Impact AI

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 JaskolskiLinks:Corey Jaskolski on LinkedInCorey Jaskolski on XSynthetaicResources 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 FisherLinks:Charles Fisher on LinkedInCharles Fisher on XUnlearn AIResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.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 BauchyLinks:Concrete.aiConcrete.ai on LinkedInMathieu BauchyMathieu Bauchy on LinkedInMathieu Bauchy on YouTubeMathieu Bauchy 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.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 AlberLinks:Maximilian Alber on LinkedInAignosticsAignostics 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.

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 LevangLinks: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 RivensonLinks:Yair RivensonPictorLabsPictorLabs 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:Welcoming Nikola Sivacki to the show. Nikola shares a bit about his background and how it led him to create Greyparrot. What Greyparrot does, what services they offer, and why it is so important. The role machine learning plays in this technology. How they go about gathering data and annotating it for their purposes. What they are trying to predict with the data they are gathering. Challenges they encounter in training machine learning models and how to overcome them.A breakdown of how his team plans and develops a new machine learning model or feature. Nikola shares how Greyparrot measures the impact of its technology. The two groups of machine learning developments Nikola is most excited about. Nikola shares some advice for other leaders of AI-powered startups. Where he sees the impact of Greyparrot in three to five years. 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 SivackiLinks:Nikola Sivacki on LinkedInNikola Sivacki on XGreyparrotResources 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:Background about Leo and what motivated him to start Jona.He explains the complexity of the microbiome and its role in human health.Hear more about Jona and how the company leverages AI for data analysis.How Jona applies models to analyze microbiome data and medical literature.The technical nuances and validation processes behind the company’s approach.Learn about the challenges of building models to elucidate microbiome data.Explore the intricacies of validating the company’s groundbreaking technology.Advancements in AI and machine learning that he is most excited about.Leo shares advice for leaders of AI-powered startups.Uncover the number one barrier to AI adoption: payment. What the future looks like for Jona and what the company aims to achieve.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 GradyLinks:Leo Grady on LinkedInJonaResources 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:David shares details about his background and path to Salo Sciences and Planet.The original vision and mission of Salo Sciences and what they did there.He explains how they leveraged large-scale airborne LiDAR collections and deep learning to create maps of vegetation fuels.His goals and focus at Planet.David unpacks the types of satellite imagery, models, and sensors they incorporate into their data and outputs.How they validate that their models work in places where they do not have Airborne LiDAR.Reducing the bias that results from only having data in a heterogeneous distribution of LiDAR sites around the world.How they integrate their extensive knowledge to empower their machine-learning developers in creating powerful models.The business benefits he’s seen from publishing and making it a priority.His advice to other leaders of AI-powered startups.His thoughts on the impact of the forest monitoring efforts at Planet in three to five years.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 MarvinLinks:David MarvinDavid Marvin on LinkedInDavid Marvin on xSalo SciencesPlanetResources 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:Introducing today’s guest, Razik Yousfi.An overview of Razik’s background and what led him to become Senior Vice President of Technology at Paige AI. What Paige does and why it’s important for cancer care. The role machine learning plays in pathology. Razik tells us what a foundation model is, why it’s useful, and what it takes to train one. The subtle differences in training a foundation model for pathology versus other data. How they are using foundation models at Paige AI. Razik discusses what the future of foundation models for pathology looks like. Why Razik doesn’t suggest that every organization build a foundation model. Our guest shares some advice for leaders of machine learning teams. Where he sees the impact of Paige AI in the next three to five years. 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 YousfiLinks:Razik Yousfi on LinkedInRazik Yousfi Email AddressRazik Yousfi on XPaige AIResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.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.