Transcript
00:00:00 The following is a conversation with Colin Angle.
00:00:02 He’s the CEO and co founder of iRobot,
00:00:05 a robotics company that for 29 years
00:00:08 has been creating robots that operate successfully
00:00:11 in the real world.
00:00:12 Not as a demo or on a scale of dozens,
00:00:15 but on a scale of thousands and millions.
00:00:18 As of this year, iRobot has sold more than
00:00:21 25 million robots to consumers,
00:00:25 including the Roomba vacuum cleaning robot,
00:00:28 the Bravo floor mopping robot,
00:00:29 and soon the Terra lawn mowing robot.
00:00:33 29 million robots successfully operating autonomously
00:00:37 in real people’s homes,
00:00:39 to me is an incredible accomplishment
00:00:42 of science, engineering, logistics,
00:00:45 and all kinds of general entrepreneurial innovation.
00:00:48 Most robotics companies fail.
00:00:51 iRobot has survived and succeeded for 29 years.
00:00:56 I spent all day at iRobot,
00:00:58 including a long tour and conversation with Colin
00:01:01 about the history of iRobot,
00:01:03 and then sat down for this podcast conversation
00:01:06 that would have been much longer
00:01:08 if I didn’t spend all day learning about
00:01:10 and playing with the various robots
00:01:12 and the company’s history.
00:01:13 I’ll release the video of the tour separately.
00:01:17 Colin, iRobot, its founding team, its current team,
00:01:21 and its mission has been and continues to be
00:01:24 an inspiration to me and thousands of engineers
00:01:27 who are working hard to create AI systems
00:01:30 that help real people.
00:01:33 This is the Artificial Intelligence Podcast.
00:01:35 If you enjoy it, subscribe on YouTube,
00:01:38 give it five stars on iTunes,
00:01:39 support it on Patreon,
00:01:41 or simply connect with me on Twitter
00:01:43 at Lex Friedman, spelled F R I D M A N.
00:01:47 And now, here’s my conversation with Colin Angle.
00:01:51 In his 1942 short story, Runaround,
00:01:55 from his iRobot collection, Asimov proposed
00:02:00 the three laws of robotics in order,
00:02:02 don’t harm humans, obey orders, protect yourself.
00:02:06 So two questions.
00:02:07 First, does the Roomba follow these three laws?
00:02:11 And also, more seriously,
00:02:14 what role do you hope to see robots take
00:02:17 in modern society and in the future world?
00:02:21 So the three laws are very thought provoking
00:02:25 and require such a profound understanding
00:02:31 of the world a robot lives in,
00:02:36 the ramifications of its action and its own sense of self
00:02:40 that it’s not a relevant bar,
00:02:45 at least it won’t be a relevant bar for decades to come.
00:02:50 And so if Roomba follows the three laws,
00:02:54 and I believe it does,
00:02:58 it is designed to help humans, not hurt them,
00:03:00 it’s designed to be inherently safe,
00:03:03 and we designed it to last a long time.
00:03:07 It’s not through any AI or intent on the robot’s part.
00:03:11 It’s because following the three laws
00:03:14 is aligned with being a good robot product.
00:03:19 So I guess it does,
00:03:23 but not by explicit design.
00:03:27 So then the bigger picture,
00:03:28 what role do you hope to see robotics, robots take
00:03:33 in what’s currently mostly a world of humans?
00:03:37 We need robots to help us continue
00:03:42 to improve our standard of living.
00:03:46 We need robots because the average age
00:03:51 of humanity is increasing very quickly,
00:03:55 and simply the number of people young enough
00:03:59 and spry enough to care
00:04:01 for the elder growing demographic is inadequate.
00:04:08 And so what is the role of robots?
00:04:11 Today, the role is to make our lives a little easier,
00:04:14 a little cleaner, maybe a little healthier.
00:04:18 But in time, robots are going to be the difference
00:04:22 between real gut wrenching declines
00:04:25 in our ability to live independently
00:04:28 and maintain our standard of living,
00:04:30 and a future that is the bright one
00:04:34 where we have more control over our lives,
00:04:37 can spend more of our time focused
00:04:40 on activities we choose.
00:04:44 And I’m so honored and excited
00:04:47 to be playing a role in that journey.
00:04:50 So you’ve given me a tour.
00:04:51 It showed me some of the long histories,
00:04:53 now 29 years that iRobot has been at it,
00:04:57 creating some incredible robots.
00:04:59 You showed me Pacbot.
00:05:01 You showed me a bunch of other stuff that led up to Roomba,
00:05:04 that led to Braava and Terra.
00:05:08 So let’s skip that incredible history
00:05:14 in the interest of time,
00:05:15 cause we already talked about it.
00:05:16 I’ll show this incredible footage.
00:05:18 You mentioned elderly and robotics in society.
00:05:22 I think the home is a fascinating place for robots to be.
00:05:26 So where do you see robots in the home?
00:05:29 Currently, I would say, once again,
00:05:31 probably most homes in the world don’t have a robot.
00:05:34 So how do you see that changing?
00:05:36 What do you think is the big initial value add
00:05:39 that robots can do?
00:05:41 So iRobot has sort of, over the years,
00:05:44 narrowed in on the home, the consumer’s home,
00:05:49 as the place where we want to innovate
00:05:53 and deliver tools that will help a home
00:05:59 be a more automatically maintained place,
00:06:04 a healthier place, a safer place,
00:06:06 and perhaps even a more efficient place to be.
00:06:11 And today, we vacuum, we mop,
00:06:15 soon we’ll be mowing your lawn.
00:06:16 But where things are going is,
00:06:22 when do we get to the point where the home,
00:06:27 not just the robots that live in your home,
00:06:29 but the home itself becomes part of a system
00:06:32 that maintains itself and plays an active role
00:06:35 in caring for and helping the people live in that home.
00:06:40 And I see everything that we’re doing
00:06:43 as steps along the path toward that future.
00:06:46 So what are the steps?
00:06:47 So if we can summarize some of the history of Roomba,
00:06:53 you’ve mentioned, and maybe you can elaborate on it,
00:06:55 but you mentioned that the early days
00:06:57 were really taking a robot from something that works
00:07:02 either in the lab or something that works in the field
00:07:04 that helps soldiers do the difficult work they do
00:07:10 to actually be in the hands of consumers
00:07:12 and tens of thousands, hundreds of thousands of robots
00:07:15 that don’t break down over how much people love them
00:07:18 over months of very extensive use.
00:07:21 So that was the big first step.
00:07:22 And then the second big step was the ability
00:07:26 to sense the environment, to build a map, to localize,
00:07:29 to be able to build a picture of the home
00:07:32 that the human can then attach labels to
00:07:34 in terms of giving some semantic knowledge
00:07:38 to the robot about its environment.
00:07:40 Okay, so that’s like a huge, two big, huge steps.
00:07:46 Maybe you can comment on them,
00:07:47 but also what is the next step
00:07:51 of making a robot part of the home?
00:07:54 Sure, so the goal is to make a home
00:07:57 that takes care of itself,
00:08:01 takes care of the people in the home,
00:08:03 and gives the user an experience of just living their life
00:08:07 and the home is somehow doing the right thing,
00:08:10 turning on and off lights when you leave,
00:08:14 cleaning up the environment.
00:08:17 And we went from robots that were great in the lab,
00:08:24 but were both too expensive
00:08:27 and not sufficiently capable to ever do an acceptable job
00:08:32 of anything other than being a toy or a curio in your home
00:08:37 to something that was both affordable
00:08:42 and sufficiently effective to drive,
00:08:45 be above threshold and drive purchase intent.
00:08:50 Now we’ve disrupted the entire vacuuming industry.
00:08:55 The number one selling vacuums, for example, in the US
00:08:59 are Roombas, so not robot vacuums, but vacuums,
00:09:02 and that’s really crazy and weird.
00:09:05 We need to pause that. I mean, that’s incredible.
00:09:08 That’s incredible that a robot
00:09:10 is the number one selling thing that does something.
00:09:15 Yep. Something as essential as vacuuming.
00:09:17 Yep. Congratulations.
00:09:20 Thank you. It’s still kind of fun to say,
00:09:22 but just because this was a crazy idea
00:09:26 that just started, you know, in a room here,
00:09:30 we’re like, do you think we can do this?
00:09:33 So, hey, let’s give it a try.
00:09:35 But now the robots are starting to understand their environment.
00:09:42 And if you think about the next step,
00:09:46 there’s two dimensions.
00:09:48 I’ve been working so hard since the beginning of iRobot
00:09:52 to make robots are autonomous,
00:09:55 that, you know, they’re smart enough
00:09:57 and understand their task enough,
00:09:59 they can just go do it without human involvement.
00:10:04 Now what I’m really excited and working on
00:10:07 is how do I make them less autonomous?
00:10:10 Meaning that the robot is supposed to be your partner,
00:10:15 not this automaton that just goes and does what a robot does.
00:10:20 And so that if you tell it,
00:10:23 hey, I just dropped some flour by the fridge in the kitchen,
00:10:27 can you deal with it?
00:10:28 Wouldn’t it be awesome if the right thing just happened
00:10:32 based on that utterance?
00:10:35 And to some extent, that’s less autonomous
00:10:37 because it’s actually listening to you,
00:10:40 understanding the context and intent of the sentence,
00:10:44 mapping it against its understanding
00:10:47 of the home it lives in and knowing what to do.
00:10:52 And so that’s an area of research.
00:10:56 It’s an area where we’re starting to roll out features.
00:10:59 You can now tell your robot to clean up the kitchen
00:11:02 and it knows what the kitchen is and can do that.
00:11:05 And that’s sort of 1.0 of where we’re going.
00:11:10 The other cool thing is that we’re starting
00:11:13 to know where stuff is.
00:11:14 And why is that important?
00:11:15 Well, robots are supposed to have arms, right?
00:11:21 Data had an arm, Rosie had an arm, Robbie the robot had an arm.
00:11:25 I mean, robots are, you know, they are physical things
00:11:27 that move around in an environment
00:11:29 and they’re supposed to like do work.
00:11:31 And if you think about it,
00:11:34 if a robot doesn’t know where anything is,
00:11:37 why should it have an arm?
00:11:38 But with this new dawn of home understanding
00:11:44 that we’re starting to go enjoy,
00:11:47 I know where the kitchen is.
00:11:49 I might in the future know where the refrigerator is.
00:11:52 I might, if I had an arm, be able to find the handle,
00:11:55 open it and even get myself a beer.
00:11:58 Obviously, that’s one of the true dreams of robotics
00:12:01 is to have robots bringing us a beer
00:12:03 while we watch television.
00:12:05 But, you know, I think that that new category of tasks
00:12:10 where physical manipulation, robot arms,
00:12:14 is just a potpourri of new opportunity and excitement.
00:12:20 And you see humans as a crucial part of that.
00:12:23 So you kind of mentioned that.
00:12:26 And I personally find that a really compelling idea.
00:12:28 I think full autonomy can only take us so far,
00:12:33 especially in the home.
00:12:35 So you see humans as helping the robot understand
00:12:38 or give deeper meaning to the spatial information.
00:12:43 Right. It’s a partnership.
00:12:46 The robot is supposed to operate according to descriptors
00:12:52 that you would use to describe your own home.
00:12:57 The robot is supposed to, in lieu of better direction,
00:13:02 kind of go about its routine,
00:13:03 which ought to be basically right,
00:13:07 and lead to a home maintained in a way
00:13:12 that it’s learned you like,
00:13:14 but also be perpetually ready to take direction
00:13:21 that would activate a different set of behaviors
00:13:26 or actions to meet a current need
00:13:28 to the extent it could actually perform that task.
00:13:32 So I got to ask you, I think this is a fundamental
00:13:35 and a fascinating question,
00:13:37 because iRobot has been a successful company
00:13:39 and a rare successful robotics company.
00:13:42 So Anki, Jibo, Mayfield Robotics with their robot curry,
00:13:46 SciFi Works, Rethink Robotics, these are robotics companies
00:13:50 that were founded and run by brilliant people.
00:13:54 But all, very unfortunately, at least for us roboticists,
00:13:59 all went out of business recently.
00:14:02 So why do you think they didn’t last longer?
00:14:05 Why do you think it is so hard to keep a robotics company alive?
00:14:10 You know, I say this only partially in jest
00:14:14 that back in the day before Roomba,
00:14:17 you know, I was a high tech entrepreneur building robots.
00:14:23 But it wasn’t until I became a vacuum cleaner salesman
00:14:26 that we had any success.
00:14:29 So, I mean, the point is technology alone
00:14:34 doesn’t equal a successful business.
00:14:37 We need to go and find the compelling need
00:14:43 where the robot that we’re creating
00:14:47 can deliver clearly more value to the end user
00:14:53 than it costs.
00:14:55 And this is not a marginal thing
00:14:59 where you’re looking at the scale and you’re like,
00:15:00 yeah, it’s close.
00:15:01 Maybe we can hold our breath and make it work.
00:15:04 It’s clearly more value than the cost of the robot
00:15:11 to bring, you know, in the store.
00:15:13 And I think that the challenge has been finding
00:15:17 those businesses where that’s true
00:15:24 in a sustainable fashion.
00:15:28 You know, when you get into entertainment style things,
00:15:34 you could be the cat’s meow one year,
00:15:38 but 85% of toys, regardless of their merit,
00:15:43 fail to make it to their second season.
00:15:45 It’s just super hard to do so.
00:15:48 And so that’s just a tough business.
00:15:53 And there has been a lot of experimentation
00:15:57 around what is the right type of social companion,
00:16:02 what is the right robot in the home
00:16:05 that is doing something other than tasks people do every week
00:16:14 that they’d rather not do.
00:16:17 And I’m not sure we’ve got it all figured out right.
00:16:20 And so that you get brilliant roboticists
00:16:23 with super interesting robots
00:16:25 that ultimately don’t quite have that magical user experience
00:16:32 and thus that value benefit equation remains ambiguous.
00:16:40 So you as somebody who dreams of robots changing the world,
00:16:45 what’s your estimate?
00:16:48 How big is the space of applications
00:16:53 that fit the criteria that you just described
00:16:55 where you can really demonstrate an obvious significant value
00:17:00 over the alternative non robotic solution?
00:17:05 Well, I think that we’re just about none of the way
00:17:10 to achieving the potential of robotics at home.
00:17:13 But we have to do it in a really eyes wide open,
00:17:20 honest fashion.
00:17:21 And so another way to put that is the potential is infinite
00:17:25 because we did take a few steps,
00:17:27 but you’re saying those steps are just very initial steps.
00:17:29 So the Roomba is a hugely successful product,
00:17:32 but you’re saying that’s just the very, very beginning.
00:17:34 That’s just the very, very beginning.
00:17:36 It’s the foot in the door.
00:17:38 And I think I was lucky that in the early days of robotics,
00:17:45 people would ask me, when are you going to clean my floor?
00:17:48 It was something that I grew up saying,
00:17:53 I got all these really good ideas,
00:17:54 but everyone seems to want their floor clean.
00:17:58 And so maybe we should do that.
00:18:02 Yeah, your good ideas.
00:18:03 Earn the right to do the next thing after that.
00:18:05 So the good ideas have to match with the desire of the people
00:18:10 and then the actual cost has to like the business,
00:18:13 the financial aspect has to all match together.
00:18:16 Yeah, during our partnership back a number of years ago
00:18:21 with Johnson Wax, they would explain to me
00:18:24 that they would go into homes and just watch how people lived
00:18:32 and try to figure out what were they doing
00:18:35 that they really didn’t really like to do,
00:18:39 but they had to do it frequently enough
00:18:42 that it was top of mind and understood as a burden.
00:18:51 Hey, let’s make a product or come up with a solution
00:18:55 to make that pain point less challenging.
00:19:02 And sometimes we do certain burdens so often as a society
00:19:07 that we actually don’t even realize,
00:19:09 like it’s actually hard to see that that burden
00:19:11 is something that could be removed.
00:19:13 So it does require just going into the home and staring at,
00:19:17 wait, how do I actually live life?
00:19:19 What are the pain points?
00:19:21 Yeah, and getting those insights is a lot harder
00:19:26 than it would seem it should be in retrospect.
00:19:29 So how hard on that point?
00:19:33 I mean, one of the big challenges of robotics
00:19:37 is driving the cost down to something
00:19:42 that consumers, people would afford.
00:19:45 So people would be less likely to buy a Roomba
00:19:48 if it cost $500,000, which is probably
00:19:53 sort of what a Roomba would cost several decades ago.
00:19:58 So how do you drive, which I mentioned is very difficult,
00:20:02 how do you drive the cost of a Roomba or a robot down
00:20:05 such that people would want to buy it?
00:20:07 When I started building robots, the cost of the robot
00:20:11 had a lot to do with the amount of time it took to build it.
00:20:15 And so that we build our robots out of aluminum,
00:20:18 I would go spend my time in the machine shop
00:20:21 on the milling machine, cutting out the parts and so forth.
00:20:28 And then when we got into the toy industry,
00:20:29 I realized that if we were building at scale,
00:20:34 I could determine the cost of the Roomba
00:20:35 instead of adding up all the hours to mill out the parts,
00:20:38 but by weighing it.
00:20:42 And that’s liberating.
00:20:44 You can say, wow, the world has just
00:20:48 changed as I think about construction
00:20:51 in a different way.
00:20:53 The 3D CAD tools that are available to us today,
00:20:56 the operating at scale where I can do tooling and injection
00:21:02 mold, an arbitrarily complicated part,
00:21:07 and the cost is going to be basically
00:21:09 the weight of the plastic in that part,
00:21:13 is incredibly exciting and liberating
00:21:16 and opens up all sorts of opportunities.
00:21:18 And for the sensing part of it, where we are today is instead
00:21:26 of trying to build skin, which is really hard.
00:21:30 For a long time, I spent creating strategies and ideas
00:21:38 around how could we duplicate the skin on the human body
00:21:42 because it’s such an amazing sensor.
00:21:47 Instead of going down that path, why don’t we focus on vision?
00:21:53 And how many of the problems that
00:21:57 face a robot trying to do real work
00:22:02 could be solved with a cheap camera and a big ass computer?
00:22:09 Moore’s law continues to work.
00:22:12 The cell phone industry, the mobile industry
00:22:16 is giving us better and better tools that can run
00:22:19 on these embedded computers.
00:22:21 And I think we passed an important moment maybe
00:22:27 two years ago where you could put machine vision
00:22:33 capable processors on robots at consumer price points.
00:22:39 And I was waiting for it to happen.
00:22:42 We avoided putting lasers on our robots to do navigation
00:22:49 and instead spent years researching
00:22:51 how to do vision based navigation
00:22:54 because you could just see where these technology
00:23:00 trends were going.
00:23:01 And between injection molded plastic and a camera
00:23:06 with a computer capable of running machine learning
00:23:10 and visual object recognition, I could
00:23:12 build an incredibly affordable, incredibly capable robot.
00:23:18 And that’s going to be the future.
00:23:21 So on that point with a small tangent,
00:23:23 but I think an important one, another industry in which I
00:23:27 would say the only other industry in which there
00:23:30 is automation actually touching people’s lives today
00:23:34 is autonomous vehicles.
00:23:37 What the vision you just described
00:23:40 of using computer vision and using cheap camera sensors,
00:23:44 there’s a debate on that of LIDAR versus computer vision.
00:23:48 And the Elon Musk famously said that LIDAR
00:23:54 is a crutch that really in the long term,
00:23:58 camera only is the right solution, which echoes some
00:24:02 of the ideas you’re expressing.
00:24:03 Of course, the domain in terms of its safety criticality
00:24:06 is different.
00:24:07 But what do you think about that approach
00:24:10 in the autonomous vehicle space?
00:24:13 And in general, do you see a connection
00:24:15 between the incredible real world challenges
00:24:18 you have to solve in the home with Roomba?
00:24:20 And I saw a demonstration of some of them, corner cases
00:24:24 literally, and autonomous vehicles.
00:24:27 So there’s absolutely a tremendous overlap
00:24:31 between both the problems a robot vacuum
00:24:36 and an autonomous vehicle are trying to solve
00:24:38 and the tools and the types of sensors
00:24:41 that are being applied in the pursuit of the solutions.
00:24:47 In my world, my environment is actually
00:24:53 much harder than the environment an automobile travels.
00:24:57 We don’t have roads.
00:24:58 We have t shirts.
00:25:01 We have steps.
00:25:02 We have a near infinite number of patterns and colors
00:25:07 and surface textures on the floor.
00:25:10 Especially from a visual perspective.
00:25:11 So the way the world looks is an infinitely variable.
00:25:18 On the other hand, safety is way easier on the inside.
00:25:22 My robots, they’re not very heavy.
00:25:26 They’re not very fast.
00:25:28 If they bump into your foot, you think it’s funny.
00:25:32 And autonomous vehicles kind of have the inverse problem.
00:25:39 And so that for me saying vision is the future,
00:25:45 I can say that without reservation.
00:25:49 For autonomous vehicles, I think I
00:25:51 believe what Elon’s saying about the future
00:25:56 is ultimately going to be vision.
00:25:59 Maybe if we put a cheap lighter on there as a backup sensor,
00:26:02 it might not be the worst idea in the world.
00:26:03 So the stakes are much higher.
00:26:05 The stakes are much higher.
00:26:05 You have to be much more careful thinking through how far away
00:26:09 that future is.
00:26:10 Right.
00:26:11 But I think that the primary environmental understanding
00:26:17 sensor is going to be a visual system.
00:26:21 Visual system.
00:26:23 So on that point, well, let me ask,
00:26:25 do you hope there’s an iRobot robot in every home
00:26:29 in the world one day?
00:26:31 I expect there to be at least one iRobot robot in every home.
00:26:38 We’ve sold 25 million robots.
00:26:41 So we’re in about 10% of US homes, which is a great start.
00:26:47 But I think that when we think about the numbers of things
00:26:52 that robots can do, today I can vacuum your floor,
00:26:57 mop your floor, cut your lawn, or soon
00:27:00 we’ll be able to cut your lawn.
00:27:02 But there are more things that we could do in the home.
00:27:06 And I hope that we continue using the techniques I described
00:27:12 around exploiting computer vision and low cost
00:27:16 manufacturing that we’ll be able to create these solutions
00:27:20 at affordable price points.
00:27:22 So let me ask on that point of a robot in every home,
00:27:25 that’s my dream as well.
00:27:26 I’d love to see that.
00:27:29 I think the possibilities there are indeed
00:27:31 infinite positive possibilities.
00:27:34 But in our current culture, no thanks to science fiction
00:27:39 and so on, there’s a serious kind of hesitation, anxiety,
00:27:45 concern about robots, and also a concern about privacy.
00:27:51 And it’s a fascinating question to me
00:27:55 why that concern is amongst a certain group of people
00:27:59 is as intense as it is.
00:28:02 So you have to think about it because it’s a serious concern.
00:28:05 But I wonder how you address it best.
00:28:08 So from a perspective of vision sensors,
00:28:09 so robots that move about the home and sense the world,
00:28:14 how do you alleviate people’s privacy concerns?
00:28:19 How do you make sure that they can
00:28:21 trust iRobot and the robots that they share their home with?
00:28:26 I think that’s a great question.
00:28:28 And we’ve really leaned way forward on this
00:28:33 because given our vision as to the role the company intends
00:28:39 to play in the home, really for us,
00:28:43 make or break is can our approach
00:28:48 be trusted to protecting the data
00:28:50 and the privacy of the people who have our robots?
00:28:53 And so we’ve gone out publicly with a privacy
00:28:57 manifesto stating we’ll never sell your data.
00:29:00 We’ve adopted GDPR not just where GDPR is required,
00:29:05 but globally.
00:29:09 We have ensured that images don’t leave the robot.
00:29:18 So processing data from the visual sensors
00:29:22 happens locally on the robot.
00:29:23 And only semantic knowledge of the home with the consumer’s
00:29:30 consent is sent up.
00:29:32 We show you what we know and are trying
00:29:35 to go use data as an enabler for the performance of the robots
00:29:45 with the informed consent and understanding of the people who
00:29:50 own those robots.
00:29:55 We take it very seriously.
00:29:56 And ultimately, we think that by showing a customer that
00:30:03 if you let us build a semantic map of your home
00:30:07 and know where the rooms are, well, then
00:30:09 you can say clean the kitchen.
00:30:11 If you don’t want the robot to do that, don’t make the map.
00:30:14 It’ll do its best job cleaning your home.
00:30:16 But it won’t be able to do that.
00:30:18 And if you ever want us to forget that we know that it’s
00:30:21 your kitchen, you can have confidence
00:30:24 that we will do that for you.
00:30:26 So we’re trying to go and be a data 2.0 perspective company
00:30:35 where we treat the data that the robots have
00:30:39 of the consumer’s home as if it were the consumer’s data
00:30:43 and that they have rights to it.
00:30:47 So we think by being the good guys on this front,
00:30:50 we can build the trust and thus be entrusted
00:30:55 to enable robots to do more things that are thoughtful.
00:31:00 You think people’s worries will diminish over time?
00:31:04 As a society, broadly speaking, do you
00:31:07 think you can win over trust not just for the company,
00:31:10 but just the comfort that people have with AI in their home
00:31:14 enriching their lives in some way?
00:31:17 I think we’re in an interesting place today
00:31:19 where it’s less about winning them over
00:31:22 and more about finding a way to talk about privacy in a way
00:31:26 that more people can understand.
00:31:28 I would tell you that today, when there’s a privacy breach,
00:31:33 people get very upset and then go to the store
00:31:37 and buy the cheapest thing, paying no attention
00:31:39 to whether or not the products that they’re buying
00:31:42 honor privacy standards or not.
00:31:44 In fact, if I put on the package of my Roomba,
00:31:50 the privacy commitments that we have,
00:31:53 I would sell less than I would if I did nothing at all.
00:31:58 And that needs to change.
00:32:00 So it’s not a question about earning trust.
00:32:02 I think that’s necessary but not sufficient.
00:32:04 We need to figure out how to have
00:32:06 a comfortable set of what is the grade A meat
00:32:10 standard applied to privacy that customers can trust
00:32:17 and understand and then use in their buying decisions.
00:32:23 That will reward companies for good behavior
00:32:25 and that will ultimately be how this moves forward.
00:32:29 And maybe be part of the conversation
00:32:32 between regular people about what it means,
00:32:34 what privacy means.
00:32:36 If you have some standards, you can say,
00:32:38 you can start talking about who’s following them,
00:32:41 who does not have more.
00:32:42 Because most people are actually quite clueless
00:32:45 about all aspects of artificial intelligence,
00:32:47 the data collection, and so on.
00:32:48 It would be nice to change that for people
00:32:50 to understand the good that AI can do.
00:32:52 And it’s not some system that’s trying to steal
00:32:56 all the most sensitive data.
00:32:58 Do you think, do you dream of a Roomba
00:33:02 with human level intelligence one day?
00:33:05 So you’ve mentioned a very successful localization
00:33:10 and mapping of the environment, being
00:33:12 able to do some basic communication to say,
00:33:14 go clean the kitchen.
00:33:16 Do you see in your maybe more bored moments,
00:33:22 once you get the beer, to sit back with that beer
00:33:27 and have a chat on a Friday night with a Roomba
00:33:30 about how your day went?
00:33:34 So to your latter question, absolutely.
00:33:38 To your former question as to whether a Roomba
00:33:40 can have human level intelligence, not in my lifetime.
00:33:45 You can have you.
00:33:46 I think you can have a great conversation,
00:33:49 a meaningful conversation with a Roomba
00:33:54 without it having anything that resembles
00:33:56 human level intelligence.
00:33:59 And I think that as long as you realize that conversation
00:34:04 is not about the robot and making the robot feel good.
00:34:08 That conversation is about you learning interesting things
00:34:14 that make you feel like the conversation that you
00:34:18 had with the robot is a pretty awesome way
00:34:24 of learning something.
00:34:27 And it could be about what kind of day your pet had.
00:34:30 It could be about how can I make my home more energy efficient.
00:34:36 It could be about if I’m thinking about climbing
00:34:40 Mount Everest, what should I know?
00:34:44 And that’s a very doable thing.
00:34:48 But if I think that that conversation
00:34:51 I’m going to have with the robot is
00:34:53 I’m going to be rewarded by making the robot happy,
00:34:56 well, I could just put a button on the robot
00:34:58 that you could push and the robot would smile.
00:35:00 And that sort of thing.
00:35:02 So I think you need to think about the question
00:35:04 in the right way.
00:35:06 And robots can be awesomely effective at helping people
00:35:12 feel less isolated, learn more about the home
00:35:16 that they live in, and fill some of those lonely gaps
00:35:21 that we wish we were engaged learning
00:35:24 cool stuff about our world.
00:35:26 Last question.
00:35:28 If you could hang out for a day with a robot
00:35:32 from science fiction, movies, books,
00:35:35 and safely pick its brain for that day, who would you pick?
00:35:42 Data.
00:35:43 Data.
00:35:43 From Star Trek.
00:35:45 I think that A, data is really smart.
00:35:49 Data has been through a lot trying
00:35:51 to go and save the galaxy.
00:35:53 And I’m really interested actually in emotion
00:35:59 and robotics.
00:36:01 And I think you’d have a lot to say about that.
00:36:03 Because I believe actually that emotion
00:36:08 plays an incredibly useful role in doing reasonable things
00:36:14 in situations where we have imperfect understanding of
00:36:16 what’s going on.
00:36:18 In social situations when there’s imperfect information.
00:36:20 In social situations, also in competitive or dangerous
00:36:26 situations that we have emotion for a reason.
00:36:32 And so that ultimately, my theory
00:36:36 is that as robots get smarter and smarter,
00:36:38 they’re actually going to get more emotional.
00:36:41 Because you can’t actually survive on pure logic.
00:36:49 Because only a very tiny fraction of the situations
00:36:53 we find ourselves in can be resolved reasonably with logic.
00:36:57 And so I think Data would have a lot to say about that.
00:36:59 And so I could find out whether he agrees.
00:37:02 If you could ask Data one question,
00:37:04 you would get a deep, honest answer to what would you ask.
00:37:08 What’s Captain Picard really like?
00:37:12 OK, I think that’s the perfect way to end it.
00:37:14 Colin, thank you so much for talking today.
00:37:16 I really appreciate it.
00:37:16 My pleasure.