Through her research and companies, Dr. Picard has developed wearable sensors, algorithms and systems for sensing, recognizing and responding to information about human emotion. Her products are focused on using fitness trackers to advance clinical quality treatments for a range of conditions.
Meanwhile, in just the past few years, numerous fitness tracking companies have released products with their own stress sensors and systems. You may have heard about Fitbit’s Stress Management Score, or Whoop’s Stress Monitor – these features and apps measure things like your heart rhythm and a certain type of invisible sweat to identify stress. They’re designed to raise awareness about forms of stress such as anxieties and anger, and suggest strategies like meditation to relax in real time when stress occurs.
But how well do these off-the-shelf gadgets work? There’s no one more knowledgeable and experienced than Rosalind Picard to explain the science behind these stress features, what they do exactly, how they might be able to help us, and their current shortcomings.
Dr. Picard is a member of the National Academy of Engineering and a Fellow of the National Academy of Inventors, and a popular speaker who’s given over a hundred invited keynote talks and a TED talk with over 2 million views. She holds a Bachelors in Electrical Engineering from Georgia Tech, and Masters and Doctorate degrees in Electrical Engineering and Computer Science from MIT. She lives in Newton, Massachusetts with her husband, where they’ve raised three sons.
In our conversation, we discuss stress scores on fitness trackers to improve well-being. She describes the difference between commercial products that might help people become more mindful of their health and products that are FDA approved and really capable of advancing the science. We also talk about several fascinating findings and concepts discovered in Dr. Picard’s lab including the multiple arousal theory, a phenomenon you’ll want to hear about. And we explore the complexity of stress, one reason it’s so tough to measure. For example, many forms of stress are actually good for us. Can fitness trackers tell the difference between stress that’s healthy and unhealthy?
Show links:
- Dr. Picard’s book, Affective Computing
- Dr. Picard’s bio
- Dr. Picard on Twitter
- Dr. Picard’s company, Empatica - https://www.empatica.com/ - The FDA-cleared Empatica Health Monitoring Platform provides accurate, continuous health insights for researchers and clinicians, collected in the real world
- Empatica Twitter
- Dr. Picard and her team have published hundreds of peer-reviewed articles across AI, Machine Learning, Affective Computing, Digital Health, and Human-computer interaction.
- Dr. Picard’s TED talk
Rosalind Picard
AI was integral to creating Moderna's mRNA vaccine against COVID.
But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
