The Future of Everything

Start an email with “I hope” and before you can type the next word, the program will suggest you complete it with “all is well.” You may not have realized it, but this is AI-generated text. In the past several years, this technology has advanced beyond completing sentences in emails: It can now respond to others’ emails, and write essays, hip-hop songs, public health messages, and much more. What’s more, it can sometimes be even more effective than humans at conveying certain messages. In this episode of Stanford Engineering’s The Future of Everything, Jeff Hancock, a professor of communication at Stanford, explores this phenomenon and its positive and negative implications for how we communicate and how we understand our interactions with one another and the world. Learn more with Hancock and host Stanford Professor Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The world has made remarkable gains in pediatric medicine and public health over the past several generations. The average American child of the 21st century has access to clean water and milk, fully functioning sewage systems, and antibiotics, vaccines, and other medicines. Result: Child mortality rates have declined dramatically over the past century. At the same time, a widening income gap in the United States has led to vastly different prevalence rates for health conditions between low- and high-income families, says Stanford pediatrician Lisa Chamberlain. And COVID-19, she says, has put a spotlight on many of the health challenges associated with these wealth disparities. In this episode of Stanford Engineering’s The Future of Everything, Chamberlain joins host Professor Russ Altman to discusses these issues, and how telehealth might help overcome some of the burgeoning challenges in pediatric health. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

It may not be immediately obvious, but there are huge financial, environmental and security costs associated with storing all the selfies, videos, documents and other digital assets the world is generating. One way to address this issue is by developing better compression algorithms that can represent the data more succinctly. Another is by creating new ways of storing the information itself, including, potentially, within biological molecules.In this episode of Stanford Engineering’s The Future of Everything, Stanford electrical engineer Tsachy Weissman discusses with host Professor Russ Altman the challenges associated with storing our ever-growing mountains of digital data – and how they can be addressed. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Children have an amazing capacity for healing after injury. Break a leg, the bone grows back; cut a finger, the skin heals. But as we age, most tissues no longer heal easily, and tissue loss is unavoidable due to aging, degenerative diseases such as arthritis, and cancer.In this episode of Stanford Engineering’s The Future of Everything, Fan Yang and host and fellow bioengineer Russ Altman, discuss how biomaterials created in a lab can be injected into wound sites to enable tissue regeneration or rejuvenation by modulating stem cells, vasculature, or immune responses.They also discuss the potential of exploiting such biomaterials to create 3D cancer models to facilitate discovery of novel drugs with reduced time and cost. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

You might not realize it, but AI-driven systems are integrated into virtually every aspect of our lives. But how can we be certain the values AI systems are striving for reflect what we want for ourselves and for society? And how can scientists and engineers do a better job of increasing people’s trust in AI? Stanford computer scientist Carlos Guestrin is a leading voice on how to advance and implement a more trustworthy AI. Learn about his work in this area, and his particular interest in AI and healthcare, on this episode of Stanford Engineering’s The Future of Everything, with host Professor Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Anyone who’s ever been to a hospital knows that the healthcare system is extremely complex. Every patient has their own challenges – and they will typically see multiple physicians, nurses, pharmacists, and other healthcare practitioners, and come into contact with a slew of medical technologies, protocols, and billing and insurance systems.Sara Singer, a Stanford professor of medicine, is an expert on integrated care – the development of tools, technologies, and processes designed to improve the interactions among patients, clinicians, and other providers to lower costs and improve health outcomes.In this episode of Stanford Engineering’s The Future of Everything, she explains how new technology, and its improved integration into the healthcare system, can enhance practitioners’ ability to care for patients. Learn more with host Professor Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Whether it’s autonomous vehicles or assistive technology in healthcare that can do things like help the elderly do core tasks like feeding themselves, some of the most challenging problems in the field of robotics involve how robots interact with humans, with all of our many complexities.Drawing from fields as varied as cognitive neuroscience, psychology, and behavioral economics, Stanford computer scientist Dorsa Sadigh is exploring how to train robots to better understand humans – and how to give humans the skills to more seamlessly work with robots.Learn more on this episode of Stanford Engineering’s The Future of Everything, with host Professor Russ Altman. Listen and subscribe Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Among the many areas James Zou might have chosen to apply his considerable knowledge of artificial intelligence, he opted for health care. It was the most interesting, the most complex and the most impactful area of study. In short, it was the most exciting outlet for his expertise.Since that epiphany, Zou has gone on to publish influential studies that have improved the patient experience, shaped basic research and sped the development of new drugs. Among his most important contributions, Zou says, are efforts to expose and overcome bias in the data and algorithms.His latest project, Pathfinder, uses anonymized, real-world medical records to allow researchers to conduct synthetic clinical trials on fictional (but realistic) patients, as Zou explains in this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Stanford professor Johan Ugander is an expert in making sense of messy data. Lately he’s been working to tell fact from fiction online, as news stories spread on social media. He comes at the question from a unique angle, using machine learning to study the differing patterns in how both types of information spread (or don’t). In so doing, Ugander has come to some interesting conclusions and, more important, suggests some novel strategies for preventing the spread of misinformation. False stories, he says, are more “infectious,” with wide-ranging consequences for how they spread. Strategies to slow or restrict this infectiousness range from increasing digital literacy to asking potential sharers to consider the factual accuracy of a story they are about to share. Ugander has also started to take his research in a new direction, criminal justice, working to make sense of the complex data records that a Stanford team has collected to understand California’s parole system, as he tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

For a profession that has existed essentially since the beginning of human civilization, few people fully appreciate the importance of construction in our everyday lives, but Martin Fischer does. To build the key infrastructure of society, he says, requires intimate understanding of human nature, the environment, the materials and the ever-evolving techniques of building things. Fischer has grown frustrated with the present state of his profession and decided to change its trajectory using artificial intelligence and virtual reality to redefine what construction will look like in the future. It’s an effort he hopes will unite the profession in creating more efficient, safer and more livable homes, buildings, airports, bridges and more. Fischer muses all about the future of construction in this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Much of what the world knows about genetic diseases is learned by comparing the DNA of people with a shared disease against the DNA of otherwise healthy people to learn where the differences lie. This is all well and good except that, written into all that DNA, is a lot of other information that the subjects would rather keep private. And that’s where Gill Bejerano enters the scene. He’s an expert in cryptogenomics, a discipline that marries the fields of cryptography and genomics to essentially scramble the genetic code to researchers in such a way that they can still glean valuable information from it without revealing the donor’s entire genetic code. Bejerano’s efforts have been so successful he’s now applying a similar process to medical records, as he explains to host Russ Altman and listeners of this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

As the field of computer science has evolved over the last half century, so too has the way in which computer science is taught and to whom it is taught. Stanford lecturer Cynthia Lee says she is encouraged by the diversity she sees as she looks out over her classroom. But that wasn’t always the case, particularly when she, a woman, was in college. Lee has since dedicated her career to changing that mindset from a fixed and rigid outlook to one that is more open and welcoming of diverse backgrounds and skills. Change, she says, can come from the top in how classes are structured and at the foundation in undoing preconceptions about who can excel in the field. Diverse faces, myriad skills and interests, fewer lectures and more hands-on, peer-to-peer collaboration are in order, Lee tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In one of computer science’s more meta moments, professor Chelsea Finn created an AI algorithm to evaluate the coding projects of her students. The AI model reads and analyzes code, spot flaws and gives feedback to the students. Computers learning about learning—it’s so meta that Finn calls it “meta learning.”Finn says the field should forgo training AI for highly specific tasks in favor of training it to look at a diversity of problems to divine the common structure among those problems. The result is AI able to see a problem it has not encountered before and call upon all that previous experience to solve it. This new-look AI can adapt to new courses, often enrolling thousands of students at a time, where individual instructor feedback would be prohibitive. Emboldened by results in class, Finn is now applying her breadth-over-specificity approach to her other area of focus, robotics. She hopes to develop new-age robots that can adapt to unfamiliar surroundings and can do many things well, instead of a few, as she tells host Russ Altman and listeners to this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

For experts in digital graphics and visual perception, like computer scientist Kayvon Fatahalian, the recent pandemic has been a call to arms. Fatahalian says he and others in the field felt an urgent responsibility to harness their background in computer graphics and interactive techniques to improve life for people across the globe. He says new, virtual tools have proved better than past, real ones in improving certain aspects of our everyday lives. His job as a computer scientist is to make those experiences more successful, more of the time. His role as a teacher is a case in point. While the virtual world is not a replacement for face-to-face interaction between students and instructors, Fatahalian notes there are many aspects of the live virtual lecture experience that enable more students to participate, and participate more frequently than in a physical classroom. Fatahalian is now busier than ever discovering where and how the virtual world excels and creating new tools to meet the evolving need, as he tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

One underappreciated fact about the explosion in genetic databases, like consumer sites that provide information about ancestry and health, is that they unlock valuable insights not only into an individual’s past and future, but also for that individual’s entire family. This raises serious concerns about privacy for people who have never submitted their genetic information for analysis, yet share much the same code as one who did.Today’s guest, Kuang Xu, is an expert in how genetic information can and should be used. He says that the DNA problem weighs heavily on privacy experts in fields ranging from law and engineering to public health and criminal justice. The fundamental question is: Can we create methods for accessing genetic data while maximizing the privacy of all involved?The problems will only grow more intense as time and data accumulate, Xu says, unless we resolve them now, as he explains on this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Readers of Eric Appel’s academic profile will note appointments in materials science, bioengineering and pediatrics, as well as fellowship appointments in the ChEM-H institute for human health research and the Woods Institute for the Environment. While the breadth of these appointments does not leap to mind as being particularly consistent, the connections quickly emerge for those who hear Appel talk about his research.Appel is an expert in gels, those wiggly, jiggly materials that aren’t quite solid, but not quite liquid either. Gels’ in-betweenness is precisely what gets engineers like Appel excited about them. Appel has used gels for everything from new-age fire retardants that can proactively prevent forest fires to improved drug and vaccine delivery mechanisms for everything from diabetes to COVID-19. Hence the appointments across engineering and medicine.Listen in with host and bioengineer Russ Altman as Appel explains to Stanford Engineering’s The Future of Everything podcast why gels could be the future of science. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

As the world has learned through the recent pandemic, epidemiological studies can be complicated by many unanticipated factors. Lianne Kurina is an expert in the design of epidemiological studies who says that the key to greater confidence is better design.The gold standard, she says, is the randomized controlled trial—a study that compares groups that are ​essentially identical by every apparent factor but one— the vaccinated vs. the unvaccinated, for instance. In the case of COVID-19 vaccinations, Kurina stresses that investigators did an exemplary job of this. ​In situations where we can't use a randomized controlled trial, achieving ​a similar balance and specificity is far harder. Kurina says ​that researcher​s working with observational data, rather than trial data, must always be attuned to the overlooked factors—“confounders” she calls them—that can muddy the data and render a study moot. ​ However, Kurina notes, the better one controls the confounders ​in these observational studies via better design ​and data collection, the greater confidence we can have in the end results, as she tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

For decades, the general-purpose central processing unit—the CPU—has been the workhorse of the computer industry. It could handle any task—literally—even if most of those capabilities were unnecessary.This model was all well and good as chips grew smaller, faster and more efficient by the day, but less so as the pace of progress has slowed, says electrical engineer Priyanka Raina, an expert in chip design. Raina says that, to keep chips on their ever-improving trajectory, chip makers have shifted focus to chips that do specific tasks very well. The graphics processing unit (GPU), which handles the intense mathematics necessary for video and gaming graphics, is a perfect example.Soon, there’ll be a faster, more efficient chip for every task, but it’ll take industry-wide cooperation to get there, as Raina tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Most people know the seismograph, those ultrasensitive instruments that record every small shift in the Earth’s crust.But did you know that the very latest method for measuring earthquakes involves fiber optic cables that carry internet data around the world?Stanford geophysicist Biondo Biondi says that the waves of energy sent forth by an earthquake cause fiber optic cables to stretch and contract ever so slightly. Using precise mathematical algorithms, experts like Biondi can measure earthquake intensity, making every meter of fiber optic cable a potential seismograph and dramatically increasing the data experts can gather in a day. Biondi’s sensor arrays are so sensitive they can detect sinkholes, landslides and even the rumblings of failing urban infrastructure.These new technologies – and the secrets they might reveal – are only starting to emerge, as Biondi tells listeners in this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Anyone who’s ever made weekend plans based on the weather forecast knows that prediction – about anything – is a tough business. But predictive models are increasingly used to make life-changing decisions everywhere from health and finance to justice and national elections. As the consequences have grown, so has the weight of uncertainty, says today’s guest, mathematician and statistician Emmanuel Candès. Candès knows this paradigm all too well. He is an expert in identifying flaws in today’s highly sophisticated computer models. He says the secret to better prediction rests in building models that don’t try to be right every time, but instead offer a high degree of certainty about things of real consequence. In that regard, the old scientific maxim holds, he says. Correlation does not equal causation. The statistician’s job, therefore, is helping to sort through the noise to find the nuggets of truth in the things that really matter, as Candès tell listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Electronics are everywhere these days, so much so that often we don't even register that we are using them. The use of electronics will only grow over time as engineers solve societal challenges through increased connectivity, faster computation, new high-tech gadgets, and energy sustainability. Against that backdrop, electrical engineers like Stanford’s Srabanti Chowdhury have been searching for new semiconductors that can expand the application space beyond the ubiquitous silicon. Among the options she’s exploring is an old familiar friend—diamond—and a few new ones, too, like gallium nitride.The diamonds Chowdhury works with are a far cry from the sparkly gems a jeweler might prize. These diamonds are “doped” with other elements to achieve optimal electrical performance. Meanwhile, gallium nitride has shown promise in LEDs and lasers, as well as in cutting-edge radar systems—among other applications.While these new semiconductors have raised hopes of scaling new heights where even silicon cannot reach, much work remains if they are ever to move from lab bench to laptops and myriad other electronic devices. The payoff, however, will be smaller, faster, more powerful, more energy efficient, and more versatile electronics, as Chowdhury tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

It now seems more certain than ever that the world will make the all-important transition to electric vehicles, but that shift raises important questions about global preparedness.The world is going to need a lot of batteries to make it happen and engineers are rightly concerned about everything from the availability of raw materials to how many miles can I drive before I run out of juice?Simona Onori is an electrical engineer by training and a professor of energy resources engineering as well as an expert in creating computer models of what that electric future will look like. For instance, she is developing mathematical battery management systems that assess the internal chemistry of a battery to predict how much life is left in it, how safe it is and, yes, how long until that next charge is needed.Onori likens her analyses to “battery biopsies” that can help engineers and everyday drivers get more life out of their batteries. Don’t fret, our electric future is in good hands, Onori reassures listeners in this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Engineer Irene Lo studies markets, but not traditional marketplaces based in cash.Instead, she studies markets for goods/resources that place a high value on social goods like diversity, fairness and equity.Thus, Lo came to help San Francisco create an algorithm to assign kids more fairly to public schools across geographic, social, racial and economic boundaries. As it turns out, math is just the first step. The most challenging part was getting families to trust in the system, begetting a multi-year community engagement effort.Lo is now turning her attention to other markets with social impact, like her work on the system that places medical students in residency programs across the country or one trying to make the palm oil supply chain fairer for farmers.Listen in as Irene Lo explains the changing face of markets to host Russ Altman in this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Oft-heralded 3-dimensional printers can build objects ranging from simple spoons to advanced running shoes.While those objects are usually made very slowly, the latest printing technologies portend a new era of 3D printing in real-time for use in health care. The possibilities are endless, says Joseph DeSimone, who is an expert in translational medicine – the field of transferring promising technological breakthroughs into real-world products. He says printers he developed have led to the first FDA-approved 3D printed dentures, ultra-thin microneedles that make it easier and more effective to deliver vaccines, and even implantable chemotherapy devices that kill tumors while reducing side effects for patients. From dentistry to oncology, the promise of 3D printed medical devices is only just emerging, as DeSimone explains in this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Tina Hernandez-Boussard is an expert in biomedical informatics who says a new era of understanding the real outcomes of our health care systems is on the horizon thanks to big data, artificial intelligence, and the growing availability of electronic health data. She says that the combination of these tools and data holds the promise of providing never-before-possible insights into whether health procedures truly improve patient quality of life and for which populations.With these tools, she says, her field can peer into the “real-world” details hidden in the medical records, even going so far as to use natural language processing to analyze the freeform notes and emails to and from the provider. The examples are virtually limitless: matching health records against data from wearable devices to know when a knee patient is not getting enough physical exercise, cross-referencing prescriptions to learn whether a patient might be susceptible to adverse drug combinations, or even revealing undisclosed medical events such as past mild heart attacks.It’s all there in the data, waiting for us to explore, as Tina Hernandez-Boussard tells bioengineer and host Russ Altman in this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Nate Persily is a professor at Stanford Law School and an expert in election law.He sees the most recent presidential election as a fundamental change in the way Americans vote. For the first time ever, the majority of voters cast their ballot by mail, rather than at a polling place. It “was an earthquake,” Persily says, speaking metaphorically about the 2020 election’s profound implications for future elections.But not all agree it was a success. Republicans and Democrats are further apart than ever in their beliefs as to whether the recent presidential election was free and fair. Addressing polarization in beliefs regarding the fairness of the election will be very difficult. Until leaders come together in a bipartisan fashion to affirm the legitimacy of an election winner, reform will not be able to do much to address this underlying problem.Failing that, we need to bolster the institutional position of all nonpartisan election administrators who are placing the public interest over party, as Persily tells host Russ Altman in this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Whether by injury or disease, paralysis has afflicted humans through the ages.Only now have science and technology converged to a point where scientists can contemplate a day when computers and the human mind can communicate directly to restore a certain degree of independence to people with debilitating spinal injuries and other physical conditions that impede or prevent movement.Electrical engineer Krishna Shenoy is an expert in such brain-computer interfaces and has built machinery by which humans can control the movement of computer cursors with mere thoughts. Using small chips implanted in the brain itself, Shenoy “listens intelligently” to the electrical “chatter” among a hundred or so of the 100 billion neurons of the brain’s motor cortex and then translates the meaning into language a computer can understand. In this way, Shenoy has allowed a man with paralysis to “write” his thoughts at some 17 words per minute, a record more than double the previous standard.Work remains, but the future of brain-computer interfaces is on the horizon as Krishna Shenoy tells us on this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sam Wineburg, a research psychologist at Stanford’s Graduate School of Education, recently conducted a nationwide study of the fact-checking skills of thousands of American high school students.He didn’t go about it with a survey asking the kids to self-report their own behaviors. Instead, he devised a live experiment that charged the 3,000 students in the study to determine the veracity of a now-famous bit of fake news from the 2016 election. Wineburg and team were then able to follow along as students tried to find the true source of the video, which had been produced in Russia as part of a disinformation campaign. In the end, just three students – one-tenth of one percent – arrived at the right answer. Rather than blame the kids, however, Wineburg says fault lies with the tools they are using, which have changed so dramatically in speed and scope that their fact-checking skills have had trouble keeping up. All is not lost, he promises, but fixing the problem will require changing not just what information students consume, but the way they think about it, as Wineburg tells host Russ Altman in this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Many have now become familiar with the term herd immunity, an idea few outside the infectious disease community knew just a few short months ago.It’s an elusive concept to comprehend, and harder still to achieve, but Stanford epidemiologist Dr. Julie Parsonnet says it’s important to understand just what herd immunity does – and doesn’t – mean for today’s pandemic.Broadly speaking, herd immunity is reached when enough people have either recovered from or have been fully vaccinated against an infectious disease and there are no longer enough susceptible people in the entire population (the herd) to sustain transmission. Herd immunity doesn’t mean there won’t be cases, only that when they crop up, they will then die out. Parsonnet says this term is meant for epidemiologists to model what things will and won’t work; herd immunity is never really a public health goal in and of itself. Parsonnet also says that, in models, there are many obstacles to attaining herd immunity, including vaccine hesitancy, especially in people most likely to transmit the infection (young adults); imperfect effectiveness of the vaccine; movement of people; carriage of the virus in non-human hosts; and the continuous appearance of variants. Importantly, Parsonnet says, herd immunity is unlikely to be permanent. Society must remain vigilant, continuously limiting the number of susceptible people to keep the herd safe. She therefore counsels deemphasizing the concept and instead bringing the diversity of communities into the conversation to achieve high levels of protection in the U.S. and globally. She says every vaccine given is a step in the direction of “normal.”In this episode of Stanford Engineering’s The Future of Everything podcast, host Russ Altman and Parsonnet also talk about her other research showing that average human body temperature is on the decline worldwide. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Imagine typing words into a text editor and watching on a nearby television as a well-known celebrity speaks those words within seconds.Computer graphics expert Maneesh Agrawala has imagined it and has created a video editing software that can do it, too. Given enough raw video, Agrawala’s application can produce polished, photorealistic video of any person saying virtually anything he types in.While he acknowledges concerns about manufactured “deep fakes” of political leaders or others speaking words they never said, Agrawala chooses to focus on the profound upside. He envisions the television and film industries using his technology to forgo costly reshoots, for instance, or medical professionals helping people with damaged vocal cords regain their natural voices.In the end, while ethical and legal frameworks are being developed to address deep fakes with all due seriousness they deserve, Agrawala says the benefits of the technology, and his passion for it, gets at the most basic of all human endeavors — better communication. Agrawala tells host Russ Altman all about it in this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Biology is not typically considered a mathematically intensive science, says Noah Rosenberg, an expert in genetics, but all that is about to change.Math, statistics, data and computer science have coalesced into a growing interest in applying quantitative skills to this traditionally qualitative field.The result will be better and more accurate models of life, ranging from genetic inheritance to the entirety of human society. The yield will be a greater understanding and, quite possibly, revolutionary interventions into disease, ecology, demography, and even evolution itself. The tools of mathematical biology have never been more apparent, Rosenberg says, as mathematical models of the spread of infectious disease have been central around the world in the response to the COVID-19 pandemic.With applications in health care, forensic genetics, and human evolution, the tools of mathematical biology are proving more relevant and more needed than ever, as Noah Rosenberg tells Stanford Engineering’s The Future of Everything podcast, with host bioengineer Russ Altman. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Slowly but surely, the highly centralized, industrial electric grid that supplies power to the vast majority of American homes and business is changing.Our existing system of massive power plants and huge networks of high-voltage wires is giving way to a much leaner, decentralized system of small-scale power generation on a more personal, neighborhood- or residence-level scale.In other words, we’re going from an “infrastructure-centric” model to a “human-centric” one, says grid expert Ram Rajagopal. He says that the new grid will be much smarter, more inclusive and better able to adapt to the individual needs of users, helping them to schedule power-intensive tasks, like laundry or charging of electrical vehicles, to off-peak times of the day. Before that day can come, however, Rajagopal says we’ll need new sorts of sensors and algorithms that will provide much more data about who, how and when people are using power, as he tells listeners to Stanford Engineering’s The Future of Everything podcast with host bioengineer Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The world’s once linear — take it, treat it, use it, dispose it — model of freshwater usage is changing fast.Despite two-thirds of Earth being covered in water, just 2.5% of it is fit for human consumption. And that share is dwindling by the day, says civil and environmental engineer and expert in water treatment and distribution systems Meagan Mauter. With a rapidly increasing population and climate change disrupting traditional weather and distribution patterns, access to freshwater is headed for, if not already amid, a worldwide crisis.Avoiding calamity will require industrial scale desalination and other technologies that can separate precious freshwater from other less desirable substances in the water, but also a shift to a more circular model where every drop of water is treasured and reused.Doing that, Mauter says, will demand doing away with not only inefficient practices but also the very notion of “waste” water, as she tells us in this episode of Stanford Engineering’s The Future of Everything podcast with host bioengineer Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Humankind has long harnessed the wind to its advantage. From ancient mariners to millers grinding grist, the wind has been an ally for millennia, but only now do engineers have at their disposal advanced computer simulations to better understand the details of wind flow and to optimize designs.Catherine Gorle is one such engineer who has made it her career to design better built environments able to improve walkability, temper extreme winds, shuffle air pollution far away and dissipate heat islands arising from so much sun-beaten concrete in our cities.Once, that work had to take place in wind tunnels, but now transpires through advanced computer simulations that both speed her work and add critical detail to her understanding of the close interrelationship between wind and human society. Join us as Catherine Gorle tells host bioengineer Russ Altman all about the future of wind on this episode Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

As the world moves to more efficient and cleaner energy solutions, there is a growing divide between the clean-energy haves and have-nots, says Anthony Kinslow II, PhD, a lecturer in civil and environmental engineering. Too often the divide falls along racial and socio-economic lines, as minority and low-income communities do not benefit from clean energy to the degree that whiter and wealthier communities do.The problem is founded in history and in the federal government’s askew system of financing and incentivizing clean and renewable energy systems. The money flows to certain communities and not to others, Dr. Kinslow says.Fixing the problem won’t be easy, but solutions might begin with energy audits of minority and low-income homes and communities to better understand where the gaps are and how wide they have become, as well as greater diversity in federal appointments to energy and finance positions in government. With audits will come opportunities for low-interest loans and other financing to transition to greater efficiency, as Dr. Kinslow tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Electrical engineer Kunle Olukotun has built a career out of building computer chips for the world. These days his attention is focused on new-age chips that will broaden the reach of artificial intelligence to new uses and new audiences—making AI more democratic. The future will be dominated by AI, he says, and one key to that change rests in the hardware that makes it all possible—faster, smaller, more powerful computer chips. He imagines a world filled with highly efficient, specialized chips built for specific purposes, versus the relatively inefficient but broadly applicable chips of today. Making that vision a reality will require hardware that focuses less on computation and more on streamlining the movement of data back and forth, a function that now claims 90% of computing power, as Olukotun tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Julie Owono is a lawyer, executive director of Internet Sans Frontières and a fellow at the Stanford Center on Philanthropy and Civil Society. She wants the world to know that the internet is the not the same for every person, everywhere. Born in Cameroon, and having grown up in Russia, she understands firsthand that every nation sets and maintains its own content standards.Owono has dedicated her career to establishing and securing basic digital rights, but also to developing standards by which social media giants—like Facebook, Google and Twitter—can distinguish hate speech from free speech. In many ways, Owono says, the global internet is a local endeavor.Owono tells Stanford Engineering’s The Future of Everything podcast and host Russ Altman that this dynamic means local voices will be critical to fairly determining standards of speech and, by extension, to charting the future of the global internet. You can listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Words are a window into human psychology, society, and culture, says Stanford linguist and computer scientist Dan Jurafsky. The words we choose reveal what we think, how we feel and even what our biases are. And, more and more, computers are being trained to comprehend those words, a fact easily apparent in voice-recognition apps like Siri, Alexa and Cortana.Jurafsky says that his field, known as natural language processing (NLP), is now in the midst of a shift from simply trying to understanding the literal meaning of words to digging into the human emotions and the social meanings behind those words. In the social sciences, our great digital dialog is being analyzed to tell us who we are. And, by looking at the language of the past, language analysis promises to reveal who we once were. Meanwhile, in fields such as medicine, NLP is being used to help doctors diagnose mental illnesses, like schizophrenia, and to measure how those patients respond to treatment.The next generation of NLP-driven applications must not only hear what we say, but understand and even reply in more human ways, as Dan Jurafsky explains in his own words to host Russ Altman in this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

When Riitta Katila looks at old photos or movies about the space program of the 1960s, she sees one common thread among the people depicted there — homogeneity. The engineers and technicians who first put humans on the moon were, almost without exception, white and male.While society has come a long way in the decades since, Katila, who is an expert in technology strategy and organizational learning, says there’s still a long way to go. She notes that companies need innovation not only to reach the top, but to stay there. And now more than ever, innovative companies should be hiring, promoting, and listening to a broader range of voices.The good news is that innovation can be taught. It’s like a recipe, says Katila, who encourages entrepreneurs — even those who have already built successful companies — to seek out mentors who can help them navigate the future. More important, those same entrepreneurs need to proactively identify mentors who can empower their team members to think like innovators too, as Katila tells Stanford Engineering’s The Future of Everything podcast, hosted by bioengineer Russ Altman. You can listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

As the silicon chip embarks upon its second half-century of dominance in computing and communications, the field is confronting fundamental boundaries that threaten to halt that progress in its tracks.The transistor cannot get much better or smaller and the copper wires that connect them cannot carry much more data than they do now. But, says electrical engineer David Miller, an alternative technology that uses light instead of electricity has the potential to transmit vastly more data than present technologies. It’s known as photonics.“A silicon chip these days looks like six Manhattan grids stacked atop one another,” Miller says of the challenge facing today’s technology. Photonics holds the promise of more powerful computing by beaming tiny packets of photons through light-bearing conduits that carry 100,000 times more data than today’s comparable wires, and it can do it using far less energy, too.Before that day can arrive, however, Miller says photonic components need to become much smaller and less expensive to compete with the sheer scale advantages silicon enjoys, and that will require investment. But, for once, a way forward is there for the asking, as Miller tells bioengineer Russ Altman, host of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In recent decades, medical and biological science have advanced by leaps and bounds using technologies that allow us to peer into the brain in myriad new and insightful ways — MRI, CT, PET, EEG, etc.However, Stanford electrical engineer Jin Hyung Lee says, we are still missing critical insights that could lead to a cure for currently incurable brain diseases like Alzheimer’s, Parkinson’s, epilepsy and others.Even in diagnosis, we still rely on “diagnosis of exclusion,” where tests are used to exclude other conditions that are relatively easy to identify, such as a tumor. However, there is still no way, for instance, to directly test why one’s memory is failing or why motor functions decline and lead to tremors.Lee’s approach is to directly identify the brain’s underlying algorithms and to enable quantitative diagnosis of its malfunctions in order to design approaches to cure brain diseases. She employs optogenetic MRI and various measurement tools at different scales, which she then uses to reconstruct the algorithms of brain function using artificial intelligence. Lee defines healthy circuitry and function, which in turn allows identification of the characteristics of dysfunction. Her approach has put Lee on the cusp of new understanding and new treatments for epilepsy, for instance, as she tells Stanford Engineering’s The Future of Everything podcast, hosted by bioengineer Russ Altman. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.