Impact AI

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.

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 GodwinLinks:Orbital MaterialsOrbital Materials on LinkedInOrbital Materials on XOrbital Materials on GitHubJonathan Godwin on LinkedInJonathan Godwin on XJonathan Godwin SubstackResources 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 patternsHear 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 SakellariouLinks:Dimitris Sakellariou on LinkedInDimitris Sakellariou on XPiramidalPiramidal 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.

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 groupsWhy 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 NuñoLinks:Bruno Sánchez-AndradeBruno Sánchez-Andrade Nuño on XBruno Sánchez-Andrade Nuño on LinkedInClayClay 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.

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 ZehnderLinks:Fauna BioAshley Zehnder on LinkedInAshley Zehnder on XAshley Zehnder EmailZoonomia ProjectScience Issue dedicated to the Zoonomia ProjectResources 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 GeorgescuLinks:Andrei GeorgescuVivodyneAndrei Georgescu 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.

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 CoffinLinks:CIBO TechnologiesMarie Coffin 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 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 FogtmannLinks:Mads Fogtmann on LinkedInFaunaPhotonicsFaunaPhotonics 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.

Manufacturing is a fundamental part of our economy. Unfortunately, a huge swath of the industry is still dependent on outdated methods, adversely impacting our environment. To address these challenges, one company is harnessing the power of AI to transform traditional manufacturing, driving unprecedented efficiency and sustainability in the industry. Joining me today is Berk Birand, co-founder and CEO of Fero Labs, to unpack how AI is optimizing the manufacturing sector.Tuning in, you'll learn all about Fero Labs' innovative software and how it’s empowering engineers in industries like steel and chemicals to harness machine learning, drastically reducing waste and energy consumption. We discuss how their AI analyzes historical production data to ensure factories operate at peak performance and how this is boosting sustainability and profitability. Our conversation also unpacks the critical role of explainable AI in building trust within the industrial sector, where precision and reliability are essential. Tune in to discover how Fero Labs is paving the way for a greener industrial future!Key Points:Berk Birand’s education and career background.How he co-founded Fero Labs with his business partner.An overview of Fero Labs’ AI software.Fero Labs’ role in reducing raw material waste in the steel industry.How they have helped improve energy efficiency in chemical manufacturing.Data analysis and how their software provides recommendations for efficient operations.Understanding the high stakes involved in manufacturing processes.Why AI explainability is crucial in the industrial sector.How they are building explainable models that engineers can trust and understand.Why now is the right time to build this technology.His advice to AI-powered startups: seriously consider the cost of a bad prediction.Fero Labs’ long-term vision to achieve a more circular and sustainable industrial sector.Quotes:"One of our largest customers was able to reduce the waste of raw materials, about a million pounds just throughout last year, by using our software AI system." — Berk Birand"We think AI will play a key role in the transition to a green economy." — Berk Birand"The best people to be solving these types of challenges, ultimately, are the engineers that work at the plants. The engineers that have the most domain expertise." — Berk Birand"In an environment like this, an engineer in a factory would just not want to use a software that they don't trust, because ultimately, it's their job that's on the line." — Berk Birand“With the new drive towards building an industrial sector that is more circular and more sustainable, there's incredible potential to optimize not just an individual factory, but beyond that, to optimize the entire supply chain by optimizing factories jointly.” — Berk BirandLinks:Berk Birand on LinkedInFero LabsResources 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 this episode of Impact AI, we delve into the transformative impact of AI on in-vitro fertilization (IVF) with Daniella Gilboa, co-founder and CEO of AIVF, a startup that develops AI-powered IVF solutions to help increase the certainty of a successful journey to parenthood. Join me as Daniella shares her mission to democratize fertility care and offers insight into AIVF’s proprietary technology that delivers reliable, objective, and data-driven IVF outcomes for clinicians, embryologists, and patients. We explore the role and challenges of machine learning at AIVF, strategies for validating AI models in clinical practice, and the current demand for AI-powered IVF solutions. We also discuss the metrics used to measure the impact of AIVF's technology, Daniella’s advice for other AI-powered startup leaders, and her vision for the future. Tune in to gain valuable insights into the future of fertility care and find out how AI is making IVF more effective and accessible!Key Points:How Daniella came to understand the epidemiology and data aspects of fertility.What AIVF does and why it’s so important for both patients and clinicians.The role of machine learning at AIVF and the challenges their models encounter. AIVF’s strategy for validating their models and translating KPIs into clinical settings.The value of explainability to empower embryologists to use AI as a tool.Daniella’s definition of computational embryology, assisted by machine learning.Why now is the right time for AI-powered IVF solutions.Metrics that AIVF uses to measure the impact of their technology.Danielle’s advice for leaders of AI-powered startups and her vision for the future.Quotes:“We showed that if you use AI as a tool for the embryologist – [it] increased the success rates – The decision-making is faster, more accurate. You freeze less embryos because each embryo you freeze is accurate – It changes the way the lab works and it optimizes everything.” — Daniella Gilboa“The way you interact with the patient and consult the journey ahead is changing. It’s more accurate. It allows you to make more informed decisions. This is the right way of doing medicine. It needs to be data-driven rather than subjective human analysis.” — Daniella Gilboa“AIVF needs to become the standard of care.” — Daniella GilboaLinks:AIVFDaniella Gilboa on LinkedInDaniella Gilboa 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.

Image-based machine learning is fast becoming an AI staple, and with its new Vision Intelligence Filters, Plainsight Technologies is staking its claim as an industry pioneer. Today, I am joined by Plainsight CEO, Kit Merker, who is here to share all the details behind his company’s latest innovation. Kit begins by explaining what Plainsight does and why this work matters in the AI realm. Then, we learn about the mechanics behind Plainsight’s Vision Intelligence Filters, the company’s ML models and data protocols concerning existing customers, the ins and outs of bringing a product like the Vision Intelligence Filters to life, and how bias manifests in image-trained models. We also discuss the most game-changing applications that Kit has been involved in, and he shares some critical advice for young leaders of AI-powered startups, plus so much more!Key Points:Kit’s professional background and how he ended up at Plainsight.What Plainsight does and why this work matters. The mechanics behind Plainsight's Vision Intelligence Filters.How the company's ML models and data use relate to its customers Understanding when domain expertise comes into play. The process of planning and developing a new filter.How bias manifests in image-trained models, and how Kit and his team are mitigating this.  The most interesting and game-changing applications that Kit has worked on. His advice to other leaders of AI-powered startups.Kit’s vision for the future of Plainsight Technologies.Quotes:“Our goal is to give customers very high accuracy on their models.” — Kit Merker“A lot of times, traditional enterprises are looking for a solution or an app. The filter is like an app, and so customers can start really small with us, get an app that they trust the data, and then expand from there. They don't have any machine learning expertise required.” — Kit Merker“Don't fake your demos!” — Kit MerkerLinks:Kit MerkerKit Merker on LinkedInKit Merker on X  Plainsight TechnologiesResources 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.

Infants cry when they're hungry, tired, uncomfortable, or upset. They also cry when they’re in pain or severely ill. But how can parents tell the difference? To help us address this critical question, I'm joined by Charles Onu, a health informatics researcher, software engineer, and CEO of Ubenwa. Ubenwa is a groundbreaking app that uses AI to interpret infants' needs and health by analyzing the biomarkers in their cries. Charles conceived of the idea while working in local communities in south-eastern Nigeria, where high rates of newborn mortality due to late detection of Perinatal Asphyxia inspired him to create a solution.In this episode, Charles shares insights into Ubenwa's machine-learning models and how they establish an infant's cry as a vital sign. He discusses the process of collecting and annotating data through partnerships with children's hospitals, the challenges of working with audio data, the benefits of creating a foundation model for infant cries, and much more. He also offers human-focused advice for leaders of AI-powered startups and reflects on his vision for success and the impact he hopes to achieve with Ubenwa. Tune in to discover how understanding your infant’s cries can transform healthcare and well-being for newborns and their families!Key Points:Charles' converging interests in math and healthcare, which led him to create Ubenwa.What Ubenwa does to establish an infant’s cry as a vital sign (and why it’s so important).The essential end-to-end role that machine learning plays in this technology.How Ubenwa collects and annotates data by partnering with children’s hospitals.Challenges of working with audio data and training medical ML models on it.Insight into the benefits of creating a foundation model for infant cries.Variations in infant’s cries and how Ubenwa’s models generalize for these shifts.Valuable research Ubenwa has made publicly available as a gift to the ML community.Charles’ human-focused advice for other leaders of AI-powered startups.What success means to Charles and the impact he hopes to make with Ubenwa.Quotes:“Ubenwa was born out of the idea that, if there's something that [human doctors] can listen to to come to a conclusion [about an infant’s health], then there has to be something machines can also learn from the infant's cry.” — Charles Onu“The real leap we made with self-supervised learning is that you now do not need an external annotation to learn. The model can use the data to supervise itself.” — Charles Onu“AI-powered or not, – the problem of a startup remains the same. It’s to meet a need that humans have. – At the end of the day, AI is not just there for AI only. It’s only going to be a successful and useful startup if you identify a need and [solve] that problem.” — Charles Onu“Human babies have evolved to communicate their needs and their health through their cries. We [haven’t] had the tools to understand that. Babies have been trying to talk to us for a long time. It's time to listen.” — Charles OnuLinks:Ubenwa HealthNanni AICharles Onu on LinkedInCharles Onu on XCharles Onu on GitHubUbenwa on GitHubUbenwa CryCeleb DatabaseResources 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.

Innovative AI technologies are paving the way for more efficient and impactful environmental monitoring. Joining me today to discuss remote monitoring and water forecasting is Marshall Moutenot, the co-founder and CEO of Upstream Tech. From using satellite imagery to monitor conservation projects to employing machine learning for accurate water flow predictions, Upstream Tech is at the forefront of leveraging technology to address environmental challenges.In our conversation, Marshall shares his journey from a tech-savvy childhood to co-founding a company with a mission to make environmental monitoring scalable and cost-effective. He delves into the development of Upstream Tech's two primary products: Lens, for remote monitoring of climate solutions, and HydroForecast, which uses AI to predict water flow, aiding in hydropower management. Marshall also underscores the need for integrating domain knowledge with machine learning to create reliable models before offering practical insights for AI startups. Tune in to learn more about how AI can revolutionize environmental conservation!Key Points:The details of Marshall’s tech-savvy childhood and entrepreneurial journey.An overview of Upstream Tech’s mission to improve environmental monitoring.How they use AI and satellite imagery for scalable, cost-effective monitoring.The development of their Lens product for remote monitoring of climate solutions.Why remote monitoring is so challenging at scale and their approach to solving it.Their product, HydroForecast, and its role in predicting water flow using machine learning.How integrating new inputs like satellite imagery creates reliable, adaptable models.Success stories, including outperforming traditional models in a major competition.Challenges Upstream Tech faces in acquiring and integrating geospatial data.Best practices for ensuring model reliability and effectiveness over time.Their team's approach to developing a new machine learning product or feature.Marshall’s advice for AI startups: don’t get too attached to the tools!His vision for Upstream Tech’s impact on environmental conservation.Quotes:"What these new machine learning models that we're employing allow us to do is to provide enough data to the model to create [equations] to describe physical interactions." — Marshall Moutenot“[The] adaptability of these models is something that is really exciting for the field overall." — Marshall Moutenot"We train a single model on a wide diversity, which forces the model to learn the common rules across all of them.” — Marshall Moutenot“As an organization, one of [Upstream Tech’s] purposes is to see the 100% renewable grid become a reality. We want to continue to contribute to that and to build forecasts that enable that future.” — Marshall MoutenotLinks:Marshall Moutenot on LinkedInMarshall’s BlogUpstream TechUpstream Tech on LinkedInUpstream Tech on XUpstream Tech on YouTubeResources 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.