Impact AI

These days, it seems that there are a lot of big problems in the world, especially in healthcare. Our guest today believes that there is massive value in tackling smaller problems, and, sometimes, the smaller problems are the most important to solve.I welcome to the show today Joe Brew, Co-Founder and CEO of Hyfe, and he is here to talk about detecting and tracking coughing. We hear about what led to the founding of the company Hyfe and why they’ve narrowed their respiratory health innovations down to focus on cough. Joe talks about the role of machine learning, the process of gathering cough examples, and how they train their models. He touches on challenges they’ve faced, navigating model performance in varying environments, and the benefits of publishing their work. To hear more about why Joe believes now is the time to build this type of technology don’t miss out on this episode.Key Points:How the movement of pathogens through our bodies and communities eventually led to the founding of Hyfe.What Hyfe does with respiratory health and why it’s important in overall healthcare.Why they’ve narrowed their focus down to the cough.The role machine learning plays in their cough-count technology.He explains more about acoustic epidemiology. The process of gathering cough examples and annotating them to train their models.We explore the challenges faced working with and training models on audio data.Navigating model performance in varying environments: working well in the real world.Joe shares thoughts on the benefits of publishing their work.Why now was the time to build this type of technology.How Joe and his team are measuring the impact of their technology Joe offers advice to other leaders of AI startups. We talk about the potential impact of Hyfe in three to five years.Quotes:“I realized that there are so many global health problems that are addressable, at least partially by tech. I hesitate to say, solvable, but addressable.” — Joe Brew“The really big problem that Hyfe is tackling is around respiratory health.” — Joe Brew“It felt to us that cough is perhaps, the lowest-hanging fruit, the area where the additionality of tech is greatest, because it's so prevalent and because it's currently just the status quo is so poor.” — Joe Brew“If you really want reliable medical grade annotations, you need reliable medical grade input. Garbage in, garbage out. That's why the only way to really do that is through partnerships with medical professionals.” — Joe Brew“A method, that if I were to start another company or to do another project, I would absolutely repeat, is to go quickly to the market, start collecting data, real-world data really quickly, and build a feedback loop where you're constantly training, testing, validating on real-world data.” — Joe Brew“Our aim is not just to get nice comments on the App Store or nice emails. It's to impact the lives of millions. Everybody who breathes has lungs and everybody with lungs coughs. We think cough tracking is for everybody.” — Joe Brew“Don't be turned off by problems that appear simple. Sometimes the simplest problems are the ones that are the most important to solve.” — Joe BrewLinks:Joe Brew on LinkedInJoe Brew on TwitterHyfe AIResources 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.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

The impact of AI knows no bounds. Today, I am joined by Subit Chakrabarti, Vice President of Technology at Floodbase, a mission-driven, machine-learning-powered company specializing in flood monitoring and insurance. Having grown up in Eastern India, he knows the importance of adapting to global flood risk first-hand.In this episode, Subit shares insights on how Floodbase utilizes advanced AI and diverse satellite imagery to support the design of parametric flood insurance solutions. We discover how machine learning plays a crucial role in analyzing vast datasets and bridging the insurance gap for regions vulnerable to flooding. Join us as we explore the transformative potential of Floodbase's technology and its vision for a more secure and equitable future, in the context of global warming and the associated global flood risk.Key Points:Introducing Subit Chakrabarti, Vice President of Technology at Floodbase.Subit's background: what led him to Floodbase.Insight into Floodbase and its focus on parametric flood insurance and disaster response.Subit explains parametric insurance.How Floodbase uses machine learning to design the index for parametric insurance.Their use of satellite imagery and other geospatial datasets in setting up their ML models.The challenges of working with such diverse data sets.What made it possible to build Floodbase's technology (spoiler alert: advanced AI).How Floodbase measures its impact.Subit’s advice for AI-powered startup leaders: address bias and build a skilled AI team.Floodbase’s three to five-year plan: make insurance more accessible. Quotes:“Adapting to global flood risk is something that is near and dear to my heart, having grown up in India and having seen a lot of damage from floods in Eastern India where I used to live.” — Subit Chakrabarti“Parametric insurance pays out when a pre-agreed weather condition is made separate from the physical damage.” — Subit Chakrabarti“What we use machine learning for is to design [the] index that the parametric insurance can be based on, and that is our proprietary AI technology.” — Subit Chakrabarti“One of the most important challenges with satellite imagery is that satellite imagery represents the condition of a place at a certain point in time and it’s not the continuous movement of what that flood looks like at that place.” — Subit Chakrabarti“Our policy at Floodbase is that we add more data to remove bias from the process.” — Subit Chakrabarti“The biggest thing that we can measure is the flood protection gap. So like I said, 83% of losses are uninsured and we can measure that.” — Subit ChakrabartiLinks:Subit Chakrabarti on LinkedInSubit Chakrabarti on TwitterFloodbaseResources 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.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

The role of AI in cancer detection grows more significant with each passing week. During this conversation, I welcome Marcel Gehrung, CEO and Co-Founder of Cyted, to discuss detecting gastrointestinal cancer. You’ll learn how Cyted leverages machine learning to diagnose Barrett’s Esophagus in upper GI samples. Marcel reveals some of the challenges he has faced at Cyted related to the limited autonomy an algorithm can realistically provide, and annotating data for training and validation. Hear how the company is responding to changes in AI, and why hiring for technical roles at Cyted has not been difficult, due to their location. You’ll hear Marcel’s perspective on hiring specialist generalists and some of his advice for leaders at AI-powered startups.Key Points:Introducing founder and CEO at Cyted, Marcel Gehrung. His path to focusing on gastrointestinal cancer detection.How the technology at Cyted works to diagnose Barrett’s Esophagus. What Barrett’s is and who is most susceptible to it. The role of machine learning in detecting cancer in upper GI samples.Navigating the challenge of how much autonomy an algorithm can provide.Annotating data for training and validation.How Cyted is responding to changes in AI.Hiring for technical roles at Cyted.Onboarding challenges due to the verticality of technology Cyted works with. Why Marcel advocates for hiring specialized generalists.Marcel’s advice for leaders of AI-powered startups. Where he sees the impact of Cyted in three to five years.Quotes:“We’re essentially leveraging the best of both worlds. We’re working with cytoscreeners, which we also have on our staff to generate the initial annotations, and then we have someone who looks at it and then reclassifies if necessary.” — Marcel Gehrung“The more ability the candidates have to horizontally integrate different types of knowledge from across the company or across the technology of the sector, the better.” — Marcel Gehrung“Getting carried away just happens so easily, particularly when we follow the various news outlets in the world that overwhelm us with new exciting ideas and functions of that technology.” — Marcel GehrungLinks:Marcel Gehrung on LinkedInMarcel Gehrung on TwitterCytedResources 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.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Climate change is one of the most pressing issues of our time, and today’s guest, Ankur Garg, and his team at BlocPower are using machine learning technology to mitigate it. BlocPower is a climate technology company that is focused on making buildings in low and middle-income areas more environmentally friendly. Their area of expertise lies in developing products and services to lower or eliminate the barriers that prevent access to energy efficiency and electrification retrofits. And this all starts with gathering, checking, annotating, and understanding enormous amounts of data (BlocPower currently has over 40 terabytes of data in its data lake!)In this episode, Ankur talks about the innovative ways in which BlocPower deals with its data, the challenges that they face when it comes to the size and scope of its datasets, why machine learning technology is central to the work they do, and how they measure the impact of their technology.Key Points:Ankur’s career journey prior to joining BlocPower. Why Ankur decided to join BlocPower.  The inspiring work that BlocPower is doing to contribute to solving the problem of climate change.The central role that machine learning plays in BlocPower’s approach.Ankur gives examples of some of the different types of machine learning models that BlocPower uses.The size of BlocPower’s data lake and the types of data stored within it.BlocPower’s innovative approach to annotating data. The importance of high-quality training data sets in the machine learning space.Challenges that Ankur and his team face when training machine learning models on their core dataset.Technological advancements that have allowed BlocPower to achieve what it has.How BlocPower measures the impact of its technology.What Ankur believes the future holds for BlocPower Quotes:“Climate change is one of the primary problems of our generation, and BlocPower is making a huge dent in solving that.” — Ankur Garg“Machine learning really excels at ingesting huge volumes of data and to be able to infer key relationships between these data points to come up with an optimal output or a solution.” — Ankur Garg“Labeling the data and annotating is extremely critical. If your training data set is not of a good quality, no matter what algorithm you use, it won't really perform well.” — Ankur Garg“You need a lot of high-quality data for machine learning and artificial intelligence to be productive.” — Ankur GargLinks:Ankur Garg on LinkedInBlocPowerBlocPower Email AddressResources 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.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

If you are working in the life science research space and battling with image recognition issues, firstly, you are far from alone, and secondly, there is a solution! That solution comes in the form of KML Vision, an AI-powered start-up co-founded by today’s guest, Philipp Kainz. In this episode, Philipp explains how he became aware of the image analysis problem and the process that he and his team have gone through to develop machine learning models that provide a range of benefits to a diverse cohort of end users. There is still a large gap between what is technologically possible in a research or lab setting and what is actually out there and what people can use. Through their flagship product, IKOSA, Phillip is on a mission to change that. Listen to this episode to gain an understanding of how machine learning is being used to shape the future of life science research! Key Points:The motivation behind the founding of KML Vision.The value that KML Vision’s cloud platform, IKOSA, brings to the life science research space.The diversity of end users of IKOSA.Benefits that IKOSA provides to its users.Examples of some of the most common use cases for IKOSA.The role that machine learning plays at KML Vision.How KML Vision trains their models.The challenges that the KML Vision team have run into when training their models.Philipp explains KML Vision’s approach to developing the machine learning aspects of a new product or feature.How KML Vision helps to solve the problem of reproducibility in the life science research space.Valuable advice for leaders of AI-powered start-ups.Quotes:“We basically set out to help people overcome this barrier of using new technologies for image analysis.” — Philipp Kainz“There is still a big gap between what is technologically possible in a research or lab setting and what is actually out there and what people can use. So, we are actually focusing on bridging that gap.” — Philipp Kainz“Nobody [really] has time to go into the inner workings of deep learning. They want to use it like we use this smartphone today. This is where we want to be in three to five years.” — Philipp KainzLinks:Philipp Kainz on LinkedInKML VisionResources 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.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Sustainability is finally getting the attention it deserves as the global drive to reduce our carbon emissions gets more frantic each day. Thankfully, the progression of AI has accelerated the way materials and chemical manufacturers can go about their business in an environmentally friendly and sustainable manner.Today I am joined by Greg Mulholland, the Co-Founder and CEO of Citrine Informatics, a technology company that is focused on accelerating the development of the next generation of materials and chemicals. We discuss the role of machine learning in Citrine’s technology, the challenges they are forced to overcome regarding their data sets, the model accuracy and explainability balance, and how Greg and his team validate their models. There is no doubt that Citrine’s work is vital for the global sustainability effort, and our guest explains his company’s collaborative programs, how publishing research articles has boosted Citrine’s profile, what this AI-powered business hopes to achieve in the next five years, and so much more! Key Points:Introducing Greg Mulholland, his professional background, and how he ended up at Citrine. Greg explains what Citrine does and why this work is important for sustainability. The role of machine learning in Citrine’s technology. Taking a closer look at Citrine’s data sets and the data challenges that they encounter.The techniques that Greg and his team use to successfully handle small data sets. Examining the balance between model accuracy and explainability. How he validates his models. An explanation of Citrine’s collaborative program with external researchers. The benefits of publishing research articles. Greg’s advice to other leaders of AI-powered startups. His vision for Citrine’s impact and influence over the next five years. Quotes:“I trained as an electrical engineer and got into material science because I believed that material science was really an important technology set of disciplines that we needed, to solve the world's most pressing environmental challenges.” — Greg Mulholland“We started the company 10 years ago now; we've been able to show that machine learning and artificial intelligence, among other things, can be used to really accelerate the future of the materials and chemicals industry. It was the vision all along, but it really required a lot of technology development and we're really proud of how far we've come.” — Greg Mulholland“The scientists in our community are brilliant people.” — Greg Mulholland“Explainability is important. Accuracy is also important. Neither is dominant over the other. It turns out, a less accurate model that is more explainable can often help unlock new thinking in a scientist's mind, that then unlocks the next-generation product.” — Greg Mulholland“Publishing what we do as a starter for more conversations; I think it helps us attract good talent. It helps people understand that we're doing cutting-edge research and continue to invest in driving forward the field. I take it as a little bit of a feather in our cap and a source of pride that we get to help the world move along into this new era of AI.” — Greg Mulholland“We've seen companies remove toxic chemicals from important products much more quickly than they could have otherwise. We've seen companies reduce their energy consumption. We've seen companies reduce costs and reduce carbon input. Those are all really exciting to me.” — Greg MulhollandLinks:Greg Mulholland on LinkedInGreg Mulholland on TwitterCitrine InformaticsResources 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.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Spatial biology is an important part of the research being done to gain biological insights and joining me today on Impact AI to discuss how his company, Enable Medicine, uses AI to decode biology is Aaron Mayer. You’ll hear about Aaron's background, what led him to create his company, what Enable Medicine does and why, and how they use machine learning in their endeavors. Aaron shares the struggles they face, why they publish their research, the timing their company has nailed, and so much more! Finally, he shares some words of wisdom for other leaders of AI-powered startups.Key Points:Aaron’s background and what led him to create Enable Medicine. What Enable Medicine does and why it’s important for healthcare. The role machine learning plays and how it’s used with spatial biology data. The challenges Aaron has faced working with this data.How they prepared for the use of AI by creating a data infrastructure from scratch. The importance of trust and transparency and the benefits of publishing articles. Why this is the perfect time to build this kind of company.Aaron shares some advice for other leaders of AI-powered startups. Where Aaron sees the impact of Enable Medicine in the near future. Quotes:“The goal of Enable Medicine is really to organize biological data and make it searchable to deliver insights to the questions that we really care about.” — Aaron Mayer“Machine learning and AI is deeply integrated into the platform and technology stack that we've been building [at Enable Medicine].” — Aaron Mayer“We want to take these various AI models and put them into an environment where they can operate with an expert in a loop.” — Aaron MayerLinks:Aaron Mayer on LinkedInEnable MedicineResources 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.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Karthik Kannan, Co-Founder and CTO of Envision, discusses how his company improves the lives of visually impaired people, their data gathering process, developing new features, ensuring technology performs well, and measuring impact. He also shares advice for AI-powered startup leaders and his hopes for Envision's future.

AI seems to be taking the world by storm, and it is easy to use this new technology for either good or bad. Today I am joined by Sérgio Pereira, the VP of AI Research, Oncology Group, at Lunit, a company using AI for good by conquering cancer with machine learning.You’ll hear about Sérgio’s professional background, Lunit’s missions, how they use AI for cancer screening and treatment planning, and so much more. Sérgio delves into how they read imaging before discussing the differences between supervised, self-supervised, and contrastive learning. Lunit has created an incredible dataset called Ocelot, and he tells us all about its benefits, how they published it, and why publishing a paper while ensuring that quality products are being produced is a challenge. Finally, Sérgio tells us his hopes for the future of Lunit.Key Points:A brief overview of Sérgio Pereira's background, and what led him to Lunit. Lunit’s mission and why it’s important in fighting cancer. How Lunit applies machine learning for cancer screening and treatment planning.What most of the Lunit products are based on and how genetics come into play. Why subjectivity becomes an issue when it comes to reading images in machine learning. Sérgio explains what supervised, self-supervised, and contrastive learning is.The lessons from machine learning work that can be applied to other types of imaging. He tells us about Lunit’s dataset, Ocelot, and the benefits of it.How Lunit publishes their datasets. The challenges of getting a paper out while getting a product on the market. Sérgio shares some important things AI-powered startups need to consider. Where Sérgio sees the impact of Lunit in the near future. Quotes:“Our mission at Lunit is to conquer cancer through AI.” — Sérgio Pereira“We don’t have many products at Lunit, that’s a fact, but the ones we have, we believe they are [the] best-in-class.” — Sérgio Pereira“Mistakes in healthcare can have a very big impact, so we need to be able to show and demonstrate that our products work as we promised.” — Sérgio Pereira“AI can be used for good and for bad. Let’s make sure we work on the good part.” — Sérgio PereiraLinks:Sérgio Pereira on LinkedInSérgio Pereira on Google ScholarSérgio Pereira on TwitterLunit Inc.Paper: OCELOT: Overlapped Cell on Tissue Dataset for HistopathologyDataset: OCELOTPaper: Benchmarking Self-Supervised Learning on Diverse Pathology DatasetsResources 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.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Sustainable waste disposal has been a global pain point for many decades. While the recent push toward comprehensive recycling has eased the pressure a little, there’s still much more to be done if we are to build a sustainable society. Luckily for us, the progression of AI brings new hope for feasible waste disposal, and today’s guest, the CTO and Co-Founder of CleanRobotics, Tanner Cook, is here to tell us how his company is playing its part in improving the disposal of waste, recycling, and compost.In our conversation, we learn about CleanRobotics and why the company’s work is vital for sustainability, the ins and outs of their smart recycling product, TrashBot, and how it uses machine learning, how CleanRobotics ensures that its technology is always improving and up to date, and the impact of their AI-powered systems on sustainable waste management. Plus, Tanner offers up some noteworthy advice for other leaders of AI-powered start-ups before sharing his vision of the future of CleanRobotics.  Key Points: Introducing the CTO and Co-Founder of CleanRobotics, Tanner Cook. Tanner’s background and how he ended up as a co-founder. What CleanRobotics does and why this work is important for sustainability. Assessing the information that CleanRobotics is able to extract from its TrashBot product. The role of machine learning in TrashBot technology. How they gather and annotate data with TrashBot. The challenges of training machine learning models on imagery. How Tanner and his team improve their technology and ensure that it’s always up to date. The way CleanRobotics measures the impact of its technology. Tanner’s advice to other leaders of AI-powered startups. What he’d like CleanRobotics to achieve over the next five years. Quotes:“[I] found myself looking at trash cans very closely with my co-founder, Charles Yhap, and realizing, at the bin level and where people dispose of things, there were a lot of problems going on, and a lot of problems that artificial intelligence and robotics could solve.” — Tanner Cook“The number of rule sets are very diverse throughout the United States and throughout the world. The rules can easily change for what is and isn't recyclable when you drive 20 minutes outside of your city.” — Tanner Cook“One of our personal tellers internally for CleanRobotics is sustainability. Putting in those checks and balances to make sure that we're actually doing something good - instead of just greenwashing - is very important to us.” — Tanner CookLinks:Tanner Cook on LinkedIn CleanRobotics TrashBotResources 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.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.