Artificial Intelligence, Automation, and the Future of Work

Welcome to the new retirement podcast. Today we're going to be talking with Ken Goldberg, a robotics professor from Berkeley, and Eric Brewer, a VP at Google and professor of computer science at Berkeley as well, about artificial intelligence, robots, automation, and the future of work. Both Ken and Eric live here in Mill Valley. I'm continuing to mine this town for talent, so we are live in studio. Ken's daughter and my youngest son are in the same elementary school, and Eric and I have played Sunday morning soccer in the same pickup game for the last 10 years. And Eric and I compete for the title of the least skilled player among the other Americans, while the Turks, Jamaicans, Guatemalans, Argentinians, Italians, and Mexican guys that we play with tolerate our presence. Both of us had the ball too much, but Eric's cost per header is probably 50 times mine. So, Eric and Ken, welcome to our show. It's great to have you join us.

Thank you. Thank you, Stephen.

So, Ken, why don't we start with you. I just wanted to, if you could kind of tell us in your own words, kind of, you know, a quick background and what you're working on and what you find most interesting today.

Well, I've been really working on the same problem for most of my life, which is how to not drop things. I, I was a clumsy kid and I, but my father was, was an engineer and was. Came up with this idea of building a robot when I was a kid. So I've always been fascinated by robots. And when I was in college, I, I started working a robot lab and then basically trying to solve the problem of how to make a robot less clumsy. And it turns out that it's extremely difficult. Humans are very good at this, most humans, but it is very hard. And so we've been trying different approaches for many years, and very recently we've made some very interesting progress. Haven't solved the problem, but we've made a significant improvement.

And you're thinking about or you have started a company in the space as well?

We did. We just started a company that's called Ambidextrous Robotics.

Nice. But solving this problem probably has pretty profound impacts if you can suddenly make robots be as dexterous, I guess, as humans.

Well, they'll never be that dexterous, I don't believe. But I think here's the big driving application for it is e commerce. So I think we've all noticed that there's just the more and more boxes appearing on our doorstep every morning, and it's this, it's really changing the way we shop. So it's growing at the rate of 12% a year. And the challenge is how do you get these products into the boxes fast enough? And they're building places like Amazon, Walmart, JD Are building warehouses every week and they cannot hire enough people to do this job. So that's where there's a real opportunity for robots to come in. Wow.

And before I move on to Eric, I mean, do you see them penetrating kind of the labor market in a huge way in the next 10 years?

Well, this will be. Let's come back to it because I do think that there's the technological issues and then there's also the economic or social issues. And the reality is I think we're going to have a shortage of workers for the foreseeable future.

Well, okay, all right. Well, we're going to, we're going to dive into these, you know, robotics and the singularity and stuff. But I want to let Eric kind of give us his background really quickly. I don't know if you want to.

Sure. Happy to be here. I am a systems guy, which basically means I like to design large scale systems that run all the time. The most obvious modern version is cloud, but Starting in the 90s, I worked on things like how do you make a search engine and how to. How do you find all the documents on the web, how do you distribute content all over the world? And they're all variations of the same problem, which is large scale distributed systems. And they are a foundational thing for the future in the sense that it makes computing essentially free and everyone now gets to have their own personal supercomputer.

Right. So I think specifically you run infrastructure at Google.

I do software infrastructure. I'm essentially the kind of senior designer for the software in Google Cloud.

Yeah. I think at one point. Is it true that you are the only VP at Google that has no direct reports?

I believe that is still true.

By the way, Google Home is like listening to us right now, who had heard what mentioned.

Okay.

And there's what I think five or six SVPs and 160 VPs at Google, which has how many thousand tens of.

Thousands of those are all about. Right. Numbers. I don't actually track them very closely, but yes, there's, you know, order a few hundred VPs and, you know, quite a few less senior VPs. Cloud alone probably has more than a dozen VPs by now.

Wow. So you have a very unique skill set. All right. And also you founded Inktomi as well.

I did. 1996. Different. Different era. Different era in the Internet.

But yeah, bubble and burst. Yep. I was reading your Wikipedia and it said you were a paper billionaire at one point.

That is also true. Almost paper two billionaire.

Nice. I hit 20 million on paper back in the dot com glory days. But here we are, I mean we're still doing fine. So before I move on, can you just tell me how many servers Google runs and also maybe any amazing stats that the average person might not appreciate about just how big Google is and what's what they're doing?

Yeah, we don't actually talk about the details, but I would say this, that it's tens of data centers in order. A million servers. When ink to me was big, we were thinking about oh, a thousand servers is big. Or maybe 10,000 servers would be, would be big. But it's certainly way larger than that now.

And how fast are you bringing them online?

Well, one public thing I can tell you is we spend about $5 billion per quarter on CapEx, which is basically data centers, computers, concrete.

Wow.

That's a very large number. A lot of servers for that.

That's amazing. And how does that compare to Amazon, Facebook and Microsoft?

It's hard to know exactly. I believe Google still has the largest total collection of infrastructure, including networking and servers. That's in part because the Google has I think seven or eight now services that have a billion users each.

Wow.

Gmail has more than a billion users. Apps has more than a billion users.

Yeah.

And so each of those needs their own very large cloud just for them in some sense.

So you're responsible. We were talking about this before we got started. Ken was mentioning this, you know, for making sure that all Google Docs, all photos uploaded to Google never disappear and are always available. Right, yes.

It's a lot of responsibility actually. I mean, do you sleep well at night? Do you worry about this?

I actually do sleep well at night. And of course it's not a job that an individual can do. My role is to get systems of systems really such that were robust to all kinds of different failures. So I'll work on things like how far do data centers need to be apart so that if you have a tornado take out a data center, the other data center is fine. Right. What we depend on is not that disasters don't happen, it's that they happen in uncorrelated ways so that we can bound the risk of any given accident or facility. Like for example, we had a fire in one facility at a point, took out a bunch of stuff. Yeah, Mostly people didn't notice. Right. And that's because we had copies of data other places fires limited to a section of a data center, not even the whole thing. And so you can kind of bound these risks over time. And by the way, it's the same thing with governments. You, you want to have data in different governments for different reasons.

Right.

That's another kind of risk. What data do you want to get subpoenaed? If a customer asks you to delete their data, how do you know it's actually been deleted? You have to find all the copies. Right. So all that kind of stuff is very interesting to me. And it's really, you have to plan on people making mistakes, systems breaking things, not doing what they're supposed to, and still try to be resilient despite all that.

How.

How much data is being added to Google's network every day or month?

The biggest single source is YouTube. And I, the last I checked, which was more than a while ago, it was about, let's see, I think it was about, I think it was 500 hours of video uploaded per hour.

Wow.

Which means you couldn't, you couldn't watch the video that's arriving.

Right.

If you started now, you wouldn't live long, you wouldn't catch up.

And when you build your networks out, are you staying a certain like amount in front of the data getting added, or are you accelerating away from it?

You can't really accelerate away from it on an exponential scale, so you have to basically just try to keep up. And the network actually hasn't been that bad a problem. It's not, I would say, trivial. Its biggest problem is it takes a lot of lead time. So you have to guess far in advance how much networking you need in different locations. And of course, the locations are not interchangeable, so you have to guess correctly for every location on the other end. You have to figure out how many disks to buy and what type.

Right.

And you have to actually get multiple suppliers. You have to have suppliers that make enough in advance that they can actually meet your needs.

Right. So you're just getting Amazon. You're having Amazon drop ship surfers to your doorstep every day. No, it's not like.

Nope.

But you should also ask him about. Eric is famous for a theorem about this.

All right.

Because it's a mathematical result that's actually quite beautiful. You should describe it.

Sure. It's sometimes called Brewer's theorem, but I didn't want to call it that, so I called it the cap theorem. And it says that there's three properties you'd like to have in these Giant systems and you only get to have two of them. And the three properties are that it's consistent. Which means, for example, in Google Docs, if you have two people looking at the same doc, they should see the same thing. Another is that it should be highly available. It's that even in the presence of failures, you should still be able to make updates to your doc.

Right.

And the third one is that, and this is a little more complicated, is that you'd like it to be tolerant of partitions, which means if you were to, you know, cut some fiber lines and the two documents are in different sizes that cut, you'd like the system to keep working.

And why can you only have two of three?

Well, that's why it's a theorem. It turns out the short answer is if you look at two sides of the cut, if you want to make an update on both sides, you have kind of two choices. You can update one side and then the other side will be inconsistent, so you've lost the consistency, or you can not do the update till later, which case you've lost availability, because now I can't do updates. You can both read it. By the way, reading documents on both sides is fine, but updating documents both sides won't work. It turns out for many years people tried to design systems that had all these properties and they were not able to. And that's because it's not possible.

Well, when did you come up with this theorem?

While teaching, in 1998.

Nice.

I was teaching this kind of topic. I'm like, you know, I know this is not possible. We should be able to just clarify.

Right.

And I had a simpler version at first, but eventually I realized that this nicer version, you can have any two of the three properties you want, actually. Yeah. And. But you can't have all three.

Nice. Well, appreciate that. Good to get this history.

Isn't that cool? It's very cool.

It applies to people. It applies to all. All systems.

People are inconsistent. No. You know, also, I have to tell you, I want to thank you on this program because I've been recently using Google Docs to write this big document with about 15 authors are all doing at the same time. And I was run editing it today on a in a Lyft ride, trying to catch up desperately to this. And I was. So I was offline during the drive and then when I got there, it synced up with all the 15 people and all the changes and everything else. And I was like, is this gonna. How is this gonna. But it sure Enough. It did. Wow, that is non trivial to get that right.

So a big doc other people were editing at the same time.

Yeah.

And I was like not editing. And then suddenly a whole burst of my edits come in which are inconsistent. Right. With what they were. I mean, so it has to basically figure out all that and make it reasonable.

And it was.

It'll almost always do the right thing.

Yes, that was it. It wasn't 100%, but I wasn't expecting even that.

Right.

Yeah.

Roughly what it's doing, this is not that hard to understand is it's making a little list of all your edits, like an edit list. It's not actually sending your version of the document, it's sending the changes. Your list of individual changes.

Yeah.

And it turns out it's easier to merge lists of changes from different people especially because they typically work on different parts of the document and then only if two people edit the same part of the document at the same time. Now you have to decide what to do among those edits. And the short answer is you just do the union. Meaning that if you added a word and someone else added a word, we'll just add them both. You might have added the same word or something. It doesn't matter.

That's where the, that's where the few snags were. Exactly that. But it was, but overall it was amazingly well organized.

Well, I think Google Docs is, they obviously nailed it with the whole collaborative making that the core functionality. And that's, you know, I mean we use it and it's, it is amazing. It's pretty interesting to see how email and email is getting replaced with messaging. You know, everyone's using Google Docs because it's all real time collaborative kind of every system is going in that direction for human to human communication. Anyway, I want to move on a little bit, just quick background on, you know, where we are in kind of the state of the world with AI and machine learning. But one of the things I saw today was that, you know, Waymo driverless cars from Google rolled out or self driving cars rolled out in Phoenix. They were going to go live with fully autonomous vehicles, but they have left, you know, drive, you know, human co pilots in there at least temporarily, but it doesn't seem like they're needed. But you know, that kind of really makes it real for me to see this. Even though I'm in the technology space. It's kind of amazing to me that I can click a button like calling an Uber, but a completely autonomous vehicle could show up, I could get in, it could take me somewhere, navigate, you know, the streets and traffic in a completely chaotic world and do it safely.

Well, unfortunately, that's science fiction and I'm very, very skeptical about this. Yeah, I think that the leaving humans in the car is not accidental or just temporary. Um, I don't think we can solve this problem fully in our lifetime.

Oh really?

Yeah.

In our lifetime. That's a stronger statement.

Yeah. Well, listen, I'm not as young as I used to be, but I will even say in my, for, for many decades to come, we'll solve it on, on freeways. I think we can do a lot better than human drivers there. And that's coming. I think it's already. We're very close. But trying to drive, for example, the way the, I drove to the, to your house tonight, very, very complicated.

Yeah.

And, and, and perilous. Especially a truck trying to drive through these kind of neighborhoods.

Yeah.

It is amazingly complex and you have to. The corner cases crop up fairly commonly, so someone is double parked and you have to cross into the other side of the street. And that usually requires a very complex negotiation and navigation. So I'm. We made. We may disagree on this and I know it, but I have a lot of friends in the business and even at Google who feel the same way that it's. We're not close. And it's very premature. This, this pilot study in Phoenix is gonna, I think, gonna run into problems.

Yeah, well, I, I know that. I think an Uber car or Uber self driving car killed somebody. Yeah, right. Was that completely autonomous or did that have a human driver in it too?

It had a human, but the human wasn't paying attention.

Actually killed somebody.

Yes.

And so Google had said they communicated even I think within the last month, when this goes live, it's going to have no humans in the car. And then when they went live today, they said, all right, we're going to leave humans in the car.

Well, the other thing that I believe they're doing, I don't know if this is official yet, but they're. The other approach is there's not a human in the car, but there's a human remotely monitoring situation.

So like a drone?

Like a drone, exactly.

There's definitely some of that going on. I don't know the details, but I think they're also limiting the routes they're taking.

Right.

Yeah.

So I would also say if you do this in an environment like retirement center where you have very, very well controlled environment where you very clearly labeled intersections and you have, you know, beacons and things like that. You probably can. You may be able to do it. There's a. There's a city in China that's being built right now where the entire city is being built from the design from the beginning to have no human drivers. So it's like, you know, like a. Think of it like a tram system. Right. A lot of subways run this way. You don't really need a. Humans.

It's all public transportation. Except cars.

Right. It's the cars and the pedestrians are. It gets really tricky.

Yeah. Okay.

It's also bikes and animals.

Right, right, right.

Free for all.

Yes. And that's the thing is that, you know, in suburban or urban settings where so much of it is, you know, the, A driver, like an Uber driver or Lyft, they have to do basically illegal maneuvers, constantly pulling over in intersections on the. Dodging in them. They have to find the client. Right. That's always a very complex maneuver, and it all requires human skills that are very complex.

So you're living up to your skepticist title.

Yeah, I'm sorry, I'm sorry. I don't mean to disappoint people, but, you know, I feel like Cassandra sometimes on this thing. But, you know, we wanted our jetpacks. We wanted teleportation and time travel. I mean, science fiction has been talking about those too.

Totally. Yeah. Actually, in preparation for this, I was. I was thinking about this. I was like, all right. I was googling failed future predictions. And so everyone's like, oh, flying cars and jetpacks. Yeah, teleportation. Right. But then there's stuff like, you know, we're doing some pretty fancy. It seems like we're starting to do fancy stuff with genetic engineering. You know, we've got these smartphones that are doing real time translation. You know, we're able to get access to the whole world's information at our fingertips and communicate with anybody. You know, I mean, we're sitting here in, in the garage in Mill Valley, and I regularly talk to people in Europe or whatever through video calls that are amazing. And that's happening. There's a lot of amazing things that are happening.

Absolutely. So I'm not, I'm not trying to say that, you know, these kind of miracles don't happen. I mean, technological progress has been amazing. And there's, you know, everything. The, the. The history of anything from air transportation to automobiles to X rays, I mean, nuclear power. There's a huge number of miraculous breakthroughs. And I'm perfectly. I Perfectly believe that a number of them will happen. But this is one category where I think we need to be really careful that it's what sometimes people call Moore's curse, because Moore's law has been so successful in making this growth in the capacity of computation and memory. People believe that anything is possible. And this myth of exponential technologies, it's very distortion because in so many other technologies it doesn't happen like that. And air travel is a perfect example. Air travel hasn't really improved much since the 1960s. 60s.

Yeah.

It's actually, some people may argue it's gotten worse.

Sure. Yeah. But it, Yeah, I, I do. I mean, my sense is that the world is amazing me in at a faster rate these days. I know I'm seeing headlines and like.

You'Re kind of like, exactly.

God, this stuff's happening. Right, right.

So that's why I want to say, you know, that's, I understand that and I, I agree with you. I, I, I'm, and I don't want to rain on the parade because I think that, look, I'm a technologist, you know, we both are. I think we want to be optimistic about the future of various technologies. So we are very excited about everything that's that's happening and will happen. I just, you know, this, this idea of AI as reaching, you know, human equivalent intelligence, artificial general intelligence, is the one that I really feel it's important to be, to be more realistic about. That one we haven't made much.

That one I definitely agree with.

Yeah, right. So you'll hear this from many academics, by the way. Look at like understanding the human brain. That's another one. If you talk to a neuroscientist, you know, there's a question of if understanding the human brain is like walking a mile. How far have we gotten so far? And the answer is three inches. Okay.

Really?

Yeah. And that's, I would say it's very similar for general AI.

Yeah, I think, Eric, we were talking about this, right? Artificial general versus artificial specific intelligence. Like, how would you define these things in machine learning? How do you, you know.

Well, I think of it this way, which is on a, on a narrow, well defined task, machine learning in general should be able to do excellent and in general, better than humans. Okay, Right. But the problem is it's a narrow, well defined task. You even have 100 of those tasks and collectively say that, oh, that's a very smart thing. But it's really, there's 100 things. Well, and still not general intelligence. It's just a Collection of narrow intelligences. And I will see many, many variations of that. That's really.

Can you give me an example of this?

Well, like one recent one is.

Machine.

Learning is better at looking at cataract images to figure out if you have a disease in your eye or not.

Right. So radiology and stuff like that.

Yes, those are basically image analyses that humans do pretty well, but the best humans do much better than well trained humans that are average. And machine learning can do as well as the best humans.

Right, right.

But it's a narrow task and you have to actually rebuild models for that narrow task for every different narrow.

But I would also say what's important to keep in mind there is, that doesn't mean that those doctors are going, going to be unemployed.

Absolutely not.

Right. So a radiologist, you know, there's a, there's a, there's this fallacy that, okay, we can, we can analyze an X ray better than humans. So we can run a bunch of trials and then we show a bunch of human doctors, same X rays. Do they spot the cancers as well? And yes, you can train a system, but that doesn't mean in any way that you're going to put these, these doctors and radiologists out of work. What it means is you're going to have better tools, make them more effective and efficient at what they do.

Yeah. Well, does that mean you need less of them, though?

I don't think so. I mean, here's the, here's the, the three examples everyone, you know, I always come back to is ATM machines. Everyone thought that was going to wipe out bank tellers. There's now huge numbers of bank tellers out there. Second one was spreadsheets. People thought that was going to eliminate accountants. Not at all. And the third one was barcodes. They thought, oh, just have these, no more cashiers. And in all this, that might still happen.

That might happen. Amazon's got their store now, right?

Yeah. Right. Well, the thing is, it's very interesting because generally when you're given the choice of do you go to the cashier or do you have to check yourself out? People go to the cashier. It's just like a, do I have to really do this myself? It's very annoying. So I think there's this phenomenon that many times the innovations are great because they actually make our jobs, us as workers, better.

Sure.

And that doesn't eliminate jobs.

I have a good related story on this, how I got into cataract images as I doing work with a very large hospital in India, southern India, and they do thousands and thousands of cataract surgeries per year. And in fact, their problem was simply they don't have enough doctors. In fact, they can't even train enough doctors. Because if you train a doctor in these rural hospitals, they'll eventually move to an urban area where they can make more money or have more options for their kids and things like that. So in fact, when we were able to give them tools that helped, like video conferencing and image analysis, and it really meant that they spent less time doing things they didn't want to do, like being in the van, going to rural areas and more time being doctors. But it eventually led to is a system where the best cataract surgeons only did cataract surgery. There was one guy that could do about seven an hour. Right. Because all of the other stuff, including the pre and post operation stuff, was done by somebody else and the paperwork was done by else. And. And nurses did all of the kind of things you'd think of as bedside manner. Yeah, right. And that's a kind of a Model T approach to medicine. But when you have such a great need in a place like rural India, it ended up being critically important that we take things off their plate.

Right. Well, I think that is one of the things that's definitely happening is, you know, when in preparing for this, I was looking at the poverty numbers and you know, in the 1820s, 90% of the world population lived in what we consider poverty. And by 2015, that's 10%. And we're also bringing online billions of people, you know, onto the Internet where they can get educated. Their educational ramp is going to be much steeper than it would have been 20 years ago. Right. So they can get access to the whole world's information and potentially, you know, become students of Berkeley or whatever. If they're, you know, the talent can rise to the top and contribute to make things get better faster. So that's the good scenario, right?

It is. And I think, you know, historically, if you look back over the last 100 years or, or further, I mean, there used to be huge number, a fraction of the population that worked on farms. Right. That is very small.

2% in all countries, including the United States. Right. 1700s, they all look the same. They were all 99% agrarian, agrarian.

Right. And so what happened is, you know, historically, time and time again, that, that new jobs emerge and people get more efficient. This is this productivity idea. So I'm not. And then people say, well, this time it's different because it's Happening much faster. But it happened relatively fast for the time when air travel was introduced. Right. That radically changed. Yeah, Automobiles, same thing. So the, there are disruptions and temporarily a lot of people do lose their jobs. So, you know, blacksmiths and others, there's no doubt. And we'll see analogous things in the future. But the idea of this widespread unemployment, I think is a myth.

Yeah, I know you wrote about that for the Economist. And I mean, it definitely feels like it's accelerating. Technological change is accelerating and we're all much more productive. We could do many more things with fewer, fewer people. So. But you think that we'll innovate even more quickly and soak up all this excess labor?

I do. I think we're going to have. Look at the predictions about truck drivers and taxi drivers, bus drivers that we're going to have. I've heard of this. Five million, you know, angry white men out, unemployed. What are we going to do about this? Well, the fact is, truck driving is actually not a great job, but it's actually a pretty well paying job. Some people really like it because they are, they're free. There's freedom. You're not like, you know, sitting in a factory. And it turns out there's a shortage of truck drivers, massive shortage. They cannot fill those jobs.

Right.

So I think that's gonna probably persist and it's not that we're gonna completely eliminate. You're gonna. Their job will get better. So when they get on the freeway, they can switch to autopilot, maybe take a nap. Yeah, that's gonna be great. They'll be fresher and better drivers when they get off. They're frequently.

Right, right. Or they can do other things. They can be computer programmers.

Right, Right. Yeah, they'll do other things like, you know, spend time thinking about their next route and being optimal, more productive. So, you know, in so many fields, I feel, I think this is true. I mean, look at education, how much the Internet has radically changed the way we teach my kids, our kids. Right. Are all using this, this resource amazingly. And it's making them incredibly. You know, I think that they're, they're, they're smarter. I mean, would you guys agree? I think I would go smarter.

I would go step farther and say that I think for me one of the biggest impacts of the Internet is the ability to teach yourself. Yes. The number of things I learn, things I'm willing to try to learn that are kind of unapproachable before is really unlimited.

So that's interesting. So you, that's Actually, an area I've been thinking about lately, because one of the things is, I feel like we're in this very fragmented stage right now where there's all these resources out there, but they're all spread out and there's nowhere to really. It's very hard to find. I mean, you can search, but it would be nice. What I would love to do is have something that would know my skills, know my time availability, and it would say, listen, I know you have a layover on this flight, and I've. I found this great mini course in three hours that's going to teach you this skill that will. I think you'll like. Right. I would love tools like that. Because you're absolutely right. I mean, I think we are. And all of us can be. Benefit from essentially continuous education, learning new skills. And especially for certain people who are. Whose skills will become less in demand, I'm still optimistic that they can be retrained and do new things.

Sure.

I just find it empowering. Like, I don't know. I've. I recently made brisket, which is actually hard to make really, really well. And I've made it like three times now, and now I think I make it really, really well.

Hey, dude, that's one of my skills, actually. I couldn't. I could teach you that. My grandmother taught me.

Nice.

But I wouldn't have occurred to me to try to learn how to make a great brisket 20 years ago.

And you. You found it online.

I did. I got to start there and I learned. I actually taught myself cooking. In the past, I've taught myself a huge number of things about building our house, including everything from codes to details about materials. It's all out there. It's just a matter of feeling like it's approachable or whether you're confident enough to try and take on some of these things.

Yeah. I'd be curious to see as we sit here, right. You two are in the. Definitely the top point, you know, 1% or point. You know, point. Yeah, 0.1 of 1%, whatever. In terms of intelligence, probably curiosity. You know, do average Americans, you know, embrace this and say, hey, it's a great opportunity for me to. Yeah. Learn the guitar or. Or whatever it is. Or, you know, pursue my passions or do they see it more as a threat and, like, wow, it's going to take my job.

Right. Well, this is. I'm glad you brought that up, Stephen, because I think that's actually a big issue, which is that there. There's a. What's sometimes called automation anxiety out there. People are, their morale is being weakened by a lot of this discussion. This is why I have this problem with the world, the word singularity. I, I just feel it's very counterproductive and it appeals to certain people who shall not be named. But there's a, there's certain people who just love to vision this kind of, you know, science fiction hypothetical point when we're gonna supersede humans and humans will be left, you know, in the dust. But it sounds okay if you're in the, you know, it sounds interesting if you're in the tech world. But for many people, if you're in the Rust Belt, you hear that and you start, you know, believing it and it's demoralizing. Right. You know, like I talk to drivers in on Lyft, for example. I always ask how, what do you think about this self driving car? And they're always like, yeah, it's coming. I've got probably a year left and then I'm out of work, I'm going to work for something else. But it's not true. So I think that that's where, that's this idea of multiplicity I've been advocating, which is really the future is going to be much more about humans and machines working together and in a cooperative, positive way and actually going to make people better at their jobs more enjoyable. They'll enjoy their jobs more and also hope that it will lead to sort of more of this mobility where people will feel comfortable learning in the same way that Eric just described of, okay, I want to learn, you know, how to do drywall or plumbing or some skill like that. By the way, if you're a plumber, you're, you've got job security for life.

Yeah.

I do think it's a confidence thing though. I kind of feel like our kids will grow up in a generation that expects that they can learn whatever they want at any time. Right. And that's super empowering.

I do think it's empowering. I mean, but as a parent of two teenagers and elementary school kid, I also see how much these devices encroach on their lives and influence their lives and thinking and almost drive us. Right. So this information is always available, there's always more stimulus out there. And the software is designed, you know, social media and games and so forth to, you know, appeal to our habits and, you know, brains, right. And give us dopamine hits and create habits and loops and stuff like that. And you know, these, there's a lot of, there's A huge amount of money, Right. Being spent to keep people doing more stuff, doing more and more on their computers, consuming more and more and being in front of their screens all the time, which I think has, you know, bad downside and bad long term effects or could have bad long term effects.

It's certainly a risk for adults and I think a big risk for children. We actually intentionally have our kids as a relatively low tech school. It's not that we're anti tech, quite the opposite. It's that I want to control the introduction of technology over time. The way I think about it is by the time they're 18, they have to have full command and they're on their own. Right. But you have kind of 10 years before that where you can incrementally, in a guided way say okay, let's start with email. Email is actually quite a bit safer than social media on many dimensions. There's a record, you can see what happened. It's not urgent. Right. The delay is actually much more like regular human interaction and so that's actually a good place to start. Oh, and the audience is relatively limited too. You can, you don't get, at least on basic new accounts, you don't get a lot of interaction with people you've never heard of. Sure, right. It's not as public, so that's an example. And another example actually is, you know, I think it's was worthwhile having my older son have a dumb phone for a while.

Yeah, right.

Just let's talk about the phone and the phone habits and simple texting.

Right.

There's no Internet, there's no games. It's just can you manage a phone and can you kind of show that we can trust you with the phone at all and then we could talk about a more advanced phone.

I just think that that's what we did with my oldest son. He had a text only phone for a long time and I think it, he has a healthier relationship with technology. The, you know, we have iPhones in our house and then the middle son got a phone earlier and I think it's had a pretty profound impact on him and it's difficult to unwind, you can't unwind it. I, I think most parents don't appreciate just how addictive these devices.

Yeah, there's a term I, I've heard recently, digital obesity. And I do think it's a, this is a real danger. I, I, Tiffany, my wife is been, is actually writing a book right now and so her, the strategy is that we take one day a week off from screens, all of us. And it's, it's based on Shabbat, the idea of, you know, we're not, we're not fanatics, so we're not orthodox in the sense that we drive and we turn lights on and things like that. But we don't, we don't use screens. And it's just been a way for the, for our daughters and us to just sort of have this, you know, ability to separate from it. And it actually turns out to be really interesting because they are capable of putting, you know, the screens down and not feeling that compulsion.

Yeah.

And that's a. I agree with you. I think that, you know, trying to get that early is really important.

Well, I think one of the things that, you know, I've definitely learned recently is 20% of the population deals with depression at some point. And I think that these devices amplify those behaviors. So what happens is if you, you know, have a, a certain type of mental, you know, health or profile, you can manage things healthily. Right. Just like alcohol probably say, right, we can drink a beer. I'm not alcoholic. Right. But some people, they drink a beer, it's like they can't drink one beer because it's gonna be fun.

No one likes to go on and see that. There's been this fabulous party and you know, you weren't invited. Right. That sucks. No matter how well adjusted you are. And that is what so much of this social media is about right here. Look at this place I'm at or this party I've been invited to and, and people, you know, just feel miserable. So, you know, I do worry about this. This is a whole other issue. I mean we could do, we could talk about this for three hours. I do think that it would, it would be interesting to try and see. It'll be to see how this evolves because right now there is a huge. The driving forces are really around commerce and the idea that if there's this whatever behavior creates more clicks, more eyeballs is, is going to be rewarded. So we have to really think about this. Is there any alternative?

Right.

I can't say that this topic is not my area, but it's taken very seriously by Google at this point. The internal or the name of the program is digital well being. And the idea is that there are ways your device can help you at least monitor your usage and help you set limits on you or your children in a way that you believe in. But a lot of it actually is just, even if it's just feedback like, you know, you've been on for four hours, is this what you want?

Right.

So I don't know where that's going to go. It's a very nascent space.

Sure.

But it's needed totally.

I think as adults you can say, oh yeah, I've been on for four hours. Maybe that's not a good idea. But I think with developing kids, you know, developing brains, yeah, big deal, you know, I'll play more video games, you know, they don't have the same judgment. An executive function.

Yeah, it's literally true. The, for the early teenage years are among the most addictive and it has to do with the fact that there's plenty of the physiology of addiction, but you don't have the developed frontal cortex yet. That is the higher reasoning that lets you say, what is this behavior doing to me? Can I assess what I'm, what I'm doing and how it's going? That actually doesn't come in until your 20s.

Right.

So even at 18, you don't have great executive judgment on top of your lizard brain.

Right.

That's actually basically the same problem AI has. AI has, is a giant lizard brain. It's lizard brain for image recognition, lizard brain for song recognition. It's missing the executive function.

Right? Yeah. Well, hopefully it emerges. I mean, in a way that is good for human humanity. All right.

I actually think it's worse than that. In the short term, the executive function is the programmers and product managers.

Right.

And in some extent the users. Right. Humans are going to have to be the limiting factor on how these things get used in the short term because it's not. There is no general AI that's going to figure this out.

Right, Right. And I think that's actually a great point is that it's that, you know, understanding what, what this, the, the, the skills are, right. Is there are image recognition, speech recognition, translation, those are, those are very subtle problems, but they can be addressed with deep learning. The new, the newest wave of technology around AI and it's. The results are astounding and also game playing. Right. So AlphaGo, the techniques that are being developed there are really interesting, but they're again solving these very specific problems. And in the case of image recognition, we also know there's a number of flaws that we can create counter examples systematically that cause these systems to fail. So, so they're vulnerable to attack or exploits from, from, you know, hackers.

Good. Well, that's good. Humans can still out.

That's right, that's right. So humans, I think humans have Many good years left.

Right. So I guess Ken, you haven't gone down and hung out at the Singularity University and.

Oh yeah. So I, I have to always, I should say this, I always forget, but I'm not against the Singularity University. I actually like that program. I think it's really interesting and I don't want to, you know, sound like I'm bashing that because people I know, they're all, every one of them has said, well, we're not a university and we're not about Singularity.

Change the name.

That's what I say. Why not just change it? But because if I see that on somebody's resume, I always like, oh, but it's an interesting program because it attracts really good people. It's now everybody gets funded completely. And, and there's like any great program, you get really great people together.

Right.

And so there are a lot of interesting results have come out of it. So I'm not, I'm not opposing that.

Okay. So before I move on, I just want to touch on this whole Singularity idea and just to define it, I mean I was looking at some Data. So by 2023, you know, the average thousand dollar computer is supposed to have enough processing power, the equivalent processing power of a human brain. Does that sound about right or does that sound crazy? That's only five years from now.

It sounds early to me.

Yeah.

Plus the rate of improvement of processing has gone down significantly.

Okay.

You know, for a long time it was like 2x every 18 months. You know, that lasted almost 50 years. It's been pretty flat the last few years.

Because we're hitting the limits of physics. Right.

We are hitting limits that are at least harder to get around. And there's also some economic limits in the sense that the things you have to invest to do a new factory that can do a new generation of equipment is, is a very large number and it's getting larger exponentially with each generation. And so it might be that it's too expensive to make.

Right.

Significantly faster chips. Now I think many changes are in progress. I don't know if we'll get back on the nice curve we were on. I think we're going to be stuck with a slower curve for a while. But we do have another decade probably of tricks we can play. But it is going to slow down. In fact, I had this, this is a half baked idea, but I'll put out there. I was wondering if. Because if you look at the plot of improvement in computing over time, it's been this you know, straight line on a exponential graph for a long time, and now it's curving, flat. So you would say it's approaching an asymptote. So I'm wondering, maybe that asymptote is. Is the singularity meaning? Maybe there's some limits that we're not going to get past by any mechanism, human or otherwise. But I don't know where that is, and I certainly don't know if that's way above or way below, or if that's way below or way below what a human brain could do compared to this kind of computing. I would suspect it might still be above. So you'd still have singularity in the sense that was originally intentioned. But it is interesting to me that our ability to improve is slowing down. Right. As we're starting to have this question.

Yeah, I mean, this is the other issue that if you look at, for example, Kurzweil, right, Who's. Who's very compelling when you listen to him. You know, he's a magnificent speaker and he really sweeps you along. But what's really interesting if you look carefully, is he's very selective about the results he shares. And the way he tells his story is very, very, I would call it biased.

Right.

It's not even handed in the sense that he's really looking at all the different possibilities and just simply being able to match some measure of the processing speed of the human mind, which, by the way, is very subject to debate because we're analog systems. There's huge nuance of, of chemical processes that go on in addition to the electrical processes in the brain. We don't understand this at all. So just trying to understand the number of, you know, the, the number of computations in some way that's happening in the brain. It's just speculation. Um, so we, we can. We'll probably increase orders several orders of magnitude in the, in the speed. But we already, you know, right now, if you look at it, you know, what's interesting is you look at the device in your pocket, it has a certain amount of. Huge amount of computation on board, but it also has the network. So what it means is that it can access, you know, Eric's data centers. And so essentially, in your pocket you have access to the amount of computation that's staggering. Right. Even though it's not living in your pocket.

Right.

So all these numbers are very easy to manipulate. And that's where, I think that's why this idea, like, you know, people have been predicting, by the way, that computers will do things Robots will do things, take over human capabilities for a long, long time. Yeah, because it even goes back before the word robot was invented.

Yeah. Well, I'll tell you that personally, my expectations are rising. I have this Google Home thing on my, on my desk here that Google sent me. And I find myself getting frustrated. Like now I'm. Now I actually have this thing and I'm kind of thinking to myself, I should be able to talk to it in a conversational way. And I can't do that. Or. But it does strange things. Like my phone, my Siri will. I'll be on a podcast or on a phone call and sir will think I'm talking to it and, like, start talking to me.

Right.

I can't get to shut up. But, you know, if it does become conversational and you know, I think that will be completely game changing.

Don't hold your breath. Yeah, that's, that's the, you know, the Turing Test. That's. We're not close to that. We can do that for a little bit of time. And as you know, you can fool some of the people. In other words, people look at a stuffed animal and think it's real and it's alive. So people will project a lot onto devices. I do think that Google Home and Alexa in particular are really capable. They're impressive. I mean, I remember this show, do you remember, in the 60s called what's the Munsters or Addams Family, where they had this thing called the thing that was on the table and it would just kind of like light the Morticia's cigarette and she would say thank you thing, and it would just kind of go back into its box. Remember that? I love that. I think of Alexa kind of like that. Like, it's just this thing, you know, that you could kind of ask simple things and you, you know, how do you spell embarrassment? Right. You get the answer. It's beautiful. But that's so far from being able to hold a conversation like we're having right now.

Right. Well, the idea that there definitely seems to be more awareness around these devices. I find myself, I'll be looking at one thing on, say, Apple News, and then I'll go to Google to search for something and Google will have a sense for what. The next word that I'm going to be typing in there is that I. I feel like it's aware of what is happening, you know, out here. So just on the singularity side. So processing power is plateauing or it's not accelerating nearly as fast. So I guess neither of you Think, hey, we need to be worried about, you know, some super, super AI taking over and destroying humanity like Elon Musk is.

It's certainly not imminent. And I think by the time it becomes imminent, we'll have some semblance of what the real risks are.

Yeah, I, I think it's very interesting because the people like Elon and Stephen Hawking and others, you know, they're, they're great minds and they're very much thinking, you know, several steps ahead, maybe hundreds of years ahead. So I think that's certainly interesting to think about, but it's the way it gets interpreted like, you know, this existential risk and it's coming and it's going to be, you know, near. I think that's where the, the real problem comes in. And there's no clear path. Right. We can speculate about it, but there's not a path like how we're going to get there. Well, other things like 5G. Right. This is, is very clear. We know we're going to get there and it's, there's no science fiction there. I mean, maybe you can say more, but I, I think it's just we have to install the transmitters, we have to upgrade our equipment and that's going to happen. I mean it's not. We know how to get there.

That's relatively straightforward.

Yeah, so that's, that's fine. I mean, I think, but there's, and there's a lot of technologies that we're, you know, I would say we probably both agree that are very exciting that are coming.

Yeah, I definitely wanted, that's what I wanted to dive into next. So yeah, 5G, you know, AR VR, kind of what you see happening with robots, solar energy, biology, genetics, 3D printing, stuff like that. I mean what any particular things that jump out at you as like, hey, this is going to be the next big game changing thing for it'll affect the most people.

What do you think? There's a lot of them.

I still feel the, there's a whole bunch of stuff still coming around, collaboration and empowerment. And I don't have many specifics but for me the first time I had this realization of some kind of collaborative power, it actually goes all the way back to Evite. And this is probably, I would say late 90s and when people were talking about what became the cloud, thinking, oh, we have all this extra computing power, we can do a search engine, we can do things that you couldn't do before. You do things that don't fit on one computer in particular. So that's one kind of thing you get by moving into the cloud, but actually a more important one in the long term is actually proven to be that in the cloud you have a shared state, meaning the state of the system is actually now in one place versus on everybody's desktop individually. And now it's very easy to say, oh, I want to have a group of friends and I want to know if they can make the same date. That's a collaborative application. And that shared state is actually super enabling. It's the same thing that enables Google Docs you talked about earlier, but also it's the thing that enables Uber and Lyft. It's not actually anything except the fact that there's a centralized way to track all the drivers and track all the passengers. That's the value.

And it's been incredible. Right. That's. I took that today and I still. It's great because I don't have to. I can get actually work done while I'm driving, you know, while I'm getting back and forth to Berkeley. I love it. You know, here's an example. About 10 years ago, you remember there was this huge fear that about spam and these spam bots were happening and we're getting our inboxes, we're getting deluged and it was a real fear of what are we going to do? This is like, you know, we can't stay ahead of it. And it's actually, it's, it's interesting because you kind of forget that it's just kind of got fixed. It's just not there.

Right.

And what was it? It was really Google, really, I give you a lot of credit, did by figuring out that you could distribute that problem you would have. As soon as someone flags something as spam, it sort of percolates. So it gets recognized by all the systems. Brilliant. Right. So that actually was able to solve that system. So that problem, I think something very analogous can happen with the. What's now being called fake news and the way social media is being manipulated to, you know, basically distort and send people messages that are particularly designed to inflame them or something. We can, I think we can, we can address that.

Right.

I don't know if it's in the interest of the social media yet.

Right.

To do it, but I think it's starting to. And they're going to be able to figure that out. So I'm actually pretty confident about that one.

It's also a kind of shared state application.

Definitely. Exactly.

You need to know what is, what is your bubble? Yes. That's actually visible in the cloud in some sense in a way that should be able to say, ah, this group is getting vastly different information than this other group. That is something that is at least detectable collectively.

Yeah. And so, and for example, I don't think that we could have an AI system that will be able to look at a message and say that's that was fictitious or not. Yeah, but, but collectively the human. With humans in the loop and this is multiplicity. The, that I think we can, we'll be able to solve.

Right. Well, I think the point about the spam filter for fake news is a great idea and I totally agree to that. You know, social media, a lot of these firms are not interested in fixing it because it entices and engages half their audience or a big chunk of their audience. Right. So. And they're like, hey, I can sell more clicks or sell more ads. Good for me.

Right.

I mean, you notice all these systems is fairly easy to set up a bot to generate lots of accounts. So Twitter's known about this for a long time. They know, they know what's the bots. But it, but of course they, they want to show how many new accounts are created every month. So it's not in their interest to sort of, you know, eliminate those until recently.

Yeah.

But you know, I think. So this is where there's a lot of, you know, as Eric said, I think where we start realizing this power of the, of the collective right that's really strong and we can, we can do even more with that. I do think that here's another. In terms of safety and driving, I think that this is, we're going to see this relatively soon, is that all cars will have this, these, these equipped with these systems that will help keep you in lane, will help anticipate and avoid, you know, collisions. And I think we're going to see, you know, just like airbags, they greatly reduce the number of fatalities. We're going to have other technologies like that that are going to really make driving much, much safer.

Yeah, that's a good example of specific intelligence versus general intelligence. Right. It's really easy to make a good braking system. It's, you know, you don't have to have a fully intelligent system to do that. You just need to solve the very narrow problem of I'm about to hit an obstacle, maybe I should stop.

Right. And so you will have. All this will be the boo. We'll still have humans in the car. So in other Words you still have, let's say Lyft or Uber drivers, humans, but they'll be safer and their job will be less stressful.

Right.

One thing I would love to see right now, and it doesn't exist yet, is that you can switch on autopilot when you're in heavy traffic, because that's the most aggravating, right. You're stuck on this bridge and you're like. And that actually is. You're slow moving. It seems like that. That's actually something we, I think we could address. But you know what the biggest problem right now is, why can't be done is that merging, which happens a lot in these kind of situations, is, is a very hard problem because geometrically, it's impossible. There's nowhere to merge in on this line of cars. But we do it all the time. How do we do it? We nudge, we indicate, we wave to the person. We're like, can you.

We negotiate.

We negotiate. Exactly.

Right. Yeah. Just listening to this, the whole I'm back on this fake news thing, some of this comes down to human. I mean so much. This comes down to humans and do they want to change? I think many people, they have these strongly held beliefs despite prevailing data, but they're like, hey, I don't care. I don't believe in climate change. I don't believe that 90. Just because of. 99% of scientists say that this is actually happening.

Who cares? No, no, listen. We're all prone to our confirmation biases. And the one about AI is one example, right, where I think there's a lot of very intelligent people probably listening to the show who will say, you know what? They'll, they'll totally disagree because I run into this a lot, really, really smart people, and they say, you're an idiot.

Y.

You don't know what you're talking about. We're going to have AI and it's coming very soon and you better wake up to it.

Y.

And I'm like, well, how do you know? And they're like, I know. I mean, I know. And they're like, how are you disagreeing with Elon Musk? And, and, and, and, and, you know, all these, all these geniuses. I'm like, well, I happen to work in this area and that they. Then they still don't care. That doesn't make any difference. So it's a perfect example. They don't, they don't want to take in any information that goes against their, their already their assumptions.

Right?

And we're all the same way I mean, I'm guilty of this too.

Right. Well, that'll be a big technological advance is convincing. Finding a way to work with the humans to convince them.

Oh my God.

That's depending on how what Regardless of what their perspective is. Because you have to feed them information a certain way. But I actually think there's actually a.

Whole field called persuasion.

Yeah. Google, Facebook, I'm sure could spoon feed people information a certain way. They'll understand how they think and they can not. You're not going to go from this to this. Right. From this position to this position overnight. But you might be able to get them to take a series of steps that kind of like bring them around.

That's right. And so understanding that probably it's going to be, as you said, in small steps, not contradicting someone, but actually helping them see through, you know, making a very specific case study that helps them walk through the scenario doesn't work the way they think it will. I actually think this brings it to the issue of diversity, which I've been thinking a lot about in terms of intellectual diversity or cognitive diversity that we are, that there's this idea of the IQ that was, you know, basically was, was created 100 years ago. I somewhat blame our competitive institution to the south about this because Stanford Binet IQ test. Right. But it's not really all their fault. But it was this idea of projecting the complex multidimensional phenomenon of intelligence down into one dimension. And they used principal component analysis, you know, statistical methods, eigenvectors. But it was, it was a great disservice to the, to what intelligence really is. It was useful for, for classifying soldiers and students. But it doesn't capture the multitude of nuances where you know, any group of people, they're, they're good and bad, they have strengths and weaknesses in so many different ways. In terms of music, in terms of spatial ability, numbers, verbal reasoning, all those are different. So you don't want to project everything down. But you should start thinking, going back to the idea of intelligence is a very high dimensional phenomenon. And that way thinking about artificial intelligence as just certain other dimensions. So computers are very good at doing certain kinds of calculations, Absolutely certain kind of pattern recognition, but it doesn't mean they're going to do well in all these other ones. And so I think it's important to recognize that. And then it comes back to this idea of what we're talking about here of, of, of cooperation and collaboration, that it so benefits from having people with different perspectives.

Right. Well, I can Tell you, as a father of three kids that are very, very different in terms of how they think and how they learn, I'm hopeful because I have a traditional child, right, who's like, hey, normal. Like our standard public school education has been great for him and he's gotten a ton out of it. He's going to go hopefully to a great college and everything else. And I have a couple of kids that think very differently, but they do bring their own, they have their own capabilities and skills and they can definitely add to teams and having a world that embraces that and say, oh, well, you're great at art or you're great at creating creativity. You think about the world, you know, in a different way.

Absolutely.

Yeah, exactly. Because we see so many creative geniuses did not fit in right into the, you know, that's the conformal standards of.

What, you know, Einstein was slow and what Einstein was slow.

Yeah, absolutely. So, you know, realizing that when you get groups together, you should have them being diverse and you need to learn how to negotiate, you know, to use your word, that diversity is really important for kids that, you know, we shouldn't be saying, okay, well this is the good kid and this is, you know, this kid is bad or something. We should be realizing that they're thinking differently or learning differently. And then how do we get them together and work together? Because they're going to complement. So there's these new results now with collective iq, collective intelligence, where you can show. And they do experiments where they bring people together and they give them these problem solving or creative tasks. And if you get the homogeneous group together, they're, they'll, they're, they're always underperform compared with a heterogeneous group that has diversity. And I'm not talking about just gender or age or, you know, racial diversity. Those are important, vital, but those are, those also lead to cognitive diversity. And you can have cognitive diversity just with two people who look identical.

I did see that, like Google, for instance, had a program where they were actively trying to recruit different kinds of thinkers, people that might be on the autism spectrum or something because, you know, well, many of them, maybe they're great at math or something, but you know, for, for different, for different reasons. So do you see that?

I think it's, it's important to try to figure out for each individual what, how they can contribute. Right. And honestly, that usually happens at Google after people have been hired, not before. Because hiring is an imperfect process. But the principle is the same, which is this person's clearly Bright. And, you know, they might be having a challenge in their current situation, but I've had many successes, you know, moving people around, changing roles, changing what we call ladders, which is kind of job description. And I feel like that's a huge boon to Google that we're able to give people this kind of mobility.

Nice. So any other big technological changes that you see on the Horizon or even 10 years from now that you think will be affecting what the world is like? I mean, because when I. I think about this a lot, like, so 10 years ago, when the iPhone was created. Right. I don't think I would have imagined what it could do. Now, in some ways, it's kind of mundane, but in some ways it's amazing what the thing does and how it connects us and how different the world is because of it.

Yeah. It's hard to imagine that was only 10 years ago. 2007. Right. I agree. That was the smartphone. Amazing. I wouldn't want to give, you know, go back and give that up. I. I do think there's a. There's a few things I think, you know, coming back to the robot grasping. We're. We're. We're making some very interesting progress being able to pick things up and to be able to. To do. To sort products in. In warehouses. That's where there's a. There's a group of companies now that are really pushing that. And it's. I think we're going to make some progress there. I'm kind of optimistic about that one. Here's where I think it may impact the, you know, everyone and beyond. Just getting orders delivered faster and less and less expensively is. I think we will see. And I know it's a little risky, so I'm going to hedge my bet. But I would say that within the next decade, we will have a decluttering robot.

Mm.

So that, you know, we have Roomba now. Right. And it works fairly well just sweeping or vacuuming the rug. But a decluttering would be something that would just go around and pick things off the floor.

Yep.

And not. Not, you know, fold your laundry or anything. Not gonna be like a rosy robot, but just keep your floor relatively straightened.

Pick up water glasses that are left around the house and plates and stuff like that and bring them around and.

Yeah. Clothes that are on the floor. Just put things into bins. Bins are good.

Yeah.

And that. And it'll sort them.

Yeah.

And it's very important for senior citizens to have such a thing because their. Their eyesight isn't so good. They don't often see if there's something on the floor. Second of all, they may not stop and pick it up because it's a hassle to reach down. And third, if they slip and trip on something, it could actually be fatal.

Right.

So if, if you can say for, let's say $5,000, I can put this thing in your, you know, my mother's home, that will just keep the floors clean or clear. I'm probably going to buy that.

It's interesting how demographics are driving this. So in Japan they have a rapidly aging population and you know, they're living a long time and you know, they're not having as many children. And so you have a huge boom of older folks. And they're in the forefront of robotics for seniors and elderly, for companionship, for, I think, self care, stuff like that.

Definitely. And, and this is interesting. I mean, for your audience in particular, I think, you know, we are. There's this huge bulge, by the way, this is not speculation. Right. This is demographics. We know the birth rate, we know the death rate. We can actually see that there's this huge bulge of, of, of senior citizens coming. And so. And there's gonna be a shortage of caretakers again. Why I'm not worried about unemployment. It's always gonna be those kind of jobs. But I think people are gonna want something in the house that's gonna help them and it's gonna take different forms, but. And again, it's not gonna do everything. But here's another task that I'll predict we can, we can address one that nobody likes doing. Making your bed.

Sure.

Here's the thing. We, we started working on this actually last year and making a bed is not time critical. You just generally would like to come home and have the bed made so it can happen over a period of hours. Second of all, it doesn't have to be done perfectly. It's fault tolerant.

Rarely is.

It rarely is perfect. Right. And I'm not talking about replacing, you know, that kind of bed making in a hotel. Right. That's really got to be done. Right. That's very hard. But just making the bed is straightening up so that the sheets are pulled up and things reasonably so. We actually have a robot in the lab that's doing that. Well, it's slow, but it's getting there. And it's actually, there's a mathematical problem of how do you find out where the pick point on the COVID to pull to increase what we call coverage, which is how much of the bottom sheet is covered by the top sheet. And it's a nice problem because it's a high dimensional manifold, how that cover has been arranged and if you just look at the data points, it's not obvious where you should, you should pick it. So it's actually very ripe for deep learning. So we've been giving it a lot of examples and it seems to be learning some structure to find the points to pick that sheet. So it's able to cover and it's able to actually do reasonably well already. It's just too slow. But I think we can get there and I think that would be something people would also want to have.

It brings me back to or into, I guess, a generalization of this idea which I've been thinking about a lot, which is the kinds of things we should be doing are what I call the repetitive toil tasks. Making a bed is not a terrible example, but in some sense it's a repetitive toil task. Humans are not good at those tasks for a variety of reasons. And this actually comes up in system design for things like cloud, because it turns out if you have a system that fails rarely, you can't actually have a human monitor it, because if it fails rarely, they will not take the monitoring seriously. And then when it actually fails, they're not prepared to do anything. In fact, this is the nature of the Uber crash you mentioned before. You can't actually ask a human driver to do nothing for a long time and then expect them to do something on very short notice that's urgent and important and, and participate completely. It's not going to happen. It's against what humans are good at. So, and you see this many other fields like this is where checklists come from for pilots or surgeons. Right. You don't have the checklist. These rare things that only happen once in a while don't get covered because it hasn't happened due to a long time that you didn't have the right blood type. Right. Because usually you do have the right blood type. So, you know, checklist is a human mechanism to deal with the fact that infrequent failures are hard to handle. The checklist causes you to be more systematic. But really, if you want to be systematic at something, use a computer. They're really good at that. So if you want to do something like, you know, classic KP thing, clean all the potatoes, right?

Yeah.

Pure toil. No human needs to be doing potato cleaning.

Yeah.

Except as punishment.

The one I don't like is like snapping the ends off the peas, the string beans Right. I just, I would love to have a machine that would do that.

I would eat more green beans if I didn't have to do that.

Exactly. But you know, here's one, one part where, you know, our two areas come together, which is the idea of the cloud for, for things like robots. And that's where I do think there's also some, some, some new innovation going that's going to happen, which is where robots will start to communicate and learn from each other.

Yeah.

So in this, imagine this scenario is describing of a, something that picks up, you know, the decluttering robot in your home. It's probably gonna make mistakes, right? But over time, if it's sharing its mistakes centrally and we're able to retrain, then you can download new versions. So the system will be able to get better over time.

Right. It makes me, I mean that's one thing I see with, you know, Tesla and Waymo, I mean, pretty much everything, right. So you have these intelligent Internet of things. Right? So the car becomes a thing. Right. Or whatever it is. They're learning collectively and all that learning is being centralized. I mean that's, that's been true in computer science for a long time. Right. We all, when we're building stuff, people are, you know, especially in open source, you know, someone, you only have to write some algorithm one time, someone uploads it to GitHub and makes it available and everyone else can use it and no one has to spend their time ever again.

Absolutely fundamental to self driving cars. I think that the robotics part of the car is super interesting and powerful. But I would say equally important is the, the shared map system. The million miles of shared training and learning that you get from these systems like the maps we use for self driving cars are much more sophisticated than the maps that you think of on, on your phone.

Right.

They need much more level of detail in terms of where are the lanes and you know, where are the signs. Can we, We've actually started finding all the signs by, you know, in the video that we're watching of cars, you'll see, oh, that's a sign that gives us the speed limit. Now we know the speed limit for that part of the road which is not documented anywhere.

Right.

There's no database to where are all the speed signs.

Yeah, right.

So you have to learn all that essentially in these super advanced maps. Right.

And then it can be stored and shared and updated real time. And yeah, so the world, the world changes. But you know, the next driver that drives by that speed sign says okay, the speed limit got updated in, in Mill Valley. So now all cars know that you can go 20 miles under an hour instead of 25 or whatever it is.

Right. I mean I, I often say part of the reason to my just speculation, why would Google get involved in self driving cars? Because it is, they saw it as a cloud issue. It was a task that could be hugely benefit by having the, all this streaming information coming in where you have maps, you have traffic information, you have weather information and you're, it's constantly being updated. So as you approach an intersection, I don't know if this is what they do exactly, but they're probably downloading as you get closer to intersection and downloading a new policy. It's the latest policy for that intersection given that that traffic condition and that time of day and that weather conditions, etc. So you want to be constantly basically sharing data over the cloud if you just have a self contained system. Right. It's really big, it's really at a disadvantage.

Right. I mean this is where I do think some of this stuff is going to emerge and be faster than we anticipated because we have all these humans that are now wired up all the time to these devices, educating them, speaking to them, looking stuff up. Right. Asking questions and Google's I'm sure, storing every single question. I did hear something amazing that there's something like 15% of the queries every day are new and there's some kajillion number of queries every day. It was something shocking but they had never seen them before.

Yeah, there's a surprisingly large number of new things every day. You would think there'd be no new things today compared to yesterday really. But there are every day.

But I do see Google. I mean I see it when I'm doing searches. It anticipates what I want to ask better and better. So and I have the sense also that these devices, you know, people say oh it's baloney but you know, you'll be on a car ride and you'll ask about some, maybe have a conversation that's completely random about some very random task or I mean topic and then you'll do a search or you'll see not even a search, you'll see just see presented to you in some stream or another, something related to that and you're like how did that get in here? Because I only talked about it, you.

Know, that may also be confirmation bias. But I do think there's another thing going on there too, which is current events are given relatively speaking A lot of weight. So depends why you were talking about something. Is it likely that other people were talking about it too then? They've actually been doing that search recently, that day in unusual numbers. And you definitely see effects like that where like, you know, how did Noah's going to ask about this fire? That's. Well, actually it's because lots of people are asking about the fire even though it's a new topic that wasn't here yesterday.

Right, right. Like if you. That's the other thing is that this constantly changing. So if you asked about yellow vests in Paris two weeks ago, would have been very different than, than searching it today. So that's another example where, you know, you constantly need. These systems need to be fed with human activity. So we're still going to be needing the loop.

Sure. Well, it's interesting and my wife was in Berkeley recently and she said she saw those small ice chest sized tracked robots that deliver things slowly cruising around. So these things are making their way. So now we have semi autonomous cars out and about. We've got these track delivery vehicles. Right. We have drones seating for us and drones flying around different ways. So they're, they're starting to be present in our lives in bigger ways.

I do worry about drones and I do worry about Iot. But these are all things that I think are fortunately going to evolve some.

Yeah.

But I don't think we're ready as a society for all the consequences. And how, for example, if your IoT device is taken over, is that your problem? Is it the manufacturer's problem? Is it nobody's problem?

Right.

What's the, what's the back pressure on systems that are badly behaved? What causes them to not be badly behaved in the future?

Right.

We don't have a lot of mechanisms. We have kind of liability, we have brand, we have regulation. Those are all complicated mechanisms. Right.

Wasn't it?

And they all add to the cost. So we'll see how this works out.

It was What, a year, 18 months ago when we had that giant DNS attack from smart fridges or something like that.

Yeah, cameras.

Mostly cameras.

100,000 cameras attacking the Internet and very preventable, but they're not in general to make these things safe, you need to be able to, like we do with Chromebooks, remotely upgrade the software in a reliable fashion so that when you find security bugs, you can fix them. And in general, IoT devices aren't fixable in the field, but they're still a network device that is an attack vector and that's not Sustainable. That's something that is going to change. I don't know what it's going to be.

All right, well, so just as we wrap up here, I mean I think my thought is I just feel like we with you know, you see self driving, maybe not completely self driving cars, but you see these more and more autonomous things, more intelligence being built into the network, more learning quickly from all these shared resources and humans attached to it. That many jobs and things will be. Maybe they won't be completely automated. I think, Ken, I'm coming down to your side that they're not going to be completely automated but they're going to get a lot better, a lot quicker and they're also going to amplify what an individual can do. So you may need less human capital. One thing that I've been thinking about is that, you know, the idea of a 40 hour workweek is actually a relatively recent invention in span of. I mean, you know, back in the 1900s, you know, people were working all the time and then you work till you drop, kids were working in factories and now we have 40 hour work weeks and maybe it's going to go to 35 hours. So maybe we will work less or have more control or be able to, you know, ideally choose the kind of work.

You see variants of that now besides the obvious one of France, a 35 hour work week. You see, I believe there's some shared funding in both Alaska and I believe Finland is the other place.

The university universal basic income idea.

There's, I think that is certainly has a role to play. Nothing else is a safety net but is a, is a long term consequence of being more productive. Those things are on the table.

Right, right.

That doesn't mean people won't want to work. But I do feel like our goal should be to make work more fun actually. Let's take out the toil of work and get to the creative parts and let everyone be creative and working the things they want to work on. That I think is an achievable goal.

Absolutely. And I. Here's one that I would love to see. What I would love to see automated, which is scheduling. The amount of time I spend trying to schedule things is just enormous and it drives me crazy. And I would love to have a real digital assistant.

You could just do half of the stuff.

Maybe. I'd say that, you know, there's Clara and these various things out there. There are artificial, artificial intelligence. Right. You know about this. So they're, they're supposed to be AI but there's actually people doing it, but getting. That's something I would love to see done. And I do think that's. That may be doable. It's going to be. You have to essentially learn my preferences and have to be able to have these networked calendars and things. But, you know, it'd just be great if it could just say, hey, we want to get together and then it figures it out.

I feel like I'm sure Google's working on this, that there's. That could. That wouldn't surprise me if that emerged.

Well, I think the thing three to.

Five years, like it was a pretty bulletproof thing.

I think it will emerge, but I think one of the things that makes it tricky is to do it well. You really need to know, as Ken called it, the preferences of the people being scheduled. And that's actually much harder than you think because many times they can't express the preferences if asked. Like they don't realize that, oh wait, I never really want to have a lunch meeting on Friday because sometimes I do stuff with friends on Friday at lunch. Right. That's something they would know if you ask them specifically about Friday lunches. But if you ask them in general about scheduling, they're not. It's not going to come up. So which means that if the system is going to work, you're going to infer a whole bunch of preferences over time by say whenever I schedule stuff on. On Friday, you never pick that one. You don't seem to want me to schedule stuff on Friday in the city.

Right.

That requires some repeated interaction, which means people have to kind of trust the system and use it for a while while it learns those preferences. And we're not that good at using systems that don't work the first day and get better.

Sure.

Which might be a good reason to use humans the first days, if maybe the humans can do a better job until we learn the preferences and then could do it more automatically.

But these devices do know, like where I'm traveling and what I'm doing kind of like, you know, most of many of us have a rhythm to our lives. Right. So if it sees that I'm riding my bike around this time and that time and I'm going here or there, that it might understand. And if it could see access to my Google Calendar and all my emails, oh, here's what you might like to do or where you might like to eat and where you might like to meet somebody.

Yeah, I've looked into that some. It's. I mean, I think it's a good direction. We should definitely, definitely pursue it. But if, I bet if I looked at your calendar, it'd be hard for me to tell on every hour of every day where you actually intend to be. Because even if you know this meeting is in San Francisco, you didn't actually put the address or location in the meeting. Right. It's in your head.

Yeah.

And so maybe I can infer that because you went last time, and last time it was in San Francisco, but it's, It's. It's much more incomplete on what's actually in the calendar than what is what you think it is.

Yeah.

But, you know, if anyone's going to do it, it's going to be Google. And Google's been pretty good about doing things like this. So I don't know if you would tell us if you're working on it, but Google now and things like that are really, you know, sophisticated. And they can learn. Right. By looking at lots of history for us. And look, it would have made it easier for us to get together, the three of us, tonight.

I know. It only took us eight months. Exactly.

And we all are in the same city.

Right. All right, well, I'll put my. We wrap up here. I want to put my. My hope in here. This is one thing I've talked about with my. With my wife. It's like vacation planning. You know, hey, just say, hey, listen, you know, we have this block of time. We want to go somewhere. It will know. This, to me seems doable. Yeah. Kind of what you're like, what you might like, what other people have liked. You know, it's like the Netflix of vacations and. And then it optimally finds the travel, you know, the how you're going to get there, where you're going to stay, puts together a rough itinerary, does all the pricing. To me, that feels like could happen.

You know, I think it could give you a menu of options.

Right.

Give you some. A shortened list. Like, hey, this is like Netflix. Hey, it's a 99% match. Or you're probably going to like this because you like that. Yeah, I think it's coming.

Having worked with travel professionals, a lot of what they do, I think actually is help the traveler figure out what it is they actually want. Like, you might say, oh, I want to do all this stuff in Tuscany and do some painting and things like that. But then you realize, well, actually, my kids will be with me and they're not actually going to want to do that. And that would actually be a terrible Trip. Let's figure out what holistically is going to work for the family. And those are things that people self discover as they go through the process.

And that problem exists, I mean back just a couple, you know, loop back to what we do. So we're trying to help people build financial plans for their future. And so how do you optimally use your resources that are available to you? Not just kind of your savings, you know, your, your, your investments and, and what's in your bank, but you know, your human capital, home equity insurance, where do you want to live, how do you want to live, health, all that stuff. And I, I can totally see, okay, you know what, Computers could look at patterns. I had this discussion with our dev team today. I was like, oh, can we look across and learn from the thousands of users on our site and learn more quickly and then use that to help other people in aggregate? Right. But still in delivering this, humans want to talk to other humans and you can quickly kind of sense where someone's at because when you look at somebody, you're getting so much information. You know, like Ken's like, I gotta get out here and work, work on my work on this paper that due tonight. This is gone way too long. But you know, computers can't do all that stuff. Yeah.

One thing I think we do know is that to complete a task, it's actually okay if the computer suggests multiple steps, as long as the steps are generally in the right direction. Like I say, know, do you want to do things with your family on this part of the trip? Right. Even if the answer is no or it's not a good question, it's at least a reasonable question. Which means the dialogue will continue and the, and the traveler is, is stays engaged. Right. And so I think it's, you can present options through which you can in a dialogue way, navigate to a decent solution. We're not good at that yet either, but there's absolutely better work on that coming as well.

Right. Well, I'm looking forward to robotic declutter decluttering. That would be awesome. As long as one robot. I don't want to think I have like five robots in the house doing different things, but. And then also digitally same thing. Digitally decluttering, my life would be awesome.

Try Google Photos.

Yeah, well that's for, I mean there's so many other things. Like think about, we, I think we have used this, the ability to do so many things, to do so many things and then our lives are super complicated.

The feature I love in Google Photos that people don't know about is you can search for people by their face like your kids and they can find all the pictures that my kids are in, including all the way back to their birth pictures. Even though they don't look the same.

Yeah.

You can actually extrapolate over time to say this person's the same as this person and even though 10 years apart, they don't look at all the same because it has some intermediate data can connect them all. Right. Before that there was no good way to search for pictures of my kids.

And it will put together these highlight reels and stuff like that that are pretty cool. You know some awesome stuff. All right, well, I know we've gone long here as we wrap up. Any last things you guys want to share about like good resources people look at to learn more about this stuff or you want to send them to your company or anything like that? I don't know. Anything you want to call out?

I'd say feel empowered. You can teach yourself whatever you want to know.

Awesome.

Yeah, I'll end on something I saw someone else posted recently which was it was said that may the person who invented Autocorrect burn in hello.

Nice. That's funny. Thanks Ken and Eric for being on our show. Thanks Dado Robeson for being our sound engineer. Anyone listening? Thanks for listening. Hopefully you found this useful. Our goal at New Retirement is to help anyone plan and manage their retirement so they can make the most of their money and time. And if you made it this far, I encourage you to check out Ken Goldberg or Eric Brewer, their work at Berkeley. You can look them up online. You can also check out our site@newretirement.com and you can join our Facebook group or follow us on Twitter newretirement. And finally, we are trying to build the audience for this podcast. So if you have a chance, you can leave us a review on itunes or Stitcher or anywhere. We'd appreciate it. We read them and try to improve our show based on the feedback. All right, thank you and have a great day.