April 9, 2024
Harry's guest this week is Raffi Krikorian, chief technology officer and managing director at Emerson Collective, the social change organization founded by Laurene Powell Jobs. Krikorian is the former vice president of engineering at Twitter (now X), where he was responsible for getting rid of the Fail Whale and making the company’s backend infrastructure more reliable; the former director of Uber's Advanced Technology Center in Pittsburgh, where he oversaw the launch of the world's first fleet of self-driving cars; and then the chief technology officer at the Democratic National Committee, where he helped rebuild the party's technology infrastructure after the Russian hacking debacle of 2016. At Emerson Collective, Krikorian built the technology organization, leads the development of data products, and works to upgrade the back offices of the non-profits Emerson works with. On top of all that, he recently launched a podcast called Technically Optimistic, where he’s taking a deep dive into the way AI is challenging us all to think differently about the future of work, education, policy, regulation, creativity, copyright, and many other areas. The show is a must-listen for anyone who cares about how we can build on AI to transform society for the better while minimizing the collateral damage. Harry talked with Krikorian about why he moved to Emerson Collective, why and how he started the podcast, and what he really thinks about what government should be doing to prepare for the waves of social change AI will bring.
59 min 27 sec
3.26.24
Generative AI isn’t magic. You can’t just sprinkle it like pixie dust over an existing project or dataset and expect wonderful things to happen automatically. In fact, just to use the data you already have, you have to you may have to invest a lot in the new infrastructure and tools needed to train a generative model. And that’s the part of the puzzle Harry focuses on in today's interview with David Buniatyan. He’s the founder of a company called ActiveLoop, which is trying to address the need for infrastructure capable of handling large-scale data for AI applications. He has a background in neuroscience from Princeton University, where he was part of a team working on reconstructing neural connectivity in mouse brains using petabyte-scale imaging data. At ActiveLoop, David has led the development of Deep Lake, a database optimized for AI and deep learning models trained on equally large datasets. He says the company’s goal is to take over the boring stuff. That means removing the burden of data management from scientists and engineers, so they can focus on the bigger questions—like making sure their models are training on the right data—and ultimately innovate faster.
63 min 04 sec
3.12.24
Harry's guests this week are Frank Cheng, CEO of UK-based Caristo Diagnostics, and Keith Channon, Caristo's co-founder and chief medical officer. Under their leadership, Caristo has introduced an AI-based test called CariHeart that applies machine learning to the data in a three-dimensional CT scan of the heart. It looks for signs of inflammation in the fat tissue around the major coronary arteries—a risk factor that's often overlooked because it isn't always accompanied by plaque or narrowing of the arteries. Doctors can use that information to decide whether a patient needs to take a cholesterol-lowering drug like a statin or an anti-inflammatory drug like colchicine. Caristo’s test is being used on an experimental basis in the UK, and it hasn’t yet been approved for use in the US. But it’s a leading example of the way AI, put together with fundamental advances in our understanding of human biology, is really beginning to change the practice of medicine.
64 min 42 sec
2.27.24
If you believe that computation will help companies get better at developing new drugs, then what specific kind of computation and software should you invest in? Quantum chemistry simulations? Molecular dynamics simulations? Generative AI models? Harry's guests this week, Garegin Papaoian and Michael Antonov, lead a company called Deep Origin that's taking an all-of-the-above approach. The company’s philosophy is that physics-based modeling by itself won’t be enough to build a powerful drug discovery engine. But neither will generative AI, which requires more training data than lab scientists will ever be able to provide. They think the only reasonable approach today is to combine the two, and use both physics and AI to try to get better at predicting which molecules could become effective drugs. It’s important stuff, because if Deep Origin is right, then a lot of other more specialized biotech and techbio startups could be going down the wrong path.
51 min 39 sec
1.30.24
If you look back at all the health-tech and drug development companies Harry has hosted on the show, an interesting pattern starts to emerge: a very large number of those companies have gone on to enormous growth and success in their markets. It could be that being on the podcast is like a catapult to success—or it could be that we're pretty good at finding companies that are already on a promising trajectory. Either way, there's no better example than Concert AI. The company’s CEO, Jeff Elton, first spoke with Harry back in July of 2021. At that time, the company was already one of the leaders in gathering and analyzing broad collections of data about cancer patients involved in clinical trials for new treatments. Its specialty was, and is, going beyond the very specific endpoints measured in clinical trials and looking to electronic medical records, genome sequencing data, insurance claims data, and other sources in order to build a more comprehensive picture of cancer patients and their journeys through the healthcare system. That kind of data can be very useful to companies trying to track the performance of their drugs after they’ve reached the market, and to researchers planning new clinical trials. And since that first conversation, the company has grown by leaps and bounds. It’s taken over management of more data sources, including the massive CancerLinq database formerly maintained by the American Society of Clinical Oncology. It’s struck up partnerships with some of the leading technology startups, research centers, and drug companies working to beat cancer. And it’s leaning hard into the new wave of deep-learning AI tools and their potential to help find patterns in vast amounts of data about patients. It’s probably safe to say that ConcertAI has gathered up more data about cancer patients than any other company on the planet. And investors have been rushing to pour money into the company, on the conviction that data is going to be the key to getting more and better cancer drugs to market. That’s certainly Jeff Elton's conviction too, as you’ll hear in today's interview.
60 min 23 sec
1.16.24
One of the most amazing successes in the battle against cancer over the last two decades has been the introduction of antibody drugs that harness the body’s own immune system to kill tumor cells. Finding those drugs may sound like a biology problem rather than a machine learning or a big-data problem. But actually, these days, it’s both. Harry's guest this week is Leonard Wossnig, who’s the chief technology officer for a UK company called LabGenius. The company uses a combination of synthetic biology, high-throughput assays, and machine learning to hunt for new drugs within a subclass of antibody medicines called T cell engagers that, loosely speaking, can grab tumor cells with one end and then grab tumor-killing T cells from the bloodstream with the other end. And Wossnig says the key to the whole thing is having the best data possible—meaning, data about their candidate T cell engagers and how specifically they bind to their targets in the lab assays. LabGenius has built an automated platform called EVA that runs experiment after experiment and uses active learning to zero in on T cell engagers with just the right ability to bind to their intended targets. One of the big takeaways from the interview is that companies that want to use AI to speed up drug discovery need the biggest, cleanest, and most consistent data sets possible.
38 min 48 sec
12.19.23
The combination of better data and more powerful computing is helping researchers reinvent the process of discovering new drugs. Within 5-10 years, we’ll likely see a huge wave of new medicines that were either discovered or designed using AI—drugs that will finally help us get control of our most stubborn health problems, from cancer to cardiovascular disease to obesity and metabolic disorders to neurodegenerative diseases. And the biotech startups that will do most to contribute are the ones that have both proprietary data, and original ways to use AI to sift through that data. Harry's guests this week are from a startup called Pangea Bio that’s working hard on both. As Pangea's co-founder and COO, John Boghossian, and its president of AI, Sona Chandra, explain, the company specializes in gathering data from the natural world, especially data about compounds manufactured inside the cells of plants and fungi. They narrow down the possibilities by working with indigenous cultures to find the plants or mushrooms that people have already been using for centuries in traditional medicine. They've also built three separate computational platforms that filter through all that data, to single out the small molecules that have the biggest effects in the human body, especially the central nervous system.
58 min 49 sec
12.5.23
The microbiome has been getting more and more attention from researchers and doctors now that we’re starting to have the tools we need to identify and measure all those microbes and see what they’re up to. Harry's guest this week is serial healthcare and AI entrepreneur Leo Grady, whose company Jona is on a mission is to help patients and physicians keep up with the skyrocketing amount of scientific literature about the microbiome and try to translate it into real steps people can take to improve their health. If you’re a Jona customer, you start by sending in a fecal sample. Then the company uses a large-scale gene sequencing technique called shotgun metagenomics to get a profile of all the microbes in your GI tract. Since everyone’s microbiome contains a different mix of microbes, the next step is to use large language models to sift through the published science about the microbiome and find the studies that relate to the specific bugs in your microbiome. Then the company gives patients and their doctors a report that parses out whether their microbiome makeup might be contributing to their health problems, and whether there might be any health or nutritional interventions that would help. It’s all in the early stages. And right now Jona’s test is mostly available through concierge medical services, executive health clinics, and other offices that do a lot of cash-pay tests. But Grady thinks that over the long term the service has the potential to turn the microbiome from a former black box into something closer to what he calls an “organ of data”—meaning, a part of the body that doctors can, in a sense, visualize and analyze in the same way we can use MRI and other forms of imaging to scan our other organs.
55 min 45 sec
11.21.23
Quality control is one of those things that only a select few people pay attention to—until something goes wrong, then everyone cares. That's especially true in the drug manufacturing industry, where episodes like cross-contamination in a drug factory can shut down a production line and create instant shortages of important medicines. And if a contaminated medicines ever does get shipped out to clinics or stores, people’s lives can be at stake. So drug makers are usually pretty receptive toward any new technology that can help them detect manufacturing problems before they get out of hand. That’s the market opening that Harry's guest this week, Taylor Chartier, says she saw back in 2020, during the coronavirus pandemic. Chartier watched the stories about the Baltimore company Emergent BioSolutions, which was manufacturing vaccines for Johnson & Johnson and AstraZeneca and had to throw out millions of doses of both vaccines due to suspected cross-contamination, and thought: there has to be a better way. So she started her own company. And today her startup Modicus Prime is partnering with top pharma companies to use new machine vision and AI capabilities to catch drug manufacturing problems faster.
44 min 54 sec
11.7.23
There’s a lot of talk out there about how artificial intelligence will change the way doctors and nurses take care of patients; you hear some of it right here on this show. But all of that still feels like a forecast rather than a present reality. When you look really closely, it’s hard to find concrete examples where AI is already helping healthcare providers make better decisions that improve patient outcomes and take costs out of the system. That’s why Harry wanted to have Nassib Chamoun on the show. Chamoun is the founder and CEO of Health Data Analytics Institute (HDAI), which has been working with a major healthcare system, Houston Methodist, to test out a working platform called HealthVision. It's a collection of AI-driven models that use huge amounts of data, both from Medicare and from Houston’s own electronic health record system, to make predictions that help doctors and administrators spend less time poring over records and data, and more time interacting with actual patients and making good clinical and management decisions.
73 min 18 sec
10.24.23
There’s a good chance that we’re all going to live a lot longer than we think. Or at least, that’s what Harry's guest Sergey Young argues in his book The Science and Technology of Growing Young. Young is an investor who leads a $100 million venture capital fund called the Longevity Vision Fund, and through his investing, he says he meets innovators who are coming up with the technologies that will extend our healthy lifespans not just by years but by decades. Those technologies include better drugs, of course, but also gene editing to rejuvenate our DNA and methods for regenerating or replacing old organs, just the way you’d replace the worn-out parts in an old car. All these technologies are coming faster than we think, Young says, and the big question is how widely they’ll be available and whether everyone who wants them will have access to them. That’s the theme of Young’s work at the Longevity Vision Fund, which focuses on companies creating affordable and accessible life extension technologies.
58 min 38 sec
10.10.23
It's practically the theme of our show that AI is going to change almost everything about the way drugs get developed and the way healthcare gets delivered. But there’s probably nobody better placed to see how this transformation is already happening than Harry's guest this week, Scott Penberthy. Scott works at Google Cloud, where he’s the director of Applied AI in the Office of the CTO. He and his team work with Google’s big corporate customers, including a variety of customers in healthcare and pharmaceutical R&D, to help them solve business problems that require large-scale computing and deep learning. Scott compares Google’s cloud computing capabilities to a racecar that can be adapted to any type of race—whether that’s a customer like Ginkgo Bioworks that leans on computation to reprogram bacterial cells to pump out pharmaceuticals and other products, or a giant health network like Anthem that uses AI to deliver personalized services to members. Because Scott helps set up these partnerships, and because he gets the first look at the Google’s emerging products and services, he has a unique picture of how computing is changing the everyday practice of doing R&D and running a healthcare company. As he himself puts it, he’s in the catbird seat. So listen along as Scott and Harry geek out about how far things have come in AI's transformation of healthcare, and how much more is just around the corner.
80 min 23 sec
9.26.23
Building any kind of startup is hard. Starting a business in healthcare or medical technology is even more challenging, given the long timelines for product development and all the regulatory requirements companies have to meet. But imagine how much harder it would be to start a company if you were still just a senior in high school! Recently Harry learned about a company called Vytal that’s building eye-tracking technology to measure brain health, and he knew he wanted to have the co-founders on the show. Not just because the technology is interesting, but because CEO Rohan Kalahasty and the CTO Sai Mattapali are both 18 years old, and both entering their senior years at Thomas Jefferson High School of Science and Technology in Fairfax County, Virginia. Very few teenagers have ten employees and over a million dollars in seed capital. But that's exactly where Rohan and Sai are right now. Some of the challenges they’ve faced have been absolutely typical—like how to build a network of partners and how to meet government standards for new medical devices. And others have been a little unusual, like how to get time off from school to meet with investors and how to convince their parents that the business won’t take too much time away from their studies. Listen in to hear their whole startup story.
40 min 38 sec
9.12.23
If you’re looking for help thinking about the implications of exponential change in all areas of technology, one of the best people you can turn to is Azeem Azhar. He's a writer, entrepreneur, and investor who publishes the incredibly popular and influential Substack newsletter Exponential View, which takes deep dives into AI and other subjects with world experts. In 2021 Azeem published a whole book along the same lines called The Exponential Age: How Accelerating Technology is Transforming Business, Politics, and Society, and he joined Harry on the show in early 2022 to talk about that. This summer, the book came out in paperback—and just this month, Azeem worked with Bloomberg Originals to launch a limited-run TV show and podcast called Exponentially with Azeem Azhar. So it seemed like a great time to revisit Harry's 2022 interview, which resonates with current events even more now than it did when we first aired it.
56 min 35 sec
8.29.23
Professionals in drug discovery, drug development, and healthcare may not grasp the scale of the change that’s coming to their business thanks to generative AI models like GPT-4. They need to get up to speed fast if they want to stay competitive and incorporate generative AI into their work in a way that’s effective and safe. Fortunately there are plenty of people in the life sciences industry thinking about how to help with that. And one of them is Harry's guest this week, Lana Feng. She’s the CEO and co-founder of Huma.ai, and under her leadership the company has been working with OpenAI to find ways to adapt large language models for use inside biotech and pharmaceutical companies. GPT-4 and competing models are extremely powerful. But for a bunch of reasons that Lana explains in this episode, it wouldn’t be smart to apply them directly to the kinds of data gathering and data analysis that go on in the biopharma world. Huma.ai is working on that problem. They’re building on top of GPT-4 to make the model more private, more secure, more reliable, and more transparent, so that companies in drug development can really trust it with their data and not get tripped up by issues like the hallucination problem.
50 min 13 sec
8.15.23
Harry's guest this week is Joe DeVivo, the new CEO of Butterfly Network. The company's goal is to make it radically easier for doctors or medical technicians to perform an ultrasound exam on any part of the body, and radically cheaper for a patient to get one. The companyt makes an FDA-cleared, handheld ultrasound scanner called the Butterfly iQ. The first big thing that’s different about the iQ is that it uses silicon-based microelectromechanical sensors, instead of a traditional piezoelectric crystal element, to generate and receive the ultrasound waves. That means the device is fully digital, rather than analog. The second big thing that’s different is that the iQ transmits the ultrasound data to a standard iPhone or iPad instead of a big, expensive ultrasound cart. The doctor or technician can see the live ultrasound image right on a handheld device, and use the image to aim the sensor correctly to get the best possible picture to make a diagnosis. All of that is bringing down the cost of equipping a clinic with ultrasound technology dramatically, and over time it should also bring down the cost of administering an ultrasound exam. It also opens up the possibility of adding AI assistance to the software, so that doctors or technicians can get usable images with less training. The net result is that Butterfly is making it economically feasible to use ultrasound for diagnostic imaging in a lot more places, including clinics in developing countries where ultrasound was out of reach before due to the high cost of the technology and a shortage of trained ultrasonographers.
54 min 20 sec
8.1.23
This week Harry's guest is....Harry! We're flipping the script and giving Harry a chance to wax eloquent about AI in healthcare and drug research, the growing role of personal health monitoring devices, the unique features of the Boston life science ecosystem, the meaning of the recent downturn in biotech investment, the most common mistakes made by new entrepreneurs, and much more. This week's guest interviewer is Wade Roush, who hosts the tech-and-culture podcast Soonish and has been the behind-the-scenes producer of The Harry Glorikian Show ever since Harry started the show in 2018.
65 min 34 sec
7.18.23
Harry's guest this week is Dr. Isaac Kohane, chair of the Department of Biomedical Informatics at Harvard Medical School and co-author of the new book The AI Revolution in Medicine: GPT-4 and Beyond. Large language models such as GPT-4 are obviously starting to change industries like search, advertising, and customer service—but Dr. Kohane says they're also quickly becoming indispensable reference tools and office helpmates for doctors. It's easy to see why, since GPT-4 and its ilk can offer high-quality medical insights, and can also quickly auto-generate text such as prior authorization, lowering doctors' daily paperwork burden. But it's all a little scary, since there are no real guidelines yet for how large language models should be deployed in medical settings, how to guard against the new kinds of errors that AI can introduce, or how to use the technology without compromising patient privacy. How to manage those challenges, and how to use the latest generation of AI tools to make healthcare delivery more efficient without endangering patients along the way, are among the topis covered in Dr. Kohane's book, which was co-written with Microsoft vice president Peter Lee and journalist Carey Goldberg.
58 min 38 sec
7.5.23
In the same way that written English is built around an alphabet of just 26 letters, all life on Earth is built around a standard set of just 20 amino acids, which are the building blocks of all proteins. And just as we've invented special characters like emoji to go beyond our standard letters, it turns out that biologists can expand their repertoire of powers using non-standard amino acids—those that either occur rarely in nature, or that can only be made in the lab. GRO Biosciences, a spinout from the laboratory of the renowned synthetic biology pioneer George Church at Harvard Medical School, is one of the companies working to explore the exciting applications of non-standard amino acids (NSAAs), and Harry's guest this weeks is GRO's co-founder and CEO, Dan Mandell. He says NSAAs could help overcome some of the limitations that keep today’s gene and protein therapies from being used more widely, while also expanding the kinds of jobs that protein-based therapies can do.
60 min 38 sec
6.20.23
Owning a dog can be a joy, but one sad downside is that dogs are highly prone to cancer—six million of them are diagnosed with the disease in the U.S. each year. Harry's guest this week, Christina Lopes, is co-founder and CEO of a company called One Health that's working to improve cancer outcomes for our canine friends. The company offers a precision cancer diagnosis and treatment service called FidoCure that takes what we’ve learned about genomic testing of tumors in humans and uses it in veterinary clinics. Vets can submit a dog’s tumor sample for DNA sequencing, and FidoCure's report will show whether the animal has specific mutations that could help determine which cancer drug will be most effective. Harry and Christina talk about how that process works, why dogs are more vulnerable to cancer in the first place, where she got the idea for the company, and how One Health's work could benefit dogs and humans alike.
62 min 1 sec
6.6.23
Unlike cancer, brain diseases like epilepsy, Alzheimer’s disease, or depression don't tend to have easily measured biomarkers that could help doctors tailor treatments, or that could help researchers develop more effective drugs. So in neurology and psychiatry, the precision medicine revolution hasn't really arrived yet. But Beacon Biosignals, where Harry's guest Jacob Donoghue is the co-founder and CEO, is trying to change that. Beacon is focused on making electroencephalography into a more reliable and useful data source for diagnosing and treating neurological disease. EEG has been a common medical tool for almost 100 years, but interpreting an EEG readout is slow and expensive—all of which makes it the perfect candidate for machine learning analysis. By using computation to peer deeper into EEG data, Donoghue thinks it should be possible to identify subtypes of problems like epilepsy or Alzheimer’s, and help neurologists understand which patients will respond best to which therapies. On top of that, better EEG measurements could also give drug developers and regulators more clinical endpoints to measure when they’re trying to evaluate the safety and efficacy of new drugs for CNS diseases. If Beacon’s vision comes true, the precision medicine revolution might finally start to reach the brain.
42 min 54 sec
