Impact AI

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.

Marshall Moutenot, co-founder and CEO of Upstream Tech, dives into the transformative power of AI in environmental monitoring. He discusses pioneering tools like Lens for remote climate project oversight and HydroForecast, which leverages machine learning for precise water flow predictions crucial for hydropower. Marshall shares his entrepreneurial journey and emphasizes the importance of integrating domain expertise with technology to tackle the challenges of climate change effectively. Tune in for insights on revolutionary approaches to conservation-leaning AI!

What will it take to bring affordable, accessible, and timely healthcare to all? Curai, an AI-powered virtual clinic, is on a mission to do just that by leveraging AI to enhance the efficiency of licensed physicians through text-based virtual primary care. In today’s episode, I sit down with Anitha Kannan, head of AI and founding member of Curai, to talk about the transformative potential of virtual primary care and its role in scaling healthcare access.In our conversation, Anitha delves into the technical aspects of using large language models for patient data processing, the challenges of training models with clinical data, and the strategies Curai employs to ensure high-quality care. We also discuss the innovative ways Curai integrates AI into healthcare, the significance of multidisciplinary teams, and Anitha’s vision for the future of virtual care. Tune in for an insightful conversation on scaling healthcare through virtual primary care and learn how Curai is making a real impact!Key Points:Some background on Anitha Kannan, and how she joined Curai.An overview of Curai’s services as a virtual healthcare practice.How they provide affordable and timely healthcare access through AI-enhanced systems.Machine learning’s role in history taking, information gathering, and summarization.How AI streamlines the workflow for physicians.Their use of large language models to process patient data.Training model challenges: ensuring clinical correctness and handling data omission issues.Best practices they’ve developed for validating models and the importance of evaluation.Fundamental differences between their work and how other LLMs, like ChatGPT, are trained.Their strategy for balancing long-term research aspirations with short-term product development.An overview of their multidisciplinary teams and how this contributes to their success.Anitha’s hopes for the future of Curai; particularly through partnerships with healthcare organizations.Quotes:"Our mission is to provide the best health care to everyone." — Anitha Kannan“Today, [Carai runs] a text-based virtual primary care practice. We have our licensed physicians or experts in their fields. Then we supercharge them and bring about a lot of efficiencies by leveraging AI.” — Anitha Kannan"It's very easy to build 80% of a good product with AI today, but I think to get it to 100%, [and] to get it to scale, to be useful in [the] real world — evaluation is the number one thing." — Anitha Kannan“At Curai, the AI team is composed of clinical experts, subject matter experts, researchers, and machine learning engineers. Every project, long-term or short-term, has a mix of these types of expertise in it. This allows us to work through the problem much more effectively.” — Anitha KannanLinks:Anitha Kannan on LinkedIn Anitha Kannan on XCurai HealthResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Batteries are arguably the most important technological innovation of the century, powering everything from mobile phones to electric vehicles (EVs). Unfortunately, most batteries have a significant impact on the environment, requiring increasingly scarce and valuable resources to manufacture and typically not designed for easy repair, reuse, or recycling.Today on Impact AI, I'm joined by Jason Koeller, Co-Founder and CTO of Chemix, to find out how his company is leveraging AI to create better, more sustainable EV batteries that could reduce our reliance on elements like lithium, nickel, and cobalt, all without compromising vehicle performance. For a fascinating conversation with a data-driven physicist working at the intersection of software, machine learning, chemistry, and materials science, be sure to tune in today!Key Points:Jason’s background in theoretical physics and how it led him to create Chemix.Products and services offered by Chemix and the role that AI plays.Four reasons that machine learning (ML) is at the core of everything Chemix does.Unique challenges that their ML models need to contend with.What goes into validating these models to ensure accuracy.Why now is the right time for the technology that Chemix is developing.Metrics for measuring the impact of a better EV battery.Jason’s data-driven advice for leaders of AI-powered startups.His “electrifying” vision for Chemix in the next three to five years.Quotes:“All data analysis and decision-making is automated by our AI system. This includes analyzing terabytes of battery test data each day.” — Jason Koeller“Looking at broad trends, [electric vehicles (EVs)] and AI have both become [things] that people have been talking a lot more about in the past 10 years and even more so in the past four or five years, and that has happened simultaneously.” — Jason Koeller“Why is everyone not buying an EV? It's largely because they're too expensive or because people are worried they're not charging fast enough or they don't hold enough range for long road trips. – Improving any one of these metrics would be a measure of impact.” — Jason KoellerLinks:Jason Koeller on LinkedInChemixChemix 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.

Being given a cancer diagnosis is one of the worst pieces of news you can receive as a patient. This is often made even more difficult by the fact that choosing a treatment option is rarely simple or easy. Clinicians need to make multiple assessments before they can move forward, and even then it is often difficult or impossible to make unambiguous predictions. That’s where Artera comes in, a company using multimodal AI tests to provide individualized results for cancer patients, which enables clinicians and patients to make personalized treatment decisions, together.I am joined today by Nathan Silberman, Vice President of Machine Learning and Engineering at Artera, to talk about how Artera’s technology is paving the way for personalized cancer treatment decisions. Join us today, as we get into how Artera is contributing to the cancer treatment process, some of the biggest challenges they face, and how they are addressing these through specifically trained algorithms and robust validation protocols. Be sure to tune in to this important conversation on how Artera is impacting cancer treatment outcomes for the better!Key Points:Background on our guest, Nathan Silberman, and what led him to Artera.How Artera is helping clinicians make informed decisions for cancer treatments.The role of machine learning in their personalized risk assessments for patients.Key challenges they’ve encountered with pathology data.How they deal with slide variations through well-trained algorithms.Bias in pathology data and what Artera is doing to mitigate bias.Their partnerships with academics, clinicians, and oncologists.Insight into the variety of approaches they use to validate their models.How their tests fit in with clinical workflows and assist doctors and patients.The agonizing wait time associated with traditional non-AI testing methods.How Artera is providing quick and reliable test results.Advice to leaders of AI-powered startups: stay focused on the ultimate goal of patient impact.Looking ahead at Artera’s impact in the next three to five years.Quotes:“Which therapy to choose is simply not an easy choice. Clinicians would ideally be able to accurately assess a patient's risk of a cancer spreading, or adversely affecting the patient's health in the short term. But often, that's hard or impossible for a clinician to predict.” — Nathan Silberman“Clinicians have been wanting and waiting for tools that can predict whether or not a therapy will work for that particular patient. This is ultimately where Artera steps in.” — Nathan Silberman“Rather than wait a month, Artera's test provides the answer within two to three days after the lab receives the biopsy slide. And it is so rewarding to hear from clinicians, and especially patients about the relief we can provide by giving clarity sooner.” — Nathan Silberman“I think the biggest piece of advice I can give is really just making sure that you're laser-focused on the ultimate goal of patient impact.” — Nathan SilbermanLinks:ArteraNathan Silberman 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.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.