Foundation Model Series: Enabling Digital Pathology Workflows with Dmitry Nechaev from HistAI

What if we could unlock the hidden potential of unstructured health data to improve patient outcomes? In this episode, I sit down with David Sontag, co-founder and CEO of Layer Health, to discuss the transformative role of AI in healthcare. David, a former MIT professor and leading machine learning researcher, delves into how Layer Health addresses one of healthcare’s most persistent challenges: extracting actionable insights from unstructured medical data. In our conversation, David explains how Layer Health’s AI platform automates complex chart review tasks, tackles data generalization issues across diverse healthcare systems, and overcomes challenges like bias and dataset shifts. We explore Layer Health’s groundbreaking use of large language models (LLMs), the importance of scalable AI solutions, and the integration of AI into clinical workflows. Join us to discover how Layer Health is reducing administrative burdens, improving data accessibility, and shaping the future of AI-powered healthcare with David Sontag.Key Points:Hear about David's career journey from MIT professor to CEO of Layer Health.How Layer Health transforms chart reviews and enhances healthcare workflows.The role of large language models in solving the company's scalability problems.Learn about Layer Health's approach to benchmarking performance for institutions.Explore how the company navigates dataset shifts and ensures robust model performance.Discover Layer Health's strategies to identify and mitigate bias in clinical AI models.Find out about the challenges of implementing reasoning across diverse medical records.Why building trust through data transparency, auditing, and compliance are essential.David’s advice for AI startup leaders on balancing research with practical implementation.Layer Health's long-term vision for reshaping healthcare and improving patient outcomes.Quotes:“Our vision for Layer Health is to transform healthcare with artificial intelligence, really building upon all of the work that we've been doing over the past decade in the AI and health field and academic space.” — David Sontag“What we realized very quickly is that where [Layer Health] would have the biggest impact was bringing the right information to the physician's fingertips at the right point in time.” — David Sontag“We're using large language models to drive the abstraction of those clinical variables that we need for these either retrospective or prospective use cases.” — David Sontag“Where I think we're going to see the biggest source of bias is likely going to be not along the traditional demographic-related quantities, but rather on more clinical quantities.” — David Sontag“A lot of the friction that we currently see in healthcare, [Layer Health] is going to really take a big bite out of [it].” — David SontagLinks:David SontagDavid Sontag on LinkedInLayer HealthResources 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.

How can advancements in biotechnology and machine learning lead to revolutionary treatments for age-related diseases? In this episode, I speak with Hanadie Yousef, CEO and Co-Founder of Juvena Therapeutics, to discuss her work on protein-based therapeutics. Hanadie, a neurobiologist specializing in aging and tissue degeneration, has pioneered research at Juvena to identify regenerative proteins that can restore tissue function and combat chronic diseases.In our conversation, Hanadie details Juvena’s AI-driven platform that identifies, validates, and engineers protein candidates with therapeutic potential. We explore the power of machine learning models in drug discovery, the challenges of working with multi-omics data, and the potential for new treatments to revolutionize healthcare by targeting disease at the molecular level. Join us to hear how Juvena Therapeutics is setting a new standard in precision medicine aimed at helping individuals age with vitality.Key Points:The founding story of Juvena Therapeutics and its mission to restore tissue health.How the company leverages AI to identify regenerative proteins from stem cell secretions.Learn how Juvena's machine learning models enable targeted protein engineering.Explore the different types of data that Juvena utilizes and how they are structured.Hear about the benefits of in-house data generation for model training and validation.Discover the challenges of generating sufficient data for accurate model predictions.Technological advancements in proteomics and multi-omics that support its platform.Hanadie shares advice for AI-driven startups and her hopes for Juvena's future impact.Quotes:“Juvena is part of really, a new approach to leveraging the biology of aging and underlying mechanisms associated with why our tissues decline in function, in order to target this biology so that we can treat a broad swath of diseases.” — Hanadie Yousef“That's ultimately the goal of Juvena, to really enable people to age with dignity, to continue to contribute to society, and to really maintain their health until the very end.” — Hanadie Yousef“Ultimately, [machine learning is] leveraged at every stage of the process from in silico prediction, and screening through to the actual engineering and drug development.” — Hanadie Yousef“When it comes to wet lab data generation, sometimes you're really limited by just the quantity of data that you can generate.” — Hanadie Yousef“AI isn't the solution to everything. Oftentimes, you do still want to have that human in the loop and really test the accuracy of these models.” — Hanadie YousefLinks:Hanadie Yousef on LinkedInJuvena TherapeuticsJuvena Therapeutics on LinkedInResources 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.

To succeed at an AI startup, you have to be able to show your work and its value. During this episode, I am joined by Brigham Hyde, Co-Founder and CEO of Atropos Health, to talk about his app that gathers real-world evidence for healthcare. He is an entrepreneur, operator, and investor who is deeply immersed in the potential of data and AI. Join us as he shares his journey to creating Atropos Health, why he believes AI is important for healthcare, and the potential it holds to bridge the evidence gap. We discuss how the lack of diversity in healthcare data has impacted patient outcomes leading up to this point and explore some of the methods Atropos uses to get the most out of machine learning. We discuss the AI data-gathering process, how each setup is validated and adapted, and how he measures the impact of his technology. In closing, he shares advice for other leaders of AI-powered startups and offers his vision for the future impact of Atropos.Key Points:Welcoming Brigham Hyde, co-founder and CEO of Atropos Health.His journey to creating Atropos Health after working in other medical AI arenas. Why AI is important for healthcare: the evidence gap. Atropos’s perspective on the role of real-world evidence.How the lack of diversity in healthcare data sets impacts patient outcomes.Methods Atropos uses to leverage machine learning to ensure that patient populations are supported.The data-gathering process.How the setup is validated and adapted according to need.Measuring the impact of the technology. Advice for other leaders of AI-powered startups. Where Brigham foresees the impact of Atropos in three to five years. Quotes:“At Atropos, we focus on the automation and generation of high-quality real-world evidence to support clinical decision-making with the dream of creating personalized evidence for everyone.” — Brigham Hyde“We see the role of real-world evidence and observational research as a great way to supplement that gap.” — Brigham Hyde“It's our ability to create that evidence, transparently show you the populations that are being used and the bias that is involved, and the techniques to remove that bias that are the key.” — Brigham Hyde“You've got to be able to show how what you're doing works, that it's not biased, and that it's applicable to the health system you're working with, and it's got to be done in extremely high quality.” — Brigham HydeLinks:Brigham Hyde on LinkedIn Brigham Hyde on XAtropos HealthAtropos Health on LinkedInAtropos Health on XResources 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.

As machine learning becomes increasingly widespread, AI holds the potential to revolutionize drug development, making it faster, safer, and more affordable than ever. In this episode, I'm joined by Jo Varshney, Founder and CEO of VeriSIM Life, to explore how her company is transforming drug translation through hybrid AI.With her unique blend of expertise as a veterinarian and computer scientist, Jo leverages biology, chemistry, and machine learning knowledge to tackle the translational gap between animal models and human patients. You’ll learn about VeriSIM Life’s innovative approach to overcoming data limitations, synthesizing new data, and applying ML models tailored to various diseases, from rare conditions to neurological disorders. Jo also reveals VeriSIM’s unique translational index score, a tool that predicts clinical trial success rates and helps pharma companies identify promising drugs early and avoid costly failures.For anyone curious about the future of AI in healthcare, this episode offers a fascinating glimpse into the world of biotech innovation. To discover how VeriSIM Life’s technology is poised to bring life-saving treatments to patients faster and more safely than ever before, be sure to tune in today!Key Points:How Jo's background and interest in translational challenges led her to found VeriSIM Life.Addressing translational gaps between animal models and human trials with hybrid AI.Combining biology-based models with ML to enhance drug testing accuracy.Small molecules, peptides, large molecules, clinical trial outcomes, and other data inputs.Ways that VeriSIM’s models are tailored per data type, ensuring maximum accuracy.Insight into the challenge of overcoming data gaps and how VeriSIM solves it.How hybrid AI reduces overfitting, boosting model accuracy in data-limited scenarios.What goes into validating VeriSIM’s models through partnerships and external testing.Measuring the impact of this technology with VeriSIM’s translational index score.Jo’s advice for AI-powered startups: be specific, validate technology, and be adaptable.Her predictions for the impact VeriSIM will have in the next few years.Quotes:“[Hybrid AI] helps us not only unravel newer methods and mechanisms of actions or novel targets but also helps us identify better drug candidates that could eventually be safer and more effective in human patients.” — Jo Varshney“Biology is complex. We need to understand it enough to create a codified version of that biology.” — Jo Varshney“If you're just using machine learning-based methods, you may not get the right features to see the accuracy that you would see with the hybrid AI approach that we take.” — Jo Varshney“Focus on validation and showing some real-world outcomes [rather than] just building the marketing outcome because, ultimately, we want it to get to the patients. We want to know if the technology really works. If it doesn't work, you can still pivot.” — Jo VarshneyLinks:VeriSIM LifeJo Varshney on LinkedInJo Varshney on XResources 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.

Today’s guest believes that decoding the immune system is at the heart of improving drug efficacy. He is currently focused on this effort as the CEO and Co-founder of Immunai – a company that is building an AI model of the immune system to facilitate the development of next-generation immunomodulatory therapeutics. Noam Solomon begins our conversation by detailing his professional history and how it led to Immunai before explaining what Immunai does and why this work is vital for healthcare. Then, we discover how understanding the immune system will help to improve how drugs work in our bodies, how the team at Immunai accomplishes its goals, the major challenges of working with complex ML models, and some helpful recommendations for processing the high-dimensional nature of biological data. Noam also explains the collaborative landscape of Immunai, how the evolution of technology made his work possible, Immunai’s plans for the future, and his advice to others on a similar career path. Key Points:Unpacking Noam Solomon’s professional journey that led to his founding of Immunai. What Immunai does and why this work is vital for the healthcare industry. How understanding the immune system will help to improve drug efficacy. Exploring how Noam and his team use AI to accomplish their goals. The standardization of data and other challenges of working with complex ML models. Techniques for handling the high-dimensional nature of biological data.How ML experts collaborate with other domains to inform and build Immunai’s models. The technical advancements that have made Noam’s work possible. His advice to other leaders of AI-powered startups, and imagining the future of Immunai. How to connect with Noam and his work.  Quotes:“First, let’s talk about the problem, which is today, getting a drug from IND approval to FDA approval—which is the process of doing clinical trials—has less than a 10% chance of success, usually about a 5% chance, takes more than 10 years, and more than $2 billion of open immune therapy.” — Noam Solomon“Different people respond differently to the same drug, and the reason they respond differently is because their immune system is different.” — Noam Solomon“You first need to fall in love with the problems. Many ML people—physicists, mathematicians, computer scientists—we love building models; we love solving puzzles. In biology, you need to really fall in love with the question you are trying to answer.” — Noam Solomon“It’s a great decade for biology.” — Noam SolomonLinks:Noam Solomon on LinkedInNoam Solomon on XImmunaiResources 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.

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 BakosLinks:Robert BakosHOPPRRobert Bakos on LinkedInResources 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 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 PanneershelvamLinks:Vedavyas Panneershelvam on LinkedInPhaidraResources 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’ConnellLinks:Tim O’Connell on LinkedInEmtelligentResources 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 NechaevLinks:Dmitry Nechaev on LinkedInDmitry Nechaev on GitHubHistAICELLDXHibou on Hugging FaceResources 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 RolfeLinks:Variational AIVariational AI BlogJason Rolfe on LinkedInResources 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.