In the meantime, I'm thrilled to share another way you can continue to learn and engage with the world of AI through Pixel Scientia Labs. While the podcast is on pause, I invite you to explore our Webinar Initiative at pixelscientia.com/webinars.

Links:

• Webinars from Pixel Scientia

Key Points:

• What led Nico to turn decades of research into a breast imaging AI startup.
• How ScreenPoint uses AI to support radiologists in early detection.
• Challenges of working with diverse data from different imaging devices.
• The importance of training models with clean, representative data.
• Strategies for reducing bias across vendors and populations.
• How independent, real-world validation drives trust and clinical adoption.
• Finding a balance between model accuracy and explainability.
• Why domain expertise is crucial for building a successful AI-powered startup.
• Driving adoption in medical AI through clinical partnerships and rigorous trials.

Quotes:

“It’s amazing how much more information you can get out of the mammograms [using AI]. That surprises me all the time.” — Nico Karssemeijer

“You can't just say, ‘This mammogram is abnormal,’ because then [the radiologists] are puzzled. – The algorithm is getting so good that it identifies areas the radiologists would probably not see by themselves. – You have to – mark the area in the exam where a lesion is found.” — Nico Karssemeijer

“It's incredibly important to have enough domain expertise when you start a company, because it's easy to fail because you don't understand well enough what the customer wants [or] where the field is going.” — Nico Karssemeijer

Links:

Nico Karssemeijer

ScreenPoint Medical

Nico Karssemeijer on LinkedIn

Nico Karssemeijer on Google Scholar

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.

In our conversation, we hear how his early work in radiology and engineering led to the founding of Quibim and how the company’s AI-based technology transforms medical images into predictive biomarkers. We unpack the challenges of data heterogeneity, how Quibim tackles image harmonization using self-supervised learning, and why accounting for regulations is critical when building healthcare AI products. Ángel also shares his perspective on the value of model explainability, the concept of digital twins, and the future of preventative imaging. Join us to discover how AI is disrupting clinical decision-making and preventive healthcare with Ángel Alberich-Bayarri.

Key Points:

• Hear about Ángel’s background and how his career led to founding Quibim.
• Find out how Quibim turns radiology images into predictive clinical insights.
• Different use cases of Quibim’s technology and why biopsy data is important.
• He explains why Quibim avoids relying solely on radiologist annotations.
• Challenges of using medical imaging: data fragmentation and scanner variability.
• Explore Quibim’s self-supervised image learning harmonization techniques.
• How Quibim increases the explainability of the model while maintaining accuracy.
• Why understanding clinical workflows and radiologist adoption behavior is critical.
• Uncover how regulations influence the development of Quibim’s technology.
• Ángel’s advice for entrepreneurs and leaders of AI-powered startups.
• Quibim’s plans for predictive modeling, digital twins, and AI for preventative medicine.

Quotes:

“We would like AI to be able to mine all this hidden information we have right now in the images. Our vision is long-term, being able to understand what is happening until this point within the human body.” — Ángel Alberich-Bayarri

“What [Quibim is] investing in is the next frontier that not only detects and diagnoses disease, but also predicts or prognoses what is going to happen.” — Ángel Alberich-Bayarri

“Human behavior has a lot of nuances that need to be appreciated when AI is adopted.” — Ángel Alberich-Bayarri

“The bolder the claims you make, it’s the higher level of evidence you need to achieve.” — Ángel Alberich-Bayarri

“Taking care of health before we have symptoms, it’s just going to be a growing business, and therefore, a lot of AI tools will be needed to understand our inner us.” — Ángel Alberich-Bayarri

Links:

Ángel Alberich-Bayarri on LinkedIn

Ángel Alberich-Bayarri on X

Quibim

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.

Orr shares how QuantHealth uses real-world patient data and detailed drug biology to build deep-learning models capable of forecasting patient responses to new therapies. He breaks down their biggest challenges, like the complexities of messy healthcare data, hidden biases, and the importance of domain knowledge when building AI tools for regulated environments. He also shares a key lesson for any AI startup: focus on solving real problems, not just building clever models. Tune in for a fascinating look at how AI is reshaping drug development and what the future of clinical trials could look like!

Key Points:

• Some background on Orr, his parents, and how he founded QuantHealth.
• Key problems QuantHealth is solving as a clinical trial simulation company.
• A breakdown of the biggest challenges facing clinical trials.
• Why we need to improve data-driven trials of drugs.
• How QuantHealth builds their foundation models for trial simulations.
• Examples of the type of predictions their models make in clinical contexts.
• How they use patient and drug data to make predictions and build “digital drugs”.
• Key challenges of working with these different types of data.
• Methods for combating bias, including the use of exogenous data. 
• How they incorporate the medical context in model development.
• QuantHealth’s validation process: how they meet rigorous industry standards.
• Orr’s advice to other AI startups on creating value, not just smart models.
• Where you can expect to see QuantHeath in the next three to five years.

Quotes:

“There is a constant desire in drug development and pharmaceutical research to get your hands on more data. This makes sense since it's a very data-driven industry. But at the same time, there was a mismatch there, because there's actually quite a lot of data already out there.” — Orr Inbar

“How do we bridge the gap between the data that we already have and the insights that we need to generate to answer those questions?” — Orr Inbar

“If you take a step back and look at how drugs are being developed today and with an emphasis on clinical trials, we're essentially doing the same things that we were doing 50 years ago.” — Orr Inbar

“Even in a world of GenAI, you can't just snap your fingers and get the solution. It requires a lot of work to structure and harmonize the data.” — Orr Inbar

“Every trial that we simulate, we first go through a data enrichment process where we look for the latest information in terms of research publications, recently completed trials that are relevant to our drug of interest, and incorporate that data into our data sets.” — Orr Inbar

Links:

Orr Inbar on LinkedIn
QuantHealth

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.

Shahab is passionate about using interdisciplinary research to bridge the gap between machine learning and optimization, and he begins today’s conversation by detailing his professional background and how it led him to co-found SenseNet. Then, we unpack SenseNet and how its technology works, how it gathers data for its AI models, the challenges of relying on images and other sensor data to train machine learning models, and how SenseNet uses multiple sources to detect or define any one problem. To end, we learn why and how SenseNet uses various AI models in a single sensor, how it measures the overall impact of its tech, where the company plans to be in the next five years, and Shahab’s valuable advice for other leaders of AI-powered startups.     

Key Points:

• Shahab Bahrami walks us through his professional background and how it led to SenseNet. 
• The ins and outs of SenseNet and how its technology works. 
• How machine learning fits into SenseNet’s offerings, and how it gathers the necessary data. 
• The challenges of working with images and other sensor data to train models.  
• How SenseNet integrates information from different sources to zero in on a single anomaly. 
• Understanding how it uses multiple AI models to adapt to variations post-installation. 
• How the system chooses which AI model to apply and when. 
• Shahab describes how his company measures the overall impact of its technology. 
• His advice to other leaders of AI-powered startups, and his five-year vision for SenseNet. 

Quotes:

“We have one of the most comprehensive wildfire detection solutions in the world, and it is proven by multiple, real-world projects.” — Shahab Bahrami

“Having separate AI models is the solution that we are now implementing.” — Shahab Bahrami

“For the sensor’s AI – because it is a semi-supervised AI, it automatically adapts itself to local conditions. It learns gradually what is normal and what is abnormal, and it is a continuous learning. It won’t stop.” — Shahab Bahrami

“AI changes fast. Every day we have a new AI engine, we have a new model, and leaders, I believe, need to stay updated and make sure their teams have the support and also the resources to keep innovating.” — Shahab Bahrami

Links:

Shahab Bahrami 

Shahab Bahrami on LinkedIn

SenseNet

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.

In this episode of Impact AI, we hear from the CEO and Co-founder of Helical to find out how bio-foundation models are transforming pharmaceutical research. Rick shares how Helical’s AI platform enables drug discovery by leveraging biological sequencing data without requiring companies to build their own models from scratch. He also reveals the challenges of working with high-dimensional biological data, the power of model specialization for specific therapeutic areas, and the growing role of open-source AI in healthcare innovation.

Whether you're in biotech, AI, or simply curious about the future of medicine, this episode offers invaluable insights into how AI is shaping the next generation of drug discovery. Tune in today!

Key Points:

• Introducing Rick, his engineering background, and Helical’s mission.
• The challenges of leveraging biological foundation models for drug discovery.
• Understanding biological sequencing data and its complexities.
• Key technical challenges: messy datasets, long-range dependencies, and model architecture.
• How Helix, Helical’s mRNA foundation model, competes with industry leaders.
• Three key factors in building biological foundation models: data, compute, and talent
• The shift from narrow AI to general-purpose AI in pharma.
• Benchmarking and evaluating foundation models for different use cases.
• Commercializing Helical’s platform through partnerships with pharma companies.
• Insight into the role of open-source AI in advancing biological research.
• The future of biological foundation models: scaling up for greater impact.
• Rick’s vision for Helical as the backbone of in silico pharma labs.

Quotes:

“The question is, how do I leverage [powerful biological foundation models] and – build use cases that matter and bring value? Helical is building a therapeutic area, an agnostic AI platform that is empowering single-cell RNA and DNA bio foundation models for drug discovery.” — Rick Schneider

“In bio, you can still innovate on the architecture side and not simply [with] the scale of the models. It's not simply by throwing more compute at the models that you get to the very best outcomes.” — Rick Schneider

“Be okay with being different in your approach and accept [that you will] be contrarian to certain things.” — Rick Schneider

Links:

Rick Schneider on LinkedIn

Helical

Helical on GitHub

Helical on Hugging Face

Introducing Helix-mRNA-v0

Helix-mRNA

Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA Therapeutics

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.

Junaid begins with his professional background and what led him to found NeuroCareAI before explaining what his company does and the products and services it offers. Then, we unpack the primary data sets that inform NeuroCareAI’s work, how to overcome the challenges of combining varied data types, the ethical responsibilities of AI, and how to ensure generalizability is upheld over long periods. To end, we learn why it’s essential to distinguish explainability from reason, how to mitigate the effects of bias on radiology data, how the regulatory process stunts the development of machine learning solutions, and Junaid’s vision of the future of NeuroCareAI. 

Key Points:

• Junaid Kalia walks us through his professional background and why he formed NeuroCareAI.
• The ins and outs of NeuroCareAI and how it incorporates AI into its products and services. 
• Understanding the two main forms of data that govern the company’s work. 
• The challenges of combining different data types and how to overcome them.  
• Unpacking the ethical responsibilities of AI. 
• Generalizability over time: How Junaid and his team ensure their models continue to perform.
• Model accuracy versus explainability, and distinguishing explainability from reason. 
• How bias affects models trained on radiology data and how to mitigate this. 
• The way the regulatory process affects the development of machine learning solutions.
• Junaid Kalia’s advice for other leaders of AI-powered startups. 
• His view on the future of NeuroCareAI. 

Quotes:

“Coming from a very low resource country like Pakistan, I wanted to start a project in which AI can help democratize in countries with low resource settings.” — Junaid Kalia

“Our mission is if you save a life, it is as if you save the life of all mankind.” — Junaid Kalia

“When you are deploying artificial intelligence, you need to make sure that it's deployed ethically. [For] some of these things, we do expect our partner sites – [to] have a real quality assurance system in place before they can deploy my artificial intelligence, because I just want to be ethical.” — Junaid Kalia

“We need to differentiate [and] distinguish between reasoning and explainability. In the vision world, I believe that explainability is nice to have. In the large language models space, reasoning, in my opinion, is a must-have.” — Junaid Kalia

Links:

Junaid Kalia on LinkedIn

Junaid Kalia on X
NeuroCareAI

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.

Paul shares his journey from radiologist to AI entrepreneur, explaining how his background in cognitive science and medical imaging led him to co-found Raidium. He breaks down the challenges of building a foundation model for radiology, from handling massive datasets to addressing bias and regulatory hurdles, and their approach at Raidium. We also explore Raidium’s vision for the future: its plans to refine multimodal AI, expand its applications beyond radiology, and commercialize its technology to improve patient care worldwide. Tune in to learn how foundation models could shape the future of radiology, enhance patient care, and expand global access to medical imaging!

Key Points:

• Paul Hérent’s background in radiology, cognitive science, and founding Raidium.
• Why existing AI tools in radiology are fragmented and have limited adoption.
• How Raidium’s foundation model unifies multiple radiology tasks.
• Raidium’s multimodal AI: handling diverse imaging types in one system.
• Outlining the vast, diverse data used to train Raidium’s model, including radiology reports.
• The teams, compute power, and infrastructure behind Raidium’s AI development.
• Challenges in data curation, regulatory hurdles, and proving clinical value.
• What makes a good foundation model and the role of self-supervised learning (SSL).
• Insights into how Raidium benchmarks its model using rigorous medical imaging tests.
• The role of diverse data, human oversight, and continuous learning in reducing bias.
• Their current R&D phase and plans for commercialization.
• Key lessons Paul learned about AI startups, from data needs to product-market fit.
• The future of foundation models in radiology and beyond.
• Paul’s advice to AI founders: Build a team with both AI and domain expertise.
• Raidium’s vision: Improving the lives of patients and global healthcare access.

Quotes:

“In practice, there is still little AI adoption because every solution solves only a tiny part of what radiologist do. [For radiologists] it's a wider job. We want, as a radiologist, to have one tool to rule all modalities.” — Paul Hérent

“Data is key. If you have good data, not only to build a data set, but proprietary data, challenging data, rare data in a specific domain. It's very valuable because the architecture is not particularly innovative.” — Paul Hérent

“Build a team with people you trust. Entrepreneurship is not trivial. Be complementary.” — Paul Hérent

“The dream of Raidium is to build something that has a huge impact on a patient's life.” — Paul Hérent

“If we go beyond the rich countries, many, many people have no access to radiology. Two-thirds of countries don’t have access to radiologists. It's a big need. If we can contribute with our approach to more accessible health, we will be very happy.” — Paul Hérent

Links:

Paul Hérent on LinkedIn

Paul Hérent on Google Scholar

Raidium

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.

Tuning in, you’ll learn how Virgo’s foundation model, EndoDINO, trained on the largest endoscopic video dataset in the world, is unlocking new possibilities in gastroenterology. Matt also shares how automated video capture, AI-powered diagnostics, and predictive analytics are reshaping patient care, with a particular focus on improving treatment for inflammatory bowel disease (IBD). Join us to discover how domain-specific foundation models are redefining healthcare and what this means for the future of precision medicine!

Key Points:

• An introduction to Matt Schwartz and Virgo’s mission.
• The importance of video documentation in endoscopy and its impact on healthcare.
• Machine learning’s role in automating endoscopic video capture and clinical trial recruitment.
• Building the EndoDINO foundation model to unlock endoscopy data for precision medicine.
• Data collection: the process of gathering 130,000+ procedure videos for model training.
• Foundation model development using self-supervised learning and DINOv2.
• Model development challenges, from hyper-parameter tuning to domain-specific adjustments.
• Applying EndoDINO to predict inflammatory bowel disease (IBD) treatment responses.
• Commercializing EndoDINO through licensing to health systems and pharma companies.
• The future of foundation models in endoscopy: expanding applications beyond GI diseases.
• Advice for AI startup founders to prioritize data capture as a foundation for AI success.
• Insight into Virgo’s vision to transform IBD treatment and preventative care.

Quotes:

“There's a massive amount of endoscopic video data being generated across a wide range of endoscopic procedures, and nobody was capturing that data – [Virgo] realized early on that endoscopy data could hold the key to unlocking all sorts of opportunities in precision medicine.” — Matt Schwartz

“With the foundation model paradigm, you can compress a lot of heavy compute needs into a single model and then build different applications on top of the foundation. This is going to have a positive impact on the clinical deployment of foundation models.” — Matt Schwartz

“Our foundation model can turn something like a routine colonoscopy into a precision medicine screening tool for IBD patients.” — Matt Schwartz

“There are a lot of untapped data resources in healthcare. If a founder can build a first product that is the data capture engine, it will set them up for a ton of future success when it comes to AI development.” — Matt Schwartz

Links:

Virgo

Matt Schwartz on LinkedIn

Matt Schwartz on X

EndoML

Introducing EndoDINO: A Breakthrough in Endoscopic AI

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• Who is Zelda Mariet and what led her to create Bioptimus. 
• What Bioptimus does and why it’s so important.
• Why their first model announced was for pathology.
• Zelda breaks down three core components that go into building a foundation model.
• How their histopathology foundation model is different from the number of other models published at this point.
• Their methodology behind properly benchmarking how well their foundation model performs.
• Different challenges they’ve encountered on their foundation model journey.
• How they plan to commercialize their technology at Bioptimus. 
• Thoughts on whether open source is part of their long-term strategy for the model, and why.  
• Developing a product roadmap for a foundation model.
• She shares some information regarding their next step, beyond pathology, at Bioptimus.
• The importance of understanding what kind of structure you want to capture in your data.
• Where she sees the impact of Bioptimus in the next three to five years. 

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:

Zelda Mariet on LinkedIn

Zelda Mariet

Bioptimus

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.

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:

• Max's background in philosophy, his transition to machine learning, and his path to Nixtla.
• Why time series data is the “DNA of the world” and its role in businesses and institutions.
• Nixtla's advanced forecasting algorithms, the benefits, and their application to industry.
• Historical overview of time series forecasting and the development of modern approaches.
• Learn about the advantages of foundation models for scalability, speed, and ease of use.
• Uncover the range of datasets used to train Nixtla's foundation models and their sources.
• Similarities and differences between training TimeGPT and large language models (LLMs).
• Hear about the main challenges of building time series foundation models for forecasting. 
• How Nixtla ensures the quality of its models and the limitations of conventional benchmarks.
• Explore the gap between benchmark performance and effectiveness in the real world.
• He shares the current and upcoming plans for Nixtla and its TimeGPT foundation model. 
• He shares his predictions for the future of time series foundation models.
• Advice for leaders of AI-powered startups and what impact he aims to make with Nixtla.

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

Nixtla

Nixtla on X

Nixtla on LinkedIn

Nixtla on GitHub

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.

Key Points:

• Ron shares his background and how his journey led to Noetik.
• Why a foundation model is important in their work.
• What goes into building a foundation model that understands biology.
• Building the dataset: where does the data come from?
• The types of data they generate from the samples they use in their models.
• He further explains the components necessary to build a foundation model.
• The scale and what it takes to train these models. 
• Ron sheds light on the challenges they’ve encountered in building their foundation model.
• How to determine if your foundation model is good. 
• Utilizing analysis to help identify ways to improve your model. 
• The current purpose for their foundation model and how they plan to use it in the future.
• Key insights gained from developing foundation models and how these can be adapted to other types of data.
• His advice to other leaders of AI-powered startups.
• Ron digs deeper into their goal to impact patient care by developing new therapeutics.

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:

Ron Alfa on LinkedIn

Ron Alfa on X

Noetik

Noetik Octo Virtual Cell (OTCO)

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.

Key Points:

• An overview of Julianna’s biomedical engineering background and Proscia's mission.
• Insight into Proscia’s Concentriq platform, aiding more than two million diagnoses annually.
• Ways that Concentriq Embeddings streamlines AI development by eliminating data friction.
• How Concentriq Embeddings make model creation 13x faster than traditional methods.
• Why Proscia integrates external foundation models for versatility and superior performance.
• Flexible and efficient: how Concentriq lets users test, swap, and select models with ease.
• Types of solutions built using these embeddings, including rapid biomarker detection.
• Tackling AI challenges like reducing overfitting and addressing bias in medical applications.
• Lessons from pathology: simplifying complex workflows for faster AI adoption in other fields.
• A look at the future of foundation models for pathology and Julianna’s advice for innovators.

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:

Proscia

Julianna Ianni on LinkedIn

Julianna Ianni on X

Julianna Ianni on Google Scholar

Concentriq Embeddings
Concentriq Embeddings internal case study
Proscia AI Toolkit
Zero-Shot Tumor Detection Example

Previous episode of Impact AI: Data-Driven Pathology with Coleman Stavish and Julianna Ianni from Proscia

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.

In this episode of Impact AI, Benjamin dives into the journey of creating this innovative tool, the challenges of working with complex agricultural data, and the role of machine learning in empowering farmers to make sustainable, data-driven decisions. Tune in to discover how this technology is not only boosting farming efficiency but also contributing to a healthier ecosystem and the fight against climate change!

Key Points:

• Benjamin’s biomedical engineering background and how it led him to start ChrysaLabs.
• How ChrysaLabs’ portable probe provides real-time soil analysis.
• The role of machine learning in converting spectroscopy data into actionable soil insights.
• Challenges in acquiring diverse, high-quality soil data for model training.
• Addressing variability in soil and lab measurements to ensure model accuracy.
• What goes into ChrysaLabs’ validation techniques to maintain robust, reliable AI models.
• Considerations for overcoming seasonal constraints in agricultural data collection.
• Technological advancements that have enabled portable, cost-effective sensors.
• Advice for AI-powered startups: balance data volume with variability management.
• Collaborative efforts between agronomists and machine learning engineers at ChrysaLabs.
• ChrysaLabs’ vision for improving soil health and combating climate change.

Quotes:

“There’s a translation between the light information that we receive from the spectrometer and the information that is actionable for the farmers and agronomists. The machine learning models are between the hardware, the application, and what the farmers can do.” — Benjamin De Leener

“The main challenge that the agronomists and the farmers have is the data about what’s in the soil. So, that’s what we provide.” — Benjamin De Leener

“The more data you accumulate, the bigger the variability that you need to take into account. It’s not always better to think, ‘The more data I have, the better’ because sometimes, the less data, the more focused the models are.” — Benjamin De Leener

“We want to combat climate change – [We believe] that the soil can sequester a lot of carbon through agriculture, and we want to provide a way to measure that so that, when we choose one agronomical practice over another, we understand what we’re doing.” — Benjamin De Leener

Links:

ChrysaLabs

ChrysaLabs InsightLabs

Benjamin De Leener on LinkedIn

Benjamin De Leener on Google Scholar

Benjamin De Leener on X

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• How Kfir's background in computer science and applied math led him to found DeepCure.
• Insight into DeepCure’s mission to leverage proprietary technology to create small molecule drugs for inflammation and autoimmunity.
• Augmenting human expertise with AI: the role of machine learning in drug discovery.
• Layers of using AI to analyze targets and design small molecules with optimized properties.
• Challenges in small molecule datasets and how DeepCure develops tailored models.
• The influence of molecule representations like SMILES on machine learning models.
• Combining publicly available datasets with data generated in DeepCure’s automation lab.
• Model validation techniques to address out-of-distribution challenges in small molecule data.
• Collaboration between machine learning experts and chemists to refine drug discovery.
• Recruiting top talent by highlighting DeepCure’s impactful mission in healthcare.
• The process of onboarding machine learning developers with no prior chemistry knowledge.
• Problem-solving advice for leaders of AI-powered startups: it’s not about the AI!
• DeepCure’s future plans for clinical trials and expansion into other autoimmune diseases.

Quotes:

“Machine learning in our space is almost never a complete solution. It's a way to augment our chemists [and] our biologists [to] try to make them capable of solving problems that were unsolved before.” — Kfir Schreiber

“One of the best things about DeepCure [is the] very tight collaboration between the domain experts and our machine learning scientists.” — Kfir Schreiber

“Your average machine-learning scientist doesn't have chemistry intuition. We need this feedback and we need to integrate this feedback back into our models to make the predictions make sense.” — Kfir Schreiber

“Focus on the problem, focus on the value, and work your way backwards to the best tools to use.” — Kfir Schreiber

Links:

DeepCure
Kfir Schreiber on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• Hear about David's career journey from MIT professor to CEO of Layer Health.
• How Layer Health transforms chart reviews and enhances healthcare workflows.
• The role of large language models in solving the company's scalability problems.
• Learn about Layer Health's approach to benchmarking performance for institutions.
• Explore how the company navigates dataset shifts and ensures robust model performance.
• Discover Layer Health's strategies to identify and mitigate bias in clinical AI models.
• Find out about the challenges of implementing reasoning across diverse medical records.
• Why building trust through data transparency, auditing, and compliance are essential.
• David’s advice for AI startup leaders on balancing research with practical implementation.
• Layer Health's long-term vision for reshaping healthcare and improving patient outcomes.

Quotes:

“Our vision for Layer Health is to transform healthcare with artificial intelligence, really building upon all of the work that we've been doing over the past decade in the AI and health field and academic space.” — David Sontag

“What we realized very quickly is that where [Layer Health] would have the biggest impact was bringing the right information to the physician's fingertips at the right point in time.” — David Sontag

“We're using large language models to drive the abstraction of those clinical variables that we need for these either retrospective or prospective use cases.” — David Sontag

“Where I think we're going to see the biggest source of bias is likely going to be not along the traditional demographic-related quantities, but rather on more clinical quantities.” — David Sontag

“A lot of the friction that we currently see in healthcare, [Layer Health] is going to really take a big bite out of [it].” — David Sontag

Links:

David Sontag

David Sontag on LinkedIn

Layer Health

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.

In our conversation, Hanadie details Juvena’s AI-driven platform that identifies, validates, and engineers protein candidates with therapeutic potential. We explore the power of machine learning models in drug discovery, the challenges of working with multi-omics data, and the potential for new treatments to revolutionize healthcare by targeting disease at the molecular level. Join us to hear how Juvena Therapeutics is setting a new standard in precision medicine aimed at helping individuals age with vitality.

Key Points:

• The founding story of Juvena Therapeutics and its mission to restore tissue health.
• How the company leverages AI to identify regenerative proteins from stem cell secretions.
• Learn how Juvena's machine learning models enable targeted protein engineering.
• Explore the different types of data that Juvena utilizes and how they are structured.
• Hear about the benefits of in-house data generation for model training and validation.
• Discover the challenges of generating sufficient data for accurate model predictions.
• Technological advancements in proteomics and multi-omics that support its platform.
• Hanadie shares advice for AI-driven startups and her hopes for Juvena's future impact.

Quotes:

“Juvena is part of really, a new approach to leveraging the biology of aging and underlying mechanisms associated with why our tissues decline in function, in order to target this biology so that we can treat a broad swath of diseases.” — Hanadie Yousef

“That's ultimately the goal of Juvena, to really enable people to age with dignity, to continue to contribute to society, and to really maintain their health until the very end.” — Hanadie Yousef

“Ultimately, [machine learning is] leveraged at every stage of the process from in silico prediction, and screening through to the actual engineering and drug development.” — Hanadie Yousef

“When it comes to wet lab data generation, sometimes you're really limited by just the quantity of data that you can generate.” — Hanadie Yousef

“AI isn't the solution to everything. Oftentimes, you do still want to have that human in the loop and really test the accuracy of these models.” — Hanadie Yousef

Links:

Hanadie Yousef on LinkedIn

Juvena Therapeutics

Juvena Therapeutics on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• Welcoming Brigham Hyde, co-founder and CEO of Atropos Health.
• His journey to creating Atropos Health after working in other medical AI arenas. 
• Why AI is important for healthcare: the evidence gap. 
• Atropos’s perspective on the role of real-world evidence.
• How the lack of diversity in healthcare data sets impacts patient outcomes.
• Methods Atropos uses to leverage machine learning to ensure that patient populations are supported.
• The data-gathering process.
• How the setup is validated and adapted according to need.
• Measuring the impact of the technology. 
• Advice for other leaders of AI-powered startups. 
• Where Brigham foresees the impact of Atropos in three to five years. 

Quotes:

“At Atropos, we focus on the automation and generation of high-quality real-world evidence to support clinical decision-making with the dream of creating personalized evidence for everyone.” — Brigham Hyde

“We see the role of real-world evidence and observational research as a great way to supplement that gap.” — Brigham Hyde

“It's our ability to create that evidence, transparently show you the populations that are being used and the bias that is involved, and the techniques to remove that bias that are the key.” — Brigham Hyde

“You've got to be able to show how what you're doing works, that it's not biased, and that it's applicable to the health system you're working with, and it's got to be done in extremely high quality.” — Brigham Hyde

Links:

Brigham Hyde on LinkedIn 

Brigham Hyde on X
Atropos Health
Atropos Health on LinkedIn

Atropos Health on X

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

With her unique blend of expertise as a veterinarian and computer scientist, Jo leverages biology, chemistry, and machine learning knowledge to tackle the translational gap between animal models and human patients. You’ll learn about VeriSIM Life’s innovative approach to overcoming data limitations, synthesizing new data, and applying ML models tailored to various diseases, from rare conditions to neurological disorders. Jo also reveals VeriSIM’s unique translational index score, a tool that predicts clinical trial success rates and helps pharma companies identify promising drugs early and avoid costly failures.

For anyone curious about the future of AI in healthcare, this episode offers a fascinating glimpse into the world of biotech innovation. To discover how VeriSIM Life’s technology is poised to bring life-saving treatments to patients faster and more safely than ever before, be sure to tune in today!

Key Points:

• How Jo's background and interest in translational challenges led her to found VeriSIM Life.
• Addressing translational gaps between animal models and human trials with hybrid AI.
• Combining biology-based models with ML to enhance drug testing accuracy.
• Small molecules, peptides, large molecules, clinical trial outcomes, and other data inputs.
• Ways that VeriSIM’s models are tailored per data type, ensuring maximum accuracy.
• Insight into the challenge of overcoming data gaps and how VeriSIM solves it.
• How hybrid AI reduces overfitting, boosting model accuracy in data-limited scenarios.
• What goes into validating VeriSIM’s models through partnerships and external testing.
• Measuring the impact of this technology with VeriSIM’s translational index score.
• Jo’s advice for AI-powered startups: be specific, validate technology, and be adaptable.
• Her predictions for the impact VeriSIM will have in the next few years.

Quotes:

“[Hybrid AI] helps us not only unravel newer methods and mechanisms of actions or novel targets but also helps us identify better drug candidates that could eventually be safer and more effective in human patients.” — Jo Varshney

“Biology is complex. We need to understand it enough to create a codified version of that biology.” — Jo Varshney

“If you're just using machine learning-based methods, you may not get the right features to see the accuracy that you would see with the hybrid AI approach that we take.” — Jo Varshney

“Focus on validation and showing some real-world outcomes [rather than] just building the marketing outcome because, ultimately, we want it to get to the patients. We want to know if the technology really works. If it doesn't work, you can still pivot.” — Jo Varshney

Links:

VeriSIM Life

Jo Varshney on LinkedIn

Jo Varshney on X

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• Unpacking Noam Solomon’s professional journey that led to his founding of Immunai. 
• What Immunai does and why this work is vital for the healthcare industry. 
• How understanding the immune system will help to improve drug efficacy. 
• Exploring how Noam and his team use AI to accomplish their goals. 
• The standardization of data and other challenges of working with complex ML models. 
• Techniques for handling the high-dimensional nature of biological data.
• How ML experts collaborate with other domains to inform and build Immunai’s models. 
• The technical advancements that have made Noam’s work possible. 
• His advice to other leaders of AI-powered startups, and imagining the future of Immunai. 
• How to connect with Noam and his work.  

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:

Noam Solomon on LinkedIn

Noam Solomon on X

Immunai

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.

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:

• Robert’s background in medical imaging and tech and how it led him to create HOPPR.
• Ways that HOPPR’s AI models improve diagnostic speed and accuracy.
• The significant data and compute resources required to build a foundation model like this.
• Partnering with imaging organizations to collect diverse data across multiple modalities.
• How HOPPR differentiates itself with ISO-compliant development and multimodal training.
• The quantitative metrics and clinical review involved in validating its foundation model.
• Key challenges in building this model include data access, diversity, and secure handling.
• Reasons that proper data diversity and balance are essential to reduce model bias.
• How API integration makes HOPPR’s models easy to adopt into existing workflows.
• The real-world clinical needs and input that go into building an AI product roadmap.
• Robert’s take on what the future of foundation models for medical imaging looks like.
• Valuable lessons on the importance of strong labeling, compute scalability, and more.
• Practical, real-world advice for other leaders of AI-powered startups.
• The broader impact in healthcare that HOPPR aims to make.

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:

Robert Bakos

HOPPR

Robert Bakos on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• Hear how collaborating on data center optimization at Google led to the founding of Phaidra.
• How Phaidra’s AI-based autonomous control system optimizes data centers in real-time.
• Discover how reinforcement learning is leveraged to improve data center operations.
• Explore the range of data needed to continuously optimize the performance of data centers.
• The challenges of using real-world data and the advantages of redundant data sources. 
• He explains how Phaidra ensures its models remain accurate even as conditions change.
• Uncover Phaidra’s approach to validation and incorporating scalability across facilities. 
• Vedavyas shares why he thinks this type of technology is valuable and needed.
• Recommendations for leaders of AI-powered startups and the future impact of Phaidra.

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

Phaidra

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.

Key Points:

• An overview of Tim’s background in engineering and radiology.
• How Tim co-founded Emtelligent to solve pressing data issues in healthcare.
• The importance of turning unstructured medical text into searchable, structured data.
• How Emtelligent’s models extract metadata and structure from faxed patient records.
• Why healthcare data is so challenging to work with, from shorthand to messy notes.
• The role of precision and recall in assessing and improving model performance in healthcare.
• Ensuring AI models continue to perform well after deployment with ongoing updates.
• How Tim’s team maintains safety and ethical standards in AI healthcare solutions.
• Creating technology that serves the end user; how it is informed by firsthand experience.
• The importance of clinical input to develop relevant and practical AI healthcare tools.
• Where Tim sees AI's impact in healthcare evolving over the next three to five years.

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:

Tim O’Connell on LinkedIn

Emtelligent

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.

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:

• Background about Dmitry, his path to HistAI, and his role at the company.
• What whole slide images are and the challenges of working with them.
• How AI can streamline diagnostics and reduce the workload for pathologists.
• Why foundation models are a core component of HistAI’s technology. 
• The scale of data and compute power required to build foundation models.
• Outline of the different approaches to building a foundation model.
• Privacy aspects of building models based on medical data.
• Challenges Dmitry has faced developing HistAI’s foundation model. 
• Hear what makes HistAI’s foundation model different from other models.
• Learn about his approach to benchmarking and improving a model. 
• Explore how foundation models are leveraged in HistAI’s technology. 
• The future of foundation models and his lessons from developing them.
• Final takeaways and how to access HistAI’s open-source models.

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:

Dmitry Nechaev on LinkedIn

Dmitry Nechaev on GitHub

HistAI

CELLDX

Hibou on Hugging Face

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• An overview of Jason’s background and how it led him to create Variational AI.
• What Variational AI does for the small molecule domain for drug discovery.
• How they use foundation models to predict and enhance the design of small molecules.
• Defining small molecules, their appeal, and an overview of Variational AI's data sets.
• What goes into training Variational AI's foundation model.
• The computational infrastructure and algorithms necessary to process this data.
• Challenges of predicting molecular potency against disease-related protein targets.
• Various ways that Variational AI’s foundation model underpins everything they do.
• Evaluating progress: balancing predictive success with experimental validation.
• Lessons from developing foundation models that could apply to other data types.
• Jason’s funding and research-focused advice for leaders of AI-powered startups.
• The transformative impact of Variational AI’s technology on drug development.

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:

Variational AI

Variational AI Blog

Jason Rolfe on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

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:

• Insight into Jonathan’s diverse career path and how it led him to Orbital Materials.
• What types of advanced materials Orbital develops and their potential impact.
• The critical role AI plays in developing materials for decarbonization purposes.
• Defining foundation models and why they’re an essential part of leveraging AI.
• 3D atomic simulations and other types of data that go into Orbital’s foundation model.
• The computing infrastructure required to build a foundation model for materials.
• Engineering and other challenges encountered while building models at this scale. 
• How AI enhances scientific discovery without replacing human expertise.
• Why open-sourcing Orbital’s foundation model, Orb, is key for innovation.
• Lessons from developing this model that could be applied to other data types.
• Jonathan’s detail-oriented advice for leaders of AI-powered startups.
• Orbital’s exciting mission to accelerate new materials development.

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:

Orbital Materials

Orbital Materials on LinkedIn

Orbital Materials on X

Orbital Materials on GitHub

Jonathan Godwin on LinkedIn

Jonathan Godwin on X

Jonathan Godwin Substack

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.

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:

• Dimitris discusses his journey from physics to a career in neuroscience.
• Explore Piramidal's mission to automate EEG interpretation.
• Learn about the complexity and variability of brainwave patterns
• Hear how machine learning can better analyze brain activity.
• Uncover the challenges of building a foundation model for EEG data.
• Why diverse data sets are vital for training the foundational model.
• Piramidal's plans for making EEG analysis more accessible.
• Future use cases for Piramidal’s model in healthcare and beyond.
• Discover why domain knowledge for model building is essential.
• He shares advice for AI startup founders.

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

Dimitris Sakellariou on X

Piramidal

Piramidal on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• Introducing guest, Bruno Sánchez-Andrade Nuño, Executive Director at Clay.
• His journey from NASA astrophysicist to climate change, social development, and AI researcher.
• What Clay focuses on: using remote sensing maps to interpret the Earth’s data.
• The mechanics of how Clay is used and how different feature sets compare to one another.
• A broad explanation of the tool’s foundation model and why it is quicker, cheaper, and more environmentally friendly.
• Two main benefits of the tool that Bruno finds most exciting. 
• Data and infrastructure required to build Clay including 70 million satellite and aerial images.
• Measuring what the model understands and the process of compressing an image into 700 numbers.
• Privacy and intellectual property in the realm of satellite imaging and mapping. 
• What commercial imagery could add to the model and how it might be integrated in the future. 
• Clay’s partnerships with university and company groups
• Why the focus of Clay is to make it as easy as possible for anyone to use the tool for anything they want to do. 
• Challenges encountered on the road to building Clay: explaining what it is.
• The complexity of benchmarking foundation models and how this relates to Clay. 
• Working with partners to build Clay and the rest of the ecosystem. 
• Lessons from building Clay that may apply to other foundation models.
• Bruno’s predictions for the future of foundation models and Clay. 
• What is certain about the future of Clay and our understanding of Earth. 

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

Bruno Sánchez-Andrade Nuño on X

Bruno Sánchez-Andrade Nuño on LinkedIn

Clay

Clay on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

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:

• Insight into the diverse backgrounds of Fauna Bio’s founding members.
• Ways that Fauna Bio uses AI and genomics to identify key targets for new therapeutics.
• The role machine learning plays in analyzing and annotating large volumes of data.
• Gene expression and other data inputs that drive Fauna Bio’s discoveries.
• The collaborative effort required to collate datasets from 400+ mammals.
• Challenges of working with genomic data and training ML models on it.
• How Fauna Bio rigorously validates their AI-driven discoveries.
• Cooperation between ML developers and domain experts to advance this technology.
• Technological advancements that enable Fauna Bio’s innovations.
• Ashely’s advice on differentiation for leaders of AI-powered startups.
• Where she sees Fauna Bio making the biggest impact in the future.

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:

Fauna Bio

Ashley Zehnder on LinkedIn

Ashley Zehnder on X

Ashley Zehnder Email

Zoonomia Project

Science Issue dedicated to the Zoonomia Project

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.

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:

• Insight into Andrei’s background and how it led him to create Vivodyne.
• What Vivodyne does and why it’s so important for drug discovery.
• The role that AI and machine learning play in analyzing vast amounts of data.
• Different data inputs and outputs for Vivodyne’s advanced multimodal AI.
• The value of biased and unbiased AI outputs depending on the context.
• Why interpretability and explainability are crucial in fields like biotechnology.
• Challenges associated with collecting human tissue data to train Vivodyne’s models.
• What goes into validating Vivodyne’s machine learning models.
• Difficulties in integrating biology knowledge with robotics and machine learning.
• Andrei’s business-focused advice for technical founders.
• The profound impact that Vivodyne will have on drug discovery in the future.

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:

Andrei Georgescu

Vivodyne

Andrei Georgescu on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• Introducing Marie Coffin and her background leading up to her role at CIBO Technologies.
• CIBO’s work to influence company carbon footprints to improve agricultural sustainability.
• The role of machine learning in this process: remote sensing.
• What remote sensing is used for at CIBO.
• How satellite imagery interacts with their computer vision system. 
• Gathering, labeling, and annotating data with a focus on the boundary of the field. 
• Obtaining this information through a farmer’s recording process. 
• Why their work is largely limited to the US at the moment. 
• Challenges related to privacy and data protection while working with training models.
• Managing data quality issues.
• Validating models within a geographical context. 
• Collaborating with domain experts and external agronomists to understand and validate thier approaches.
• How the seasonal nature of agriculture impacts the timing of reports and outputs. 
• Advice for tech startups; addressing trends, who to hire, and more.
• Qualities Marie seeks in new hires. 
• Her prediction for CIBO’s growing impact in the next three to five years. 

Quotes:

“It’s pretty straightforward to estimate the carbon footprint of a single farmer’s field or even the carbon footprint of a whole farm, but, to make an impact, we need to be able to scale that across the landscape.” — Marie Coffin

“That is really the biggest challenge; it’s just getting enough data.” — Marie Coffin

“When you’re working in a really cutting-edge area, it’s tempting to sort of get caught up in the buzz of the new technology and lose sight of what the customer needs.” — Marie Coffin

“We need to not always be following the latest, greatest advance. We need to be going in a direction that’s going to really provide value.” — Marie Coffin

Links:

CIBO Technologies

Marie Coffin on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• Some background on Mads and his transition from academia to the private sector.
• The FaunaPhotonics platform and how it monitors biodiversity.
• An overview of the biodiversity crisis and the urgent need to address it.
• Understanding our connection to, and dependence on, nature.
• The risks that the biodiversity crisis poses for supply chains.
• FaunaPhotonics’ role in measuring the biodiversity crisis: why this protects ecosystems.
• Why insects are the best available proxy for measuring ecosystem health.
• How sensing technology and machine learning are utilized by FaunaPhotonics.
• Case studies showcasing the impact of FaunaPhotonics' technology.
• Future directions and innovations in biodiversity monitoring.
• Key challenges faced in developing and deploying biodiversity monitoring technology.
• FaunaPhotonics’ collaboration with other domain experts and researchers in the field.
• Why there is such an urgent need for scaleable biodiversity monitoring.
• The importance of public awareness and education in addressing the biodiversity crisis.
• Mads’ advice to leaders of other AI-powered startups and the future of FaunaPhotonics.

Quotes:

“The clothes we wear, the food we eat, the water we drink, the material we use to build houses: everything comes from nature. And right now, we are destroying that foundation rapidly.” — Mads Fogtmann

“I think it’s important that we become more aware that we are an integral part of nature.” — Mads Fogtmann

“If you can’t measure it, then how can you protect the rights? – [We come with the solution] that allows them to measure [the impact on biodiversity] so they can protect it. We do this by using insect sensing. The reason we do this is that insects are so fundamental to the ecosystem.” — Mads Fogtmann

“Insects are the best proxy that you can have for actually measuring the health of [an] ecosystem.” — Mads Fogtmann

“There’s a huge need and an interest in ‘how we can actually scale biodiversity monitoring to kind of help us understand what’s going on with nature at the moment.’” — Mads Fogtmann

Links:

Mads Fogtmann on LinkedIn
FaunaPhotonics

FaunaPhotonics on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Tuning in, you'll learn all about Fero Labs' innovative software and how it’s empowering engineers in industries like steel and chemicals to harness machine learning, drastically reducing waste and energy consumption. We discuss how their AI analyzes historical production data to ensure factories operate at peak performance and how this is boosting sustainability and profitability. Our conversation also unpacks the critical role of explainable AI in building trust within the industrial sector, where precision and reliability are essential. Tune in to discover how Fero Labs is paving the way for a greener industrial future!

Key Points:

• Berk Birand’s education and career background.
• How he co-founded Fero Labs with his business partner.
• An overview of Fero Labs’ AI software.
• Fero Labs’ role in reducing raw material waste in the steel industry.
• How they have helped improve energy efficiency in chemical manufacturing.
• Data analysis and how their software provides recommendations for efficient operations.
• Understanding the high stakes involved in manufacturing processes.
• Why AI explainability is crucial in the industrial sector.
• How they are building explainable models that engineers can trust and understand.
• Why now is the right time to build this technology.
• His advice to AI-powered startups: seriously consider the cost of a bad prediction.
• Fero Labs’ long-term vision to achieve a more circular and sustainable industrial sector.

Quotes:

"One of our largest customers was able to reduce the waste of raw materials, about a million pounds just throughout last year, by using our software AI system." — Berk Birand

"We think AI will play a key role in the transition to a green economy." — Berk Birand

"The best people to be solving these types of challenges, ultimately, are the engineers that work at the plants. The engineers that have the most domain expertise." — Berk Birand

"In an environment like this, an engineer in a factory would just not want to use a software that they don't trust, because ultimately, it's their job that's on the line." — Berk Birand

“With the new drive towards building an industrial sector that is more circular and more sustainable, there's incredible potential to optimize not just an individual factory, but beyond that, to optimize the entire supply chain by optimizing factories jointly.” — Berk Birand

Links:

Berk Birand on LinkedIn

Fero Labs

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.

Key Points:

• How Daniella came to understand the epidemiology and data aspects of fertility.
• What AIVF does and why it’s so important for both patients and clinicians.
• The role of machine learning at AIVF and the challenges their models encounter. 
• AIVF’s strategy for validating their models and translating KPIs into clinical settings.
• The value of explainability to empower embryologists to use AI as a tool.
• Daniella’s definition of computational embryology, assisted by machine learning.
• Why now is the right time for AI-powered IVF solutions.
• Metrics that AIVF uses to measure the impact of their technology.
• Danielle’s advice for leaders of AI-powered startups and her vision for the future.

Quotes:

“We showed that if you use AI as a tool for the embryologist – [it] increased the success rates – The decision-making is faster, more accurate. You freeze less embryos because each embryo you freeze is accurate – It changes the way the lab works and it optimizes everything.” — Daniella Gilboa

“The way you interact with the patient and consult the journey ahead is changing. It’s more accurate. It allows you to make more informed decisions. This is the right way of doing medicine. It needs to be data-driven rather than subjective human analysis.” — Daniella Gilboa

“AIVF needs to become the standard of care.” — Daniella Gilboa

Links:

AIVF

Daniella Gilboa on LinkedIn

Daniella Gilboa on X

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Key Points:

• Kit’s professional background and how he ended up at Plainsight.
• What Plainsight does and why this work matters. 
• The mechanics behind Plainsight's Vision Intelligence Filters.
• How the company's ML models and data use relate to its customers 
• Understanding when domain expertise comes into play. 
• The process of planning and developing a new filter.
• How bias manifests in image-trained models, and how Kit and his team are mitigating this.  
• The most interesting and game-changing applications that Kit has worked on. 
• His advice to other leaders of AI-powered startups.
• Kit’s vision for the future of Plainsight Technologies.

Quotes:

“Our goal is to give customers very high accuracy on their models.” — Kit Merker

“A lot of times, traditional enterprises are looking for a solution or an app. The filter is like an app, and so customers can start really small with us, get an app that they trust the data, and then expand from there. They don't have any machine learning expertise required.” — Kit Merker

“Don't fake your demos!” — Kit Merker

Links:

Kit Merker

Kit Merker on LinkedIn

Kit Merker on X  

Plainsight Technologies

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.

In this episode, Charles shares insights into Ubenwa's machine-learning models and how they establish an infant's cry as a vital sign. He discusses the process of collecting and annotating data through partnerships with children's hospitals, the challenges of working with audio data, the benefits of creating a foundation model for infant cries, and much more. He also offers human-focused advice for leaders of AI-powered startups and reflects on his vision for success and the impact he hopes to achieve with Ubenwa. Tune in to discover how understanding your infant’s cries can transform healthcare and well-being for newborns and their families!

Key Points:

• Charles' converging interests in math and healthcare, which led him to create Ubenwa.
• What Ubenwa does to establish an infant’s cry as a vital sign (and why it’s so important).
• The essential end-to-end role that machine learning plays in this technology.
• How Ubenwa collects and annotates data by partnering with children’s hospitals.
• Challenges of working with audio data and training medical ML models on it.
• Insight into the benefits of creating a foundation model for infant cries.
• Variations in infant’s cries and how Ubenwa’s models generalize for these shifts.
• Valuable research Ubenwa has made publicly available as a gift to the ML community.
• Charles’ human-focused advice for other leaders of AI-powered startups.
• What success means to Charles and the impact he hopes to make with Ubenwa.

Quotes:

“Ubenwa was born out of the idea that, if there's something that [human doctors] can listen to to come to a conclusion [about an infant’s health], then there has to be something machines can also learn from the infant's cry.” — Charles Onu

“The real leap we made with self-supervised learning is that you now do not need an external annotation to learn. The model can use the data to supervise itself.” — Charles Onu

“AI-powered or not, – the problem of a startup remains the same. It’s to meet a need that humans have. – At the end of the day, AI is not just there for AI only. It’s only going to be a successful and useful startup if you identify a need and [solve] that problem.” — Charles Onu

“Human babies have evolved to communicate their needs and their health through their cries. We [haven’t] had the tools to understand that. Babies have been trying to talk to us for a long time. It's time to listen.” — Charles Onu

Links:

Ubenwa Health

Nanni AI

Charles Onu on LinkedIn

Charles Onu on X

Charles Onu on GitHub

Ubenwa on GitHub

Ubenwa CryCeleb Database

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.

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:

• The details of Marshall’s tech-savvy childhood and entrepreneurial journey.
• An overview of Upstream Tech’s mission to improve environmental monitoring.
• How they use AI and satellite imagery for scalable, cost-effective monitoring.
• The development of their Lens product for remote monitoring of climate solutions.
• Why remote monitoring is so challenging at scale and their approach to solving it.
• Their product, HydroForecast, and its role in predicting water flow using machine learning.
• How integrating new inputs like satellite imagery creates reliable, adaptable models.
• Success stories, including outperforming traditional models in a major competition.
• Challenges Upstream Tech faces in acquiring and integrating geospatial data.
• Best practices for ensuring model reliability and effectiveness over time.
• Their team's approach to developing a new machine learning product or feature.
• Marshall’s advice for AI startups: don’t get too attached to the tools!
• His vision for Upstream Tech’s impact on environmental conservation.

Quotes:
"What these new machine learning models that we're employing allow us to do is to provide enough data to the model to create [equations] to describe physical interactions." — Marshall Moutenot

“[The] adaptability of these models is something that is really exciting for the field overall." — Marshall Moutenot

"We train a single model on a wide diversity, which forces the model to learn the common rules across all of them.” — 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:

Marshall Moutenot on LinkedIn

Marshall’s Blog

Upstream Tech

Upstream Tech on LinkedIn

Upstream Tech on X

Upstream Tech on YouTube

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.

In our conversation, Anitha delves into the technical aspects of using large language models for patient data processing, the challenges of training models with clinical data, and the strategies Curai employs to ensure high-quality care. We also discuss the innovative ways Curai integrates AI into healthcare, the significance of multidisciplinary teams, and Anitha’s vision for the future of virtual care. Tune in for an insightful conversation on scaling healthcare through virtual primary care and learn how Curai is making a real impact!

Key Points:

• Some background on Anitha Kannan, and how she joined Curai.
• An overview of Curai’s services as a virtual healthcare practice.
• How they provide affordable and timely healthcare access through AI-enhanced systems.
• Machine learning’s role in history taking, information gathering, and summarization.
• How AI streamlines the workflow for physicians.
• Their use of large language models to process patient data.
• Training model challenges: ensuring clinical correctness and handling data omission issues.
• Best practices they’ve developed for validating models and the importance of evaluation.
• Fundamental differences between their work and how other LLMs, like ChatGPT, are trained.
• Their strategy for balancing long-term research aspirations with short-term product development.
• An overview of their multidisciplinary teams and how this contributes to their success.
• Anitha’s hopes for the future of Curai; particularly through partnerships with healthcare organizations.

Quotes:

"Our mission is to provide the best health care to everyone." — Anitha Kannan

“Today, [Carai runs] a text-based virtual primary care practice. We have our licensed physicians or experts in their fields. Then we supercharge them and bring about a lot of efficiencies by leveraging AI.” — Anitha Kannan

"It's very easy to build 80% of a good product with AI today, but I think to get it to 100%, [and] to get it to scale, to be useful in [the] real world — evaluation is the number one thing." — Anitha Kannan

“At Curai, the AI team is composed of clinical experts, subject matter experts, researchers, and machine learning engineers. Every project, long-term or short-term, has a mix of these types of expertise in it. This allows us to work through the problem much more effectively.” — Anitha Kannan

Links:

Anitha Kannan on LinkedIn 

Anitha Kannan on X

Curai Health

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.

Today on Impact AI, I'm joined by Jason Koeller, Co-Founder and CTO of Chemix, to find out how his company is leveraging AI to create better, more sustainable EV batteries that could reduce our reliance on elements like lithium, nickel, and cobalt, all without compromising vehicle performance. For a fascinating conversation with a data-driven physicist working at the intersection of software, machine learning, chemistry, and materials science, be sure to tune in today!

Key Points:

• Jason’s background in theoretical physics and how it led him to create Chemix.
• Products and services offered by Chemix and the role that AI plays.
• Four reasons that machine learning (ML) is at the core of everything Chemix does.
• Unique challenges that their ML models need to contend with.
• What goes into validating these models to ensure accuracy.
• Why now is the right time for the technology that Chemix is developing.
• Metrics for measuring the impact of a better EV battery.
• Jason’s data-driven advice for leaders of AI-powered startups.
• His “electrifying” vision for Chemix in the next three to five years.

Quotes:

“All data analysis and decision-making is automated by our AI system. This includes analyzing terabytes of battery test data each day.” — Jason Koeller

“Looking at broad trends, [electric vehicles (EVs)] and AI have both become [things] that people have been talking a lot more about in the past 10 years and even more so in the past four or five years, and that has happened simultaneously.” — Jason Koeller

“Why is everyone not buying an EV? It's largely because they're too expensive or because people are worried they're not charging fast enough or they don't hold enough range for long road trips. – Improving any one of these metrics would be a measure of impact.” — Jason Koeller

Links:

Jason Koeller on LinkedIn

Chemix

Chemix on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

I am joined today by Nathan Silberman, Vice President of Machine Learning and Engineering at Artera, to talk about how Artera’s technology is paving the way for personalized cancer treatment decisions. Join us today, as we get into how Artera is contributing to the cancer treatment process, some of the biggest challenges they face, and how they are addressing these through specifically trained algorithms and robust validation protocols. Be sure to tune in to this important conversation on how Artera is impacting cancer treatment outcomes for the better!

Key Points:

• Background on our guest, Nathan Silberman, and what led him to Artera.
• How Artera is helping clinicians make informed decisions for cancer treatments.
• The role of machine learning in their personalized risk assessments for patients.
• Key challenges they’ve encountered with pathology data.
• How they deal with slide variations through well-trained algorithms.
• Bias in pathology data and what Artera is doing to mitigate bias.
• Their partnerships with academics, clinicians, and oncologists.
• Insight into the variety of approaches they use to validate their models.
• How their tests fit in with clinical workflows and assist doctors and patients.
• The agonizing wait time associated with traditional non-AI testing methods.
• How Artera is providing quick and reliable test results.
• Advice to leaders of AI-powered startups: stay focused on the ultimate goal of patient impact.
• Looking ahead at Artera’s impact in the next three to five years.

Quotes:

“Which therapy to choose is simply not an easy choice. Clinicians would ideally be able to accurately assess a patient's risk of a cancer spreading, or adversely affecting the patient's health in the short term. But often, that's hard or impossible for a clinician to predict.” — Nathan Silberman

“Clinicians have been wanting and waiting for tools that can predict whether or not a therapy will work for that particular patient. This is ultimately where Artera steps in.” — Nathan Silberman

“Rather than wait a month, Artera's test provides the answer within two to three days after the lab receives the biopsy slide. And it is so rewarding to hear from clinicians, and especially patients about the relief we can provide by giving clarity sooner.” — Nathan Silberman

“I think the biggest piece of advice I can give is really just making sure that you're laser-focused on the ultimate goal of patient impact.” — Nathan Silberman

Links:

Artera

Nathan Silberman on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In our conversation, we delve into the groundbreaking technology behind Synthetaic's flagship product and how it is revolutionizing image and video processing. Explore how it utilizes an unsupervised backend to swiftly analyze and interpret data, how it is able to work with any kind of image data, and the process behind ingesting and embedding image objects. Discover how Synthetaic navigates biased data and leverages domain expertise to ensure accurate and ethical AI solutions. Gain insights into the gaps holding AI’s application to images back, the different ways the company’s technology can be applied, the future development of Synthetaic, and more!

Key Points:

• Corey’s background in AI and ML and what led to the creation of Synthetaic.
• Why Synthetaic focuses on processing images and videos quickly.
• How the company leverages ML in its approach. 
• Details about image ingestion and embedding processes.
• How the definition of potential objects varies depending on the type of imagery used.
• Explore the role of domain expertise in addressing challenges. 
• Hear examples of the technology’s diverse range of applications.
• Recommendations to leaders of AI-powered startups. 
• His hope for the future trajectory of Synthetaic.

Quotes:

“We think about the machine learning problems a little bit differently, because we're not labeling data to go ahead and build a bespoke frozen traditional AI model.” — Corey Jaskolski

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

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

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

Links:

Corey Jaskolski on LinkedIn

Corey Jaskolski on X

Synthetaic

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Key Points:

• A rundown of Charles Fisher’s background and what led him to create Unlearn AI. 
• What Unlearn does, what digital twins are, and why they’re important. 
• How clinical trials work and how they are used within Unlearn. 
• The kinds of data they use and how they tackle these clinical trials using machine learning. 
• What a zero-trust solution is and how Unlearn guarantees that their results are accurate. 
• Charles shares his thoughts on the role of domain expertise in machine learning. 
• His advice for any leaders of AI-powered startups. 
• What we can expect from Unlearn in the next three to five years. 

Quotes:

“[Unlearn is] typically working on running clinical trials where we might be able to reduce the number of patients who get the placebo by somewhere like – 50%.” — Charles Fisher

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

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

Links:

Charles Fisher on LinkedIn

Charles Fisher on X

Unlearn AI

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Key Points:

• Insight into Mathieu’s research focus and how it led him to create Concrete.ai.
• What Concrete.ai does and why it’s important for reducing CO2 emissions.
• The role of machine learning, particularly generative AI, in this technology.
• How Concrete.ai develops ML models that are reliably able to extrapolate.
• Why estimating uncertainty is important and how Concrete.ai approaches it.
• What goes into validating these models, including systematic testing in the field.
• Reasons that the timing for Concrete.ai’s technology is critical.
• Dollars saved and other metrics for measuring the impact of this technology.
• Mathieu’s humanity-focused advice for other leaders of AI-powered startups.
• How Concrete.ai’s impact will continue to expand and evolve.

Quotes:

“Concrete is responsible for 8% of the total CO2 emissions in the world. To give you some context, that's about three times more emissions than the entire aviation industry.” — Mathieu Bauchy

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

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

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

Links:

Concrete.ai
Concrete.ai on LinkedIn

Mathieu Bauchy

Mathieu Bauchy on LinkedIn

Mathieu Bauchy on YouTube

Mathieu Bauchy on X

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Key Points:

• Insight into Max’s role at Aignostics and how the company is impacting healthcare.
• How they use machine learning to set themselves apart from their competitors.
• A rundown of their models and datasets.
• The definition of a foundation model and how Aignostics built theirs.
• How to use foundation models as a starting point for building machine learning applications.
• What sets Aignostics’ foundation model for histopathology apart from other similar models.
• How their foundation model enables them to develop other models more quickly.
• Top lessons Max has learned from developing foundation models.
• How they navigate explainability with concepts that are challenging for machine learning.
• The positive impact that foundational models have had on explainability.
• Recent advancements that Max is excited about as potential use cases for Aignostics.
• Max’s advice to leaders of other AI-powered startups.
• The impact of Aignostics and where he expects it will be in the next three to five years.

Quotes:

“Our mission is to turn biomedical data into insights.” — Maximilian Alber

“Everything we do is driven by the belief that machine learning and data science will help us improve healthcare.” — Maximilian Alber

“A foundation model is a model that can be used as a starting point for building a machine learning application, with the promise that the foundation model already has a great understanding of the domain.” — Maximilian Alber

“We are in active discussions for licensing our foundation model to other companies in order to enable their development as well. [What’s] important here is that we develop our foundation model along regulatory requirements, which will allow it to be used in medical products.” — Maximilian Alber

“One needs to build a technology that either makes a difference in the long run, or one must be able to innovate at a very fast pace.” — Maximilian Alber

Links:

Maximilian Alber on LinkedIn

Aignostics

Aignostics on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

In our conversation, we discuss the ins and outs of the company’s innovative approach to weather forecasting. We delve into the hurdles of subseasonal-to-seasonal forecasting, how machine learning is replacing traditional weather modeling approaches, and the various inputs it uses. Discover the value of machine learning for post-processing of data, the type of data the company utilizes, and why it uses probabilistic models in its approach. Gain insights into how Salient is catering to the impacts of climate change in its weather predictions, the company’s approach to validation, how AI has made it all possible, and much more!

Key Points:

• Sam's background in science and the creation of Salient.
• Hear how Salient is revolutionizing weather forecasting and why.
• How Salient is utilizing machine learning in its forecasting models.
• Examples of the data and models the company uses.
• The challenges of working with weather data to build models.
• Explore why Salient also uses probabilistic models in its approach.
• Salient’s approach to validation and how it deals with data uncertainty.
• Ways AI has made the company’s approach to forecasting possible. 
• He shares advice for leaders of other AI-powered startups.

Quotes:

“Salient produces weather forecasts that extend further into the future than most people are used to seeing. We go up to a year in advance.” — Sam Levang

“ML (Machine Learning) models have proved to be actually a very effective replacement for the traditional approach to weather modeling.” — Sam Levang

“The only difference about making forecasts longer timescales of weeks and months ahead is that there are some differences in the particular parts of the climate system that provide the most predictability.” — Sam Levang

“While ML and AI are extremely powerful tools, they are still just tools and there's so much else that goes into building a really valuable product, or a service, or a company.” — Sam Levang

Links:

Sam Levang on LinkedIn 

Salient

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.

Key Points:

• The origin story of PictorLabs and the research that informed it.
• Why Pictor’s work is so important for patients and the healthcare system.
• What Yair means when he says machine learning is the “engine” for virtual staining.
• How Pictor mitigates the challenge of AI hallucinations.
• Insight into what goes into validating virtual staining models.
• Large files, bandwidth dependency, and other challenges that Pictor faces.
• A look at how this technology fits smoothly into the clinical workflow.
• Collaborating with economic partners while staying focused on business objectives.
• Yair’s product-focused advice for leaders of AI-powered startups
• What the next three to five years looks like for PictorLabs.

Quotes:

“The most important factor for the healthcare system, for the patient is the fact that you can get all the results, all the workup, and all the different stains from a single tissue section very, very fast.” — Yair Rivenson

“Machine learning is the engine behind virtual staining. In a sense, that’s what takes those images from the autofluorescence of the unstained tissue section and converts [them] into a stain that pathologists can use for their diagnostics.” — Yair Rivenson

“At the end of the day, the network is as good as the data that it learns from.” — Yair Rivenson

“The more you automate, the better off you’ll be in the long run.” — Yair Rivenson

Links:

Yair Rivenson