Impact AI

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:

• Sam’s engineering and entrepreneurship background and EnsoData’s origin story.
• What EnsoData does and why it’s important for healthcare.
• Using ML to process collected waveforms and identify therapy opportunities.
• Input and output models EnsoData uses to navigate the noise of tricky signal types.
• Examples of what they are trying to predict with these models.
• Diagnostic processes used in sleep medicine and the role of EnsoData.
• Major challenges of working with and training models on waveform data.
• Different approaches EnsoData has implemented to tackle generalizability.
• Ways that the role of ML has evolved since EnsoData was founded nine years ago.
• Insight into their team’s process for developing new products and features.
• EnsoData’s place in the clinical workflow and how they assist doctors and patients.
• Sam’s advice for other leaders of AI-powered startups.
• What’s next for EnsoData and where you can go to learn more!

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:

• Introducing today’s guest, Ranveer Chandra.
• A bit about Ranveer’s background and how he landed up at Microsoft Research. 
• How Microsoft got involved in agriculture. 
• Ranveer tells us about data-driven agriculture, what it means, and how he plans to achieve it. 
• The kinds of data they collect from farms in order to achieve these goals. 
• Challenges associated with multimodal AI.
• How these technologies have been deployed so far.  
• What new technology Ranveer is excited about in the world of machine learning.
• Ranveer shares some advice for other leaders of AI-based products. 
• The potential impact of data-driven and AI technologies for agriculture in the future. 
• Ranveer warns us about the dangers of creating an AI-divide and what that would mean. 

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:

• Reflecting on the personal and professional paths that led Bill to create Signos.
• What Signos does for glycemic dysregulation and why it’s so important for healthcare.
• Insight into the role that ML plays in Signos’ technology.
• How Signos trains their ML algorithms using various sources of data.
• Food logging and other challenges that come with gathering CGM data.
• Ways that external factors influence model performance and how Signos mitigates that.
• Qualitative user responses that help Bill measure the impact of this technology.
• Bill’s mission-driven advice for other leaders of AI-powered startups.
• How he believes the impact of Signos will continue to evolve going forward.

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.

In an emergency setting, making a quick diagnosis under pressure is often a matter of life or death. This is especially true when it comes to diagnosing infectious diseases. Unfortunately, diagnosing infections in an emergency department is rife with challenges. Current tests either take too long, deliver unreliable results, or both. That’s where Inflammatix comes in. They are using machine learning technology to develop a point-of-care instrument that will diagnose the type of infection, and severity of infection, in emergency care quickly and effectively. Their first main product is currently in the late stages of development and can deliver a test report in about half an hour using cold blood as a sample source.

Joining me today to shed light on this incredible initiative is Ljubomir Buturovic, Vice President of Machine Learning at Inflammatix. We hear from Ljubomir about the role that machine learning played in this technology, key challenges they’ve encountered while training models on gene expression data, how they selected the 29 clinically relevant genes based on published scientific papers, plus a whole lot more. Tune in today to learn more about the groundbreaking work being done at Inflammatix and what you can expect from them in future!

Key Points:

• A warm welcome to today’s guest Ljubomir Buturovic.
• Ljubomir’s background in machine learning and what led him to Inflammatix.
• An overview of the important work being done at Inflammatix in healthcare.
• Details about their main product for diagnosis in emergency care.
• The role of machine learning in their technology to measure gene expression.
• How they selected the 29 clinically relevant genes based on published scientific papers.
• Key challenges they encountered while training models on gene expression data.
• Ground truth labels; the strategies they used to identify infections and validate their models.
• How they made sure that their models would work for multiple assay platforms.
• Using grouped analysis to ensure their models would serve a diverse patient population.
• Their approach to developing technology that would fit in with the clinical workflow and provide the right assistance to doctors and patients.
• The benefits that Inflammatix has seen from publishing their work.
• Ljubomir’s advice to other leaders of AI-powered startups working in healthcare.
• Where you can expect to see Inflammatix in five years.

Quotes:

“We developed an instrument which measures this gene expression for 29 clinically relevant genes for infections.” — Ljubomir Buturovic

“It takes a long time to achieve adoption. This is basically applying AI in medicine. When you are applying AI in medicine, the whole process of development and adoption works on medicine timescales, not on AI timescales.” — Ljubomir Buturovic

“One of the key challenges in applying machine learning in clinical test design is the availability of samples for training and validation. This is in sharp contrast to other applications, like maybe movie recommendations, or shopping, where you have a lot of input data, because it's relatively easy to collect.” — Ljubomir Buturovic

Links:

Inflammatix

Inflammatix's Machine Learning Blog

Ljubomir Buturovic on LinkedIn

Ljubomir Buturovic on X

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.

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.

There are now a few different AI foundation models available for Earth Observation (EO) data. These vast neural networks can be rapidly fine-tuned for many downstream tasks, making them a highly versatile and appealing tool.

Today on Impact AI, I am joined by Hamed Alemohammad, Associate Professor in the Department of Geography at Clark University, Director of the Clark Center for Geospatial Analytics, and former Chief Data Scientist of the Radiant Earth Foundation, to discuss the applications of foundation models for remote sensing. Hamed’s research interests lie at the intersection of geographic information science and geography, using observations and analytical methods like machine learning to better understand the changing systems of our planet.

In this episode, he shares his perspective on the myriad purposes that foundation models serve and offers insight into training and fine-tuning them for different downstream applications. We also discuss how to choose the right one for a given project, ethical considerations for using them responsibly, and more. For a glimpse at the future of foundation models for remote sensing, tune in today!

Key Points:

• A look at Hamed’s professional journey and the research topics he focuses on today.
• Defining foundation models and the purposes they serve.
• The vast amount of data and resources required to train and fine-tune a foundation model.
• Ways to determine whether or not a foundation model will be beneficial.
• How foundation models improve generalizability for downstream tasks.
• Factors to consider when selecting a foundation model for a given downstream task.
• Insight into the future of foundation models for remote sensing.
• Hamed’s advice for machine learning teams looking to give foundation models a try.
• His take on the impact of foundation models in the next three to five years.
• Ethical considerations for the responsible use of AI that apply to foundation models too.

Quotes:

“[Foundation models] are pre-trained on a large amount of unlabeled data. Secondly, they use self-supervised learning techniques – The third property is that you can fine-tune this model with a very small set of labeled data for multiple downstream tasks.” — Hamed Alemohammad

“It takes a lot to train a model, but you would not [do it] as frequently as you would [fine-tune] the model. You can use shared resources from different teams to do that - share it as an open-source model, and then anybody can fine-tune it for their downstream application.” — Hamed Alemohammad

“The promising future [for foundation models] will be combining different modes of data as input.” — Hamed Alemohammad

“There is a lot to do and the community is eager to learn, so if people are looking for challenging problems, I would encourage them to explore [the foundation model domain] and work with domain experts.” — Hamed Alemohammad

Links:

Hamed Alemohammad

Hamed Alemohammad, Clark University 

Hamed Alemohammad on LinkedIn

Hamed Alemohammad on X

Hamed Alemohammad on GitHub

Foundation Models for Generalist Geospatial Artificial Intelligence

Prithvi-100M on Hugging Face

HLS Multi-Temporal Crop Classification Model on Hugging Face

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.

Could there be a future where not using AI is considered unethical? With the growing efficiency created by AI support, radiologists are able to focus on the most important aspects of their work. During this conversation, I am joined by Stefan Bunk and Christian Leibig from Vara. Tuning in, you’ll hear about the essential practice of maintaining a high standard of data quality and how AI technology is revolutionizing breast cancer detection and treatment. We discuss the relevance of German innovation and research on a global community, and the step-by-step process that Vara adopts to test and introduce AI products. You’ll also hear about Stefan and Christian’s vision for the future of Vara. Don’t miss this episode, packed with powerful insights!

Key Points:

• Introducing Stefan Bunk and Christian Leibig from Vara. 
• Vara’s mission for breast cancer outcomes in line with WHO’s Global Breast Cancer Initiative.
• The role of machine learning in Vara’s technology.
• What the AI technology predicts and the software that goes into this. 
• Why it is essential to maintain a high standard of data quality.
• The relationship between images from earlier exams and current procedures. 
• How models are trained to manage different variations. 
• The relevance of German data for global application.
• Why it is important to have strong processes around AI deployment. 
• What it means to run in Shadow Mode first and why Vara chooses to do this with AI products.
• How they established the best way to integrate AI into the workflow.
• The crucial role of trust in machine learning models. 
• Monitoring AI models constantly and creating the means to react quickly.
• Where Stefan and Christian see the impact of Vara in five years. 
• The enduring goal of Vara: to support radiologists as they focus on the most important factors. 
• Considering the possibility that not using AI will become unethical in the future. 

Quotes:

“Our ambition is to find every deadly breast cancer early. Breast cancer is the most common cancer actually worldwide, one out of eight women will have it at some point in their lifetime.” —  Stefan Bunk

“At Vara, we want to empower health systems to systematically find more cancers much earlier and systematically downstage cancers.” — Stefan Bunk

“A machine learning model can actually outperform a radiologist with a single image, but nevertheless, can still benefit from taking comparisons across images into account.” —  Christian Leibig

“When you roll out a technology such as AI, which is the technology that is hard to understand, and you cannot always predict how it behaves in certain edge cases. We believe there must be strong processes around it wherever you will deploy your AI.” — Stefan Bunk

Links:

Stefan Bunk on LinkedIn

Christian Leibig on LinkedIn 

Vara

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.

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.

Ready to dive deep into the world of online security and identity verification? In this episode, I sit down with Vyacheslav Zholudev from Sumsub to discuss user verification, fraud detection, and the role of machine learning in ensuring the safety of digital interactions. Vyacheslav is the co-founder and CTO of Sumsub, an online verification platform that secures the whole user journey using innovative transaction monitoring and fraud prevention solutions.

In our conversation, Vyacheslav discusses the evolution of Sumsub, its role in online identity verification, and the challenges posed by deepfakes in the digital world. We explore the cat-and-mouse game against the rising threat of deepfakes, the pivotal role of machine learning in user verification, the challenges posed by generative AI advancements, the ethical considerations in combating biases, and much more. Tune in and discover the future of user verification with Vyacheslav Zholudev from Sumsub!

Key Points:

• Vyacheslav's background and the journey that led to the creation of Sumsub.
• Evolution of Sumsub from an anti-Photoshop project to a user verification platform.
• Hear why online user verification is vital for implementing digital features.
• Sumsub’s overall mission and shifting from physical to online identity verification.
• The crucial role of machine learning in Sumsub’s user verification technology.
• How the latest generative AI advancements impact user verification efficiency.
• Implications of deepfakes on society and their potential to facilitate fraud.
• Approaches and techniques used by Sumsub to detect and combat deepfakes.
• Continuous learning and adaptation in the rapidly evolving field of machine learning.
• Ethical concerns and potential biases in models trained for fraud detection.
• Monitoring and preparing to address potential bias in Sumsub’s models.
• Advice for leaders of AI-powered startups and Sumsub's future goals.

Quotes:

“Basically, [machine learning] is everywhere. I can’t imagine that our company could exist without machine learning and different algorithms in this area.” — Vyacheslav Zholudev

“It was really expensive and difficult to create a deepfake that looks realistic. Nowadays, you can do it with a click of a button on your smartphone. That became a problem [for user verification].” — Vyacheslav Zholudev

“We have a very strong machine learning team and we’re really focusing a lot nowadays on fighting those deepfakes, trying new and new ways how we can protect ourselves and our customers against them.” — Vyacheslav Zholudev

“Think like a hacker and don’t compromise security. Don’t think that some things won’t be revealed, they will.” — Vyacheslav Zholudev

Links:

Vyacheslav Zholudev on LinkedIn

Sumsub

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.

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.

During this conversation, I am joined by guest Gard Hauge, CTO of StormGeo, a weather prediction specialist and researcher with a background in software. We discuss Gard’s extensive research and its application at StormGeo, his historical experience with the company’s evolving relationship with machine learning, how weather and markets are related, and more. Touching on challenges in the field, my guest reveals the growing volume of data he deals with on a daily basis. We discuss the fundamental role of data engineering alongside machine learning, the key role of third-party data, and more before Gard shares his perspective on the future of StormGeo. He also delves into his experience to give informed advice to listeners. Join in to hear more from this thought leader today! 

Key Points:

• Gard Hauge, CTO of StormGeo, shares his background and introduction to StormGeo. 
• The topic of his Ph.D. research: weather prediction.
• Products and services offered by StormGeo beyond weather prediction. 
• The evolving role of machine learning at StormGeo and how it is integrated today. 
• How weather and markets are related. 
• Investments StormGeo is making into generative AI.
• Gard’s relationship with data collection and processing.
• The biggest challenges he faces especially in relation to the volume of data.
• The fundamental role of data engineering in building successful algorithms. 
• The key role of third-party data.
• Advice for other AI startup leaders. 
• Gard’s predictions for the future of StormGeo. 

Quotes:

“The data pipeline is something we put a lot of effort into developing over the last decade. Actually, streamlining how we actually process and make this data available in products and services is key.” — Gard Hauge

“The amount of data we face is typically doubled every two years. So, we need to be quite smart in handling and processing data and what we're actually archiving for machine learning.” —  Gard Hauge

“Everybody talks about AI and machine learning. But our experience is that 80% to 85% of the work is basically data engineering, and that's a key fundament if you want to build successful algorithms.” — Gard Hauge

Links:

Gard Hauge on LinkedIn

StormGeo

StormGeo 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.

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.

Using AI for medical data analysis for eye diseases has the potential to significantly improve diagnosis, treatment, and patient care in ophthalmology. In this episode, I sit down with Carlos Ciller, Co-Founder and CEO of RetinAI, to discuss the impact of AI in the field of healthcare, specifically in the context of RetinAI, a company focused on using AI for medical data analysis for eye diseases. In our conversation, we unpack the world of mission-driven impact in healthcare as Carlos shares his journey from engineer to innovator.

Uncover how RetinAI's flagship product, "Discovery," is revolutionizing healthcare with AI-powered medical image and data management. Explore the diverse data sources and AI models used, the importance of model robustness, and the influence of regulatory processes. Carlos also discusses the benefits of publishing research and the potential of generative AI, and he offers valuable advice for AI startup leaders. Finally, learn about RetinAI's vision for the future, including its expansion into new therapeutic areas and the pursuit of digital precision medicine. Tune in to uncover the incredible impact of AI in healthcare and RetinAI's pivotal role in this transformation with Carlos Ciller!

Key Points:

• Carlos’ professional background and his passion for meaningful healthcare solutions.
• RetinAI’s mission and the range of healthcare products it provides.
• The types of data and AI models that RetinAI leverages.
• Challenges of dealing with diverse data sources, devices, and patient characteristics.
• Ensuring model performance and accuracy in the long term.
• Frozen with fixed weights versus continuous learning models.
• Discover how regulatory processes influence AI development.
• He explains the benefits of publishing research for the development process.
• Explore the potential of generative AI in healthcare.
• Learn the importance of ‘wrapping’ the technology with the right product
• Focusing on the customer, starting small, and letting the market define the product.
• His vision for RetinAI's impact in the next three to five years.

Quotes:

“[RetinAI is] a software company. We are software that is enabling the right decisions sooner in healthcare, and that, of course, goes a long way.” — Carlos Ciller

“One of the secret sauces of the company is that around 40% to 50% of the team has actually a very strong academic training, specifically in the ophthalmology space.” — Carlos Ciller

“Quality is the most important aspect. If you work on quality [data], you will create stronger models.” — Carlos Ciller

“I think the regulations that we have today, and some of the guidelines and support material provided by regulatory agencies and some of the leaders in academic space are precisely [there] to help you not commit the same mistakes that others committed in the past.” — Carlos Ciller

“I think that it's important to share your research, and you can still make a good company out of sharing your own research, and letting others build on top of what you are building.” — Carlos Ciller

Links:

Carlos Ciller

Carlos Ciller on LinkedIn

Carlos Ciller on X

RetinAI

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.

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.

AI in agriculture offers numerous benefits and plays a crucial role in addressing the challenges of feeding a growing global population while minimizing environmental impact. Joining me today is Praveen Pankajakshan, Vice President of Data Science and AI at Cropin, to talk about intelligent agriculture and how Cropin is paving the way forward for sustainable agricultural practices. Cropin is a technology company that offers services and solutions for the agriculture industry, including AI/ML models, data processing, and applications to digitize farm operations and enable data-driven decision-making.

In our conversation, Praveen discusses various aspects of how machine learning and AI are being applied to agriculture to improve farming practices, sustainability, and climate resilience. Discover how Cropin employs AI to identify crops, monitor crop health, and provide timely advice to farmers on planting and harvest timings. He highlights the importance of combining satellite data with ground-level insights and the rigorous data annotation process, emphasizing the significance of field visits. We also delve into crop-cutting experiments for machine learning, overcoming out-of-distribution (OOD) problems, how climate change makes training models difficult, and much more! Tune in and discover how Cropin is revolutionizing farming and sustainable agriculture with Praveen Pankajakshan!

Key Points:

• Praveen's background and how he got into agriculture and machine learning.
• Cropin's mission and its digitization and monitoring services for farmers.
• Discover the role of machine learning in enhancing agricultural tasks.
• Learn about the types of data Cropin leverages for crop digitization.
• Why ground data and field visits are essential for the validation process.
• Insights into the challenges of working with agriculture data.
• Developing and deploying machine learning products for agriculture.
• Maintaining machine learning advancements around seasons.
• Agritech innovations that Praveen finds the most interesting.
• Words of advice for leaders of AI-powered startups: stay grounded.
• The future impact of Cropin on sustainable agricultural practices.

Quotes:

“There are many areas where machine learning has actually worked wonders. And I would say that because we have been digitizing farmlands now for over a decade.” — Praveen Pankajakshan

“One of the major challenges of working with satellite data is it definitely needs ground data [for validation].” — Praveen Pankajakshan

“Agriculture is very complex, and it's also very nice to work with because it's also profoundly impactful.” — Praveen Pankajakshan

“[In terms of development], we have to ensure that first we have some baseline models ready for deployment, for inferencing. And development happens almost simultaneously.” — Praveen Pankajakshan

“[I] insist more on data quality rather than the quantity of the data.” — Praveen Pankajakshan

Links:

Praveen Pankajakshan on LinkedIn

Praveen Pankajakshan Email

Cropin

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.

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.