the biodiversity crisis.
• Mads’ advice to leaders of other AI-powered startups and the future of FaunaPhotonics.

Quotes:

“The clothes we wear, the food we eat, the water we drink, the material we use to build houses: everything comes from nature. And right now, we are destroying that foundation rapidly.” — Mads Fogtmann

“I think it’s important that we become more aware that we are an integral part of nature.” — Mads Fogtmann

“If you can’t measure it, then how can you protect the rights? – [We come with the solution] that allows them to measure [the impact on biodiversity] so they can protect it. We do this by using insect sensing. The reason we do this is that insects are so fundamental to the ecosystem.” — Mads Fogtmann

“Insects are the best proxy that you can have for actually measuring the health of [an] ecosystem.” — Mads Fogtmann

“There’s a huge need and an interest in ‘how we can actually scale biodiversity monitoring to kind of help us understand what’s going on with nature at the moment.’” — Mads Fogtmann

Links:

Mads Fogtmann on LinkedIn
FaunaPhotonics

FaunaPhotonics 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.