Fixing the Future

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Stopping Infection Outbreaks with AI and Big Data Dec 21, 2021

Show notes

Hospitals are where we go to get cured of infections and diseases, but sadly, sometimes tragically, and ironically, they are also places we go to get them. According to the Centers for Disease Control, “On any given day, about one in 31 hospital patients has at least one healthcare-associated infection.”

Yet, according to Dr Lee Harrison, “The current method used by hospitals to find and stop infectious disease transmission among patients is antiquated. These practices haven’t changed significantly in over a century.”

Until perhaps now. Doctors at the University of Pittsburgh and the University of Pittsburgh Medical Center have developed a new method that uses three distinct, relatively new, technologies, whole-genome sequencing surveillance, and machine learning, and electronic health records to identify undetected outbreaks and their transmission routes.

Dr Lee Harrison is a Professor at the University of Pittsburgh, where he’s the Associate Chief of Epidemiology and Education and, more to our point today, the head of its Infectious Diseases Epidemiology Research Unit. He’s the corresponding author of a new paper that describes the new methodology and he’s my guest today.

Fixing the Future is the weekly podcast of @IEEE Spectrum and is sponsored by @COMSOL

A Small Startup Fights Rare Diseases With Big Data Nov 09, 2021

Show notes

Rare diseases are, well, rare. In two not unrelated ways. By definition, they’re diseases that afflict fewer than 200,000 people. But because, in the world of big business, in particular big pharma, that’s not enough to bother with, that is, it’s not profitable enough to bother with, rare diseases are rarely worked, to say nothing of cured.

For example, hypertryptophanemia is a rare condition that likely occurs due to abnormalities in the body's ability to process the amino acid, tryptophan. How rare? I don’t know. A Google search didn’t yield an answer to that question. In fact, it’s rare enough that Google didn’t even autocomplete the word even with 15 of its 19 letters typed in.

Paradoxically, big data has the potential to change that. Because 200,000 is, after all, a lot of data points. But it presents problems of its own. There isn’t one giant pool of 200,000 data points. So the first challenge is to aggregate all the potential data that’s out there. And the big challenge there is that a lot of the data is contained, not in beautifully homogeneous, joinable, relatable databases. It’s buried deep in documents like PubMed articles and patent filings.

Deep Learning can help researchers pull that data out of those documents. At least, that’s the strategy of a startup called Vyasa. Here to explain it is Vyasa’s CEO and founder, Christopher Bouton.

Solving the Electric Vehicle Charging Conundrum Oct 26, 2021

Show notes

Like a lot of people, you may be thinking about trading in your car. Me too. The case, morally and even financially, for an all-electric car is becoming stronger and stronger.

And yet, what about recharging?

What’s it like going from, say Pittsburgh to New York’s Hudson Valley—a trip that doesn’t even have a solid cellular connection? What about a road trip my partner to Yosemite and back? And even locally, how do you charge up if you live in a townhouse or apartment? Without a driveway and a garage, can you set up charging at home? Will we have a universal standard for charging? What exactly is fast charging?

Basically, if you’re like me, you’re a bundle of questions. Fortunately, a fellow IEEE Spectrum contributing editor is a bundle of answers.

John Voelcker has been reporting on cars and the automotive industry for almost as long as he’s been driving. He’s also a contributing editor to Car and Driver, and is the editor of Green Car Reports. His work has also been featured in Wired, Popular Science, and elsewhere. He’s an actual engineer, with a B.S. in Industrial Engineering from Stanford. And he’s our guest today.

IBM’s Fall From World Dominance Aug 11, 2021

Show notes

IBM is a remarkable company, known for many things—the tabulating machines that calculated the 1890 U.S. Census, the mainframe computer, legitimizing the person computer, and developing the software that beat the best in the world at chess and then Jeopardy. The company is, though, even more remarkable for the businesses it departed—often while they were still highly profitable—and pivoting to new ones before their profitability was obvious or assured.

The pivot that people are most familiar with is the one into the PC market in the 1980s and then out of it in the 2000s. In fact, August 2020 marks the 40th anniversary of the introduction of the IBM PC. Joining me to talk about it—and IBM’s other pivots, past and future—is a person uniquely qualified to do so.

James Cortada is both a Ph.D. historian and a 38-year veteran of IBM. He’s currently a senior research fellow at the University of Minnesota’sCharles Babbage Institute, where he specializes in the history of technology. He was therefore perfectly positioned to be the author of the definitive corporate history of the company he used to work for, in a book entitled IBM: The Rise and Fall and Reinvention of a Global Icon, which was published in 2019 by MIT Press.

It’s Easy for Computers to Detect Sarcasm, Right? Jul 01, 2021

Show notes

There’s no question that computers don’t understand sarcasm—or didn’t, until some researchers at the University of Central Florida starting them on a path to learning it.

Software engineers have been working on various flavors of sentiment analysis for quite some time. Back in 2005, I wrote an article in Spectrum about call centers automatically scanning conversations for anger—either by the caller or the service operator—one of the early use-cases behind messages like “This call may be monitored for quality assurance purposes.” Since then, software has been getting better and batter at detecting joy, fear, sadness, and confidence, and now, finally, sarcasm.

My guest today, Ramya Akula, is a Ph.D. student and a Graduate Research Assistant at the University of Central Florida's Complex Adaptive Systems Laboratory.

Fixing the Chemical Industry’s Sustainability Problem Jun 21, 2021

Show notes

The most honest and inadvertently funny marketing message I ever saw was at a gas station that was closed for remodeling; it had been an Amaco station before that company was bought by BP. The sign said, “Rebranding, to serve you better.”

I’m afraid we’re a bit guilty of that here at Spectrum. This is the 30th episode of IEEE Spectrum’s relaunched podcast series, but the first under a new name, “Fixing the Future.”

We’ve changed the name partly for marketing and searchability reasons. But it also signals our intention to focus more intently on ways that technology is being deployed to improve our lives, specifically in three—to be sure overlapping—areas: climate change; machine learning and other smart technologies; and the effects of automation on the nature of work and the future of jobs.

I’m hard-pressed to imagine a more on-point guest to help me usher in this change than Myriam Sbeiti. She’s the CEO and co-founder of Sunthetics, a startup that’s reinventing the industrial processes by which we make nylon by replacing a thermal operation with an electrical one, and has both grown that business and pivoted toward other industrial processes as well.

Fixing the Future is sponsored by COMSOL, makers of mathematical modeling software and a longtime supporter of IEEE Spectrum as a way to connect and communicate with engineers.

Let’s Put Cheap, Portable Nuclear Reactors onto Barges Jun 11, 2021

Show notes

Today’s startup invites us to rethink nuclear energy. Their plan? To put cheap, portable nuclear reactors onto barges and float them out to sea. What could go wrong? According to today’s guest, basically nothing. The reactor design avoids the type of fuel rods that gave us the fictional meltdown in The China Syndrome and the real-life ones in Chernobyl and Fukushima. In fact, my guest will claim his reactor cannot meltdown or explode.

One of these reactors would be able to supply electricity, clean water, heating, and cooling to 200 000 households. All with a carbon footprint as low as any other technology—and there are co-generation opportunities that would seem to lower it even further.

The startup is Seaborg Technologies, based in Copenhagen, and we’re lucky to have its co-founder and CEO, Troels Schönefeldt, with us today to explain how this isn’t all too good to be true.

Until We Get Rid of Fossil Fuels, Can Data Make Them More Efficient? Jun 03, 2021

Show notes

A few months ago, we had on the show an economist who specialized in the energy sector. She noted that while the Trump administration had put drilling rights the Alaska Natural Wildlife Refuge, or ANWAR, on the block, there wasn’t much interest from the oil industry, and, more generally, the Arctic and other cold climes, presented logistical—and therefore financial—problems for oil companies.

To be sure, oil companies have been drilling in the frigid North Sea for decades, but that doesn’t mean it’s been easy. For example, at BP’s Valhall oil field in the Norwegian sector of the North Sea, drilling began in 1982, and the company is still pulling 8000 barrels per day, but losses are considerable—or have been until BP began working with a data science company. Yes, a data science company.

Further out, in the middle of the North Sea, another set of BP oil fields, known as Alvheim, has been rediscovered to have greater reserves than previously thought. There, the same data science company optimized a calibration process and in so doing reduced production losses and saved BP considerable money.

The data science company’s work isn’t limited to oil and gas. For example, it recently won a research contract with the California Energy Commission to use modeling and data analytics to help it improve production efficiencies in

wind energy.

The data science company is called Cognite, and my guest today is its Senior Director in charge of Energy Industry Transformation, Carolina Torres.

Can a Robot Be Arrested? Hold a Patent? Pay Income Taxes? May 25, 2021

Show notes

When horses were replaced by engines, for work and transportation, we didn’t need to rethink our legal frameworks. So when a fixed-in-place factory machine is replaced by a free-standing AI robot, or when human truck driver is replaced by autonomous driving software, do we really need to make any fundamental changes to the law?

My guest today seems to think so. Or perhaps more accurately, he thinks that surprisingly, we do not; he says we need to change the laws less than we think. In case after case, he says, we just need to treat the robot more or less the same way we treat a person.

A year ago, he was giving presentations in which he argued that Ais can be patentholders. Since then, his views have advanced even further. And so last summer, Cambridge University Press published a short but powerful treatise, The Reasonable Robot: Artificial Intelligence and the Law. In it, he argues that the law more often than not should not discriminate between AI and human behavior.

Ryan Abbott is a Professor of Law and Health Sciences at the University of Surrey and an Adjunct Assistant Professor of Medicine at the David Geffen School of Medicine at UCLA. He’s a licensed physician, and an attorney, and an acupuncturist in the United States, as well as a solicitor in England and Wales. His M.D. is from UC San Diego’s School of Medicine; his J.D. is from Yale Law School and his M.T.O.M.—Master of Traditional Oriental Medicine—degree is from Emperor's College.

The Future of Post-Industrial Cities May 18, 2021

Show notes

As we begin to finally address climate change in a serious way, we need to look at our cities in a serious way. And not just first-tier cities like, well, New York, San Francisco, Seattle, and Los Angeles, and not just flashy growing cities like Las Vegas, Austin, Atlanta, and Columbus. We need to look at cities like Baltimore, Cleveland, Detroit, Philadelphia, Pittsburgh, St Louis—cities that haven’t come back from the problems—deindustrialization, disinvestment, white flight—of 50 and 60 years ago.

These cities are at a crossroads, according to my guest today. They can, he says, enjoy a comeback, stagnate, or continue to decline. There is, in fact, a unique opportunity presented by the pandemic: as working remotely becomes more widely accepted, there could be a migration to cities such as these by people not ready to give up on city life, but looking for greater affordability.

Matthew Kahn is a Distinguished Professor of Economics and Business at Johns Hopkins University; he’s the Business Director of its 21st Century Cities Initiative; and he’s co-author of a new book that addresses these questions about these very cities, titled Unlocking the Potential of Post-Industrial Cities.

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