The Future of Everything - Stanford Engineering

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Mark Schnitzer: How to better understand the brain Jan 29, 2021

Show notes

Stanford’s Mark Schnitzer says several of the more exciting recent advances in his field of applied physics have come through developing new imaging technologies that peer into the brain as never before. What’s more, Schnitzer says the insights gained have put the world closer to solving long-vexing brain diseases, like Parkinson’s and others, where the circuitry of the brain seems to be malfunctioning.

Schnitzer says that these new imaging methods are helping medical science discern the specific functions of various cells that make up the brain’s complex communications systems. No longer is the brain seen as a monolith of neurons, but instead as a complex organ made up of numerous cell types, each with its own role to play in proper function.

Best of all, medical science is starting to move toward manipulating these cells with new drugs and other treatments that could lead to a cure or effective treatment for previously untreatable diseases and chronic pain, as Schnitzer tells Stanford Engineering’s The Future of Everything podcast and host, bioengineer Russ Altman. Listen and subscribe here.

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Mutale Nkonde: How to get more truth from social media Jan 23, 2021

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The old maxim holds that a lie spreads much faster than a truth, but it has taken the global reach and lightning speed of social media to lay it bare before the world.

One problem of the age of misinformation, says sociologist and former journalist Mutale Nkonde, a fellow at the Stanford Center on Philanthropy and Civil Society (PACS), is that the artificial intelligence algorithms used to profile users and disseminate information to them, whether truthful or not, are inherently biased against minority groups, because they are underrepresented in the historical data upon which the algorithms are based.

Now, Nkonde and others like her are holding social media’s feet to the fire, so to speak, to get them to root out bias from their algorithms. One approach she promotes is the Algorithmic Accountability Act, which would authorize the Federal Trade Commission (FTC) to create regulations requiring companies under its jurisdiction to assess the impact of new and existing automated decision systems. Another approach she has favored is called “Strategic Silence,” which seeks to deny untruthful users and groups the media exposure that amplifies their false claims and helps them attract new adherents.

Nkonde explores the hidden biases of the age of misinformation in this episode of Stanford Engineering’s The Future of Everything podcast, hosted by bioengineer Russ Altman. Listen and subscribe here.

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Karen Liu: How robots perceive the physical world Jan 15, 2021

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Stanford’s Karen Liu is a computer scientist who works in robotics. She hopes that someday machines might take on caregiving roles, like helping medical patients get dressed and undressed each day. That quest has provided her a special insight into just what a monumental challenge such seemingly simple tasks are. After all, she points out, it takes a human child several years to learn to dress themselves — imagine what it takes to teach a robot to help a person who is frail or physically compromised?

Liu is among a growing coterie of scientists who are promoting “physics-based simulations” that are speeding up the learning process for robots. That is, rather than building actual robots and refining them as they go, she’s using computer simulations to improve how robots sense the physical world around them and to make intelligent decisions under changes and perturbations in the real world, like those involved in tasks like getting dressed for the day.

To do that, a robot must understand the physical characteristics of human flesh and bone as well as the movements and underlying human intention to be able to comprehend when a garment is or is not going on as expected.

The stakes are high. The downside consequence could be physical harm to the patient, as Liu tells Stanford Engineering’s The Future of Everything podcast hosted by bioengineer Russ Altman. Listen and subscribe here.

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Jef Caers: How better mineral exploration makes better batteries Jan 08, 2021

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It has been said that batteries hold the key to a sustainable future.

But so-called “clean energy” does not come without environmental costs. For instance, says Stanford geoscientist Jef Caers, the batteries in a single Tesla contain some 4.5 kilograms — about 10 pounds — of cobalt, in addition to plenty of lithium and nickel, too.

With some 300 million cars in the U.S. right now, a full transition to electric vehicles would be impossible without new resources. But, finding new deposits and getting them safely out of the ground is an expensive and environmentally fraught proposition. Half of all cobalt reserves and most of current production come from just one unregulated country, Congo. To close the gap using environmentally and labor-regulated resources, Caers says we need AI to rapidly explore countries with stricter safeguards.

To help, geoscientists like Caers are turning to data science and artificial intelligence to quickly identify new resources, to get the most out of those we already know about and to improve refining processes to leave as small an environmental footprint as possible. Their success, he says, could be key to America’s environmental future and its long-term energy independence. Learn more on this episode of Stanford Engineering’s The Future of Everything podcast, hosted by Stanford bioengineer Russ Altman. Listen and subscribe here.

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Evan Reed: How to discover a magic material Dec 10, 2020

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Evan Reed and a team of scientists recently identified a promising solid material that could replace highly flammable liquid electrolytes in lithium-ion batteries.

The trick? Reed didn’t discover the material the old-fashioned way, using trial and error to narrow down a list of candidates. Instead, he used computers to do the legwork for him. He says that until recent advances in computer science, the seemingly never-ending search for new materials was more like a quest for unicorns. Breakthrough materials must possess that rarest of combinations: precise physical characteristics with few if any downsides.

It's exacting and time-consuming work, Reed says, but computers are accelerating the pace of discovery. He now believes the future of materials science lies at the heart of a computer algorithm, as he tells listeners in this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here.

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Renée DiResta: How to beat bad information Nov 18, 2020

Show notes

Renée DiResta is research manager at the Stanford Internet Observatory, a multi-disciplinary center that focuses on abuses of information technology, particularly social media. She’s an expert in the role technology platforms and their “curatorial” algorithms play in the rise and spread of misinformation and disinformation.

Fresh off an intense period keeping watch over the 2020 U.S. elections for disinformation as part of the Election Integrity Partnership, DiResta says the campaign became one of the most closely observed political dramas in American history.

She says that whether it comes from the top down or the bottom up, bad information can be spotted and beaten, but overcoming falsehoods in the future will require vigilance and a commitment to the truth. She explains more on Stanford Engineering’s The Future of Everything podcast, with host Russ Altman. Listen and subscribe here.

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Will Tarpeh: How to take the waste out of wastewater Nov 13, 2020

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Once the bathwater is drained, the toilet flushed or the laundry done, few give a passing thought to the wastewater that leaves our homes. But chemical engineer Will Tarpeh might change your mind, if you give him the chance.

Tarpeh says that that water is a literal mine of valuable chemicals. Chemicals like nitrogen, phosphorus and potassium make great fertilizers. Lithium can be used in lithium ion batteries. And even pharmaceuticals could be recovered and reused. In fact, Tarpeh points out that if we could harvest all the world’s urine, it could supplant 20–30% of our nitrogen needs — and in some places can be cheaper to do than existing production and transport methods.

Waste, Tarpeh says, is just a state of mind. His “pipe dream,” he says, is to develop next-generation treatment plants on the neighborhood or even household scale able to extract the valuable chemicals in water most would rather send down the drain. Tarpeh tells bioengineer Russ Altman all about it in this the latest episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here.

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Kwabena Boahen: How to build a super-efficient super-computer Nov 09, 2020

Show notes

Bioengineer Kwabena Boahen builds highly efficient “neuromorphic” supercomputers modeled on the human brain. He hopes they will drive the artificial intelligence future. He uses an analogy when describing the goal of his work: “It’s LA versus Manhattan.”

Boahen means structurally. Today’s chips are two dimensional — flat and spread out, like LA. Tomorrow’s chips will be stacked, like the floors of the skyscrapers on a New York block. In this analogy, the humans are the electrons shuffling data back and forth. The shorter distances they have to travel to work, and the more they can accomplish before traveling home, will drive profound leaps in energy efficiency. The consequences could not be greater. Boahen says that the lean chips he imagines could prove tens-of-thousands times less expensive to operate than today’s power hogs.

To learn how it works, listen in as Kwabena Boahen describes neuromorphic computing to fellow bioengineer Russ Altman in the latest episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here.

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Daphne Koller: How machine learning is transforming drug discovery Nov 02, 2020

Show notes

In a world where a drug takes years and billions of dollars to develop, just one in 20 candidates makes it to market. Daphne Koller is betting artificial intelligence can change that dynamic.

Twenty years ago, when she first started using artificial intelligence to venture into medicine and biology, Koller was stymied by a lack of data. There wasn’t enough of it and what there was, was often not well suited to the problems she wanted to solve. Fast-forward 20 years, however, and both the quantity and quality of data, and the tools for studying biology, have advanced so dramatically that the adjunct professor of computer science at Stanford founded a company, insitro, that uses machine learning (a subspecialty of artificial intelligence) to explore the causes and potential treatments for some very serious diseases.

She tells bioengineer Russ Altman about the lessons she’s learned along the way, and the challenges and rewards of getting diverse teams of experts from many fields to speak the same language. It’s all on this episode of Stanford Engineering’s The Future of Everything podcast. Listen here, and subscribe to the podcast here.

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Markus Covert: How to build a computer model of a cell Oct 19, 2020

Show notes

When Stanford bioengineer Markus Covert first decided to create a computer model able to simulate the behavior of a single cell, he was held back by more than an incomplete understanding of how a cell functions, but also by a lack of computer power. His early models would take more than 10 hours to churn through a single simulation and that was when using a supercomputer capable of billions of calculations per second.

Nevertheless, in his quest toward what had been deemed "a grand challenge of the 21st century," Covert pressed on and eventually published a paper announcing his success in building a model of just one microbe: E. coli, a popular subject in biological research. The model would allow researchers to run experiments not on living bacteria in a lab, but on a simulated cell on a computer.

After all was said and done, however, the greatest takeaway for Covert was that a cell is a very, very complex thing. There were fits and starts and at least one transcendent conceptual leap — which Covert has dubbed “deep curation” — needed to make it all happen, but he found a way. As Covert points out, no model is perfect, but some are useful. And that is how usefulness, not perfection, became the goal of his work, as he tells fellow bioengineer Russ Altman in this episode of Stanford Engineering’s The Future of Everything podcast. Listen here, and subscribe to the podcast here.

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