The Future of Everything

There was a time when all great cities were built near water. Whether for agriculture, aesthetics, energy or just plain drinking, water was a life-affirming, life-sustaining resource. But with the advent of advanced engineering in the form of dams, pumps and pipes, cities like Los Angeles thrived in places with very little fresh water. Now, global climate change is leaving many of those cities in danger of running dry.

But there is hope on the horizon, says Newsha Ajami, senior research engineer at the Woods Institute for the Environment and director of urban water policy with Stanford University’s Water in the West program. Just as engineering made it possible to store and pump fresh water great distances, the field is developing new ways to use less water, to store more of this prized resource, to repurpose used “gray water” for non-potable uses like agriculture, and to inform inventive policy approaches to conserve fresh water.

It won’t be easy, she says. California alone has over 7,000 independent water agencies that must be wrangled into a cohesive team to make it real, but recent progress has people believing once again that our parched cities can be saved. It’s all here on this episode of Stanford Engineering’s The Future of Everything podcast. Listen here.

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In recent years, biologists have learned that the vaginal microbiome — the make-up of the bacteria in the vagina — during pregnancy may be the best predictor of pre-term birth. It is a valuable finding that could reshape obstetrics. What is perhaps more revelatory about this emerging knowledge is that biologists have learned it from a surprising source: statistics.

Stanford’s Susan Holmes is one such statistician in the rapidly evolving science of using statistics to understand biology. Holmes is now turning her attention to improving our understanding of the remarkable human immune system to help fight cancer and other deadly diseases. She says that the statistician’s greatest contribution to biology may not necessarily reside in analyzing the myriad numbers and data points available these days, but rather in divining and explaining which patterns are replicable and which are not.

Join bioengineering Professor Russ Altman for the latest episode of Stanford Engineering’s The Future of Everything podcast to discuss the fascinating and fast-evolving field of statistical biology with a leading proponent of the science, Susan Holmes. Listen here.

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Mechanical engineer Sheri Sheppard got her start in engineering working on the Corvette for General Motors and later worked for both Ford and Chrysler.

Back then, she was among a handful of women engineers in the auto industry, where she learned firsthand the risks a monolithic culture presents.

Today, Sheppard is a professor at Stanford University, where she works to encourage diversity in the student body, in the classroom and in the curriculum. She says that engineering needs to reach beyond the traditional disciplines to tap into sociology, history, ethics, psychology and even philosophy to help engineers explore the “peopleness” in the challenges they are trying to solve.

In that pursuit, she encourages women and minorities eager to transform their field to become what her colleague Deb Meyerson has dubbed “tempered radicals” — leaders who can rock the boat while remaining in the boat. The result, Sheppard tells Stanford Engineering’s The Future of Everything podcast, is more empathetic engineering that benefits everyone in society.

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We’re all familiar with those algorithms on our favorite e-commerce and streaming services that recommend purchases, books or movies based on what “others like you” have enjoyed. In the industry, they are known as “recommender engines.”

Medical doctor Jonathan Chen is an assistant professor of medicine at Stanford and an expert in bioinformatics who wondered if the medical profession might benefit from similar artificial intelligence. He now creates recommender engines for doctors that comb real-world clinical data to help them make key decisions based on steps other doctors have taken with similar patients, empowering individuals with the collective experience of the many.

Chen tells Stanford Engineering’s The Future of Everything podcast that such programs will soon be commonplace in exam rooms, helping doctors become better at what they already do and making medical practice a more consistent, universal experience for everyone.

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Artificial intelligence can help us design safety-critical systems for aircraft and other vehicles that are more robust to the many sources of uncertainty in the real world, says aerospace professor Mykel Kochenderfer.

Building systems that meet the exceptionally high level of safety expected of commercial air transport is challenging, but Kochenderfer says that the key is in modeling the likelihood of the full spectrum of outcomes and planning accordingly. Validating the safety of these systems is also difficult, often requiring billions of simulations. He tells Stanford Engineering’s The Future of Everything how AI, empowered by algorithms such as “dynamic programming,” can make autonomous systems safer.

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With a degree in photography with a concentration in mathematics and boasting high-profile jobs at two of the most influential visual outlets in the last century, National Geographic and Instagram, Pamela Chen knows a bit about the state of modern photography and the algorithms that shape popular tastes.

Now, as the Human-Centered Artificial Intelligence and John S. Knight Journalism (HAI-JSK) Fellow at Stanford, she studies how artificial intelligence is shaping the role of photography in society: particularly the rise of memes, which she refers to as “packets of culture.” Chen says mathematics is redefining photography as much as artistic vision, altering both consumer tastes and the creative eye of photographers who want to become – or remain – relevant in a rapidly changing world.

Chen joins The Future of Everything host Russ Altman to discuss why artificial intelligence’s influence on photography is only just in its infancy and why lovers of photography still have power to shape AI as much as it shapes us. [Listen here.]

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Stanford engineering alumnus Michael O’Sullivan, now at the University of Auckland, likes to say his business is the “science of decision-making,” and that expertise paid off handsomely in his native New Zealand’s successful response to COVID-19.

O’Sullivan pivoted his knowledge of computer modeling, usually reserved for optimizing business processes, to help predict how quickly the disease might have spread through the island nation’s 5 million inhabitants, and to gauge various national response strategies. Based on expert models from a team of researchers that included O’Sullivan, New Zealand’s leadership took an aggressive approach and quelled the disease after just a month of lockdown.

O’Sullivan tells Stanford Engineering's The Future of Everything he is now turning his attention to highly detailed geographic models to better understand how COVID-19 could spread geographically if a future outbreak occurs. He is also putting his modeling to work to help analyze how response to the disease will impact the coming flu season and how the lockdown might have had negative effects on the treatment of other illnesses such as the early detection of cancer. 

Michael O’Sullivan, MS ’97, PhD '01, would like to acknowledge the work of Kevin Ross, MS '01, PhD '04 (Precision Driven Health), and Pieta Brown (Orion Health), who have been instrumental in making a pipeline for the modeling work discussed in this podcast readily available to the New Zealand government.

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Megan Palmer, executive director of Biopolicy and Leadership Initiatives at Stanford, joins bioengineer Russ Altman for this episode of Stanford Engineering’s The Future of Everything podcast, to discuss how we can better prepare for future virus outbreaks and how the world could ultimately become a more secure, peaceful and prosperous place as a result of the lessons learned from COVID-19.
 

The key to that future, she says, will be better coordination and communication among world leaders in science, security and policy, who will be charged with foreseeing and preventing the next crisis. Likewise, it will take better cooperation between humankind and the natural world.

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As she tells it, the life of immunologist Catherine Blish has not changed all that much from what it was just a couple months ago.

Her lab still studies deadly infectious diseases, but instead of myriad killers like HIV, dengue fever, influenza and the like, her team is now focused solely on the SARS-CoV-2 virus that causes COVID-19. Only a select group of researchers in the world are qualified to work with such serious viruses, and fewer still are properly equipped with the protective gear and sophisticated ventilation systems needed to guarantee the safety in the lab.

Blish recently joined Russ Altman for this special COVID-19 edition of Stanford Engineering’s The Future of Everything podcast, to talk about the unique character of the virus, a few surprises she and others have unearthed in their research, and how once-competitive scientists around the world have united to find treatments and a vaccine that are critical to ending the pandemic for good.

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Seema Yasmin is a rarity in public health: a medical doctor who is also a journalist. As such, she’s seen a lot, from Ebola in West Africa to SARS and MERS, and now COVID-19, the most serious pandemic in a century.

Yasmin is currently director of research and education at the Stanford Center for Health Communication. From her years in the Epidemic Intelligence Service at the U.S. Centers for Disease Control and Prevention — a group widely described as “the disease detectives” — and as a reporter for The Dallas Morning News, Yasmin says that the greatest impediment to halting an outbreak is the rapid spread of bad information, and even abject disinformation, which when abetted by social media can spread faster than the disease itself. To halt the pandemic, she says medical science and public health experts — and the journalists who cover them — must become better storytellers to get ahead of bad information and to “pre-bunk” false claims that lead to bad decision-making.

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