Impact AI Heather D. Couture

What if there was a way to revolutionize image-based AI, eliminating the need for extensive prework? In this episode, I sit down with Corey Jaskolski, Founder and President of Synthetaic, to talk about finding objects in images and video quickly. Synthetaic is redefining the landscape of data analysis with its groundbreaking technology that eliminates the need for time-consuming human labeling or pre-built models. It specializes in the rapid analysis of large, unlabeled video and image datasets.
In our conversation, we delve into the groundbreaking technology behind Synthetaic's flagship product and how it is revolutionizing image and video processing. Explore how it utilizes an unsupervised backend to swiftly analyze and interpret data, how it is able to work with any kind of image data, and the process behind ingesting and embedding image objects. Discover how Synthetaic navigates biased data and leverages domain expertise to ensure accurate and ethical AI solutions. Gain insights into the gaps holding AI’s application to images back, the different ways the company’s technology can be applied, the future development of Synthetaic, and more!

Key Points:
Corey’s background in AI and ML and what led to the creation of Synthetaic.Why Synthetaic focuses on processing images and videos quickly.How the company leverages ML in its approach. Details about image ingestion and embedding processes.How the definition of potential objects varies depending on the type of imagery used.Explore the role of domain expertise in addressing challenges. Hear examples of the technology’s diverse range of applications.Recommendations to leaders of AI-powered startups. His hope for the future trajectory of Synthetaic.
Quotes:
“We think about the machine learning problems a little bit differently, because we're not labeling data to go ahead and build a bespoke frozen traditional AI model.” — Corey Jaskolski

“We take this very broad view of objects where anything that could be discrete from anything else in the imagery gets called an object, at the risk of basically finding, if you will, too many objects.” — Corey Jaskolski

“We think of RAIC as something that solves the cold start problem really well.” — Corey Jaskolski

“By and large, we're training image and video-based AIs the same way. We need a paradigm shift that really allows AI to be the force multiplier that it can be.” — Corey Jaskolski

Links:
Corey Jaskolski on LinkedIn
Corey Jaskolski on X
Synthetaic
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 improve the outcomes of clinical trials by making them more efficient and reducing the number of patients receiving placebos? Well, today’s guest, Charles Fisher is here to tell us all about how his company, Unlearn AI, is creating digital twins to do just that! In this conversation, you’ll hear all about Charles' academic background, what made him decide to create Unlearn AI, what the company does, and how they work within clinical trials. We delve into the problems they focus on and the data they collect before Charles tells us about their zero-trust solution. We even discuss Charles’ opinions of how domain knowledge should be used in machine learning. Finally, our guest shares advice for leaders of AI-powered startups. To hear all this and even find out what to expect from Unlearn in the near future, tune in now!

Key Points:
A rundown of Charles Fisher’s background and what led him to create Unlearn AI. What Unlearn does, what digital twins are, and why they’re important. How clinical trials work and how they are used within Unlearn. The kinds of data they use and how they tackle these clinical trials using machine learning. What a zero-trust solution is and how Unlearn guarantees that their results are accurate. Charles shares his thoughts on the role of domain expertise in machine learning. His advice for any leaders of AI-powered startups. What we can expect from Unlearn in the next three to five years. 
Quotes:
“[Unlearn is] typically working on running clinical trials where we might be able to reduce the number of patients who get the placebo by somewhere like – 50%.” — Charles Fisher

“[Unlearn] can prove that these studies produce the right answer, even though they leverage these AI algorithms.” — Charles Fisher

“It's very difficult to find examples where you can actually have a zero-trust application of AI. I actually don't know of another one besides [Unlearn’s].” — Charles Fisher

Links:
Charles Fisher on LinkedIn
Charles Fisher on X
Unlearn 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.

Did you know that concrete is the second most-used material in the world after water? Although it has largely defined modern society, concrete has a hidden climate cost: it is responsible for 1.6 billion tons of carbon dioxide entering the atmosphere annually. For context, that’s more than the entire aviation industry! With these statistics in mind, today’s guest is on a mission to decarbonize the construction industry. As the CTO and co-founder of cleantech startup, Concrete.ai, Mathieu Bauchy is using his expertise in artificial intelligence and materials modeling to prescribe new concrete formulations that are less carbon-intensive and more economical. Today, Mathieu joins me to offer insight into Concrete.ai's exciting technology, why it’s important for the planet, and how it can reduce concrete emissions by a third while also ensuring that concrete producers maximize margins and streamline their supply chains. To find out how this is possible without any changes to the raw materials, no modification of the production process, and no cost premium, be sure to tune in today!

Key Points:
Insight into Mathieu’s research focus and how it led him to create Concrete.ai.What Concrete.ai does and why it’s important for reducing CO2 emissions.The role of machine learning, particularly generative AI, in this technology.How Concrete.ai develops ML models that are reliably able to extrapolate.Why estimating uncertainty is important and how Concrete.ai approaches it.What goes into validating these models, including systematic testing in the field.Reasons that the timing for Concrete.ai’s technology is critical.Dollars saved and other metrics for measuring the impact of this technology.Mathieu’s humanity-focused advice for other leaders of AI-powered startups.How Concrete.ai’s impact will continue to expand and evolve.
Quotes:
“Concrete is responsible for 8% of the total CO2 emissions in the world. To give you some context, that's about three times more emissions than the entire aviation industry.” — Mathieu Bauchy

“We think that it's the right time for the concrete industry to benefit from what AI can offer to avoid waste during the production of concrete. The idea is that, if we adopt these new technologies, then we can continue to improve our quality of life.” — Mathieu Bauchy

“It's not like we are changing the way concrete is made. It's still made in the same plant. It's still made using the same materials. We are just changing the recipe, and just that [can] save about a third of the emissions of concrete.” — Mathieu Bauchy

“AI also comes with its own carbon footprint and, to some extent, also contributes to climate change. We should think about how we use AI to solve climate change and not further contribute to it.” — Mathieu Bauchy

Links:
Concrete.aiConcrete.ai on LinkedIn
Mathieu Bauchy
Mathieu Bauchy on LinkedIn
Mathieu Bauchy on YouTube
Mathieu Bauchy 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.
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.

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:
Insight into Max’s role at Aignostics and how the company is impacting healthcare.How they use machine learning to set themselves apart from their competitors.A rundown of their models and datasets.The definition of a foundation model and how Aignostics built theirs.How to use foundation models as a starting point for building machine learning applications.What sets Aignostics’ foundation model for histopathology apart from other similar models.How their foundation model enables them to develop other models more quickly.Top lessons Max has learned from developing foundation models.How they navigate explainability with concepts that are challenging for machine learning.The positive impact that foundational models have had on explainability.Recent advancements that Max is excited about as potential use cases for Aignostics.Max’s advice to leaders of other AI-powered startups.The impact of Aignostics and where he expects it will be in the next three to five years.
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:
Sam's background in science and the creation of Salient.Hear how Salient is revolutionizing weather forecasting and why.How Salient is utilizing machine learning in its forecasting models.Examples of the data and models the company uses.The challenges of working with weather data to build models.Explore why Salient also uses probabilistic models in its approach.Salient’s approach to validation and how it deals with data uncertainty.Ways AI has made the company’s approach to forecasting possible. He shares advice for leaders of other AI-powered startups.
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:
The origin story of PictorLabs and the research that informed it.Why Pictor’s work is so important for patients and the healthcare system.What Yair means when he says machine learning is the “engine” for virtual staining.How Pictor mitigates the challenge of AI hallucinations.Insight into what goes into validating virtual staining models.Large files, bandwidth dependency, and other challenges that Pictor faces.A look at how this technology fits smoothly into the clinical workflow.Collaborating with economic partners while staying focused on business objectives.Yair’s product-focused advice for leaders of AI-powered startupsWhat the next three to five years looks like for PictorLabs.
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.