Speaker 1: Hi, and welcome back to Bloomberg Benchmark. It is October
Speaker 1: twenty two, Wednesday. Oh God, why are dates so hard
Speaker 1: for me? I mean, would never make that mistake. Hi,
Speaker 1: and welcome back to Bloomberg Benchmark, a podcast about the
Speaker 1: global economy. It is Thursday, October twenty two. I'm Tori Stillwell,
Speaker 1: a US economics reporter in d C with Bloomberg News.
Speaker 1: I am with my colleagues and go hosts. Dan Moss,
Speaker 1: our executive editor for International Economics who just landed in Ottawa,
Speaker 1: and Akiedo, our editor for Benchmark in San Francisco. Hey guys,
Speaker 1: you guys are always traveling. I'm just stuck here in
Speaker 1: d C. Dan, send me to Sydney. I'm ready to
Speaker 1: beat it up there. Well, I'll have to give you
Speaker 1: some great recommendations before you go. Okay, deal. So Aki,
Speaker 1: you've been in Japan for part of the last week.
Speaker 1: When did you get back and what perspectives on the
Speaker 1: news did you bring back with you? Well, I got
Speaker 1: back late Saturday night and had a wonderful time there.
Speaker 1: I thought i'd talked about the Bank of Japan or
Speaker 1: maybe Chinese economic statistics today because I was in Asia
Speaker 1: all week. But then I just saw this piece of
Speaker 1: news roll in this morning that was really interesting. It's
Speaker 1: a little nerdy and a little niche, but there's this
Speaker 1: government committee that oversees Sweden's central bank and apparently they're
Speaker 1: getting together to potentially revisit the central bank's mandate there,
Speaker 1: which sounds like really technical jargon and really boring, but
Speaker 1: it could potentially be a really big deal if it
Speaker 1: does end up leading to some legal changes to the
Speaker 1: way Sweden's central bank operates. UM. And right now Sweden
Speaker 1: has this inflation target of two. Some people are saying
Speaker 1: maybe Sweden needs to raise the central bank inflation target
Speaker 1: to create more of a cushion between the target and
Speaker 1: zero percent UM. Some people we're saying, maybe you need
Speaker 1: a lawer the inflation target because you shouldn't really have
Speaker 1: the school that you can't achieve at the end of
Speaker 1: the day. Um. But it comes down to this question
Speaker 1: of what happens when you have the school but you're
Speaker 1: not able to deliver on it for years and years
Speaker 1: and years. So I'm gonna be watching this really closely. Yeah,
Speaker 1: I agree with you, that's really quite sexy. That country
Speaker 1: central bank, the ricks Bank was one of the first
Speaker 1: to adopt the flesh inflation targeting, and more recently, they
Speaker 1: found themselves in the crosshairs um principally but not only,
Speaker 1: the cross hairs of Paul Krookman for raising rates quickly
Speaker 1: once what appeared to be the worst of the global
Speaker 1: recession was over, only to find themselves in a situation
Speaker 1: where they had to reverse and not only cut but
Speaker 1: do que So it's quite fascinating. There's almost a parable
Speaker 1: of moderns in full banking day. Yeah, definitely, they're really
Speaker 1: ahead of the pack. Um, Tori, what's your what's your
Speaker 1: current event chatter of the week. Well, I am focused
Speaker 1: as always on the US and right now we're in
Speaker 1: the midst of this like monthly cycle of housing data
Speaker 1: that we get, and it actually turns out things are
Speaker 1: looking pretty good. Um. Home builder confidence is at a
Speaker 1: decade high, which is great. We got housing starts data
Speaker 1: on Tuesday that showed that construction of new homes rose
Speaker 1: to the second highest level in eight years, So that's
Speaker 1: great news. We're gonna get more data over the next week,
Speaker 1: so I'm definitely keeping an eye on that. Cool. So, Tori,
Speaker 1: the idea is housing will help underpin things while manufacturing
Speaker 1: and exports of suffering. Precisely, consumers have really been doing
Speaker 1: the heavy lifting for growth in the US, and this
Speaker 1: shows that housing is no exception. Well, I want to
Speaker 1: talk this week, Tory about a story that you published
Speaker 1: on Monday called Social skills are the Last Line of
Speaker 1: Defense for humans seeking work? And what better date? We're
Speaker 1: recording this on Wednesday, October, the date Marty McFly arrived
Speaker 1: in the future. Now, we've all written and read stories
Speaker 1: about robotics and their increasing news in say, vehicle assembly lines,
Speaker 1: but most of that commentary has also suggested that more
Speaker 1: people focused softer emotional intelligence skills in the workplace. They're
Speaker 1: still some years away for robots. But you had an
Speaker 1: interesting adventure with somebody or rather something called Amy Ingram
Speaker 1: when you were developing this story. Why don't you talk
Speaker 1: about Amy and how she is essentially what this story
Speaker 1: is about. Yeah, So, when I was doing research into
Speaker 1: this story, I initially saw a great paper out by
Speaker 1: David Demming over at Harvard University about social skills in
Speaker 1: the job market, and he basically found that almost all
Speaker 1: the job growth since nineteen eighty has been in work
Speaker 1: that is social skill intensive. So um, while I was
Speaker 1: doing a little more research. I wanted to reach out
Speaker 1: to this founder of a technology startup. They do virtual
Speaker 1: personal assistance. I send him an email. Can we set
Speaker 1: up a time to chat over the phone. He's like,
Speaker 1: no problem, I'm gonna have Amy set it up. Amy,
Speaker 1: can you just take it from here. Um. So she
Speaker 1: sent me preferred day and time. This is around seven PM,
Speaker 1: and I actually had an event that night, so I
Speaker 1: wasn't checking email. Then at three twenty one am, she
Speaker 1: emails me again and it's like I wanted to follow up.
Speaker 1: Of course I didn't see it because I was sound asleep.
Speaker 1: And four hours later she sent me another mel This
Speaker 1: is about seven twenty. At this point, it's like, I
Speaker 1: haven't heard back from you about this meeting. So fortunately
Speaker 1: for Amy, I'm up and getting ready for work and
Speaker 1: I'm checking my emails. I'm already stressed about getting out
Speaker 1: the door, and Amy keeps bugging me about this meeting
Speaker 1: that I could handle like as soon as I got
Speaker 1: to work, and so I think I got a little
Speaker 1: irritated with her. I thought it was there's no way
Speaker 1: that a humans going to email me at three am
Speaker 1: about a meeting. Um. So it was at that point
Speaker 1: that I was like, all right, this has got to
Speaker 1: be a machine. And when I brought up this whole
Speaker 1: experience to Dennis Mortenson, who is the founder of AMY,
Speaker 1: I guess I should say. Um. He was like, this
Speaker 1: is exactly the thing that we're looking to figure out.
Speaker 1: You know, how many social skills do we need to
Speaker 1: embed these agents, these machines with and how do we
Speaker 1: do that? Okay, So just so we're clear, Amy Ingram, Um,
Speaker 1: she's a she's a machine. She's a virtual person assistant. UM,
Speaker 1: she answers emails, she sends emails as she sets up
Speaker 1: your meetings. And for AMY, ingram stands for artificial intelligence. Yeah,
Speaker 1: they share the share of the same initials UM and
Speaker 1: ingram is actually, God, this is like so over my
Speaker 1: head he was trying to explain it TV. But it's
Speaker 1: like a model used in natural language processing that helps
Speaker 1: machines understand human speech. UM. So you know, for all
Speaker 1: like the suber Nerds out there, they're probably oh, yeah,
Speaker 1: that's so cool. But but yeah, she's she's a robot
Speaker 1: for sure. And every any listeners who are interested in this,
Speaker 1: it's uh. The website is x dot ai. If you
Speaker 1: guys want to check it out. You know, one of
Speaker 1: the things that intrigued me about this story is it
Speaker 1: suggests that because Amy is sort of groping for some
Speaker 1: emotional and empathetic characteristics, that it's not that far off
Speaker 1: in the future. Yeah, I mean, I think computer scientists
Speaker 1: will quickly tell you that they are trying to figure
Speaker 1: out how to at least get robots to mimic social skills. Well, actually,
Speaker 1: you're in San Francisco, You're in the heart of all this,
Speaker 1: and you've spent a lot of time writing about the
Speaker 1: impact of technological advances on the economy, not just the
Speaker 1: macro economy, at what's happening in the micro economy at
Speaker 1: an enterprise level. Did tory story mesh with your experience?
Speaker 1: Didn't resonate with you? Yeah, definitely. I mean, so I
Speaker 1: wrote this big story about artificial intelligence UM about a
Speaker 1: year and a half ago, and even since then, even
Speaker 1: in that year and a half, there's been a remarkable
Speaker 1: amount of progress in that field. It's amazing how quickly
Speaker 1: scientists are making progress in this field. But at the
Speaker 1: same time, you know, one thing that I learned that
Speaker 1: was really important is that there are a lot of
Speaker 1: things that computers can do, but there are many, many,
Speaker 1: many other things that computers cannot do. Um Our human
Speaker 1: brains are amazing. They're so complex in ways that you
Speaker 1: never would have thought before, and are capable of doing
Speaker 1: so many things that robots aren't able to do. Daniel said, Like,
Speaker 1: it feels like we're not that far away from computers
Speaker 1: being able to replicate the full social skills of a
Speaker 1: human being. UM. From the conversations that I had with
Speaker 1: artificial intelligence experts, most people felt that this was something
Speaker 1: that was decades and decades away. We should probably define
Speaker 1: what where, what social skills aren't, what qualifies as social skills?
Speaker 1: What you guys think, Yeah, definitely, I mean, let's let's
Speaker 1: start with this. So, like, you know, imagine this kind
Speaker 1: of spectrum between of tasks. Some's tasks are really repetitive.
Speaker 1: You're doing the same thing over and over again, and
Speaker 1: it's easy to write out a full instruction manual of
Speaker 1: what you're supposed to do. There's this, you know, on
Speaker 1: the other end of the spectrum, there are tasks that
Speaker 1: are really complex, really diverse. You're doing something different every
Speaker 1: single day. It's really creative. You're you're coming up with
Speaker 1: novel solutions all the time. The kind of tasks that
Speaker 1: are on the repetitive end of the spectrum are really
Speaker 1: easy to replicate, and those jobs are already gone. You
Speaker 1: can think of something you would do in a factory
Speaker 1: and like an auto factory, for example, where you're putting
Speaker 1: on the same part over and over again. Those kind
Speaker 1: of jobs are gone. But you think of something really complicated,
Speaker 1: like um being an executive editor like Dan, where you're
Speaker 1: making all these different decisions every single day, you're providing oversight,
Speaker 1: You're coming up with new things. That's the kind of
Speaker 1: thing a computer hasn't been able to do yet. Right,
Speaker 1: So it's a sense of collaboration, reading people like, kind
Speaker 1: of working off of social cues. All those things qualify
Speaker 1: social skills, and at least one expert that I talked
Speaker 1: to said that upward of the workforce needs at least
Speaker 1: some sort of collaboration to get their job done. So
Speaker 1: it's a lot. If we've advanced this quickly, what makes
Speaker 1: us think that that final stage is still some steps away?
Speaker 1: Because zacky, this made me recall a story you wrote
Speaker 1: in twenty fourteen about robots being deployed in lawyers offices
Speaker 1: doing legal work. You know, that's a whole other step
Speaker 1: away from the generic shot of a robot putting an
Speaker 1: engine head into an suv. Yeah, definitely. So this was
Speaker 1: you know, a very specific task in that lawyers UM
Speaker 1: currently are performing called you know, it's document reading in
Speaker 1: this initial stage of litigation where you have to decide
Speaker 1: which documents are relevant and which documents aren't relevant to
Speaker 1: your case. Before, it was impossible to have computers do
Speaker 1: that because it was just too complicated of a task.
Speaker 1: Every case of litigation was too different in order to
Speaker 1: kind of come up with this like master set of rules.
Speaker 1: But they found a way to do that do that
Speaker 1: by um giving a small group of lawyers the small
Speaker 1: subset of documents and having them say whether something is
Speaker 1: relevant or not relevant, and the computer is watching those
Speaker 1: humans make those decisions, and then the computer learns from
Speaker 1: that experience and is able to amplify that experience across
Speaker 1: a much broader set of documents for that specific case. UM.
Speaker 1: This leads to a lot of savings and dollars you know,
Speaker 1: in terms of like the labor that you have to employ,
Speaker 1: and it also makes the litigation process go a lot more,
Speaker 1: um go much faster. But I feel like to really
Speaker 1: illustrate how far away robots are in terms of just
Speaker 1: really being able to moot very well, maybe we should
Speaker 1: try to have a little bit of a conversation with
Speaker 1: Siri and see how that goes. Yeah, let's do it.
Speaker 1: Let's see, Siri, I'm feeling really sad today. I would
Speaker 1: give you a shoulder to cry on, Victoria if I
Speaker 1: had one. Thanks, that's really nice, but it didn't really
Speaker 1: make me feel better. You're welcome, okay, all right, Well,
Speaker 1: I'm not sure that has quite the same effect as
Speaker 1: if you know, Aki was asking me exactly why I
Speaker 1: was sad. I feel like if if I told you
Speaker 1: I was sad and you didn't ask me why or
Speaker 1: try to make me feel a little bit better about it,
Speaker 1: I might have to unfriend you. So that's that's a
Speaker 1: you know, a kind of social skill that Siri has
Speaker 1: been unable to learn yet. Can we follow or um
Speaker 1: framed Amy? I mean, let's talk a bit about Amy.
Speaker 1: Does she or it exists inside a computer, inside a
Speaker 1: micro ship or in this chap's office tory? Is there
Speaker 1: a humanoid look machine typing away to keyboard and her
Speaker 1: or it the name happens to be Amy. What are
Speaker 1: the dimensions that we're dealing with here? Amy is a machine.
Speaker 1: I mean she's not. She's not a robot sitting at
Speaker 1: a computer typing. You know, she she recognized. The machine
Speaker 1: recognizes patterns in the emails, you know, today, tomorrow, a time,
Speaker 1: and they use that very similar to how Google does.
Speaker 1: If if any of the listeners out there have Gmail,
Speaker 1: I'm sure they see when when someone sends an email
Speaker 1: with a time and a date, they'll see these dash
Speaker 1: lines under it and you can add it to your calendar. Um.
Speaker 1: You know, Amy is analyzing these these dates and these
Speaker 1: times and these words, um and being able to respond
Speaker 1: to that and and added to your calendar. So UM,
Speaker 1: I don't think you should think of it as like
Speaker 1: a robot sitting at a at a computer, like slaving
Speaker 1: away at a keyboard. Should we be scared of Amy? Well,
Speaker 1: I don't think we should be scared of Amy. As
Speaker 1: a Dennis Mortinson again, the creator Um said he's just
Speaker 1: trying to sort of eliminate this email back and forth
Speaker 1: that any human has to do. Whether you're lucky enough
Speaker 1: to have a personal assistant and your personal assistant does it,
Speaker 1: or like Aggie and I have to do it. Um,
Speaker 1: he just wants to eliminate that, so it frees you
Speaker 1: up to do things that are more productive and more
Speaker 1: worth a humans time. So I don't think we should
Speaker 1: be scared of amy. It's really interesting because I was
Speaker 1: doing research for this story, I was able to talk
Speaker 1: to people about what they think the future looks like
Speaker 1: in terms of how much work robots will be taking over,
Speaker 1: et cetera. And Paedro Domingos over at the University of Washington.
Speaker 1: He's he's the author of a new book on machine
Speaker 1: learning called The Master Algorithm, if any of you guys
Speaker 1: want to check it out. Um, but he sort of
Speaker 1: envisioned this world are robots will be able to do
Speaker 1: basically everything that humans currently do now in their work,
Speaker 1: but they're going to be certain things that will want
Speaker 1: a human touch for. So, you know, you don't want
Speaker 1: to go to the bar and like pour out like
Speaker 1: your feelings about how your girlfriend dumped you to a
Speaker 1: robot bartender. You want a real bartender who can take
Speaker 1: a shot with you and who you can like go
Speaker 1: back and forth on how terrible she was. Like. You
Speaker 1: want someone who really understands what you're looking for. And
Speaker 1: he says that these things are going to start to
Speaker 1: command a premium in the labor market, and there's gonna
Speaker 1: be way fewer of them, but they will still exist.
Speaker 1: They'll be a luxury sort of. But also in that
Speaker 1: new world, we're gonna totally have to rethink how people
Speaker 1: get money, how people live. Um. There's this idea floating
Speaker 1: around about basic income, and it's this this theory that
Speaker 1: you know, as robots basically take over the way that
Speaker 1: we earn a livelihood, and you're gonna have to be
Speaker 1: able to guarantee people some sort of fixed amount of
Speaker 1: income um that they can spend, whether that comes from
Speaker 1: the government, whether that comes from taxing the people who
Speaker 1: create robots that are gonna take all our jobs. We're
Speaker 1: gonna have to change sort of the distribution of capital
Speaker 1: because right now, you earn money based on you know,
Speaker 1: the scarcity of your labor um, and and once robots
Speaker 1: are taking that over, the money is going to be
Speaker 1: held by the people who control the robots, who invented them.
Speaker 1: So we're gonna have to rethink that. Going back to
Speaker 1: your bartending example, like if you're really really amazing bartender
Speaker 1: with these incredible, interpersonable skills and you're really warm and
Speaker 1: you make these really cool creative cocktails, then yeah, your
Speaker 1: future is golden. You're gonna be fine, and if anything,
Speaker 1: you're probably going to be making even more money in
Speaker 1: the future. But if you're kind of like the middle
Speaker 1: of the road bartender, like you know, you're you're kind
Speaker 1: of friendly, but not that friendly, and your cocktails aren't
Speaker 1: that created of it's pretty much, you know, the same
Speaker 1: stuff as what's on other menus around the city, then uh,
Speaker 1: your future isn't that bright. That probably means that your
Speaker 1: job is um much more vulnerable to robots and software. Right,
Speaker 1: creativity is going to be just a huge game changer.
Speaker 1: It already is now, but that's going to be really
Speaker 1: how you can leverage yourself, is what these experts said. Right,
Speaker 1: So I can't help but feel like it would lead
Speaker 1: to more inequality in terms of who wins and who loses. Yeah,
Speaker 1: you know, it reminds me of some of the issues
Speaker 1: we talked about in our inaugural episode with our friend
Speaker 1: Barry Bosworth of Brookings. He sketched for us a history
Speaker 1: of automation going back to the spinning wheel, steam engine, electricity,
Speaker 1: personal computers, where do you think Amy and machines like
Speaker 1: her fit into that historical sweepe. There's this weird dichotomy
Speaker 1: where we have people who are both afraid that robots
Speaker 1: are going to take all our jobs and also afraid
Speaker 1: that we're not innovating enough. Uh, that productivity is just
Speaker 1: gonna be sluggish forever. I don't I don't know which
Speaker 1: one is right. I don't think it's probably either extreme.
Speaker 1: I think it's going to be somewhere in the middle. Um,
Speaker 1: the so called Internet of things is really changing how
Speaker 1: we look at productivity and how we use technology to
Speaker 1: get data and to drive decision making and to to
Speaker 1: make our processes more efficient and smarter. Um. It just
Speaker 1: it may take some time for these things to show
Speaker 1: up in the data. I think that's what people are
Speaker 1: trying to figure out. But Dan, I think I have
Speaker 1: a more important question, and that is are you a robot?
Speaker 1: And Victoria we should probably wrap up, Tori. Tori, you
Speaker 1: know we started this episode with a question of whether
Speaker 1: a robot will be able to take our jobs? Should
Speaker 1: we ask Sirie? Oh? Yeah, we should definitely ask Seria. Okay,
Speaker 1: let's see what she says. Is a robot going to
Speaker 1: take all of our jobs. Interesting question, Victoria, all right,
Speaker 1: that was a super late answer, Siri. Yes, yes, old
Speaker 1: technology for the win. Sirie needs some work. Thanks again
Speaker 1: for listening to Bloomberg Benchmark. We will be back next
Speaker 1: week and you can find us on Bloomberg dot com, iTunes, Podcasts, SoundCloud, Stitcher,
Speaker 1: all the places um as well as on the Bloomberg terminal.
Speaker 1: And if you're on any of those platforms, please take
Speaker 1: a moment to rate and review the show so other
Speaker 1: listeners can find us and let us know what you
Speaker 1: thought of this show. You can reach us and follow
Speaker 1: us on Twitter at Daniel mass d C, Tori Stillwell,
Speaker 1: and Kita seven. We'll see you next week. And I
Speaker 1: am not a robot.