Impact AI

Imagine a world where radiology backlogs are a thing of the past, and AI seamlessly augments the expertise of radiologists. Today, I'm joined by Robert Bakos, Co-Founder and CTO of HOPPR, to discuss how his company is bringing this vision to life. HOPPR is pioneering foundation models for medical imaging that have the potential to transform healthcare. With access to over 15 million diverse imaging studies, HOPPR is developing multimodal AI models that tackle radiology’s most significant challenges: high imaging volumes, limited specialist availability, and the growing demand for rapid, accurate diagnostics.

In this episode, Robert offers insight into the rigorous process of training these models on complex data while ensuring they integrate seamlessly into medical workflows. From data partnerships to specialized clinical collaboration, HOPPR’s approach sets new standards in healthcare AI. To discover how foundation models like these are revolutionizing radiology and making healthcare more efficient, accessible, and equitable, be sure to tune in today!

Key Points:

• Robert’s background in medical imaging and tech and how it led him to create HOPPR.
• Ways that HOPPR’s AI models improve diagnostic speed and accuracy.
• The significant data and compute resources required to build a foundation model like this.
• Partnering with imaging organizations to collect diverse data across multiple modalities.
• How HOPPR differentiates itself with ISO-compliant development and multimodal training.
• The quantitative metrics and clinical review involved in validating its foundation model.
• Key challenges in building this model include data access, diversity, and secure handling.
• Reasons that proper data diversity and balance are essential to reduce model bias.
• How API integration makes HOPPR’s models easy to adopt into existing workflows.
• The real-world clinical needs and input that go into building an AI product roadmap.
• Robert’s take on what the future of foundation models for medical imaging looks like.
• Valuable lessons on the importance of strong labeling, compute scalability, and more.
• Practical, real-world advice for other leaders of AI-powered startups.
• The broader impact in healthcare that HOPPR aims to make.

Quotes:

“Having clinical collaboration is super important. At HOPPR, our clinicians are an important part of our product development team – They're absolutely vital for helping us evaluate the performance of the model.” — Robert Bakos

“Because we are training across all these different modalities, getting access to this data can be challenging. Having great partnerships is critical for finding success in this space.” — Robert Bakos 

“Make sure that you're addressing real problems. There are a lot of great ideas and cool things you can implement with AI, but at the end of the day, you want to make sure you can deliver value to your customers.” — Robert Bakos

“Foundation models – trained on a breadth of data – can make a positive impact on underserved areas around the world. With the volume of images growing so rapidly, constraints on radiologists, and burnout, it's important to leverage these models to make a big impact.” — Robert Bakos

Links:

Robert Bakos

HOPPR

Robert Bakos on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

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What are the unique challenges of operating mission-critical facilities, and how can reinforcement learning be applied to optimize data center operations? In this episode, I sit down with Vedavyas Panneershelvam, CTO and co-founder of Phaidra, to discuss how their cutting-edge AI technology is transforming the efficiency and reliability of data centers. Phaidra is an AI company that specializes in providing intelligent control systems for industrial facilities to optimize performance and efficiency. Vedavyas is a technology entrepreneur with a strong background in artificial intelligence and its applications in industrial and operational settings. In our conversation, we discuss how Phaidra’s closed-loop, self-learning autonomous control system optimizes cooling for data centers and why reinforcement learning is the key to creating intelligent systems that learn and adapt over time. Vedavyas also explains the intricacies of working with operational data, the importance of understanding the physics behind machine learning models, and the long-term impact of Phaidra’s technology on energy efficiency and sustainability. Join us as we explore how AI can solve complex problems in industry and learn how Phaidra is paving the way for the future of autonomous control with Vedavyas Panneershelvam.

Key Points:

• Hear how collaborating on data center optimization at Google led to the founding of Phaidra.
• How Phaidra’s AI-based autonomous control system optimizes data centers in real-time.
• Discover how reinforcement learning is leveraged to improve data center operations.
• Explore the range of data needed to continuously optimize the performance of data centers.
• The challenges of using real-world data and the advantages of redundant data sources. 
• He explains how Phaidra ensures its models remain accurate even as conditions change.
• Uncover Phaidra’s approach to validation and incorporating scalability across facilities. 
• Vedavyas shares why he thinks this type of technology is valuable and needed.
• Recommendations for leaders of AI-powered startups and the future impact of Phaidra.

Quotes:

“Phaidra is like a closed-loop self-learning autonomous control system that learns from its own experience.” — Vedavyas Panneershelvam

“Data centers basically generate so much heat, and they need to be cooled, and that takes a lot of energy, and also, the constraints in that use case are very, very narrow and tight.” — Vedavyas Panneershelvam

“The trick [to validation] is finding the right balance between relying on the physics and then how much do you trust the data.” — Vedavyas Panneershelvam

“[Large Language Models] have done a favor for us in helping the common public understand the potential of these, of machine learning in general.” — Vedavyas Panneershelvam

Links:

Vedavyas Panneershelvam on LinkedIn

Phaidra

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 AI could unlock the potential of healthcare’s vast, unstructured data? In this episode, Tim O'Connell, Co-Founder and CEO of Emtelligent, explains how his company is bridging the gap between messy medical data and usable insights with AI-powered solutions. Drawing from his background in both engineering and radiology, Tim discusses how he saw firsthand the inefficiencies caused by disorganized medical notes and reports, which led to the creation of Emtelligent. He breaks down how their AI models work to process and structure this data, making it usable for healthcare professionals, researchers, and beyond. Tim also dives into the technical challenges, from handling faxed medical records to ensuring high levels of precision and recall in model training. Beyond the technology, he emphasizes the importance of safety, ethical use, and how Emtelligent continues to adapt its AI to meet the evolving needs of the healthcare industry, helping to make patient care more efficient and accurate. Don’t miss out on this important conversation with Tim O’Connell from Emtelligent!

Key Points:

• An overview of Tim’s background in engineering and radiology.
• How Tim co-founded Emtelligent to solve pressing data issues in healthcare.
• The importance of turning unstructured medical text into searchable, structured data.
• How Emtelligent’s models extract metadata and structure from faxed patient records.
• Why healthcare data is so challenging to work with, from shorthand to messy notes.
• The role of precision and recall in assessing and improving model performance in healthcare.
• Ensuring AI models continue to perform well after deployment with ongoing updates.
• How Tim’s team maintains safety and ethical standards in AI healthcare solutions.
• Creating technology that serves the end user; how it is informed by firsthand experience.
• The importance of clinical input to develop relevant and practical AI healthcare tools.
• Where Tim sees AI's impact in healthcare evolving over the next three to five years.

Quotes:

“During that year [that I was] working in the hospital, – I saw so many problems that we have in the healthcare environment and realized that quite a few of them had to do with the fact [that] we deal with so much unstructured data.” — Tim O’Connell

“Every time a human goes to see a caregiver, some kind of an unstructured text note is generated – We really can't use a lot of that data, unless it's another human who's reading that data.” — Tim O’Connell

“I’m still a practicing radiologist. – It’s not just a matter of intelligent people coming up with good ideas and going, ‘Oh, well. [Let’s throw this] against the wall and see what sticks’. We're developing solutions that are applicable in today's healthcare environment.” — Tim O’Connell

Links:

Tim O’Connell on LinkedIn

Emtelligent

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 happens when you combine AI with digital pathology? In this episode, Dmitry Nechaev, Chief AI Scientist and co-founder of HistAI, joins me to discuss the complexity of building foundation models specifically for digital pathology. Dmitry has a strong background in machine learning and experience in high-resolution image analysis. At HistAI, he leads the development of cutting-edge AI models tailored for pathology.

HistAI, a digital pathology company, focuses on developing AI-driven solutions that assist pathologists in analyzing complex tissue samples faster and more accurately. In our conversation, we unpack the development and application of foundation models for digital pathology. Dmitry explains why conventional models trained on natural images often struggle with pathology data and how HistAI’s models address this gap. Learn about the technical challenges of training these models and the steps for managing massive datasets, selecting the correct training methods, and optimizing for high-speed performance. Join me and explore how AI is transforming digital pathology workflows with Dmitry Nechaev!

Key Points:

• Background about Dmitry, his path to HistAI, and his role at the company.
• What whole slide images are and the challenges of working with them.
• How AI can streamline diagnostics and reduce the workload for pathologists.
• Why foundation models are a core component of HistAI’s technology. 
• The scale of data and compute power required to build foundation models.
• Outline of the different approaches to building a foundation model.
• Privacy aspects of building models based on medical data.
• Challenges Dmitry has faced developing HistAI’s foundation model. 
• Hear what makes HistAI’s foundation model different from other models.
• Learn about his approach to benchmarking and improving a model. 
• Explore how foundation models are leveraged in HistAI’s technology. 
• The future of foundation models and his lessons from developing them.
• Final takeaways and how to access HistAI’s open-source models.

Quotes:

“Regular foundation models are trained on natural images and I'd say they are not good at generalizing to pathological data.” — Dmitry Nechaev

“In short, [a foundational model] requires a lot of data and a lot of [compute power].” — Dmitry Nechaev

“Public benchmarks [are] a really good thing.” — Dmitry Nechaev

“Our foundation models are fully open-source. We don't really try to sell them. In a sense, they are kind of useless by themselves, since you need to train something on top of them, so we don't try to profit from these models.” — Dmitry Nechaev

“The best lesson is that you need quality data to get a quality model.” — Dmitry Nechaev

“[HistAI] don't want AI technologies to be a privilege of the richest countries. We want that to be available around the world.” — Dmitry Nechaev

Links:

Dmitry Nechaev on LinkedIn

Dmitry Nechaev on GitHub

HistAI

CELLDX

Hibou on Hugging Face

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.

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.

Can AI be applied to enhance geospatial data for climate, nature and people? This episode kicks off a miniseries about domain-specific foundation models. Following the trends in language processing, domain-specific foundation models are enabling new possibilities for a variety of applications, including Earth observation. During this conversation, I am joined by Bruno Sánchez-Andrade Nuño, Executive Director of Clay, a nonprofit organization harnessing the power of AI for satellite images, spatial data, and more. Bruno shares the functionality and concept behind Clay, and his journey to building it. He goes on to unpack the tool’s foundation model in broad strokes, before explaining why it's important, and sharing the challenges he has faced along the way. We discuss the legal aspects of building Clay, and it’s primary goal to make it as easy as possible for any user to achieve their goals. We also touch on what the future might hold for Clay and the future of Earth observation. Thanks for listening!

Key Points:

• Introducing guest, Bruno Sánchez-Andrade Nuño, Executive Director at Clay.
• His journey from NASA astrophysicist to climate change, social development, and AI researcher.
• What Clay focuses on: using remote sensing maps to interpret the Earth’s data.
• The mechanics of how Clay is used and how different feature sets compare to one another.
• A broad explanation of the tool’s foundation model and why it is quicker, cheaper, and more environmentally friendly.
• Two main benefits of the tool that Bruno finds most exciting. 
• Data and infrastructure required to build Clay including 70 million satellite and aerial images.
• Measuring what the model understands and the process of compressing an image into 700 numbers.
• Privacy and intellectual property in the realm of satellite imaging and mapping. 
• What commercial imagery could add to the model and how it might be integrated in the future. 
• Clay’s partnerships with university and company groups
• Why the focus of Clay is to make it as easy as possible for anyone to use the tool for anything they want to do. 
• Challenges encountered on the road to building Clay: explaining what it is.
• The complexity of benchmarking foundation models and how this relates to Clay. 
• Working with partners to build Clay and the rest of the ecosystem. 
• Lessons from building Clay that may apply to other foundation models.
• Bruno’s predictions for the future of foundation models and Clay. 
• What is certain about the future of Clay and our understanding of Earth. 

Quotes:

“Clay is trying to figure out how to finally increase the adoption of remote sensing by leveraging a tool that itself is very complex, but the result of that tool is very easy to use.” — Bruno Sánchez-Andrade Nuño

“If you start with a foundational model that gets you most of the way there, [then] you can create those trials much quicker, much cheaper, and much more environmentally friendly.” — Bruno Sánchez-Andrade Nuño

“This is so new, we get the chance, those of us working on it, that we can save the whole industry, if you will, the whole space of AI for it.” — Bruno Sánchez-Andrade Nuño

“Clay, I believe, is not only the largest and most efficient model AI for Earth, for any kind of like foundational model. It is also completely open source.” — Bruno Sánchez-Andrade Nuño

“What we try to focus on is how can we make it as simple as possible for anyone anywhere to use this model for anything they want to do.” — Bruno Sánchez-Andrade Nuño

Links:

Bruno Sánchez-Andrade

Bruno Sánchez-Andrade Nuño on X

Bruno Sánchez-Andrade Nuño on LinkedIn

Clay

Clay 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.