The Future of Everything

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 

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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