Explorez tous les épisodes du podcast Impact AI
| Titre | Date | Durée | |
|---|---|---|---|
| Foundation Model Series: Transforming Biology with Zelda Mariet from Bioptimus | 17 Feb 2025 | 00:21:26 | |
Zelda Mariet, Co-Founder and Principal Research Scientist at Bioptimus, joins me to continue our series of conversations on the vast possibilities and diverse applications of foundation models. Today’s discussion focuses on how foundation models are transforming biology. Zelda shares insights into Bioptimus’ work and why it’s so critical in this field. She breaks down the three core components involved in building these models and explains what sets their histopathology model apart from the many others being published today. They also explore the methodology for properly benchmarking the quality and performance of foundation models, Bioptimus’ strategy for commercializing its technology, and much more. To learn more about Bioptimus, their plans beyond pathology, and the impact they hope to make in the next three to five years, tune in now. Key Points:
Quotes: “Working on biological data became a little bit of a fascination of mine because I was so instinctively annoyed at how hard it was to do.” — Zelda Mariet “Bioptimus is building foundation models for biology. Foundation models are essentially machine learning models that take an extremely long time to train [and] are trained over an incredible amount of data.” — Zelda Mariet “There are two things that are well-known about foundation models, they’re hungry in terms of data and they’re hungry in terms of compute.” — Zelda Mariet “On the philosophical side, science is something that progresses as a community, and as much as we have, what I would say is a frankly amazing team at Bioptimus, we don’t have a monopoly on people who understand the problems we’re trying to solve. And having our model be accessible is one way to gain access into the broader community to get insight and to help people who want to use our models, get insight into maybe where we’re not doing as well that we need to improve.” — Zelda Mariet Links:
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 Series: Democratizing Time Series Data Analysis with Max Mergenthaler Canseco from Nixtla | 10 Feb 2025 | 00:27:11 | |
What if the hidden patterns of time series data could be unlocked to predict the future with remarkable accuracy? In this episode of Impact AI, I sit down with Max Mergenthaler Canseco to discuss democratizing time series data analysis through the development of foundation models. Max is the CEO and co-founder of Nixtla, a company specializing in time series research and deployment, aiming to democratize access to advanced predictive insights across various industries. In our conversation, we explore the significance of time series data in real-world applications, the evolution of time series forecasting, and the shift away from traditional econometric models to the development of TimeGPT. Learn about the challenges faced in building foundation models for time series and a time series model’s practical applications across industries. Discover the future of time series models, the integration of multimodal data, scaling challenges, and the potential for greater adoption in both small businesses and large enterprises. Max also shares Nixtla’s vision for becoming the go-to solution for time series analysis and offers advice to leaders of AI-powered startups. Key Points:
Quotes: “Time series are in one aspect, the DNA of the world.” — Max Mergenthaler Canseco “Time is an essential component to understand a change of course, but also to understand our reality. So, time series is maybe a somewhat technical term for a very familiar aspect of our reality.” — Max Mergenthaler Canseco “Given that we are all training on massive amounts of data and some of us are not disclosing which datasets we’re using, it’s always a problem for academics to try to benchmark foundation models because there might be leakage.” — Max Mergenthaler Canseco “That’s an interesting aspect of foundation models in time series, that benchmarking is not as straightforward as one might think.” — Max Mergenthaler Canseco “I think right now in our field probably benchmarks are not necessarily indicative of how well a model is going to perform in real-world data.” — Max Mergenthaler Canseco “I think that we’re also going to see some of those intuitions that come from the LLM field translated into the time series field soon.” — Max Mergenthaler Canseco Links: Max Mergenthaler Canseco on LinkedIn
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. | |||
| Decoding the Immune System for Drug Discovery with Noam Solomon from Immunai | 04 Nov 2024 | 00:18:02 | |
Today’s guest believes that decoding the immune system is at the heart of improving drug efficacy. He is currently focused on this effort as the CEO and Co-founder of Immunai – a company that is building an AI model of the immune system to facilitate the development of next-generation immunomodulatory therapeutics. Noam Solomon begins our conversation by detailing his professional history and how it led to Immunai before explaining what Immunai does and why this work is vital for healthcare. Then, we discover how understanding the immune system will help to improve how drugs work in our bodies, how the team at Immunai accomplishes its goals, the major challenges of working with complex ML models, and some helpful recommendations for processing the high-dimensional nature of biological data. Noam also explains the collaborative landscape of Immunai, how the evolution of technology made his work possible, Immunai’s plans for the future, and his advice to others on a similar career path. Key Points:
Quotes: “First, let’s talk about the problem, which is today, getting a drug from IND approval to FDA approval—which is the process of doing clinical trials—has less than a 10% chance of success, usually about a 5% chance, takes more than 10 years, and more than $2 billion of open immune therapy.” — Noam Solomon “Different people respond differently to the same drug, and the reason they respond differently is because their immune system is different.” — Noam Solomon “You first need to fall in love with the problems. Many ML people—physicists, mathematicians, computer scientists—we love building models; we love solving puzzles. In biology, you need to really fall in love with the question you are trying to answer.” — Noam Solomon “It’s a great decade for biology.” — Noam Solomon Links:
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. | |||
| Detecting Breast Cancer Earlier with Tobias Rijken from Kheiron Medical | 30 Jan 2023 | 00:27:55 | |
All women face the risk of breast cancer, but early detection can greatly increase the chances of a positive outcome and reduce the need for aggressive treatment options. In this episode, I talk with Tobias Rijken, CTO and co-founder of Kheiron Medical Technologies, about leveraging AI for detecting breast cancer. We discuss the role of AI in improving medical care, the power of vertical integration and feedback loops, and what makes Kheiron different from other AI startups. Hear about the challenges of acquiring reliable data, whether using generative models is beneficial, details about the products Kheiron has created, and much more! Key Points:
Quotes: “What I liked so much about machine learning is the ability it has to solve real-world problems. And in my opinion, real-world machine learning is very different from academic machine learning.” “Either the right information isn't available, or it is inaccurate, or there's missing information. We see AI as a tool to help address those information problems.” “The challenge when you sample uniformly from your whole dataset is that there will be cases you've sampled, where you may not have ground truth.” “For me, when I started this company, this was not about building a great model that has a great performance on a test dataset. This is about getting AI into the real world.” Links: 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. | |||
| Monitoring Fields for Precision Agriculture with Gershom Kutliroff from Taranis | 23 Jan 2023 | 00:34:39 | |
In this episode, I talk with Gershom Kutliroff, CTO of Taranis, about precision agriculture. Taranis uses computer vision to monitor fields, providing critical insights to growers. Gershom and I talked about how they gather and annotate data, the challenges they encounter in working with aerial imagery, and how they validate their models and accommodate data drift with continuous learning. Quotes: “Taranis is using drone technology to capture imagery and then use AI to process that imagery to understand what's happening in grower's field.” “It becomes increasingly difficult to maintain consistent quality levels if you're working with tens or even hundreds of annotators. But when you have AI models, then you have the ability to control the quality of the insights that you're generating.” “There's a lot of discussion in the last few years in the AI space about data-centric versus model-centric. Model centric would be the case where in your development you invest a lot in choosing the right architecture that optimizes your performance, gives you the best results for your models, or spending a lot of time with hyper parameters and that type of work. And data-centric is you spend a lot more time making sure that your data set is clean, that you've got that it's balanced, you've got the right amount of classes for the problem that you're trying to solve.” “We struggle with the problem of long tail distributions. If I take diseases as an example, there are some diseases that can cause a lot of damage to the crops. But they're very rare in terms of how often they actually occur in grower's fields.” “Because we're running our own operations and so we're flying our own drones, we've also invested in the software that's running on the drones when we're flying. So the images the drone pilot captures in the field are validated in the field. We have algorithms running on the edge to be able to check the quality of those images. And then if the images are not the quality that we expect them to get, the pilot knows while he's still there at the field and he can fly again.” “For a lot of the models that we use you really need domain experts. You really need trained agronomists who can look at these images.” “A certain percentage of all of the missions that we've flown are sent for review by our in-house agronomists before we release them to customers. So that's a really critical piece of how we do validation, and that also gives us a high level of confidence internally that the product that we're releasing to our customers stands by the quality that we expect it to.” “We do suffer from this type of data drift where the data that we're seeing in production is not exactly in the same distribution as the data that we used to train. So the most effective technique that we've seen is to implement some kind of a continuous learning type of framework whereby we are able to take data that we're capturing in production, so when we're actually live and servicing our customers' fields. And then the data that doesn't have a good correspondence with the distribution of the training data that was used for the models, we can then filter that data out. We can extract that data and use it to quickly retrain the models, to adapt the models, and then deploy those models back into production.” “The company started by offering a product based on manual tagging, which didn't have any AI technology at the beginning, which allows it to offer products and service customers and start building this very rich database that we leverage now.” Links: 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. | |||
| Improving Patient Outcomes with Vinod Subramanian from Syapse | 16 Jan 2023 | 00:35:52 | |
In this episode, I talk with Vinod Subramanian, Chief Data and Product Development Officer at Syapse, about machine learning for healthcare and advancements in cancer treatment. Syapse is a real world evidence company dedicated to improving outcomes for cancer and other serious diseases. Vinod and I talked about the types of healthcare data they work with, the data challenges they encounter, how they validate their models, and how they mitigate bias. QUOTES: "There are infinite possibilities in the terms of patient care with aggregated and harmonized data in healthcare. We all know about the point that data in general is fragmented and decentralized in the industry. Real world data comes from knowledge and knowledge comes from collecting information and of course, information stems from aggregating disparate data." "Machine learning today, especially in a life science setting, is leveraged as new ways right to garner new biological insights." "One of the things that we are also doing is not just about adopting and using (ML and NLP), we strongly believe that we want to share our work. And that would not only raise and mainstream the work of everybody doing it, but also it'll help us in adopting and applying in precision medicine through standards." "Now not all data is needed equal. When we can improve the way data is collected, connected, analyzed, and consumed, we can not only improve the lives of our community, but it also gives us a way to look at the care continuum very differently." "There's no guarantee when you get into an initiative which uses machine learning and AI, because it cannot be successful. It has to be a learning experience, but it, there's no guarantee that it will be successful. And there needs to be willingness and appetite to experiment, learn, and iterate, and taking a Socratic approach, and accelerate the journey towards success, anchor down the culture." LINKS: 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. | |||
| Ecological Restoration with Patrick Leung from Earthshot Labs | 09 Jan 2023 | 00:28:08 | |
In this episode, I talk with Patrick Leung, Co-founder and CTO of Earthshot Labs, about using machine learning to help predict and restore forests and our ecosystem. Earthshot Labs is building the technology and expert guidance to develop and finance nature-based carbon projects globally. Patrick and I talked about how Earthshot Labs gathers and annotates data, the challenges in working with remote sensing and other forms of data, the importance of collaboration across disciplines, and how machine learning tools can help save our ecosystems. QUOTES: "Machine learning is really essential because what we're trying to do here is predict the future. We're trying to predict the next 30 years of a forest regrowing in a tropical region." "We must look at the past. We must look at whatever data we can gather from the past state of the ecosystem and use various machine learning methods to predict the future in order to provide a view on what's gonna happen on this land in the future when we do this project." "These are actual mathematical simulations that take into account the current conditions of the ecosystem and actually forecast them by using a kind of simulation that incorporates photosynthesis and evapotranspiration and other forms of ecological processes." "They would look at historical flood maps and essentially combine them with flood forecasting models i order to generate what is a given area going to look like if it gets flooded in the future because of climate change or for other reasons. And I was very enamored with that. I thought that was a very, very clever use of a technology." "I think what we're doing definitely encompasses biodiverse native ecosystems and just restoring as many of them as we can throughout the most critical parts of the biosphere, that there are in this world. And also helping to switch our societal systems into more of a harmonious, and regenerative relationship with those ecosystems." LINKS: 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. | |||
| Personalized Physiology Analytics with Matt Pipke from physIQ | 02 Jan 2023 | 00:33:30 | |
In this episode, I talk with Matt Pipke, Co-founder and Chief Digital Health Officer of physIQ, about personalized medical predictions from physiology data. Matt and I talked about the challenges in working with physiology data, how to validate models and minimize bias, and the importance of collaboration. Quotes: “What physIQ does is it harvests data from those continuous data streams from wearable sensors and produces analytical results that are useful for clinical care when taking care of patients who are outside the four walls of the hospital and in scientific endeavors such as clinical trials where it's interesting to know what the efficacy of the drug is on a target disease, whether the health of the patients who might take those drugs is being improved or at least is not degrading any further.” “What we have to do is build our algorithms and our analytics based on machine learning techniques and, of course, the more recent really successful subgroup of deep neural net algorithms that can sift through this data and can highlight accurately the vital signs of physiology we need to make the assessments available.” “So part of the issue there, is to figure out how to differentiate the background variation that's normal for people as they move around in their daily lives from the telltale signs that they may be suffering from a derangement of physiology.” “There's a lot of companies and offerings out there that are in the consumer fitness market. They might be appropriate for healthy populations that are looking to track their activity, the amount of sleep that a healthy person might get, but they're really not the right target populations of interest for the medical system or for clinical trials where you have a population that's suffering from a disease that a drug is targeting.” “Now I know that a lot of companies out there tend to avoid the regulatory pathway for medical or health or fitness applications, and I don't think that's a good move. . . The FDA experience for us has been at times frustrating of course, as it is for anybody who has to deal with regulations, but at the same time, there is a core of meaningful value add there. Regulatory agencies around the world, FDA included, they have a pretty thankless job. They never get credit for what they do. They only get complained about. But what they're doing is really, really critical to outputting valuable, usable product in the healthcare and medical space.” “So bias in models really comes back to the representativeness of your data, right? So if you've got data that's not representing the target users, the target populations that you're going to analyze, you can end up with bias. You can end up with bias in surprising ways.” “If you aren't aware of what might be lurking in your data, you could be overfitting the wrong thing and then find out that your algorithm does not generalize, does not work in other areas.” “My feeling about this is that it's all about the data. physIQ got started a lot earlier than we probably should have and we've benefited in a strange way in that we've been in the game a lot longer than other players in this space. So we've been collecting data for a long time and we built a robust platform to collect data.” “There's a lot of resistance to change and, in fact, the layperson might be horrified to learn how the healthcare system actually works. But, stepping back, something definitely has to change in healthcare. We all know that it's not sustainable the way things are now. But we don't have any illusions at physIQ about how a little company like ours can change things by ourselves. It's really about timing. Right. And sometimes you have to look for those windows of opportunity when in large industries with huge amounts of existing business relationships and the way that they work today are ready for change.” Links: 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. | |||
| Early Cancer Detection with Emi Gal from Ezra | 19 Dec 2022 | 00:23:57 | |
In this episode, I talk with Emi Gal, co-founder and CEO of Ezra, about cancer screening with a full body MRI scan. Ezra is on a mission to detect cancer early for everyone by making the process more accurate, faster, and cheaper. Emi and I talked about the challenges in working with MR data, how regulatory processes affect model development, and the importance of validation. Quotes: “What we've been able to achieve is to essentially reduce the cost and the time in a scanner of an MRI from about two to three hours for full body to 60 minutes. And we're actually working on a new AI that will roll out next year that will reduce the scan time to 30 minutes.” “What we do is we acquire the scan fewer times, and then we've built machine learning models that recognize what noise looks like and then just remove that noise. And then we kind of expanded that from not just noise. If you acquire scans with lower resolution, the resulting images are a little bit blurry so we can sharpen them.” “Our focus on the scanning front is to reduce scan time, which yields these images with increased noise artifacts, and then use machine learning to enhance these images so that a radiologist can then use them for interpretation.” “I think what having to receive FDA clearance for AI does, is it really forces the company from day one to think about what are all of the things that might influence the performance of said AI, and what can we do to ensure that we maximize the chances of success?” “We have had an instance when we had to go back to the drawing board and build the model again because we failed internal validation prior to formal validation that we had to submit to the FDA.” “I think the way you ensure that the technology we develop fits the clinical workflow is actually not starting with the technology, but starting with the end goal in mind and then figuring out what you need to do in order to achieve that.” “To screen a hundred million people a year, we think, is a huge endeavor and probably going to take a decade or two to achieve. And I'm personally committed to Ezra for the rest of my career. In the next three to five years, I would hope we are making good progress towards that mission, and maybe in five years we're screening at least a million people a year.” Links: 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. | |||
| Sorting Recyclables with Amanda Marrs from AMP Robotics | 12 Dec 2022 | 00:21:15 | |
In this episode, I talk with Amanda Marrs, senior director of product at AMP Robotics about modernizing the world’s recycling infrastructure. Amanda and I talked about how they ensure their models work for a diverse set of objects, measuring the success of their technology, and some tips for building a successful ML team. Quotes: “At AMP we have a broad mission of enabling a world without waste.” “We work backwards on everything that ends up in a landfill to develop the technology we need to keep that from happening.” “We really have two main areas that we work in. One is technology that we will put in place at a material recovery facility. . . The other half of what we do at AMP is use our own technology for what's called a secondary sortation facility.” “All of this technology really has three main components. You have to be able to see the objects on the belt, and that's where the machine learning comes in. You have to be able to sort the objects effectively, and there's some ML behind that as well. And then you have to be able to report, see what's happening, and draw conclusions and make decisions and optimize further in the facilities.” “A majority of the data fits nicely within these primary categories. But, in AI, typically there's this natural long tail, and we have that as well.” “Diversity is the name of the game in this industry where you have to be able to recognize everything. And so a huge sample set of data really helps us overcome that.” “The wonderful thing about AI, it doesn't get tired, it doesn't get dizzy. And it can keep its inference at the same rate.” “What we try to do when we translate this to customers, to non deeply technical folks – they're technical in other ways, but they're not dealing with AI all day – is we really try to translate it to the outcomes.” “Start your hiring process early so that you're expecting it might take a while before you really, really need that team member joined, onboarded, trained up and enabled to help deliver on projects.” “I think, for us, recruiting and thinking about what mix of talent we really need on a team, it's looking across all of those different areas and building out a team that really compliments each other's skillsets.” Links: 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. | |||
| Cell Sorting with Mahyar Salek from Deepcell | 05 Dec 2022 | 00:32:04 | |
In this episode, I talk with Mahyar Salek, co-founder and CTO of Deepcell, about an AI powered technology for single cell analysis through the lense of high content cell morphology. Deepcell's platform blends deep learning, microfluidics, and high resolution optics to deliver novel insights about cell biology and has the capability to sort, label-free for downstream multi-omic and functional analysis for use in research, translational studies, and therapeutic research. We discussed some of the challenges and opportunities in working with single cell images and how they used self-supervised learning. Quotes: “We really use the power of computer vision and AI capabilities combined with the advances in microfluidics and imaging to create this high dimensional, high content interpretation of single cell images. And we use that in real time to purify and separate cells of interest.” “We have to see millions of cells even in just one go, one run. So you can't really do that without the scalability of an algorithm, right? And then we have to be consistent and robust.” “When I hear challenges, I equate them with opportunities and I'll tell you why. So, for instance, one of the challenges, not just with us, but any sort of AI solution that looks at biological samples is the susceptibility to artifacts.” “But as soon as you roll it out, there's a difference between your lab and the lab, you know, a block down the road because of the artifacts. So it's artifacts are definitely challenging, but for us, it's an opportunity as I mentioned, because we generate the data through our own platform and that means that we have a very controlled environment.” “Because, again, we have the full control over the imaging path and where the cells lie, where we image them, we could actually do these sort of things and come up with models that are very less reliant on labels.” “By being able to run a biological assay and validate whether the existing model, like basically errors in the existing models and existing labels, and that way you're able to iterate very quickly on your learning without even relying on arguably erroneous human labels, erroneous and obviously expensive human labels.” “Any modern life science companies that rely on data, you have to have a very tight collaboration between machine learning and data scientists and the domain experts.” “It is really important to, as you kind of come up with a development strategy and the product strategy, understand where you could rely on AI today versus where you hope that the AI could deliver, you know, two years down the road.” Links: 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. | |||
| Data-driven Pathology with Coleman Stavish and Julianna Ianni from Proscia | 28 Nov 2022 | 00:30:32 | |
In this episode, I talk with Coleman Stavish and Julianna Ianni from Proscia about data-driven pathology. Coleman is the co-founder and CTO of Proscia and Julianna is the VP of AI Research & Development. We discussed the importance of quality control systems in an ML pipeline, model generalizability, and how the regulatory process affects ML development. Quotes: “Better accuracy in diagnosis means less overdiagnosis and less under diagnosis, which typically leads to better patient outcomes and quality of life.” “Pathology is crucial in the drug development pipeline. It's helping pharmaceutical companies develop new treatments while assessing their safety and efficacy.” “You'll often find slides that have been annotated with pen ink. That's something that can be quite common to do in some settings and that, if you're trying to train a diagnostic model, can really bias the model.” “One of the heaviest impacts to development for us, just to give you an example, has been areas where we find a great level of disagreement in the ground truth data. So that will come out when you test, and we have to account for that disagreement during development.” “It also requires thinking through, not just how are we going to validate, but then how are we going to keep tabs on the different deployments and ensure that we're not seeing performance degrade as maybe the data or the conditions within the laboratory change.” “No matter how accurate or how valuable that information is that's produced by the model, if it's not actually introduced in the right way into the overall workflow, it's not going to be put into routine use.” “Prepare to iterate. A solution that you build is probably not going to be the final destination, the final solution. And I think the fast pace of this field kind of demands some constant innovation.” “I'd also say to heavily invest in your team. There's really nothing that replaces having good people and very skilled people working for you and building these AI products.” “Something that we've learned ourselves is how to balance the investor pitch about AI and its potential with the near and immediate term. Smaller successes that build you a road to that more ambitious future.” “They could have the ability to diagnose cases remotely without having and maybe assisting patients who are in far flung areas of the world that may not have access to subspecialty pathologist expertise.” “Maybe it means someone gets the right diagnosis a little bit faster in aggregate. I think that could have a really big impact.” Links: 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. | |||
| Biophysical Modeling of Cancer with Joe Peterson from SimBioSys | 14 Nov 2022 | 00:50:13 | |
In this episode, I talk with Joe Peterson, co-founder and CTO of SimBioSys, about biophysical modeling of cancer. SimBioSys is trying to revolutionize precision cancer care through individualized treatment planning, accelerated drug development, clinical trial optimization, and comprehensive biomarker development. Joe and I talked about the challenges of working with heterogeneous forms of data and the ways bias can manifest when training models on medical data. Quotes: “We use AI or ML at effectively every point in the process, both in our clinical medical devices, but also for our internal R&D.” “Have you ever seen the way weather scientists simulate a hurricane? We do a very similar thing within the body, or if you've ever seen mechanical engineers simulate the combustion of a gas and a gas turbine, we do a similar type of thing within these patient models.” “If you're able to distill the processes that go on biologically, chemically and physically to their essence, you can create building blocks that can be mixed and matched.” “Our thought was, let's not ask the models to do too much. Let's ask them to do one thing that we need them to do very, very well. This allows us to have more collected data or more directed data collection, as well as more clearly defined goals in terms of business value and delivering business value to each of the models.” “All these different types of data are much more heterogeneous. They come from many different scales. They come from many different sources. They're encoded in many different ways, and so there's a huge effort, on the research and development side, just to extract what's meaningful in those different types of data sets so that we can begin to define those biophysical building blocks that ultimately make it into the clinical application.” “It's just really about capturing the variability and trying to drive out as much variability up front as you possibly can.” “We also develop models that are generally capturing any sort of drift in the data over time.” “You wanna understand outside of just a research setting, but out there in the wild how well your models are going to work, how often you're going to return a null result or an inconclusive result to a physician and being able to track that over time is really important from a quality control standpoint.” “It's all the quality control machine learning models and deep learning models that make up the bulk of those internally.” “Our responsibility as practitioners of AI is to not only identify and understand that bias, that historical bias, but also try to account for it as best we can.” “What we need to assess when developing drugs or algorithms or devices is how they were trained, how they were tested, and really stratify those patient populations as best we can to sort of understand, at the very least, how they're behaving.” “We've spent a lot of time trying to account for that variability as best we can. That said, we don't have a perfect data set and we're constantly thinking about ways to improve it.” “I think what it comes down to is being open and transparent and really looking at the data that you have at the end of the day, If doctors are going to trust medical devices and if they're going to trust AI, they need to have information about.” “By looking into and stratifying the patient populations in that way we can better understand where we need to targetedly spend resources to collect potentially more data to better understand the performance in those places or to improve our algorithms.” “Adopt good machine learning practices early, just like good clinical practice or good manufacturing practices that are standards that are now being drafted and adopted.” “Find the right partners to sort of drive the questions that you're addressing and ultimately the clinical actions that you're trying to address.” “Models that are built to do a single task excellently well is a better approach than trying to build a model that does four or five tasks really well.” Links: 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. | |||
| Foundation Model Series: Accelerating Radiology with Robert Bakos from HOPPR | 28 Oct 2024 | 00:28:42 | |
Imagine a world where radiology backlogs are a thing of the past, and AI seamlessly augments the expertise of radiologists. Today, I'm joined by Robert Bakos, Co-Founder and CTO of HOPPR, to discuss how his company is bringing this vision to life. HOPPR is pioneering foundation models for medical imaging that have the potential to transform healthcare. With access to over 15 million diverse imaging studies, HOPPR is developing multimodal AI models that tackle radiology’s most significant challenges: high imaging volumes, limited specialist availability, and the growing demand for rapid, accurate diagnostics. In this episode, Robert offers insight into the rigorous process of training these models on complex data while ensuring they integrate seamlessly into medical workflows. From data partnerships to specialized clinical collaboration, HOPPR’s approach sets new standards in healthcare AI. To discover how foundation models like these are revolutionizing radiology and making healthcare more efficient, accessible, and equitable, be sure to tune in today! Key Points:
Quotes: “Having clinical collaboration is super important. At HOPPR, our clinicians are an important part of our product development team – They're absolutely vital for helping us evaluate the performance of the model.” — Robert Bakos “Because we are training across all these different modalities, getting access to this data can be challenging. Having great partnerships is critical for finding success in this space.” — Robert Bakos “Make sure that you're addressing real problems. There are a lot of great ideas and cool things you can implement with AI, but at the end of the day, you want to make sure you can deliver value to your customers.” — Robert Bakos “Foundation models – trained on a breadth of data – can make a positive impact on underserved areas around the world. With the volume of images growing so rapidly, constraints on radiologists, and burnout, it's important to leverage these models to make a big impact.” — Robert Bakos Links:
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. | |||
| Smarter Farming with Eric Adamson from Tortuga AgTech | 07 Nov 2022 | 00:27:11 | |
In this episode, I talk with Eric Adamson, CEO of Tortuga AgTech, about smarter farming. Tortuga AgTech builds robots for harvesting fruit and vegetables to help farms be more resilient, sustainable, and successful. Quotes: “Figuring out that pipeline from someone else's knowledge to the robot knows it is really critical.” “If you build technology because the technology is cool or because you can, you are much more likely to fail than if you start with the customer problem and then figure out what kind of technology might help to solve that problem.” “That learning happens with our machine learning engineers being in the field, being the ones who are actually taking data with handheld rigs.” “Many of our team members’ first two weeks have been immediately flying to a farm and spending time on the farm with the robots, learning a problem in very, very deep detail. And I would encourage anybody building a technology based on machine learning or certainly robots to do the same.” “We have a very efficient and effective pipeline that took us years to build. But it's exceptionally powerful for us to be able to, for example, go to a new site, run a couple robots or a small fleet of robots for a day, and then within a week have a brand new model that's been completely retrained on freshly labeled data from this new place.” “That’s very critical for us because farm environments are changing so often. You really need to be able to be reactive and continue to improve your models as you develop.” “We measure our scores based on golden data sets that we've sort of hand labeled ourselves. But we also have to make some judgment calls about what we really want in our performance versus what the conditions are in the field and what we're seeing on the farm.” “We try to convert whatever model results are spit out into language that the customer intuitively understands.” “It's really important to start with the customer problem and to start with the customer problem as an economic proposition.” “There are already very large discussions happening in the farming community around what type of farming should be used in order to, for example, deal with climate change, to deal with drought, to deal with chemical regulations, to deal with a lowering of fruit quality and an increasing of fruit waste, the challenging labor environments.” Links: 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. | |||
| Autonomous Diagnostics with John Bertrand from Digital Diagnostics | 31 Oct 2022 | 00:21:33 | |
In this episode, I talk with John Bertrand, CEO of Digital Diagnostics, about autonomous diagnostics. Digital Diagnostics transforms the quality, accessibility, equity, and affordability of healthcare with AI-powered diagnostics. They developed the first FDA-cleared autonomous AI system. Quotes: “So we look for diagnostics where there's an established understanding of what the disease is and there's a gold standard as to how to measure that.” “We'll naturally start with an area where positive and negative is a very binary decision that is almost mathematically derived.” “It goes back to picking the right types of disease states to make sure that the gold standard already exists.” “How do you take images that have different coverage of the retina but make sure that you piece them together in a way that the processing part of the system is getting a consistent image that they're looking at every single time so that the algorithm remains consistent and we don't have to have different algorithms per vendor that we're interacting with.” “We’re pretty proud of the fact we’ve been able to do that first kind of assistive feedback for the provider.” “We want every single patient, regardless of their background, to receive consistent quality of diagnostic output. What that means is that we actually have to build our training data sets as well as our clinical validation studies and trials to take into account a diverse population set.” “Continuous learning versus locked algorithms is another key factor. . . Would you really want that algorithm to adjust to the most recent data it's seeing, thinking it's attempting to become more accurate, when in fact it's really more optimizing for the ethnicity of the folks in that particular region, the sun rises on the east coast to the United States, everybody further east goes to bed. Now the algorithm’s been indexed towards another group from a ethnicity perspective, that’s no longer representative of where the testing’s being done as the sun rises in New York.” “How do we ensure that we create confidence with regulators, with providers, and with patients that we've actually thought through this?” “We can literally break down for you what the computer saw, why graded it out what it did, and why it gave you the results it did.” “Your algorithm should be explainable so people trust the technology, understand how it works.” “Also explainability helps you drive better accuracy and that you understand why you're getting the result that you're getting with the black box approach.” “You really want to work within the healthcare system when you’re building these types of businesses.” “If you're going to chart that course and really carry through to fruition, your vision of building an algorithm that impacts patient lives, I think you really need to center the culture of the business around a commonly shared vision for the mission of what you're trying to do.” Links: 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. | |||
| Capturing the Carbon Fingerprint of Soil with David Schurman from Perennial | 24 Oct 2022 | 00:23:18 | |
In this episode, I talk with David Schurman, co-founder and CTO of Perennial, about their verification platform for climate-smart agriculture. Perennial uses geospatial data and machine learning to unlock agricultural soils as the world’s largest carbon sink. Highlights:
Links: 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. | |||
| Biomarker Discovery from Pathology Images with Matt Alderdice from Sonrai Analytics | 17 Oct 2022 | 00:25:34 | |
In this episode, I talk with Matt Alderdice, Head of Data Science at Sonrai Analytics, about precision medicine. Sonrai Analytics automates laborious data processes and speeds up new drug and healthcare developments. Highlights:
Links: 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. | |||
| Diagnosing Emergent Diseases with David Golan from Viz.ai | 10 Oct 2022 | 00:32:03 | |
In this episode, I talk with David Golan, co-founder and CTO of Viz.ai, about diagnosis of acute and emergent diseases. Viz.ai increases the speed of diagnosis and care for a variety of conditions to improve the lives of patients. Highlights:
Links: 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. | |||
| Environmental Risk Analysis with Steve Brumby from Impact Observatory | 10 Oct 2022 | 00:36:51 | |
In this episode, I talk with Steve Brumby, co-founder, CEO and CTO of Impact Observatory, about sustainability and environmental risk analysis. Impact Observatory uses satellite imagery and machine learning to empower decision-makers with planetary insights. Highlights:
Links:
Resources for Computer Vision Teams: | |||
| Diagnosis and Management of Epilepsy with Dean Freestone from Seer | 10 Oct 2022 | 00:26:08 | |
In this episode, I talk with Dean Freestone, co-founder and CEO of Seer, about epilepsy. Seer uses home monitoring to diagnose and manage neurological conditions, relieving bottlenecks in the healthcare system. Highlights:
Links: 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. | |||
| Welcome to Impact AI | 05 Oct 2022 | 00:02:03 | |
Welcome to Impact AI, the podcast for startups who want to create a better future through the use of machine learning. I'm your host, Heather Couture. In this podcast, you’ll learn how to build a mission-driven machine learning company. I’ll be interviewing innovators and entrepreneurs from a variety of industries: healthcare, drug development, environmental, agriculture, and many more. Each is striving to solve a problem that they are passionate about. They will talk about the role machine learning plays in their technology and the impact of their product. They will also help me uncover machine learning challenges like data annotation, generalizability, explainability, bias, and collaboration across disciplines – and best practices for tackling them in a startup environment. Now, who am I? I’m a consultant with almost 2 decades of experience in computer vision and machine learning for a variety of applications. From manufacturing to planetary science to commercial media to cancer research. I completed a Masters at Carnegie Mellon University and a PhD in Computer Science at the University of North Carolina. As a researcher, I published in top-tier computer vision and medical imaging venues. Now I write regularly on LinkedIn, for my newsletter Pathology ML Insights, and for a variety of trade publications. I offer consulting services through my company Pixel Scientia Labs to help startups get to market faster by building more generalizable computer vision models. I make use of the latest machine learning research to amplify their results and support their in-house team for the long term. My mission is to fight cancer and climate change with AI – and I do that by strengthening the machine learning component of my clients’ most impactful projects. My hope for this podcast is to share machine learning best practices more widely so that many others can benefit as they work towards solving important problems. Thanks for listening. Please hit subscribe to be notified about new episodes. | |||
| Optimizing Data Center Operations with Vedavyas Panneershelvam from Phaidra | 21 Oct 2024 | 00:22:14 | |
What are the unique challenges of operating mission-critical facilities, and how can reinforcement learning be applied to optimize data center operations? In this episode, I sit down with Vedavyas Panneershelvam, CTO and co-founder of Phaidra, to discuss how their cutting-edge AI technology is transforming the efficiency and reliability of data centers. Phaidra is an AI company that specializes in providing intelligent control systems for industrial facilities to optimize performance and efficiency. Vedavyas is a technology entrepreneur with a strong background in artificial intelligence and its applications in industrial and operational settings. In our conversation, we discuss how Phaidra’s closed-loop, self-learning autonomous control system optimizes cooling for data centers and why reinforcement learning is the key to creating intelligent systems that learn and adapt over time. Vedavyas also explains the intricacies of working with operational data, the importance of understanding the physics behind machine learning models, and the long-term impact of Phaidra’s technology on energy efficiency and sustainability. Join us as we explore how AI can solve complex problems in industry and learn how Phaidra is paving the way for the future of autonomous control with Vedavyas Panneershelvam. Key Points:
Quotes: “Phaidra is like a closed-loop self-learning autonomous control system that learns from its own experience.” — Vedavyas Panneershelvam “Data centers basically generate so much heat, and they need to be cooled, and that takes a lot of energy, and also, the constraints in that use case are very, very narrow and tight.” — Vedavyas Panneershelvam “The trick [to validation] is finding the right balance between relying on the physics and then how much do you trust the data.” — Vedavyas Panneershelvam “[Large Language Models] have done a favor for us in helping the common public understand the potential of these, of machine learning in general.” — Vedavyas Panneershelvam Links: Vedavyas Panneershelvam on LinkedIn
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. | |||
| Structuring Medical Text with Tim O'Connell from Emtelligent | 14 Oct 2024 | 00:18:02 | |
What if AI could unlock the potential of healthcare’s vast, unstructured data? In this episode, Tim O'Connell, Co-Founder and CEO of Emtelligent, explains how his company is bridging the gap between messy medical data and usable insights with AI-powered solutions. Drawing from his background in both engineering and radiology, Tim discusses how he saw firsthand the inefficiencies caused by disorganized medical notes and reports, which led to the creation of Emtelligent. He breaks down how their AI models work to process and structure this data, making it usable for healthcare professionals, researchers, and beyond. Tim also dives into the technical challenges, from handling faxed medical records to ensuring high levels of precision and recall in model training. Beyond the technology, he emphasizes the importance of safety, ethical use, and how Emtelligent continues to adapt its AI to meet the evolving needs of the healthcare industry, helping to make patient care more efficient and accurate. Don’t miss out on this important conversation with Tim O’Connell from Emtelligent! Key Points:
Quotes: “During that year [that I was] working in the hospital, – I saw so many problems that we have in the healthcare environment and realized that quite a few of them had to do with the fact [that] we deal with so much unstructured data.” — Tim O’Connell “Every time a human goes to see a caregiver, some kind of an unstructured text note is generated – We really can't use a lot of that data, unless it's another human who's reading that data.” — Tim O’Connell “I’m still a practicing radiologist. – It’s not just a matter of intelligent people coming up with good ideas and going, ‘Oh, well. [Let’s throw this] against the wall and see what sticks’. We're developing solutions that are applicable in today's healthcare environment.” — Tim O’Connell Links:
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 Series: Enabling Digital Pathology Workflows with Dmitry Nechaev from HistAI | 07 Oct 2024 | 00:29:32 | |
What happens when you combine AI with digital pathology? In this episode, Dmitry Nechaev, Chief AI Scientist and co-founder of HistAI, joins me to discuss the complexity of building foundation models specifically for digital pathology. Dmitry has a strong background in machine learning and experience in high-resolution image analysis. At HistAI, he leads the development of cutting-edge AI models tailored for pathology. HistAI, a digital pathology company, focuses on developing AI-driven solutions that assist pathologists in analyzing complex tissue samples faster and more accurately. In our conversation, we unpack the development and application of foundation models for digital pathology. Dmitry explains why conventional models trained on natural images often struggle with pathology data and how HistAI’s models address this gap. Learn about the technical challenges of training these models and the steps for managing massive datasets, selecting the correct training methods, and optimizing for high-speed performance. Join me and explore how AI is transforming digital pathology workflows with Dmitry Nechaev! Key Points:
Quotes: “Regular foundation models are trained on natural images and I'd say they are not good at generalizing to pathological data.” — Dmitry Nechaev “In short, [a foundational model] requires a lot of data and a lot of [compute power].” — Dmitry Nechaev “Public benchmarks [are] a really good thing.” — Dmitry Nechaev “Our foundation models are fully open-source. We don't really try to sell them. In a sense, they are kind of useless by themselves, since you need to train something on top of them, so we don't try to profit from these models.” — Dmitry Nechaev “The best lesson is that you need quality data to get a quality model.” — Dmitry Nechaev “[HistAI] don't want AI technologies to be a privilege of the richest countries. We want that to be available around the world.” — Dmitry Nechaev Links:
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 Series: Creating Small Molecules for Drug Discovery with Jason Rolfe from Variational AI | 30 Sep 2024 | 00:29:29 | |
Building on the trends in language processing, domain-specific foundation models are unlocking new possibilities. In the realm of drug discovery, Jason Rolfe is spearheading innovation at the intersection of AI and pharmaceuticals. As the Co-Founder and CTO of Variational AI, Jason leads a platform designed to generate novel small molecule structures that accelerate drug development. In this episode, he delves into how Variational AI uses foundation models to predict and optimize small molecules, overcoming the immense complexity of drug discovery by leveraging vast datasets and sophisticated computational techniques. He also addresses the key challenges of modeling molecular potency and why traditional machine-learning approaches often fall short. For anyone curious about AI's impact on healthcare, this conversation offers a fascinating look into cutting-edge innovations set to reshape the pharmaceutical industry. Tune in to find out how the types of breakthroughs we discuss in this episode could revolutionize drug development, bring new therapeutics to market across disease areas, and positively impact lives! Key Points:
Quotes: “Rather than forming individual models for specific drug targets, we're creating a joint model over hundreds, eventually thousands of drug targets.” — Jason Rolfe “Data quality is essential. In particular, if you're drawing from multiple different data sources, frequently, those sources aren't commensurable.” — Jason Rolfe “If you don't have a proven track record where people are already throwing money at you, it is very challenging to try to bring a new technology from the drawing board into commercial application using venture funding.” — Jason Rolfe “Whenever you're developing a new technology or product, you need to test early and often. Some of your intuitions will be good. Most of your intuitions will be a waste of time – The more quickly you can distinguish between those two classes, the more efficiently you can move toward success.” — Jason Rolfe Links:
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 Series: Building New Materials for Climate with Jonathan Godwin from Orbital Materials | 23 Sep 2024 | 00:25:03 | |
AI is unlocking the future of materials science and today’s guest Jonathan Godwin, co-founder and CEO of Orbital Materials, is at the forefront of this transformation. With a background in AI research and experience leading groundbreaking projects at Google-owned DeepMind, Jonathan is now applying machine learning to develop advanced materials that can drive decarbonization. In this episode, he explains how Orbital Materials is using foundation models (like ChatGPT for language or MidJourney for images) to design new materials that capture carbon, store energy, and improve industrial efficiency. He also shares insights into the company’s mission, the challenges of simulating atomic-level interactions, and why open-sourcing their model, Orb, is crucial for innovation. To discover how AI is revolutionizing the fight against climate change and learn how these cutting-edge materials could shape a more sustainable future, don’t miss this inspiring conversation with Jonathan Godwin! Key Points:
Quotes: “We develop materials that can capture CO2 from specific gas streams – coming out of an industrial facility, new energy storage technologies that allow – [data centers] to operate behind the meter, or ways to improve the water efficiency of a data center or industrial facility.” — Jonathan Godwin “Foundation models are the crux of how we're able to leverage AI in this day and age. If you want to [say], 'We're pushing the limits of what AI is able to do. We're leveraging the most recent breakthroughs,' – you've got to be building foundation models or using foundation models.” — Jonathan Godwin “AI is a massively powerful creativity aid and accelerant. We’ve seen that in other areas of AI and we're bringing that to advanced materials.” — Jonathan Godwin Links:
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 Series: Understanding Brain Activity with Dimitris Sakellariou from Piramidal | 16 Sep 2024 | 00:23:41 | |
What if we could understand brain activity in real-time to better diagnose neurological conditions? In this episode, part of a special mini-series on domain-specific foundation models, I sit down with Dimitris Sakellariou, the founder and CEO of Piramidal, to talk about their groundbreaking work in automating EEG interpretation. Piramidal is focused on democratizing brain health insights, making interpreting brainwave data more accessible and accurate. With a strong foundation in neuroscience and AI, Dimitris and his team are developing models that could revolutionize how we understand brain activity and diagnose neurological conditions. In our conversation, Dimitris explains the challenges of building a foundation model for brain activity, the role of data diversity, and the future potential for personalized brain health monitoring. Discover the implications of Piramidal’s technology beyond healthcare and its application in cognitive enhancement and stress management. Tune in as we explore how Piramidal is paving the way for personalized brain health monitoring and why this could be a game-changer for the future of medicine! Key Points:
Quotes: “Piramidal is primarily focused at the moment in automating, or otherwise democratizing the interpretation of these tests, these brainwave recordings so that patients and people that have issues with their brain can get access to the diagnosis much, much, much faster.” — Dimitris Sakellariou “It's very important to have discussions with neuroscientists and clinical experts in order to understand what is the end-to-end pipeline from receiving data all the way to inference.” — Dimitris Sakellariou “Finding the right person. Someone that is very keen to build together with you and make important and difficult decisions can change massively a trajectory of your company.” — Dimitris Sakellariou Links: Dimitris Sakellariou on LinkedIn
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 Series: Better, Faster, Cheaper Earth Observation with Bruno Sánchez-Andrade Nuño from Clay | 09 Sep 2024 | 00:35:35 | |
Can AI be applied to enhance geospatial data for climate, nature and people? This episode kicks off a miniseries about domain-specific foundation models. Following the trends in language processing, domain-specific foundation models are enabling new possibilities for a variety of applications, including Earth observation. During this conversation, I am joined by Bruno Sánchez-Andrade Nuño, Executive Director of Clay, a nonprofit organization harnessing the power of AI for satellite images, spatial data, and more. Bruno shares the functionality and concept behind Clay, and his journey to building it. He goes on to unpack the tool’s foundation model in broad strokes, before explaining why it's important, and sharing the challenges he has faced along the way. We discuss the legal aspects of building Clay, and it’s primary goal to make it as easy as possible for any user to achieve their goals. We also touch on what the future might hold for Clay and the future of Earth observation. Thanks for listening! Key Points:
Quotes: “Clay is trying to figure out how to finally increase the adoption of remote sensing by leveraging a tool that itself is very complex, but the result of that tool is very easy to use.” — Bruno Sánchez-Andrade Nuño “If you start with a foundational model that gets you most of the way there, [then] you can create those trials much quicker, much cheaper, and much more environmentally friendly.” — Bruno Sánchez-Andrade Nuño “This is so new, we get the chance, those of us working on it, that we can save the whole industry, if you will, the whole space of AI for it.” — Bruno Sánchez-Andrade Nuño “Clay, I believe, is not only the largest and most efficient model AI for Earth, for any kind of like foundational model. It is also completely open source.” — Bruno Sánchez-Andrade Nuño “What we try to focus on is how can we make it as simple as possible for anyone anywhere to use this model for anything they want to do.” — Bruno Sánchez-Andrade Nuño Links: Bruno Sánchez-Andrade Nuño on X Bruno Sánchez-Andrade Nuño on LinkedIn
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. | |||
| Evolutionary Insights for Drug Discovery with Ashley Zehnder from Fauna Bio | 02 Sep 2024 | 00:27:17 | |
In a world where conventional drug discovery methods frequently fall short, today's guest addresses the critical challenge of fighting human diseases by drawing inspiration from nature’s most resilient creatures. Could the secret to overcoming our most stubborn illnesses lie in the extraordinary adaptability of extreme mammals? Veterinarian-scientist Ashley Zehnder, the Co-founder and CEO of AI-driven drug discovery company Fauna Bio, believes so. By leveraging data from 100 million years of evolved disease resistance in mammals, Ashley sees a unique opportunity at the crossroads of genomics and emerging model species to improve health for all species, including humans. In this episode, she explores how harnessing the biological secrets of these animals using AI and machine learning could revolutionize medicine, leading to breakthroughs that benefit us all. Tune in to discover how Fauna Bio is pioneering a new frontier in drug discovery and how understanding the resilience of these creatures could reshape the future of healthcare! Key Points:
Quotes: “[Fauna Bio uses] AI and genomics as a way to identify the most impactful targets for new therapeutic programs across a broad number of diseases.” — Ashley Zehnder “It’s certainly easier than it has been in the past to generate very high-quality single-cell RNA sequencing. We’re doing a lot of that. The challenges on the technical side are getting much easier. The challenges on the interpretation side are still there.” — Ashley Zehnder “There are many points along the drug discovery path where AI companies can differentiate. But that story has to be clear because, otherwise, it's very hard to get out of the signal-to-noise that is the AI discovery landscape in biopharma” — Ashley Zehnder Links: Science Issue dedicated to the Zoonomia Project
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 Series: Harnessing Multimodal Data to Advance Immunotherapies with Ron Alfa from Noetik | 03 Feb 2025 | 00:33:53 | |
In this episode, I'm joined by Ron Alfa, Co-Founder and CEO of Noetik, to discuss the groundbreaking role of foundation models in advancing cancer immunotherapy. Together, we explore why these models are essential to his work, what it takes to build a model that understands biology, and how Noetik is creating and sourcing their datasets. Ron also shares insights on scaling and training these models, the challenges his team has faced, and how effective analysis helps determine a model’s quality. To learn more about Noetik’s innovative achievements, Ron’s advice for leaders in AI-powered startups, and much more, be sure to tune in! Key Points:
Quotes: “Our thesis for Noetik is that one of the biggest problems we can impact if we want to make and bring new drugs to patients is predicting clinical success; so-called translation — that's where we focus Noetik, how can we train foundation models of biology so that we can better translate therapeutics from early discovery and preclinical models to patients.” — Ron Alfa “We think the most important thing for any application of machine learning is the data.” — Ron Alfa “The goal here is to train models that can do what humans cannot do, that can understand biology that we haven't discovered yet.” — Ron Alfa “The big aim of Noetik is to develop these [foundational] models for therapeutics discovery.” — Ron Alfa Links: Noetik Octo Virtual Cell (OTCO)
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. | |||
| Better Therapeutics Using Lab-Grown Tissue with Andrei Georgescu from Vivodyne | 26 Aug 2024 | 00:33:57 | |
One of the biggest hurdles in medical research is the gap between animal studies and human trials, a disconnect that often leads to failed drug tests and wasted resources. But what if there was a way to bridge that gap and create treatments that are more effective for humans from the start? Today, I am joined by Dr. Andrei Georgescu, Founder and CEO of Vivodyne, a groundbreaking biotechnology company that is transforming how scientists study human biology and develop new therapeutics. In this episode, he reveals how Vivodyne harnesses lab-grown tissue and advanced multimodal AI to create more effective therapeutics. We explore the challenges of gathering human tissue data, the collaboration between biologists, robotics engineers, and machine learning developers to build powerful machine learning models, and the profound impact that Vivodyne is poised to make in the fight against diseases. To discover how Vivodyne’s innovations can lead to more successful treatments and faster drug development, tune in today! Key Points:
Quotes: “Vivodyne grows human tissues at a very large scale so that we can understand human physiology and we can test directly on it in order to discover and develop better drugs that are both safer and more efficacious.” — Andrei Georgescu “We use machine learning and AI as a mechanism to understand the complexity of very deep data and to very efficiently apply that complexity and infer from what we've learned across the very large breadth of data that we collect.” — Andrei Georgescu “To address [the problem of a] glaring lack of trainable data, we create that data by growing it at scale.” — Andrei Georgescu “If you're a technical founder, do something that is incredibly hard because the ability for you to do that thing will grant you much more leverage than creating what is otherwise a much more simple and generic business.” — Andrei Georgescu “[With Vivodyne], we will enter a world of plenty where the development of new drugs against diseases becomes a far more successful, reliable, and predictive process, and we're able to make much safer and much more effective drugs just by virtue of being able to optimize that therapeutic on human tissues before giving it to people for the first time in-clinic.” — Andrei Georgescu Links:
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. | |||
| Accelerating Regenerative Agriculture with Marie Coffin from CIBO Technologies | 19 Aug 2024 | 00:16:02 | |
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:
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 Coffin Links:
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. | |||
| Measuring Biodiversity Using Insects with Mads Fogtmann from Fauna Photonics | 12 Aug 2024 | 00:20:34 | |
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:
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 Fogtmann Links: Mads Fogtmann on LinkedIn
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. | |||
| Optimizing Manufacturing with Berk Birand from Fero Labs | 05 Aug 2024 | 00:20:50 | |
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:
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
"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
Links:
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. | |||
| More Successful IVF with Daniella Gilboa from AIVF | 29 Jul 2024 | 00:27:38 | |
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:
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 Gilboa Links:
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. | |||
| Vision Intelligence Filters with Kit Merker from Plainsight Technologies | 22 Jul 2024 | 00:28:13 | |
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:
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 Merker Links:
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. | |||
| Interpreting Infant Cries with Charles Onu from Ubenwa Health | 15 Jul 2024 | 00:22:53 | |
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:
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 Onu Links:
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. | |||
| Remote Monitoring and Water Forecasting with Marshall Moutenot from Upstream Tech | 08 Jul 2024 | 00:26:35 | |
Innovative AI technologies are paving the way for more efficient and impactful environmental monitoring. Joining me today to discuss remote monitoring and water forecasting is Marshall Moutenot, the co-founder and CEO of Upstream Tech. From using satellite imagery to monitor conservation projects to employing machine learning for accurate water flow predictions, Upstream Tech is at the forefront of leveraging technology to address environmental challenges. In our conversation, Marshall shares his journey from a tech-savvy childhood to co-founding a company with a mission to make environmental monitoring scalable and cost-effective. He delves into the development of Upstream Tech's two primary products: Lens, for remote monitoring of climate solutions, and HydroForecast, which uses AI to predict water flow, aiding in hydropower management. Marshall also underscores the need for integrating domain knowledge with machine learning to create reliable models before offering practical insights for AI startups. Tune in to learn more about how AI can revolutionize environmental conservation! Key Points:
Quotes: “[The] adaptability of these models is something that is really exciting for the field overall." — Marshall Moutenot
“As an organization, one of [Upstream Tech’s] purposes is to see the 100% renewable grid become a reality. We want to continue to contribute to that and to build forecasts that enable that future.” — Marshall Moutenot Links:
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. | |||
| Scaling Healthcare Through Virtual Primary Care with Anitha Kannan from Curai | 01 Jul 2024 | 00:22:40 | |
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:
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 Kannan Links:
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. | |||
| Better EV Batteries with Jason Koeller from Chemix | 24 Jun 2024 | 00:27:30 | |
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:
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 Koeller Links:
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 Series: Accelerating Pathology Model Development Using Embeddings with Julianna Ianni from Proscia | 27 Jan 2025 | 00:20:51 | |
How can foundation models accelerate breakthroughs in precision medicine? In today’s episode of Impact AI, we explore this question with returning guest, Julianna Ianni, Vice President of AI Research and Development at Proscia, a company revolutionizing pathology through cutting-edge technology. Join us as we explore how their platform, Concentriq, and its new Embeddings feature are transforming AI model development, making pathology-driven insights faster and more accessible than ever before. You’ll also learn how Proscia is shaping the future of precision medicine and discover practical insights for leveraging AI to advance healthcare. Whether you're curious about pathology, AI, or innovations in precision medicine, this episode offers invaluable takeaways you won’t want to miss! Key Points:
Quotes: “With the rise of foundation models that are pathology-specific and more powerful than the models of yesterday, the ability to extract embeddings efficiently became even more important for us.” — Julianna Ianni “The pathology world didn't need another hit movie. It needed a streaming service.” — Julianna Ianni “[Continue] to innovate and [understand] what's out there. There's a lot of change in the [pathology] field right now – You're going to make plans and then you're going to need to remake those plans because things are changing so quickly.” — Julianna Ianni “ChatGPT didn't pervade our culture because it's fantastic technology. It pervaded our culture because the fantastic technology was easy to use. Pathology should be that easy. Our aim is to drive it there.” — Julianna Ianni Links: Julianna Ianni on Google Scholar Concentriq Embeddings Previous episode of Impact AI: Data-Driven Pathology with Coleman Stavish and Julianna Ianni from Proscia
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. | |||
| Personalized Cancer Treatment Decisions with Nathan Silberman from Artera | 17 Jun 2024 | 00:17:10 | |
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:
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 Silberman Links:
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. | |||
| Faster Object Search with Corey Jaskolski from Synthetaic | 10 Jun 2024 | 00:27:12 | |
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:
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:
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. | |||
| Digital Twins for Clinical Trials with Charles Fisher from Unlearn AI | 03 Jun 2024 | 00:30:19 | |
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:
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:
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. | |||
| Cutting Carbon in Concrete with Mathieu Bauchy from Concrete.ai | 27 May 2024 | 00:30:42 | |
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:
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.ai
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. | |||
| Decoding Pathology for Precision Medicine with Maximilian Alber from Aignostics | 20 May 2024 | 00:19:35 | |
Today, I am joined by Maximilian Alber, Co-founder and CTO of Aignostics, to talk about pathology for precision medicine. You’ll learn about Aignostics’s mission, how they are impacting healthcare, and the transformative power of foundational models. Max explains how Aignostics is driven by the belief that machine learning and data science will help improve healthcare before expanding on the role of foundational models. He describes how they built their foundational model, what sets it apart from other models, and why diversity in their datasets is key. He also breaks down how foundational models have allowed them to develop other models more quickly and better navigate explainability with concepts that are challenging for machine learning. We wrap up with Max’s advice for leaders of other AI-powered startups and where he expects Aignostics will be in the next five years. Tune in now to learn all about foundational models and the innovative work being done at Aignostics! Key Points:
Quotes: “Our mission is to turn biomedical data into insights.” — Maximilian Alber “Everything we do is driven by the belief that machine learning and data science will help us improve healthcare.” — Maximilian Alber “A foundation model is a model that can be used as a starting point for building a machine learning application, with the promise that the foundation model already has a great understanding of the domain.” — Maximilian Alber “We are in active discussions for licensing our foundation model to other companies in order to enable their development as well. [What’s] important here is that we develop our foundation model along regulatory requirements, which will allow it to be used in medical products.” — Maximilian Alber “One needs to build a technology that either makes a difference in the long run, or one must be able to innovate at a very fast pace.” — Maximilian Alber Links:
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. | |||
| Subseasonal-to-Seasonal Weather Forecasting with Sam Levang from Salient Predictions | 13 May 2024 | 00:16:51 | |
Advanced weather forecasts are the new frontier in meteorology. Long-term forecasting has garnered significant attention due to its potential to provide valuable insights to various sectors of society and the economy. In today’s episode, Sam Levang, Chief Scientist at Salient, joins me to discuss Salient’s innovative approach to weather forecasting. Salient specializes in providing highly accurate subseasonal-to-seasonal weather forecasts ranging from 2 to 52 weeks in advance. In our conversation, we discuss the ins and outs of the company’s innovative approach to weather forecasting. We delve into the hurdles of subseasonal-to-seasonal forecasting, how machine learning is replacing traditional weather modeling approaches, and the various inputs it uses. Discover the value of machine learning for post-processing of data, the type of data the company utilizes, and why it uses probabilistic models in its approach. Gain insights into how Salient is catering to the impacts of climate change in its weather predictions, the company’s approach to validation, how AI has made it all possible, and much more! Key Points:
Quotes: “Salient produces weather forecasts that extend further into the future than most people are used to seeing. We go up to a year in advance.” — Sam Levang “ML (Machine Learning) models have proved to be actually a very effective replacement for the traditional approach to weather modeling.” — Sam Levang “The only difference about making forecasts longer timescales of weeks and months ahead is that there are some differences in the particular parts of the climate system that provide the most predictability.” — Sam Levang “While ML and AI are extremely powerful tools, they are still just tools and there's so much else that goes into building a really valuable product, or a service, or a company.” — Sam Levang Links: 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. | |||
| Virtual Tissue Staining with Yair Rivenson from PictorLabs | 06 May 2024 | 00:34:11 | |
Welcome to today’s episode of Impact AI, where we dive into the groundbreaking world of virtual tissue staining with Yair Rivenson, the co-founder and CEO of PictorLabs, a digital pathology company advancing AI-powered virtual staining technology to revolutionize histopathology and accelerate clinical research to improve patient outcomes. You’ll find out how machine learning is used to translate unstained tissue autofluorescence into diagnostic-ready images, gain insight into overcoming AI hallucinations and the rigorous validation processes behind virtual staining models, and discover how PictorLabs navigates challenges like large files and bandwidth dependency while seamlessly integrating technology into clinical workflows. Yair also provides invaluable advice for AI-powered startup leaders, emphasizing the importance of automation and data quality. To gain deeper insights into the transformative potential of virtual tissue staining, tune in today! Key Points:
Quotes: “The most important factor for the healthcare system, for the patient is the fact that you can get all the results, all the workup, and all the different stains from a single tissue section very, very fast.” — Yair Rivenson “Machine learning is the engine behind virtual staining. In a sense, that’s what takes those images from the autofluorescence of the unstained tissue section and converts [them] into a stain that pathologists can use for their diagnostics.” — Yair Rivenson “At the end of the day, the network is as good as the data that it learns from.” — Yair Rivenson “The more you automate, the better off you’ll be in the long run.” — Yair Rivenson Links: ‘Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning’
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. | |||
| Improving Recycling Efficiency with Nikola Sivacki from Greyparrot | 29 Apr 2024 | 00:20:31 | |
One of the most powerful impacts machine learning can make is helping to solve environmental challenges all around the world. Today on Impact AI, I am joined by the founder of Greyparrot, Nikola Sivacki to discuss how his company uses machine learning to improve recycling efficiency. Learn all about Nikola’s background, what Greyparrot does, their services, the importance of their work, the role machine learning plays in it, how they gather and annotate data, the challenges they face, how they develop new models, and so much more. Tune in to hear the newest AI innovations Nikola is most excited about before hearing his goals for Greyparrot in the near future. Lastly, get some valuable advice for running AI-powered startups. Key Points:
Quotes: “Greyparrot basically monitors the flow of waste materials, recyclable materials in material recovery facilities, and offers compositional analysis of these materials.” — Nikola Sivacki “It's very helpful, – if thinking of a new product, to start with a data set that is really tailored to answering the main uncertain question that is posed there.” — Nikola Sivacki “Start thinking about data from the start. I think that it’s very important to understand the data in detail.” — Nikola Sivacki “Our goal is to improve, of course, recycling rates globally so that we can reduce reliance on virgin materials.” — Nikola Sivacki Links:
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. | |||