Digital breadcrumbs, quantum computing, and potholes Highlights from our 2017 tech-focused interviews


From dark analytics or probing the depths of the deep web, to tapping the wisdom of the crowd through predictive analysis or the brave new world of quantum computers, 2017 was a breakthrough year. Tanya Ott presents excepts from some of our thought-provoking technology podcasts of 2017. 

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TANYA OTT: Each year, Oxford Dictionary picks a Word of the Year. In 2013, it was “selfie.” In 2014, “vape.” 2015 offered not a word, but a pictograph. You know, that emoji—you might call the “face with tears of joy.” 2016’s word of the year was “post truth”—no explanation necessary on that one. And this year? Well, they haven’t chosen one yet, but I’m going to beat them to the punch, at least for the corporate world. My money is on two words actually:  “symphonic enterprise.”

It’s a phrase that appears in Deloitte’s Tech Trends 2018. And it’s the idea that companies can no longer think in silos. They have to view strategy, technology, and operations as working together, in harmony, across domains and boundaries. That can open up huge opportunities—both for bottom lines and for society.

In 2017, on this podcast, we explored some of these big ideas. Consider this episode of the show a “Year End Best Of.”

In February, we spent some time talking about some of the big tech trends that were getting ready to really pop.

BILL BRIGGS: We think that [a] 18- to 24-month horizon is the right lens to have, because it’s this fiscal cycle and the next planning cycle. The things that we think are going to actually have a mass adoption and real business impact.

TANYA OTT: That’s Deloitte’s Chief Technology Officer Bill Briggs. Every year, he and his team survey more than 3,000 chief information officers to find out what they’re thinking about, what excites them, what might worry them. And in 2017, one of the big things that came up in those conversations was dark analytics.

BILL BRIGGS: A lot of data that lives within a company, they’re still not tapping into it. It’s sitting idle. It’s sitting dark. Things that [companies] haven’t brought together to have a global view of probability, or product performance, or customer, or supplier. There’s a good chance to shine a light and do more with it.

TANYA OTT: The second trend they IDed was a move toward looking at non-traditional data sources and the ability to pull deep insight out of things like image, video, and audio files.

BILL BRIGGS: There’s an advancement in sensors and analytics techniques. Computer vision [will] be able to [get] really interesting insight, [not just] out of [understanding] traffic patterns in a retailer, but also actually [by] inferring mood by posture and facial expression. That could feed into customer demographics.

TANYA OTT: Bill says that would help retailers identify and capitalize on potential sales and marketing opportunities.

The third big trend to watch over the next year or so is the deep web. That’s the part of the Internet where things are not indexed by standard search engines. You can’t find them unless you know exactly what you’re looking for. The deep web includes some common things like web mail and online banking. It also includes academic networks and a lot of private networks, including some unsavory ones.

BILL BRIGGS: The Dark Web, which is the back alley [of] the deep web—that sometimes gets some news coverage.

TANYA OTT: Yeah. The Dark Web generally gets news coverage that’s not terribly positive. But, I mean, basically what this is all pointing to is the idea that there is so much data, and so much information, and so much power in that data and information that isn’t being tapped. Some of it’s right underneath our noses, and some of it, we might have to go a little hunting for, but it’s there.

BILL BRIGGS: Yeah. A great example [is] a retailer that’s using image analytics—video analytics off of Instagram feeds [from] influencers to actually influence their merchandising decisions, their fashion decisions—what lines they’re going to invest in and roll out, [and] understanding patterns and using it to drive [their decisions].

In oil and gas, they’ve been doing it for a long time. They use acoustic sensors and fiber optics down in the well to know exactly what’s happening hundreds, thousands of miles deep into the earth. Is it oil? Is it gas? Is it sediment? Those techniques can be applied to other industries. It’s really fascinating.

TANYA OTT: Another tech trend they’re watching is what they call IT unbounded. If every company is a technology company, how can your company get the most of the IT division?

BILL BRIGGS: How do we think about people, how [do] we organize our people in IT [and] the skills that they have? How do they practice their craft? It’s really transforming the IT function like we’ve transformed the supply chain and manufacturing and sales over the years.

TANYA OTT: This idea of getting the most [out] of our people was a theme that emerged in many of our interviews this year. And it’s not just people who work within our companies. I talked with Jim Guszcza and Bill Eggers about how to tap the knowledge of the masses. Jim is Deloitte Consulting’s chief data scientist and Bill is executive director of Deloitte’s Center for Government and Higher Education. I invited them onto the podcast to talk about how we can build smarter cities. You’ll hear Jim first, then Bill.

JIM GUSZCZA: Now we’re leaving digital breadcrumbs behind as we go about our day-to-day activities because we're all carrying these little smartphones with us. They capture our locations or the buses we're riding in, or the cars we're driving are connected [to] the sensors. Now it is much more possible to gather information about people's day-to-day activities and use this behavioral data to actually figure out, “What do people want? What are the services, what are the bits of infrastructure improvements that would really make their lives easier and safer and more prosperous?”

It’s kind of like tapping into the decentralized knowledge and preferences and behaviors of the multitude. Use that to inform the decisions rather than relying more on theory or ideas of central planning.

BILL EGGERS: The fact of the matter is cities are some of the smartest, most intelligent things ever invented by humankind. Think about how cities work, and how we are able to get all sorts of groceries and everything else that goes on in city life, and that complexity. Cities are already really, really smart. What the smart city movement is all about is [to] take that wisdom that's already there and accelerate it vis-à-vis technology and behavioral economics, tapping the wisdom of the crowd and some other new tools that we have today.

TANYA OTT: You've got some really interesting examples. The New York City department of building, for instance, for years chose which properties to inspect for problems based on complaints. Which makes a whole lot of sense—except in 2011 alone, the city received almost 25,000 complaints about just one very specific type of problem.

BILL EGGERS: But they only had 200 inspectors to cover the workload. So how do you decide which places to inspect? It's a really, really difficult issue. And if you end up inspecting a lot that are fine, and you miss the ones that are bad, you could end up with people dying because of fires and unsafe conditions. So that's the issue that the city was facing, and what they did was that they started applying analytics. They talked to the building inspectors, and they found out the inspectors knew a lot of things. They actually built a guide into a predictive data model to help understand what are the triggers that are most important, and what do we know that will let us get to these properties that are the most likely to be unsafe, and [are] fire hazards due to these illegal conversions.

JIM GUSZCZA: They fed this into a predictive model that resulted in the risk score. They were able to literally come up with a prediction score of each building in New York City, each building site, for its riskiness, its need of a vacate order. And so by rank order, all the buildings from most risky to least risky, they were able to target their inspection activities accordingly.

BILL EGGERS: Before they were doing it, they had about a 70 percent false-positive rate, or even higher than that. Essentially, previously only 13 percent of the complaints ended up requiring vacate orders—which meant that all but 13 percent of the time, when they would go into one of these properties, it was actually fine. So they were missing a lot of other ones.

Now after they filtered out a lot of these false positives, the share of complaints leading to vacate orders escalated up to 70 percent. They didn't have to re-engineer anything. They just had a better predictive model, again, using a little bit of the wisdom of the crowds. The wonderful thing that happened was that you actually saw a lowered risk for firefighters in fires in illegal conversions—they went down dramatically. We saw New York City experience zero fire deaths for the first time since 1916, in June 2015, using this analytics-driven approach.

TANYA OTT: Cities are using technology and data in other interesting ways. In some cities, they’re encouraging residents to use their cellphones to snap photos of potholes and broken water mains. Using geolocation, they can map the problems and then deploy resources accordingly.

But there can be problems. Several years ago, there was a media company in a major metropolitan area that decided to crowdsource reports of closed roads and excessive snow and ice during a blizzard. They encouraged people to report problems, and then they mapped the locations. But a funny thing happened. The map showed problems in all but one quarter of the metro area. It was clear. Clean. No snow—or, actually, just no respondents from that area. Maybe they weren’t big fans of the media company. Maybe they couldn’t afford a subscription.

Jim Guszcza says cities have to be really sure they’re actually capturing the data they think they’re capturing—otherwise they might fall victim to algorithmic bias.

JIM GUSZCZA: As a data scientist, I can tell you that whenever we build predictive models or artificial intelligence apps or what have you, those models or those apps are going to be dependable and reliable and accurate only to the extent that the data that they were built on is representative and complete of the world going forward in which you’re going to implement the models. You know the world has changed, then your model might not be accurate. We've seen this in elections, right? If the data you're feeding into a predictive model or forecasting model is not representative of who's actually going to vote, you might get a wrong indication or a misleading indication. Or if you're not smart enough to adjust for those discrepancies.

So that's a general point about predictive models, that the data has to be suitably representative in a way that helps you create better indications going forward. In the case of cities, you’re exactly right. If only one segment of the population owns the mobile phones or has the cars connected to sensors or whatever it is, then you're going to get a lot of blanked-out parts of your map. Metaphorically speaking, maybe literally speaking.

TANYA OTT: One of the other things that I found really interesting is you write about how smart cities are making their data publicly available, and you talk about an example in Boston where they're using Yelp restaurant reviews as raw data for predictive algorithms. What are they trying to do there?

JIM GUSZCZA: Yeah, that was an ingenious example. Again, it's this idea of digital breadcrumbs. It’s very analogous to this New York City building inspection example in some ways. In both cases, you've got health inspectors or hygiene inspectors issuing labels: this restaurant is very clean and very safe. This other restaurant maybe has a health violation. It’s very similar to the idea of, this building is unsafe so let’s issue a vacate order.

So the logic is very similar, and in both cases, we can use a predictive model to identify, flag ahead of time, restaurants that might need a health violation. We can build a predictive model to send out our health inspectors to the restaurants in most need of attention.

And the data they're using to do that, what was particularly thought-provoking about this example, is they're using restaurant review data. It's unstructured text, so from a technical point of view, it’s interesting. But it's also interesting from this perspective that we were saying earlier: Again, these are examples of digital breadcrumbs.

In the old days, if you went to a restaurant, and you had a bad experience, there it would be word of mouth. You might just tell your friends. You might write a letter to somebody. You might just verbally tell somebody, “Oh yeah, I felt queasy after eating at this restaurant last night.”

Now a lot of this is on social media reviews. So why not just take that data and use that as inputs to predict ahead of time which restaurants are most likely to need a health citation? The logic is very similar to the New York City building inspection example, but the data being used was particularly thought-provoking, and it really does exemplify this idea of people leaving behind digital breadcrumbs they go about the day to day lives.

TANYA OTT: That actually makes a whole lot of sense. Want to really blow your mind? Fast forward to a conversation we had in May of this year, about quantum computing. Never heard of it? You will! We asked David Schatsky to come onto the show to explain what it is and why this sci-fi sounding development is going to—no hyperbole here—change the way the world works. Here we go!

TANYA OTT: I was telling a friend, a very smart friend I should say, that I was going to be talking to you today about quantum computing, and he said good luck making that make sense to people!


TANYA OTT: So your first task is to explain quantum computing in a way that average people can understand. Are you ready for it?

DAVID SCHATSKY: Sure, I can do that.

TANYA OTT: Okay! Let's hear it. Hit me.

DAVID SCHATSKY: Quantum computing is a totally new design for computers that can perform really complicated calculations thousands of times faster than normal computers.

TANYA OTT: How is that possible? How do quantum computers differ from our traditional electronic computers?

DAVID SCHATSKY: Well, that's just it. Electronic computers use electronics. They use electrical signals to indicate true and false, or yes and no. And quantum computers use quantum mechanics, which is the bizarre behavior of subatomic particles, to perform their calculations. They use quantum mechanical behavior, which performs things that we don't fully understand, but we know that it works really, really fast.

TANYA OTT: Basically these very, very, very small particles act in a bizarre way, a strange way. We don't exactly know why they do that, but we know that they do it, and it can speed things up dramatically?

DAVID SCHATSKY: Yeah. The subatomic particles behave in pretty wacky, but consistent, ways. And they're so wacky that traditional computers can't readily simulate this behavior. So the idea is to harness the particles themselves and their wacky behavior as a kind of computation and to use them then to perform calculations that are beyond the reach of traditional computers.

TANYA OTT: You say it's going to be a lot faster and there's an example that I read—it's the phone book problem. Maybe that's a good way of expressing how much faster this is than a traditional electronic computer.

DAVID SCHATSKY: Absolutely. The phone book problem is a good example because it's easy to understand what it is and how a traditional computer would solve it. The phone book problem is the name for the task of finding something in an unsorted list, like looking up someone in the phone book when you only have their phone number, because the phone book is sorted by name, not phone number.

In classical computing, the standard way to do this, and really there's no way that's better than this, is to simply go through every entry in the phone book until you find a matching phone number. And that, in theory, requires as many steps as there are entries in the phone book. A researcher at Bell Labs found an algorithm that, if it could be run on a quantum computer, could do that in a small fraction of the time. Take, for instance, a billion entries—it would only take a little over 31,000 steps to find the right answer versus potentially a billion steps using a conventional computer.

TANYA OTT: [You] use the word “theoretical” because scientists believe this is going to work, but we don't actually have quantum computers yet, right?

DAVID SCHATSKY: We have things that are quantum computeresque or special cases of quantum computers, but they're all—I don't want to denigrate them—they're kind of experimental or jerry-rigged, you might say. And this proof that the phone book problem could be solved in fewer steps was a theoretical proof focusing on the number of steps and not exactly how to execute the steps. Now researchers and even companies are building devices that exhibit some of these quantum behaviors and are able to start testing these algorithms in practice, at least in experimental situations.

TANYA OTT: Venture capital firms, governments, and other entities are investing hundreds of billions of dollars into this research and development that you're talking about. What's the allure?

DAVID SCHATSKY: The allure is very simply that if these quantum computers can be built, they have the potential to create great wealth by making it possible to solve some of the most difficult problems. There are problems in financial services—in managing risk and identifying optimal investments. There are problems in research around simulating the behavior of atoms and molecules that would enable researchers to design entirely new forms of matter.

One example that's pretty concrete, that I like, that some researchers have looked at is the chemical processes used to produce fertilizer. There's a process that was invented over a hundred years ago that produces fertilizer using hydrogen, and fertilizer production is crucial to feeding the world. But the process that we have consumes, some people say, as much as 5 percent of the global annual output of natural gas. And so people are saying, if we can understand how to simulate the behavior of molecules, we could use that to devise a new process for producing fertilizer that could be significantly more efficient, save billions of cubic feet of gas, and the natural resource and the money associated with it.

TANYA OTT: We've got this idea that we can build a quantum computer. The beginning processes of that [are] happening, at least at the experimental level. But there are some pretty serious engineering challenges that they're facing. Can you sort of walk us through the big engineering challenges in terms of making this actually happen?

DAVID SCHATSKY: Yeah. So these devices are pretty exotic. For instance, one of the commercial quantum computers on the market has to operate in an enclosure that's carefully isolated from the outside environment at a super, super cold temperature that they say is far colder than interstellar space (which I'll have to take their word for).

TANYA OTT: I can't even imagine what that is!

DAVID SCHATSKY: It's very cold, is the point. And the other thing is that the fundamental computing unit, which is known as a quantum bit as opposed to a regular bit from a conventional computer, is perishable. So it can only maintain its state for a tiny, literally a fraction of a second or 50 microseconds, before [it starts] kind of decaying and errors creep in. Even reading the value of [a quantum bit], whether it's a zero or a one, is a very exacting process. It requires incredibly precise measurements.

So the engineering—go past the physics to the engineering of these devices—is super, super complicated. And that's why they're not rolling off assembly lines right now. There's a lot of fundamental science and engineering going into devising these devices, making them more stable, [and] reducing the errors that accumulate in operating them. And then down the line, there'll be exploration of how to make them economical.

TANYA OTT: By now, you’re probably asking yourself, “What does this mean for me and my employees? What will big data, machine learning, quantum computing, and the like mean to us mere mortals who put on our shoes every day and walk into a workplace?”

JEFF SCHWARTZ: The headlines, especially in the last couple of years, have been a version of the robots are coming, we’re doomed, or the robots are coming, we’re saved.

TANYA OTT: Jeff Schwartz is a principal at Deloitte Consulting and he co-wrote an article this year titled Navigating the future of work, which raises some big questions: What will work look like in 10, 20, 50 years? What’s driving the changes? What does this mean for workers and companies?

JEFF SCHWARTZ: Here's how I think about the future of work. The phrase that we often come back to is the idea of an augmented workforce. The idea that all of the work that we do will be augmented, will be in some ways extended in different ways. It's really being extended in three different ways. One is the way that the work that we do is extended by working with smart machines. One of the predictions that I often make is that in the next five to seven years, we will all be working next to and with smart machines that we're not working with today. That will change and augment what we do.

A very simple example of this is only 10 years ago in 2007, Steve Jobs and the team at Apple invented the modern smartphone that we're all carrying. Ten years later, there are billions of them in the world. It’s an absolutely pervasive technology [that] has changed the way everybody works. We expect to see not just digital machines but machines that are capable of machine learning and all of us will be working with these smart machines. All of our capabilities will, in some sense, be extended. That's a big challenge for us: how do we actually work with these machines.

THOMAS FRIEDMAN: I do give the example of Qualcomm in my book…

TANYA OTT: That’s Tom Friedman, New York Times columnist and three-time Pulitzer Prize winner. His latest book is Thank You for Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations. He sat down with John Hagel—co-director of Deloitte’s Center for the Edge—and Deloitte CEO Cathy Engelbert for a wide-ranging conversation on the future of work, including an interesting, early step that Qualcomm is taking.

THOMAS FRIEDMAN: They took six buildings and put sensors on every door, window, HVAC system, computer, light, pipe, and faucet. They beamed all that data up to the cloud and then they beamed it down onto [an] incredibly user-friendly interface for their janitors. They turned their janitors into maintenance technologists. So now if John Hagel leaves his computer on or a pipe burst above his head, the janitor knows it as fast as John does. They've turned their janitors into maintenance technologists and their janitors now give tours to foreign visitors.

TANYA OTT: Tom says the other big trend he sees is…

THOMAS FRIEDMAN: Work is being disconnected from jobs. And jobs and work are being disconnected from companies, which are increasingly becoming platforms.

TANYA OTT: Basically, in the future, many of us will be working in an off-balance sheet economy. You might also call it the gig economy or lots of side hustles.

THOMAS FRIEDMAN: We have in Bethesda a cab company that owns cars and has employees who have a job. They drive those cars. They're competing now with a company called Uber, which owns no cars, which has no employees, and just provides a platform of work that brings together ride needers and ride providers. I do think that [the] Uber platform model, and the way it is turning a job into work and monetizing work, the broad trend, I think that is the future of work and that will have a huge impact on the future of learning. Because if work is being extracted from jobs and jobs and work are being extracted from companies and because we're now in a world of flows, learning has to become lifelong.

TANYA OTT: There was a recent report from the National Bureau of Economic Research and there were some leading labor economists who did an analysis of the net new employment in the United States between 2005 and 2015. [They] found that 94 percent of that net new employment was from these alternative work arrangements that you talk about—everything from gig to freelance to off-balance sheets kinds of work. For many of us, we thought that that was a consequence of the economic downturn of the late aughts, but what you're saying is it's just a new reality, perhaps driven by the economy, but perhaps driven even more so by technology.

JEFF SCHWARTZ: The report you're referring to is one of my favorite reports. I love the statistic that 94 percent of net new employment in the last decade in the United States is characterized as alternative work arrangements. Ninety four percent is about as close to 100 percent as you can get and effectively what the data [is] showing us is that the growth in net new employment is coming from this off-balance sheet, this gig economy, this freelance economy. I think it's driven by a couple of things. Technology is part of it. One of the things that John Hagel and Josh Bersin, and I have looked at is where we think the gig economy is going to go and we think that gig economy is going to evolve from individual workers working on platforms like Uber to teams of workers working in a project-based world.

THOMAS FRIEDMAN: Whatever can be done will be done. The only question is, will it be done by you or to you? Let's use an example that people wouldn't normally think about. So General Electric woke up one day in 2013 and said, “Geez, whatever can be done will be done.” So I'm GE now and I'm trying to figure out how to take the most weight out of a fastener that fastens an airplane engine to the wing of an airplane because that's my business. When you take weight out of anything, especially on an airplane, you save fuel. Over the life of a plane, if you can actually reduce the weight of a fastener by 70 or 80 percent, you've saved enormous money.

But GE sort of looked at itself internally and said, “Well, I live in a world now where I can actually take advantage of the brains of anybody to take weight out of this fastener.” So they went to the main engineering website and they simply created a contest, what they call a jump ball. They described the fastener they were currently using, the weight of that fastener under the wing of the plane attached to the engine, and simply threw up a jump ball: Who in the world can take the most weight out of this fastener? And they offered $20,000 in prize money; I think it was $10,000 to the winner and then several thousand to those who came in second, third, fourth, fifth—the top ten, I believe. Within six weeks, they got over 600 responses. The 10 finalists were all tested by GE engineers and they picked the winner. None of the 10 finalists was an American. I believe the winner was a 21-year-old from Indonesia who is not an aeronautical engineer and he took 80 percent of the weight out of this fastener. The notion that within our stock of engineers we have all the best talent in the world, what are the odds of that in a flat fast world? Let's actually create jump balls and access all the talent wherever it is.

TANYA OTT: What's the one thing that you would advise leaders of organizations to do now to prepare themselves and their organizations for the work of the future?

THOMAS FRIEDMAN: The first thing that comes to mind is something I'm arguing for America in general right now which is to do something that would strike many as deeply counterintuitive—that when we move into a world of flows and the flows are the source of strategic advantage where you extract value and the flows are getting faster, it seems to me that rule No. 1 is you want to be radically open. That's a really hard sell right now because it feels so counterintuitive and everyone's putting up walls, right when you want to be actually radically open.

Why do you want to be radically open? Because you'll get more flows. You'll get the signals first. And you will attract more flow-minded people, which I would call high-IQ risk takers. That's from a country point of view. I have to believe that's also right from a company point of view, that is, you want to be plugged in to as many discussions, as many places, and as many flow generators as possible because you'll simply get the signals first in order to understand where the work of the future is coming from.

JEFF SCHWARTZ: The major recommendation that we have for business leaders, and maybe this is a funny way to put [it], is to actually have a point of view and to actually have a plan that integrates the different elements that we've been looking at. One of the challenges that we're seeing now is that the programs that companies are beginning to put in place around the future of work are very fragmented. It's almost like the scarecrow in the Wizard of Oz—a piece of the program over there, a piece of the program over there, and a piece of the program somewhere else. What we're encouraging companies to do is to think about over the next three to five to seven years, how the work and the workforce that you have is going to be probably dramatically redefined and reinvented. What's your plan for automation and artificial intelligence? I call that the “what” question. What work is going to be done by machines? Who is going to do the work? Who's going to do the work in terms of people that are on your balance sheet and off your balance sheet? How can you use both freelancers in the gig economy and the crowd to actually get your work done and do you have a plan for that? And the third question is where the work is going to be done. How can you virtualize work?

THOMAS FRIEDMAN: Stage one is we all go solo. Stage two is some real estate developer comes along and says, “Well, you're all solo, so I think I might go into the worker-space business.” And then they create a great place for solo innovators and entrepreneurs to rent office space. Then somebody is going to come along and say, “Gosh, y’all need meals and y’all might need health care advice. And by the way, you might need pension advice.” And so I think it'll all start to adapt around this. And again, if you're free flowing and you're flexible and adaptive, human creativity is boundless.

I'm not sure what the work of the future is, but I know that the future of companies is to be hiring people and constantly be training people who are prepared for a job that has not been invented yet. If you're training people for a job that's already been invented, if you're going to school in preparation for a job that's already been invented, I would suggest that you're going to have problems somewhere down the road.

JEFF SCHWARTZ: One of the ways that we often describe the future of work is this notion that we're all going to be working next to and with smart machines that we're not working with today. Machines are not only really good at automating work. Machines are good at learning really, really fast and there's going to be a new partnership between individual workers and machines and I think we're just beginning to figure this out.

One way to look at how people and machines are going to work together in the future is to pick up on the work of Andy McAfee and Erik Brynjolfsson at MIT, who just published a book earlier this spring. The book is entitled Machine, Platform, Crowd. Part of what they do in the book very, very well is they look at the evolution of machine learning and they point out something very interesting. Starting in the 1980s and the 1990s, we developed what they refer to was the standard contract between machines and people. The idea was that machines do the math, machines do the calculation, and people make the decisions. What we're seeing now is that machines are not only good at calculating and processing and transacting, but there are many kinds of decisions that machines are as good or better at making than people. And one of the challenges for us now is to really understand what are the essential human skills, what are the things that we as humans can do, and what are the enduring human skills.

Some people talk about this as soft skills. I don't like the expression of soft skills. In business, we like the idea of hard skills generally. But what becomes important is to understand [that] in every profession, in every business, in every organization, as machines get smarter, what are the unique things that we can do as individuals? We know what some of those are, right? Defining the problem. Pablo Picasso said a very interesting quote: “The problem with machines is that they only know the answers. What we know as people is we know what the questions are.”

So deciding what questions are important, what are the design principles, [and] what are the questions that we should really be pursuing. What's the role of empathy? What's the role of nurturing? What's the role of communication? What's the role of narrative? Really trying to understand what we can do uniquely and essentially as humans, as machines get smarter is really going to be a central question. This is an individual question and this is a business question and obviously, as we said, we think this is a government question as well.

THOMAS FRIEDMAN: You know, if you think of [IBM’s] Watson—who's the best doctor in the age of Watson? It's very different. It’s the doctor [who] can ask Watson the best questions. If Watson's read every article ever written on cancer and no doctor can even think about approaching that, [and] then being able to ask Watson the right question about this patient and then translate that in an empathetic way to that patient and use Watson not as a substitute, but [as] an augmenter for that doctor’s own innate skills. And I think [as] anyone who's had an elderly parent in an Alzheimer's unit, as I have, or a nursing home—boy, they know the difference between that nurse, that caregiver who has both some medical knowledge and the kind of empathy to relate to your parent. And how much more would I pay for that person to be looking after my mom as opposed to the person who doesn't have those skills? I’d pay a lot.

TANYA OTT: People are living longer. Research suggests that those born in the 1990s and in the first part of the current century can expect to live to be 100. The question Jeff Schwartz, Tom Friedman, and many others are asking is: What does a 70-year career even look like?

Well, I’m going to leave you to ponder that. This is the end of our 2017 Year in Review. Hope you heard some things that got you thinking. There’s so much more to explore at our website,

I’m Tanya Ott for the Press Room. Catch you again in 2018!

This podcast is provided by Deloitte and is intended to provide general information only. This podcast is not intended to constitute advice or services of any kind. For additional information about Deloitte, go to