The Future of Everything

Anthony Kinslow II discusses the clean-energy divide, focusing on racial and socio-economic disparities. Solutions include energy audits for minority communities and diverse federal appointments. The podcast explores systemic issues and potential pathways to a more equitable energy future.

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.