Impact AI

Today, I am joined by Maximilian Alber, Co-founder and CTO of Aignostics, to talk about pathology for precision medicine. You’ll learn about Aignostics’s mission, how they are impacting healthcare, and the transformative power of foundational models. Max explains how Aignostics is driven by the belief that machine learning and data science will help improve healthcare before expanding on the role of foundational models. He describes how they built their foundational model, what sets it apart from other models, and why diversity in their datasets is key. He also breaks down how foundational models have allowed them to develop other models more quickly and better navigate explainability with concepts that are challenging for machine learning. We wrap up with Max’s advice for leaders of other AI-powered startups and where he expects Aignostics will be in the next five years. Tune in now to learn all about foundational models and the innovative work being done at Aignostics! Key Points: Quotes: “Our mission is to turn biomedical data into insights.” — Maximilian Alber “Everything we do is driven by the belief that machine learning and data science will help us improve healthcare.” — Maximilian Alber “A foundation model is a model that can be used as a starting point for building a machine learning application, with the promise that the foundation model already has a great understanding of the domain.” — Maximilian Alber “We are in active discussions for licensing our foundation model to other companies in order to enable their development as well. [What’s] important here is that we develop our foundation model along regulatory requirements, which will allow it to be used in medical products.” — Maximilian Alber “One needs to build a technology that either makes a difference in the long run, or one must be able to innovate at a very fast pace.” — Maximilian Alber Links: Maximilian Alber on LinkedIn Aignostics Aignostics on LinkedIn Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Advanced weather forecasts are the new frontier in meteorology. Long-term forecasting has garnered significant attention due to its potential to provide valuable insights to various sectors of society and the economy. In today’s episode, Sam Levang, Chief Scientist at Salient, joins me to discuss Salient’s innovative approach to weather forecasting. Salient specializes in providing highly accurate subseasonal-to-seasonal weather forecasts ranging from 2 to 52 weeks in advance. In our conversation, we discuss the ins and outs of the company’s innovative approach to weather forecasting. We delve into the hurdles of subseasonal-to-seasonal forecasting, how machine learning is replacing traditional weather modeling approaches, and the various inputs it uses. Discover the value of machine learning for post-processing of data, the type of data the company utilizes, and why it uses probabilistic models in its approach. Gain insights into how Salient is catering to the impacts of climate change in its weather predictions, the company’s approach to validation, how AI has made it all possible, and much more! Key Points: Quotes: “Salient produces weather forecasts that extend further into the future than most people are used to seeing. We go up to a year in advance.” — Sam Levang “ML (Machine Learning) models have proved to be actually a very effective replacement for the traditional approach to weather modeling.” — Sam Levang “The only difference about making forecasts longer timescales of weeks and months ahead is that there are some differences in the particular parts of the climate system that provide the most predictability.” — Sam Levang “While ML and AI are extremely powerful tools, they are still just tools and there's so much else that goes into building a really valuable product, or a service, or a company.” — Sam Levang Links: Sam Levang on LinkedIn Salient Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Welcome to today’s episode of Impact AI, where we dive into the groundbreaking world of virtual tissue staining with Yair Rivenson, the co-founder and CEO of PictorLabs, a digital pathology company advancing AI-powered virtual staining technology to revolutionize histopathology and accelerate clinical research to improve patient outcomes. You’ll find out how machine learning is used to translate unstained tissue autofluorescence into diagnostic-ready images, gain insight into overcoming AI hallucinations and the rigorous validation processes behind virtual staining models, and discover how PictorLabs navigates challenges like large files and bandwidth dependency while seamlessly integrating technology into clinical workflows. Yair also provides invaluable advice for AI-powered startup leaders, emphasizing the importance of automation and data quality. To gain deeper insights into the transformative potential of virtual tissue staining, tune in today! Key Points: Quotes: “The most important factor for the healthcare system, for the patient is the fact that you can get all the results, all the workup, and all the different stains from a single tissue section very, very fast.” — Yair Rivenson “Machine learning is the engine behind virtual staining. In a sense, that’s what takes those images from the autofluorescence of the unstained tissue section and converts [them] into a stain that pathologists can use for their diagnostics.” — Yair Rivenson “At the end of the day, the network is as good as the data that it learns from.” — Yair Rivenson “The more you automate, the better off you’ll be in the long run.” — Yair Rivenson Links: Yair Rivenson PictorLabs PictorLabs on LinkedIn ‘Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning’ ‘Assessment of AI Computational H&E Staining Versus Chemical H&E Staining For Primary Diagnosis in Lymphomas’ Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

One of the most powerful impacts machine learning can make is helping to solve environmental challenges all around the world. Today on Impact AI, I am joined by the founder of Greyparrot, Nikola Sivacki to discuss how his company uses machine learning to improve recycling efficiency. Learn all about Nikola’s background, what Greyparrot does, their services, the importance of their work, the role machine learning plays in it, how they gather and annotate data, the challenges they face, how they develop new models, and so much more. Tune in to hear the newest AI innovations Nikola is most excited about before hearing his goals for Greyparrot in the near future. Lastly, get some valuable advice for running AI-powered startups. Key Points: Quotes: “Greyparrot basically monitors the flow of waste materials, recyclable materials in material recovery facilities, and offers compositional analysis of these materials.” — Nikola Sivacki “It's very helpful, – if thinking of a new product, to start with a data set that is really tailored to answering the main uncertain question that is posed there.” — Nikola Sivacki “Start thinking about data from the start. I think that it’s very important to understand the data in detail.” — Nikola Sivacki “Our goal is to improve, of course, recycling rates globally so that we can reduce reliance on virgin materials.” — Nikola Sivacki Links: Nikola Sivacki on LinkedIn Nikola Sivacki on X Greyparrot Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

What if AI could decode the mysteries of your microbiome for a healthier you? In this episode, I sit down with Leo Grady, Founder and CEO of Jona, to discuss his groundbreaking work in microbiome research. Jona is a health technology company that specializes in microbiome profiling and analysis. It offers microbiome testing kits for individuals to use at home, along with AI-powered analysis of the associated microbiome data. In our conversation, we delve into the human microbiome and how Jona is harnessing the power of AI to unlock its secrets and revolutionize healthcare practices. Discover how Jona bridges the gap between research and clinical practice and utilizes deep shotgun metagenomic sequencing. We discuss why he thinks AI is a critical technology for decoding the microbiome, how Jona is able to connect research findings to microbiome profiles, and the company’s approach to model validation. Gain insights into the evolving landscape of AI in healthcare, the number one barrier to clinical translation and adoption of AI technology, what needs to be done to overcome it, and much more. Key Points: Quotes: “What's really remarkable to me about the microbiome is that it's been linked to almost every aspect of human health.” — Leo Grady “There are a lot of challenges that forced us to really develop new kinds of [machine learning] techniques that are really suited to this problem. We can't just rely on taking what's out there today.” — Leo Grady “The AI is doing that extraction. We have human oversight to make corrections to it. But once that paper has been extracted correctly, then we don't need to look at it again. It’s a one-time review process on every study.” — Leo Grady “I think the biggest challenges with AI and healthcare today are no longer technical, and they're no longer regulatory. The fact is that with current AI technology and enough data, we can solve almost any AI problem that we want to.” — Leo Grady Links: Leo Grady on LinkedIn Jona Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Bringing transparency and accuracy to the marketplace by producing high-quality data on all types of hard problems is a main focus for today’s guest and the company he works for. I am pleased to welcome David Marvin to Impact AI. David was the Co-Founder and CEO of Salo Sciences, which was acquired by Planet last year, and is now the Product Lead for Forest Ecosystems there! He joins me today to talk about monitoring forests. We delve into his background and path to Salo Sciences and their eventual acquisition by Planet; including the original mission and vision and what they worked to accomplish at Salo. David then explains his goals and focus at Planet, and unpacks the types of satellite imagery, models, and sensors they incorporate into their data and outputs. He highlights their approach to validation, how they are reducing bias, and how they are integrating extensive knowledge to empower their machine learning developers to create powerful models. Key Points: Quotes: “A company like Planet was essentially probably the only company we would have really ever been acquired by just given their vision and the fact that they have their own satellites and we’re a satellite software company.” — David Marvin “[At Salo Sciences] we leveraged high-quality airborne LiDAR measurements of forests all over California. Airborne LiDAR is one of these technologies, these sensors, that was on that airplane back in my post-doc lab. It shoots out hundreds of thousands of pulses of laser light per second and reflects back to the sensor, and it can basically recreate in three dimensions a forest, or a city, whatever your mapping target is. It's extremely precise. It's centimeter-level accuracy, and it's very high-quality data. We consider that the gold standard of forest measurement.” — David Marvin “Ultimately, we want to produce a near-tree-level map of the world's forests, and we're well on our way to doing that and expect to be releasing that later this summer, or in the fall.” — David Marvin “We approach the validation aspect from a few different angles, trying to source as many different independent data sets as possible to do validation. Then we also like to do comparisons to well-known public data sets; either from academia or from governments.” — David Marvin “You really do have to have the three legs of the stool to be able to build a quality operational product that is meant for forest monitoring.” — David Marvin “Making sure you have scientists on your team, making sure you're still active in the scientific publishing community, that you're up on the latest papers that are coming out, and basically acting like a scientist in an industry position is crucial to make any product work; especially in branding markets, like forest monitoring and carbon markets.” — David Marvin Links: David Marvin David Marvin on LinkedIn David Marvin on x Salo Sciences Planet Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Foundation models have been at the forefront of AI discussions for a while now and joining me today on Impact AI is a leader in the creation of foundation models for pathology, Senior Vice President of Technology at Paige AI, Razik Yousfi. Tuning in, you’ll hear all about Razik’s incredible background leading him to Paige, what the company does and how it’s revolutionizing cancer care, and the role machine learning plays in pathology. Razik goes on to explain what foundation models are, why they are so helpful, how to train one, the differences in training one for pathology specifically, and how they use foundation models at Paige AI. We then delve into the challenges associated with the creation of foundation models before my guest shares some advice for leaders in machine learning. Finally, Razik tells us where he sees Paige AI in the next few years. Key Points: Quotes: “Paige is focusing on digital and computational pathology. In other words, we really bring AI and novel AI solutions to the field of pathology to help pathologists make better-informed decisions.” — Razik Yousfi “A foundational model is a model trained on a very large set of data. The idea there is that you can, in turn, use that foundation model to build a wide range of downstream applications.” — Razik Yousfi “Building a foundation model is not easy. So, I wouldn't necessarily recommend to every organization to build a foundation model.” — Razik Yousfi Links: Razik Yousfi on LinkedIn Razik Yousfi Email Address Razik Yousfi on X Paige AI Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Biodiversity is not just an ecological concern. As you’ll learn in this episode, it has tangible economic implications too. Today on Impact AI, I'm joined by Dr. Noelia Jimenez Martinez, Head of Insights and Machine Learning at NatureMetrics, to talk about biodiversity monitoring. NatureMetrics is a global nature intelligence technology company providing end-to-end nature monitoring and impact reporting. Powered by eDNA, their Nature Intelligence Platform allows any company to manage its impacts and dependencies on biodiversity at scale, translating the complexities of nature into simple insights that help to inform the best decisions for both the planet and business. Tuning in, you’ll learn about the importance of NatureMetrics’ work, the role that machine learning plays in their technology, and some of the challenges that come with working with sometimes unpredictable data from nature. In my conversation with Noelia, we also touched on why biodiversity is an increasingly urgent imperative for businesses of all kinds, how NatureMetrics is democratizing biodiversity monitoring, and much more! Key Points: Jurassic Park Quotes: “I couldn't focus too much on solving galaxy formation with the amount of bad news I was seeing in the climate space and biodiversity collapse. I made a transition – [to] looking for jobs to apply [my astrophysics skills to] related problems in climate and biodiversity.” — Noelia Jiménez Martínez “Nature does not seem to behave [as well] as we would want. It might be that you have exactly the same covariates and your model is predicting species, and then you go, and it's not there.” — Noelia Jiménez Martínez “[Most companies] will have to report on their sustainability strategies in the world to keep on functioning. In that context, what we can do here is make biodiversity monitoring achievable and democratically easy to access.” — Noelia Jiménez Martínez “The success of [an AI startup is] – tied up to the diverse, strong teams you build.” — Noelia Jiménez Martínez Links: NatureMetrics Dr. Noelia Jiménez Martínez on LinkedIn Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In today’s episode, I am joined by Simon Arkell, the visionary CEO and co-founder of Ryght, to talk about copilots and the application of generative AI in life sciences. Ryght is dedicated to revolutionizing the field of life sciences through the power of AI. By leveraging cutting-edge technology and innovative solutions, Ryght aims to empower professionals and organizations within the life sciences industry to streamline processes, enhance productivity, and drive meaningful outcomes. In our conversation, we discuss Simon's entrepreneurial background, the various companies he has founded, and what led him to create Ryght. We delve into the pivotal role of enterprise-scale, secure AI solutions in healthcare, and learn how Ryght's platform is reshaping the landscape of drug development and clinical research. Discover the intricate workings of generative AI copilots, the challenges of minimizing hallucinations and validating AI models, and why the utility of the approach at the enterprise level is essential. Simon also shares Ryght’s long-term goals and invaluable advice for leaders of AI startups. Join us, as we explore a world where healthcare and life sciences are transformed by cutting-edge technology with Simon Arkell from Ryght! Key Points: Quotes: “We built an enterprise-secure version of Generative AI that has many different features that allow large companies and small companies to very securely benefit from Generative AI without all of the issues that a very insecure, non-industry-trained solution might create.” — Simon Arkell “With this type of [generative AI] technology, you have the ability to completely unlock new formulas, and new molecules that could be life-changing.” — Simon Arkell “Improving the utility of the platform comes down to the efficacy of the output. It comes down to the in-context learning, the ensembling, and the prompting. But at the end of the day, a human has to determine, in many cases, the accuracy and relevance of a specific answer.” — Simon Arkell “It's not really about building models. It's about making sure that the right models are being utilized for the copilot.” — Simon Arkell Links: Simon Arkell on LinkedIn Ryght Ryght on LinkedIn Ryght on X Ryght on YouTube Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

AI in healthcare is one of the most researched areas today, particularly on the clinical side of healthcare. Sean Cassidy is the Co-Founder and CEO of Lucem Health. Having worked in digital health for the last twenty years, he joins me today to talk about identifying chronic diseases. Tune in to hear how AI and machine learning are creating efficiencies for different forms of healthcare data, and how changes and challenges are being addressed to improve the process. Going beyond workflow support, we discuss considerations to bear in mind when integrating AI into healthcare systems and how to meaningfully measure efficacy in a clinical context. Sean shares some hard-earned wisdom about leading an AI startup, reveals his big vision for the future of Lucem Health, and much more. Key Points: Quotes: “We are focused on early disease detection almost exclusively, and so that is using AI and machine learning algorithms to, at any point in time, evaluate the risk that a patient may have a certain disease.” — Sean Cassidy “Workflow is really important, but there are also other considerations that matter in terms of AI being more widely adopted in clinical settings and healthcare.” — Sean Cassidy “We are always evaluating and trying to get a deep understanding of whether what we said was going to happen with respect to the performance of the solution is actually manifesting itself in the real world.” — Sean Cassidy Links: Sean Cassidy on LinkedIn Lucem Health Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In this episode, I sit down with Jean-Baptiste Schiratti, Medical Imaging Group Lead and Lead Research Scientist at Owkin, to discuss the application of self-supervised learning in drug development and diagnostics. Owkin is a groundbreaking AI biotechnology company revolutionizing the field of medical research and treatment. It aims to bridge the gap between complex biological understanding and the development of innovative treatments. In our conversation, we discuss his background, Owkin's mission, and the importance of AI in healthcare. We delve into self-supervised learning, its benefits, and its application in pathology. Gain insights into the significance of data diversity and computational resources in training self-supervised models and the development of multimodal foundation models. He also shares the impact Owkin aims to achieve in the coming years and the next hurdle for self-supervised learning. Key Points: Quotes: “To be able to train efficiently, computer vision backbones, you actually need to have a lot of compute and that can be very costly.” — Jean-Baptiste Schiratti “There are some models that are indeed particular to specific types of tissue or specific sub-types of cancers and also the models can have different architectures and different sizes, they come in different flavors.” — Jean-Baptiste Schiratti “The more diverse the [training] data is, the better.” — Jean-Baptiste Schiratti “I’m convinced that the foundation models will play a very important role in digital pathology and I think this is already happening.” — Jean-Baptiste Schiratti Links: Jean-Baptiste Schiratti on LinkedIn Jean-Baptiste Schiratti on X Owkin Phikon Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Joining me today are Linda Chung and Michael Mullarkey to discuss the transformative potential of AI in mental health care. Linda is the co-CEO and Co-Founder of Aiberry, a groundbreaking AI company redefining mental healthcare accessibility. With a background in speech-language pathology, Linda pioneered telehealth services and now leads Aiberry in leveraging innovative technology for objective mental health screenings. Michael, the Senior Clinical Data Scientist at Aiberry, is dedicated to translating complex data science into tangible human value. His unique background in clinical psychology merged with a passion for coding drives his mission to address pressing human concerns through data. In our conversation, we explore the fascinating intersection of clinical expertise and artificial intelligence, unlocking personalized insights and proactive strategies for mental well-being. Hear about Aiberry’s innovative chatbot “Botberry” and how it helps provide insights into the user’s mental health. We also get into the weeds and unpack how Aiberry develops its models, data source challenges, the value of custom models, mitigating model biases, and much more! Our guests also provide invaluable advice for other startups and share their future vision for the company. Tune in and discover AI technology at the forefront of mental health innovation with Linda Chung and Michael Mullarkey from Aiberry! Key Points: Quotes: “We know that early detection leads to early intervention and better outcomes.” — Linda Chung “Our models take the messy, natural human way that people talk about their mental health, and we turn it into systematic data that are necessary for the healthcare industry and report it back to the user.” — Linda Chung “As a health tech company, we have to take the health and the tech elements of our business equally seriously. So, one of our guiding principles from a health perspective is we have to keep people's data secure.” — Michael Mullarkey “We really value letting people talk about their mental health in their own words, and that can lead to some unexpected outcomes on the modeling side of the operation.” — Michael Mullarkey Links: Linda Chung on LinkedIn Michael Mullarkey on LinkedIn Aiberry Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Machine learning can be used as an innovative method to contribute to climate change resiliency. Today on Impact AI, I am joined by the co-founder and CTO of Vibrant Planet, Guy Bayes, to discuss how they are using AI to revitalize forests. Listening in, you’ll hear all about our guest’s background, why he started Vibrant Planet, what the company does, how they apply machine learning to their work, and a breakdown of how they collect the four sets of data they need. We delve into any problem areas they face in their individual and integrated data types before Guy tells us how they cross-validate their models. We even talk about how the teams collaborate, how machine learning and forest knowledge come together, and where he sees the company in the next three to five years. Finally, our guest shares some pearls of wisdom for any leaders of AI-powered startups. Key Points: Quotes: “Getting the forest back into a state that's more able to tolerate fire and more able to produce low-intensity fire rather than high-intensity fire is [Vibrant Planet’s] goal.” — Guy Bayes “We have – not only super good engineers but also very talented ecological scientists and people that have done physical hands-on forestry for their careers. – This mix of those three personas – work together pretty harmoniously actually because we all share a common goal.” — Guy Bayes “I don't think you can ever find one person who has all that in their head, but you can find a team that does.” — Guy Bayes “You will not have an impact without having a combined team that all respects each other and brings different things to the table.” — Guy Bayes Links: Guy Bayes on LinkedIn Vibrant Planet Vibrant Planet on LinkedIn Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In this episode, I sit down with Erez Naaman, co-founder and CTO of Scopio Labs, to delve into the transformative potential of AI in healthcare, particularly in blood cell morphology analysis. Erez shares the intriguing journey behind the inception of Scopio Labs which was driven by a desire to revolutionize healthcare practices. Discover how Scopio Labs' platforms digitize and streamline the process of blood cell analysis and the pivotal role of machine learning in distinguishing and classifying various cell types. Gain insights into the significance of data collection and algorithm development, the evolution of AI infrastructure over the past decade, regulatory considerations on product development, and more. He also shares invaluable insights for AI startup leaders, the future trajectory of Scopio Labs, and the profound impact envisioned for the healthcare landscape. Join me as we explore the intersection of AI and healthcare innovation with Erez Naaman. Key Points: Quotes: “In terms of the approach [to AI], I think we saw it the same way that we do today in terms of its importance but I think that the infrastructure for using ML has greatly evolved.” — Erez Naaman “Getting a large enough data set to get a reliable classification on specific more rare cell types is the most difficult problem in my opinion.” — Erez Naaman “In a way, we look at it backward. Machine learning is a tool and not a goal. So, we always start with the patient in mind or the user.” — Erez Naaman “Everyone is dealing with AI and so the front runners are clearly becoming the leaders with time. So, it is much easier to choose the right tools for every task as time progresses.” — Erez Naaman Links: Erez Naaman on LinkedIn Scopio Labs Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Customer service calls often start and end at the operator’s headset, but there is so much untapped data from these conversations that could be used to improve business systems on a holistic level. Today’s guest, Amy Brown has seen the value of unlocking conversational data to improve healthcare systems across the country, and as the Founder and CEO of Authenticx, she has taken giant strides towards accomplishing this goal. Authenticx is an AI-powered platform that makes it possible for healthcare organizations to have a single source of conversational data, creating powerful and immersive customer insight analysis that informs business decisions. In today’s conversation, Amy explains why she founded Authenticx, what the company does, and why her business is important for healthcare. We also learn about how the company uses machine learning in its processes, the challenges of working with conversational data, how Authenticx upholds a high ethical standard, and how the impact of its technology can be measured across healthcare systems nationwide. After sharing some important advice for other leaders of AI-powered startups, Amy explains why Authenticx will be a key player in healthcare for the foreseeable future. Key Points: Quotes: “That’s really what I’m trying to get at; using technology to help explain customer and consumer perception of their care, and using that; putting that to work for the healthcare industry so it can start to improve its systems in a way that allows patients and consumers to actually get a better outcome.” — Amy Brown “Our data team has had to become extremely proficient at dealing with all kinds of messy data.” — Amy Brown “We’ve hired a diverse group of human beings because we want to make sure that we’re inclusive in our interpretations of what’s happening in these conversations.” — Amy Brown “You can never eliminate all bias – we would never purport of doing that – but we can be very intentional about how we train the data.” — Amy Brown “[The] dream scenario is that the healthcare system in this country starts to make room for and evolve in how it makes its business decisions to include the voices of their customers as a key source of insight, intel, and data.” — Amy Brown Links: Amy Brown on LinkedIn Amy Brown on X Authenticx Authenticx on Instagram Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Using biological intelligence, human intelligence, and artificial intelligence, the company in the spotlight today aims to demystify health, make science accessible, and honor the biochemical individuality of every human. Today on Impact AI, I am joined by the founding CTO and Head of Discovery AI at Viome, Guru Banavar! He is here to talk all about AI and the human microbiome. As you tune in, you’ll hear about Guru’s background and what led to the creation of Viome, including what they do and why their work is crucial to chronic disease. He unpacks their use of machine learning to turn RNA data into insights for their customers, the challenges they face in training models for the work they do, and Guru sheds light on the early steps of their process for planning and developing new machine-learning products or features. Be sure not to miss out on this insightful conversation about how Guru and the team at Viome are working to decode the human microbiome. Key Points: Quotes: “At some point in time, I decided that the impact that I wanted to make in the field of computational biology, life sciences, and healthcare could be done only if I joined a few of my friends from the broader community, and started a new company — [Viome].” — Guru Banavar “I am one of those AI people who believes that you first focus on the problem, and you bring all of the tools you need to solve the problem. AI, to me, is not just one thing, like the latest buzzword. For me, AI is an ML, a set of tools, and you take the right tool for the right problem.” — Guru Banavar “One of our core intellectual property elements is the meta-transcriptomic laboratory technology, which essentially, isolates, detects, and processes what we call the informative RNA molecules in any given sample. That required a number of sort of biochemistry-level technology breakthroughs.” — Guru Banavar “I would advise other leaders of AI-powered startups to be very careful about how you pick your solution toolset, based upon the problem that you want to solve.” — Guru Banavar Links: Guruduth Banavar on LinkedIn Guruduth Banavar on X Viome Viome Blog Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In this episode, I sit down with Konstantinos Kyriakopoulos, CEO of DeepSea, to discuss the transformative world of AI-powered shipping optimization. DeepSea focuses on enhancing vessel performance, fuel efficiency, and overall logistics management in the shipping and logistics industry. Konstantinos has been a key figure in advocating for digitalization in the maritime sector, pushing for technologies to streamline processes, cut costs, and reduce environmental impact. In our conversation, Konstantinos shares the captivating journey behind DeepSea's inception, revealing how its AI-driven solutions emerged from a desire to revolutionize the shipping industry's efficiency and environmental impact. We explore the intricate use of machine learning to predict fuel consumption, optimize vessel operations, and navigate the shift toward decarbonization. Gain insights into the intricacies of data architecture, the critical role of scalability, measuring impact, the future vision of the company, and much more. Don't miss out on discovering the cutting-edge applications of AI that are steering the shipping industry toward a more sustainable future with Konstantinos Kyriakopoulos. Tune in now! Key Points: Quotes: “If you really want to create impact, it’s not enough to just show people what’s happening and give them analytics, but you also have to, in some way, produce a tangible ROI.” — Konstantinos Kyriakopoulos “The most important thing is to evaluate performance, so to make sure that the proof of performance is constantly being tested and you have good benchmarks and analytics.” — Konstantinos Kyriakopoulos “It’s really important to also be able to check internally what is going on but also how the customer wants to see what’s created.” — Konstantinos Kyriakopoulos “For us, the impact is actually very straightforward. It’s dollars and the metrics tonnes of CO2.” — Konstantinos Kyriakopoulos “I think what I always say when people talk to me about starting an AI company is to focus on your data architecture early.” — Konstantinos Kyriakopoulos Links: Konstantinos Kyriakopoulos DeepSea DeepSea on LinkedIn Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

AI and machine learning have had a huge impact on the healthcare industry, but there are still plenty of advances to be made. Joining me today is Sam Rusk, Co-founder and CAIO of EnsoData, to talk about how their team is using machine learning to optimize sleep. Tuning in, you’ll learn about the founding of EnsoData, their implementation of ML, and the important role they play in the healthcare sector. We discuss the primary challenges of working with and training models on waveform data, EnsoData’s diagnostic processes, and how they use ML to process collected waveforms and identify therapy opportunities. Sam also shares his thoughts on how ML has developed since they first founded the company nine years ago, his advice for other leaders of AI-powered startups, and what his hopes are for EnsoData in the next five years. To learn how EnsoData is making waves in healthcare, be sure to listen in today! Key Points: Quotes: “We have a pretty mature process for taking feature ideas and moving them from the top of the funnel on product management all the way to testing and releasing those.” — Sam Rusk “We spend a lot of our time solving not necessarily the machine learning performance side of the problem, but more ‘how do we get this into the clinicians’ hands in a way that makes sense for everyone.’” — Sam Rusk “While we want to deliver products that change the game, we [also] invest heavily in research, and we are active in the community, publishing and engaging in the research community in sleep.” — Sam Rusk Links: Sam Rusk on LinkedIn EnsoData Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Custom Vision Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

What if you were told that AI could improve agriculture, reduce climate change, and potentially solve global food insecurity? In this episode of Impact AI, I am joined by Ranveer Chandra from Microsoft Research to discuss his work in the world of agriculture. Tuning in, you’ll hear all about Ranveer’s career, how he got his agriculture idea picked up by Microsoft, data-driven agriculture, and more! We then delve into the data needed to achieve their goals before Ranveer discusses all the challenges they face when it comes to multimodal AI. Ranveer is very hopeful that machine learning can drastically improve agriculture. He tells me what new AI technologies he is most excited about, their potential impact on agriculture, and even shares advice for other leaders in AI. Finally, my guest warns us against the potential divide society can create if AI is not made accessible to all people. You don’t want to miss out on this informative and incredibly interesting episode so press play now! Key Points: Quotes: “Technology could have a deep impact on agriculture. It could address the world's food problem; it could help improve livelihoods of a lot of smallholder farmers.” — Ranveer Chandra “The key question is, how do you sustainably nourish the planet? How do you sustainably nourish the people in this world?” — Ranveer Chandra “Microsoft is not an agriculture company. So we are not sending anything to farmers, but we are providing the tools on top of which you could build solutions for farmers, or partners, or customers build solutions and take the solutions to farmers.” — Ranveer Chandra “We need to make data consumable, and generative AI has the suitability to make that data more consumable.” — Ranveer Chandra “There are over 500 million smallholder farmers worldwide whose lives would benefit with artificial intelligence.” — Ranveer Chandra Links: Ranveer Chandra on LinkedIn Ranveer Chandra on X Ranveer Chandra on Instagram Microsoft Research – Ranveer Chandra Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Custom Vision Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Continuous glucose monitors (CGMs) are a trusted tool for diabetics, but today’s guest believes that widespread adoption could also be valuable for reversing the obesity crisis. Meet Bill Tancer, the Co-founder and Chief Data Scientist of Signos, a metabolic health platform that combines CGMs with a unique AI engine to offer real-time data and recommendations for healthy weight management. Today, Bill joins me to talk about all things metabolic health and machine learning. Tune in as we discuss how the Signos team trains their machine learning algorithms, the challenges they encounter when it comes to gathering data, and some of the other external factors that influence the performance of their model. We also touch on the value of qualitative data in the form of user feedback, the importance of keeping your mission in mind in the rapidly expanding AI space, and so much more! To find out how Signos is unlocking metabolic health with ML, don’t miss this episode of Impact AI. Key Points: Quotes: “Along with diabetes as its own health risk, having [dysregulated] glucose can lead to other medical problems. Cardiovascular disease, stroke, Alzheimer's, just to name a few. [It] is such an important goal for [Signos] to help people reduce their glycemic variability.” — Bill Tancer “That's what gets me up in the morning; hearing [positive user anecdotes]. That, in conjunction with looking at our own data and how our members are improving in terms of their wellness, tells us we're having a measurable impact.” — Bill Tancer “It is so easy [with] all the things you can do with AI to end up in a space where you've got a solution that's searching for a problem to solve. The antidote to finding yourself in that situation is always returning back to your mission.” — Bill Tancer Links: Signos Body Signals Podcast Bill Tancer on LinkedIn Bill Tancer on Instagram Resources for Computer Vision Teams: LinkedIn – Connect with Heather. Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health. Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project. Custom Vision Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.