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Speaker 1: Get technology with tech Stuff from stuff dot Com. Hey there,

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Speaker 1: and welcome to Tech Stuff. I'm your host, Jonathan Strickland,

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Speaker 1: together once again reunited with Scott Benjamin who was joining

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Speaker 1: me today in the studio. Thanks Scott reunited. Isn't there

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Speaker 1: a song? Reunited? It feels so good? Yeah, that's it. Yeah,

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Speaker 1: that's the one. Thanks for having me. I appreciate it.

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Speaker 1: And we're going to tackle a couple of different stories

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Speaker 1: that have been in the news over the past several

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Speaker 1: weeks now as at the time we're recording this, obviously

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Speaker 1: the episode will publish a little bit later, but we

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Speaker 1: wanted to talk about some stuff that has to do

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Speaker 1: with artificial intelligence, with autonomy, with vehicles, and also just

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Speaker 1: a robots in general. So we're gonna start with probably

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Speaker 1: the more sobering of the two stories, the story about

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Speaker 1: Tesla Auto Pilot um and specifically the fatal accident that

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Speaker 1: have and on May seventh, two thousand sixteen, involving Joshua Brown,

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Speaker 1: And and what does that mean? What do we know

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Speaker 1: about the accident as of the recording of this show,

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Speaker 1: What does it mean for for autonomous vehicles? Why, why

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Speaker 1: does Tesla maintain that Tesla autopilot isn't autonomous. We're gonna

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Speaker 1: get into all those details, but first I just want

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Speaker 1: to give a quick kind of layout of the order

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Speaker 1: of events. So the accident happened on May seven, six

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Speaker 1: in Florida. UM Joshua Brown was in a vehicle Tesla

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Speaker 1: Model S that what had autopilot engaged, and I was

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Speaker 1: traveling eastward down the highway when a semi truck was

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Speaker 1: turning left. The semi truck was traveling westward down that

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Speaker 1: same highway, turning left to cross onto another street, which

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Speaker 1: meant that the semi truck's trailer was crossing the traffic

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Speaker 1: that Joshua's car or the direction that Joshua's car was

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Speaker 1: going in. UH. Neither the car nor Joshua appeared to

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Speaker 1: detect the trailer. They didn't see it, didn't react to it,

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Speaker 1: and so there was a collision. We'll get more into

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Speaker 1: more details about the collision a little bit, and UM

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Speaker 1: Mr Brown passed away due to that. He died from

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Speaker 1: his injuries. The Tesla actually released information to the National

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Speaker 1: Highway Traffic Safety Administration about a week and a half

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Speaker 1: after it happened, giving them the details of the accident.

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Speaker 1: UH just a couple of days later, they held a

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Speaker 1: big shareholder meeting that's gonna come into play a little bit.

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Speaker 1: And then it wasn't until June that Tesla posted a

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Speaker 1: blog that acknowledged and disclosed this accident. And so this

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Speaker 1: is kind of it's kind of a multi tiered thing

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Speaker 1: that we're gonna chat about, not just the technology, uh,

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Speaker 1: not just what kind of of effect is this going

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Speaker 1: to have on the on on driver assist systems and

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Speaker 1: and autonomous vehicles moving forward, but also the implications some

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Speaker 1: people have raised saying that perhaps Tesla was trying to

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Speaker 1: hide the fact that there was this accident in light

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Speaker 1: of the fact that we're going to have a shareholder

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Speaker 1: meeting and and offer more stocks. They raised like one

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Speaker 1: point four billion dollars in stocks. So we're gonna attack

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Speaker 1: all of that. So the start, Scott, how would you

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Speaker 1: describe Tesla autopilot? Tesla autopilot? Okay, so I guess you

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Speaker 1: can't call it autonomous, right, That's the first thing we

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Speaker 1: need to get out of the way. It's it's a

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Speaker 1: legality issue. And we've talked about this, I think on

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Speaker 1: other podcasts that I've been here for sure tech stuff

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Speaker 1: certainly on car stuff and the it's it's again it's

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Speaker 1: a legal issue because you can't say autonomous because that

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Speaker 1: indicates that there doesn't necessarily have to be a driver

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Speaker 1: in place behind the wheel. And again it's just a

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Speaker 1: it's a word thing. It's a word play thing right now,

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Speaker 1: because most major manufacturers have autonomous like systems in the works,

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Speaker 1: autopilot like systems in the works um or they already

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Speaker 1: have them implemented, but they have a strict rule that

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Speaker 1: a driver has to be you know, in the driver

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Speaker 1: position at all times, ready to take control at any time.

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Speaker 1: And some of them even say that you have to

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Speaker 1: have hands on the wheel, as did the test La situation.

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Speaker 1: Here the Tesla, when you agree to enter autopilot mode,

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Speaker 1: you you have to I don't know, if you click

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Speaker 1: on something on that giant screen that they have essentially

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Speaker 1: like in terms of service saying like you are acknowledging

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Speaker 1: that in order to have autopilot on, you are supposed

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Speaker 1: to keep your hands on the wheel as well as

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Speaker 1: maintain alertness to your surroundings, not just you know, not

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Speaker 1: just shut down and allow the card to take over. Okay,

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Speaker 1: we have more to say about that as we go

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Speaker 1: here too, because you'll find that there's a lot of

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Speaker 1: shenanigans that go along with with people in autopilot mode

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Speaker 1: that shouldn't be happening because of this terms. You know, this,

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Speaker 1: this term of agreement that you sign, and so Mr

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Speaker 1: Brown had agreed to that, and then we will later

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Speaker 1: find out that he was in a negligence of that.

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Speaker 1: And listen, I want to also get this out of

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Speaker 1: the way too, is that we were not here to

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Speaker 1: point fingers at the dead guy or any by any means.

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Speaker 1: But um, as we go through this and see if

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Speaker 1: you agree with this as we talk here, but um,

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Speaker 1: I really feel that Tesla is not necessarily in the

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Speaker 1: wrong here, right, and I don't I feel it's more

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Speaker 1: the fault of the driver himself than it is Tesla

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Speaker 1: by by all means. Right, Let's let me see if

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Speaker 1: you if you think this is fair. I would I

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Speaker 1: would say that over the last decade, really a couple

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Speaker 1: of decades, we've seen more automated systems or driver assist

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Speaker 1: systems come into play. Some are are pretty you know,

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Speaker 1: still rely entirely upon human input. Like I would argue

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Speaker 1: power steering falls into that kind of category. It's the

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Speaker 1: car itself is not taking over any steering in power steering.

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Speaker 1: But that's a driver assist feature. It's helping someone operate

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Speaker 1: a vehicle without having to do ridiculous amounts of turning

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Speaker 1: a wheel in order to actually do what you need

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Speaker 1: to do. Sure, we've seen so many of these in

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Speaker 1: the past, I mean in the recent past, I guess

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Speaker 1: with with lane keep systems. You know that that's part

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Speaker 1: of the autonomous system that we're talking or almost said,

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Speaker 1: the driver assist system talking about from Tesla, the autopilot um.

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Speaker 1: You know it. It maintains the lane, It maintains a

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Speaker 1: distance between the vehicle in front of it and uh

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Speaker 1: and and tries to keep that at a reasonable I

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Speaker 1: would believe that you would be able to set the

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Speaker 1: distance that you want, or maybe it doesn't based on

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Speaker 1: a calculation of your speed versus the distance it needs

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Speaker 1: to break right, like like that basic rule of thumb

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Speaker 1: that you should be able to count to three when

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Speaker 1: you see the vehicle ahead of you passes something like

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Speaker 1: a lamp. Yeah, and then you street lamp, you count

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Speaker 1: you know, one, one thousand, two and thousand or three,

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Speaker 1: you know, and there's a there's a just kind of

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Speaker 1: a general rule of thumb. As you said, you know

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Speaker 1: that that uh, you know, at this speed, you have

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Speaker 1: this amount of time time in order to react, and

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Speaker 1: that's just something that humans need. I suppose it's a

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Speaker 1: little faster. Of course for an autopilot system that can

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Speaker 1: it can do things a lot quicker, although it has

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Speaker 1: to do so in a way that's safe and isn't

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Speaker 1: going to injure a person. So in other words, like

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Speaker 1: technically you could have a a a computer system follow

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Speaker 1: a little closer to a car and be able to

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Speaker 1: react faster, but momentum is still a thing. And what

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Speaker 1: about the driver behind you? Exactly? Yeah, behind got the

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Speaker 1: human driver behind you, so they may not be able

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Speaker 1: to react that quickly. And you also have the issue

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Speaker 1: of when you come to a stop, you've got so

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Speaker 1: much mass, you've got so much momentum built up that

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Speaker 1: even if your car is capable coming to a stop

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Speaker 1: at that at that reduced distance between you and the

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Speaker 1: person in front of you, they're still going to be

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Speaker 1: a very measurable uh effect on the person sitting in

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Speaker 1: that car. It's gonna you're gonna be jerked around a lot. Yeah,

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Speaker 1: and this wasn't necessarily the case with this crash. You

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Speaker 1: know it wasn't this is a truck. You have to

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Speaker 1: imagine what it would be like if a semi truck

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Speaker 1: was crossing your path in front of you, I mean

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Speaker 1: perpendicular to the road right, almost like you're coming up

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Speaker 1: to a crossroads and there's a semi truck going going

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Speaker 1: perpendicular ninety degrees from where you're going. Yeah, the cab

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Speaker 1: has passed in front of you. You're now at that

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Speaker 1: part of the trailer where there's nothing below it but

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Speaker 1: the cab or the trailers above you, and the rear

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Speaker 1: wheels have yet to cross the road. So that's the

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Speaker 1: position where this was. This was when Mr Brown went

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Speaker 1: underneath the trailer, and so you you know the you

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Speaker 1: you're thinking, well, how come he neither he nor the

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Speaker 1: car detected this. Well, there are a couple of different

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Speaker 1: things that people have said so far. One was that

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Speaker 1: the sky was brightly lit that day, that the trailer

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Speaker 1: itself was white, and therefore it was more difficult to

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Speaker 1: differentiate between the trailer and the sky. Uh So, from

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Speaker 1: just an optical standpoint like then the dual spectrum, it

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Speaker 1: was hard to see well for the computer to understand

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Speaker 1: that I mean, you would see a truck in front

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Speaker 1: of you if you had your eyes on the road.

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Speaker 1: That's That's the other part of this is that the

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Speaker 1: human element, I believe, and as we get as we

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Speaker 1: go through here, I think you're going to see more

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Speaker 1: and more that my my, my thoughts on this whole

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Speaker 1: thing are that he wasn't watching at all. I mean,

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Speaker 1: there was no there were no eyes up on the

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Speaker 1: road at all. This was completely an autopilot mode and

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Speaker 1: he was hands off for the whole thing. Because if

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Speaker 1: he were hands on, it is hard to imagine how

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Speaker 1: he could not have seen the truck simply because you're

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Speaker 1: talking about him traveling eastward. It was in the afternoon,

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Speaker 1: so the sun should be behind him, not in front

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Speaker 1: of him, so he shouldn't shouldn't have sun in his eyes.

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Speaker 1: But it's gonna be bright on the trailer, if it's

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Speaker 1: a white trailer, it's gonna be bright against that bright sky.

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Speaker 1: And I can see how that would be difficult to

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Speaker 1: differentiate between you know that in the sky for the computer,

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Speaker 1: I guess, you know, if there's no chrome trim or

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Speaker 1: anything that you know that makes a um some type

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Speaker 1: of delineation that says this is the beginning of another

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Speaker 1: chick and they can and then can detect it. It's

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Speaker 1: also moving all Another thing that this is all hinges

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Speaker 1: on is that there's an eyewitness report that came out

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Speaker 1: in early July on a site called Teslaaratti and it's

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Speaker 1: a it's kind of just a compliment compilation of Tesla news,

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Speaker 1: you know, of all all news, you know, SpaceX, Tesla Motors,

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Speaker 1: Investor News, etcetera. And one of the witnesses to this accident,

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Speaker 1: in addition to the truck driver, which we'll hear from

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Speaker 1: in just a minute, said that prior to this accident,

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Speaker 1: this is crazy, the Model S was on the same

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Speaker 1: road as this person, this this woman driver. She was

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Speaker 1: on us A in Florida, and the Model S on

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Speaker 1: on autopilot at that time, passed her while she was

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Speaker 1: driving at eighty five miles per hour. So he's going

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Speaker 1: in excessive eighty five miles per hour, where let's just

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Speaker 1: say it's it's a ballpark one hundred miles an hour.

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Speaker 1: Have you ever seen what happens to car when it

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Speaker 1: crashes a hundred miles per hour? It's it's devastating. I mean,

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Speaker 1: you've got to keep in mind that most crash tests

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Speaker 1: that are done are done at a much much lower speed,

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Speaker 1: and yet they're dramatic in the outcome. Yeah, you would

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Speaker 1: be shocked to see what kind of damage that happens.

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Speaker 1: And let's says, you know, a side impact collision, you know,

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Speaker 1: those those really dramatic ones where the whole the car

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Speaker 1: seems to bend in half in the car enters the

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Speaker 1: other vehicle. Practically that that's happening somewhere around thirty five

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Speaker 1: thirty thirty five miles per hour. This is this is

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Speaker 1: a car traveling at one hundred miles per hour, let's

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Speaker 1: say more than five to be fair, more than any

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Speaker 1: for sure. And uh and of course at that point

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Speaker 1: of the trailer which we're talking about it, what happened

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Speaker 1: was it sheared the roof off of the vehicle. And

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Speaker 1: I think we can all understand what that probably means

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Speaker 1: to the driver of that vehicle. It's horrific. So beyond that,

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Speaker 1: the car continued on, and it didn't continue on a long,

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Speaker 1: long distance. And I'm looking at a diagram that was

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Speaker 1: drawn up here of the crash investigation scene, and it's

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Speaker 1: not the scale or anything, but the car doesn't go

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Speaker 1: a whole lot farther in its in its own lane.

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Speaker 1: It did veer off the road, went through one fence

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Speaker 1: or went through a ditch, went through a fence, went

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Speaker 1: across the field, through another fence and into a pole,

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Speaker 1: so to ditch. Two fences and a pole, and that's

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Speaker 1: where it came to rest. Is After that it kind

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Speaker 1: of rotated to arrest. Um the truck. The driver of

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Speaker 1: the truck said that it all happened so fast that

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Speaker 1: by the time he didn't even I don't even know

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Speaker 1: if he felt the impact, of course, but by the

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Speaker 1: time he even checked his mirrors, the vehicle was already

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Speaker 1: beyond you know, the impact site. He couldn't he could

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Speaker 1: even pick it up. He couldn't spot it, um other

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Speaker 1: than looking, you know, I guess at this point he

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Speaker 1: had to look to his left to see it out

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Speaker 1: in the field somewhere. Um, it's moving, it's traveling that fast.

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Speaker 1: So there's this disconnect between what what happened in that

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Speaker 1: vehicle just seconds before the the the crash, the impact,

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Speaker 1: and what happened. And you know, I don't know if

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Speaker 1: I should say it that way. Maybe there's a I

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Speaker 1: guess just not enough information out there, like what was

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Speaker 1: happening in the car beforehand. It's not disconnected. It's just

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Speaker 1: we just need to know more information and we don't

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Speaker 1: quite yet. Yeah, the fact that you know, you had

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Speaker 1: you had different eyewitnesses saying that, like one saying that

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Speaker 1: there was a portable DVD player with a screen showing

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Speaker 1: a Harry Potter movie. That someone else said there was

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Speaker 1: a portable DVD player inside the car, but it wasn't

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Speaker 1: actually running at the time. Can I tell you something,

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Speaker 1: What's that The driver of the truck who went to

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Speaker 1: check on this driver out in the field as he approached,

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Speaker 1: he said that he did hear a Harry Potter movie playing,

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Speaker 1: but did not see it. But that's the way he

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Speaker 1: that's the only way that he was able to discern

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Speaker 1: that it was a Harry Potter movie. He could hear

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Speaker 1: the audio from that, because I guess it's and if

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Speaker 1: you saw the wreck, it's a mangled mess. It's it's

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Speaker 1: a it's a horrific car crash scene. Um. But he

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Speaker 1: was one of the first people on the scene, of course,

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Speaker 1: and he did, in fact here a Harry Potter movie playing.

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Speaker 1: So it appears, at least from the limited information that

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Speaker 1: we have it appears that that this was a case

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Speaker 1: where the person behind the wheel was not following the

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Speaker 1: directions stated specifically under engaging the autopilot that you you know,

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Speaker 1: need to have your full attention on the road and

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Speaker 1: have been ready to take over. Um, that's what it

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Speaker 1: looks like. Also, we should say that, yes, the optical

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Speaker 1: camera system failed in that it did not recognize the

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Speaker 1: fact that there was a truck crossing its path, which

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Speaker 1: is that's pretty dramatic. But some people have said, well

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Speaker 1: aren't there backups, like beyond optical, wasn't their radar? Well, yeah,

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Speaker 1: and as radar. Here's the thing, though, the trailer is

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Speaker 1: off the ground right, it's it's above the ground because

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Speaker 1: you've got that space between you know, where the chassis

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Speaker 1: is and the trailer a part. And the problem is

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Speaker 1: that the radar system can misinterpret that to be an

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Speaker 1: overhead sign because it's it's detecting a surface, a flat

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Speaker 1: surface that's above the ground. And Tesla autopilot essentially ignores

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Speaker 1: those because if it did, and every time we went

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Speaker 1: down the highway, your car would start to break every

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Speaker 1: time you start to approach an overhead sign. If it

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Speaker 1: has identified that as a potential collision could be a

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Speaker 1: real problem. Yeah, it could mean that you're actually causing

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Speaker 1: accidents because you're you're hitting the brake on a highway

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Speaker 1: when everything is perfectly fine. Uh So, I mean you

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Speaker 1: could argue that maybe this this was at least partly

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Speaker 1: a failure of technology in the sense that the optical

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Speaker 1: cameras did not pick up the truck. Or it could

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Speaker 1: be that the car was just traveling so fast that

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Speaker 1: even even at that advanced speed that computers can react to,

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Speaker 1: it would not have been able to react fast enough

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Speaker 1: at detecting the truck and being able to do anything

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Speaker 1: about it. Now you're hitting out. Something that I wanted

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Speaker 1: to to make clear here too, is that when we

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Speaker 1: first talked about this, when we first heard about this ourselves,

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Speaker 1: I have thought that I had imagined that this vehicle

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Speaker 1: continued on down the road and you know, the autopilot

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Speaker 1: remain engaged, and um, you know, it was it was

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Speaker 1: like it was almost like a ghost car, ghost ship

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Speaker 1: traveling down the road with no one, no one at

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Speaker 1: the wheel of course, And um, I thought I thought

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Speaker 1: it was a much greater distance that had traveled after

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Speaker 1: the accident. But now that I'm hearing that. You know,

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Speaker 1: he's traveling at such a high rate of speed. And

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Speaker 1: I look at this diagram now, I know it's not

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Speaker 1: to scale, but it's really if if it's anywhere close

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Speaker 1: to the way it's drawn here. Um, you know, it's

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Speaker 1: not like it's five miles down the road. It's right

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Speaker 1: there where this car came to rest. If he's traveling

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Speaker 1: that quickly and something like the top is being sheered

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Speaker 1: off the car in the grand scheme of things that

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Speaker 1: it's not like a full on impact into a wall

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Speaker 1: or anything that's that's it's relatively easy to take the

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Speaker 1: top off of the vehicle. It's mostly glass and a

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Speaker 1: few beams. That's it quickly handled by you know that

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Speaker 1: type of speed. So for it to just travel that

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Speaker 1: far on its own momentum, it's own you know, the

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Speaker 1: chared over velocity. I guess, um, I could see a

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Speaker 1: car taking a quarter mile to come to arrest from

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Speaker 1: something that that uh, that fast and not quite as

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Speaker 1: abrupt as a as a as a dead on him

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Speaker 1: packed sure, and you know it's I think we do

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Speaker 1: have to stress again. Tesla has always maintained that autopilot

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Speaker 1: is really a collection of integrated driver assist systems that

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Speaker 1: together make it safer to operate a vehicle, and they

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Speaker 1: have data to back that up. The big, the big

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Speaker 1: point that they like to mention is the number of

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Speaker 1: miles driven by vehicles in autopilot mode without a fatality.

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Speaker 1: This was the first fatality of anyone driving a Tesla

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Speaker 1: vehicle in autopilot mode, and they say that the cars

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Speaker 1: had accumulated something like a hundred thirty million miles of

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Speaker 1: travel without having a fatality, which is above average. Right, Yeah,

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Speaker 1: because in the United States, again, according to Tesla, they

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Speaker 1: cited a number saying that it's nine million miles for

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Speaker 1: every particular model of car. That's the that's the average,

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Speaker 1: is that every nine million miles driven by a vehicle,

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Speaker 1: there's a fatality. Yeah. That's in the US, the US.

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Speaker 1: Globally it's different, Yes, sixty it's it's it's sixty million

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Speaker 1: miles globally, which is the global numbers. I've got global

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Speaker 1: numbers if you want to hear. Yeah, they're huge. Alright.

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Speaker 1: So globally auto related deaths per year, one point three

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Speaker 1: million people died per year in auto related deaths. And

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Speaker 1: that's an It says an average of about it's just

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Speaker 1: just under thirty three hundred deaths every single day from

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Speaker 1: auto related deaths. Now in the United States, it's significantly less,

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Speaker 1: but it's still a high number. Um. It is it's

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Speaker 1: estimated in we still have estimate numbers. I think the

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Speaker 1: latest year for concrete numbers is probably twenty fourteen or

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Speaker 1: thirteen even um it's estimated that thirty eight thousand, three

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Speaker 1: hundred people are killed every year in the United States

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Speaker 1: alone on the roads, and that's about an average of

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Speaker 1: a hundred and five per day. So this is one

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Speaker 1: accident that's happened with autopilot, and it gets so much attention,

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Speaker 1: it's so much, So much focus has put on this

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Speaker 1: one accident because of the autopilot element. That's that's been

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Speaker 1: um you know, put out there into the media, and

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Speaker 1: a lot of people are of course focused on that

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Speaker 1: one thing. And I would guess that Elon Musk is uh,

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Speaker 1: you know, kind of scratched his head saying like, well,

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Speaker 1: this statistically it's about to happen. I mean, of course,

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Speaker 1: we don't want a car that is going to have

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Speaker 1: this occur. We don't want it to happen again ever,

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Speaker 1: but statistically it's going to happen, and and accidents are inevitable, inevitable,

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Speaker 1: and so our fatalities. There's just there's no way to

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Speaker 1: create a one death proof vehicle. You can't do it.

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Speaker 1: There's always gonna be some wild card that you just

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Speaker 1: can't account for. You can't there's no way to anticipate

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Speaker 1: every potential scenario. And in fact, that is an important

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Speaker 1: thing to point out, is that this this situation, that uh,

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Speaker 1: this tragic situation was kind of the perfect storm of

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Speaker 1: elements for this accident to have happened. It should in

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Speaker 1: most case, it should not have happened. And in fact,

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Speaker 1: you know, people have criticized tesla Um. Some of the

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Speaker 1: journalists have criticized tesla for uh not just for the

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Speaker 1: the fact that autopilot appears to have played a part

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Speaker 1: in a fatal accident, never mind the fact that there

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Speaker 1: so many other fatal accidents happening with so many other

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Speaker 1: vehicles that uh, you know that well, they don't garner

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Speaker 1: the attention because this one, because it's not as novel

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Speaker 1: an idea as autopilots as the first. But not only

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Speaker 1: did they they criticize them for that, but also and

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Speaker 1: I can understand this as well, the fact that Tesla

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Speaker 1: did not disclose the accident until after it had had

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Speaker 1: this shareholder meeting where it had offered more stock and

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Speaker 1: made more money. Uh. The company's response was that the

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Speaker 1: accidents not material to Tesla's business and in fact, you

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Speaker 1: would never expect other automakers to have to do the

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Speaker 1: same thing. It's bad optics, you know, it doesn't look good.

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Speaker 1: But there's also you know, again there's no precedent for

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Speaker 1: this by any this is the first time this has happened,

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Speaker 1: which automatically means that there's definitely going to be more

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Speaker 1: interest anyway. So I given the fact that it is autopilot,

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Speaker 1: I lean a little more toward it might have been

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Speaker 1: better for Tesla to get ahead of this rather than

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Speaker 1: to talk about it a month more than a month afterward,

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Speaker 1: but with a very logical explanation of why it happened

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Speaker 1: and that likely will happen again. It's just this is

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Speaker 1: simply the first time it's happened. And then also he

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Speaker 1: could he could have stressed you know that, yeah, we've

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Speaker 1: we've achieved a hundred and thirty million miles to travel

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Speaker 1: without a fatality at this point. That's that's again way

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Speaker 1: above average. Yeah. In fact, you know, he says like, well,

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Speaker 1: if you extrapolate, which, by the way, I'm just gonna

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Speaker 1: say what Musk said essentially. But this extrapolation, I completely understand,

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Speaker 1: is not uh not an apples to apples kind of thing.

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Speaker 1: Must as well, Let's assume that in two thousand fifteen,

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Speaker 1: the Tesla autopilot feature were universally available on all vehicles globally. Globally,

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Speaker 1: that would mean that there would be five hundred thousand

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Speaker 1: fewer deaths in two thousand fifteen due to car accidents.

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Speaker 1: I don't quite understand that at all. I mean, okay,

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Speaker 1: the global figure that I just gave you is one

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Speaker 1: point three million, so I can understand the half of

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Speaker 1: that point. But you can't say for sure that an

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Speaker 1: autopilot system would have corrected or made adjustments so that,

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Speaker 1: you know, half of those people would still be alive.

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Speaker 1: But yeah, it's interesting because here I understand how he's

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Speaker 1: doing his math. He's saying, globally speaking, there's one fatality

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Speaker 1: for every sixty miles driven million sixty million miles driver.

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Speaker 1: That's why I'm I'm sorry, that is a very important distinction.

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Speaker 1: That's okay, huge, huge factor there sixty million miles driven,

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Speaker 1: so one fatality every sixty million miles driven. With Tesla

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Speaker 1: autopilot records, there's one fatality for one thirty million miles driven.

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Speaker 1: Therefore that the Usla autopilot is twice as safe. Fuzzy math,

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Speaker 1: very fuzzy math. But but we get this point saying

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Speaker 1: that the driver assist systems, when used properly, are they

429
00:23:11,600 --> 00:23:16,800
Speaker 1: really do improve the safety of a vehicle. So it's true.

430
00:23:16,960 --> 00:23:21,600
Speaker 1: That leads to the the discussion about why are people

431
00:23:21,800 --> 00:23:26,840
Speaker 1: not using this properly, Why are people and whether this

432
00:23:27,040 --> 00:23:29,960
Speaker 1: was the case with Joshua Brown's particular instant or not,

433
00:23:30,119 --> 00:23:32,680
Speaker 1: Like we suspect things, but we don't know for sure.

434
00:23:32,920 --> 00:23:35,640
Speaker 1: But we do know for sure that people have been

435
00:23:36,000 --> 00:23:40,760
Speaker 1: jackasses in Tesla vehicles running on autopilot because their YouTube

436
00:23:40,880 --> 00:23:44,760
Speaker 1: videos of it. Absolutely and unfortunately for for this this case,

437
00:23:45,080 --> 00:23:48,360
Speaker 1: for this guy, uh, there are videos of him engaging

438
00:23:48,359 --> 00:23:50,280
Speaker 1: in some of these activities. I mean, I've seen videos

439
00:23:50,280 --> 00:23:54,320
Speaker 1: of people, not necessarily uh, Mr Brown, but I've also

440
00:23:54,400 --> 00:23:56,520
Speaker 1: seen people you know, that are in traffic and autopilot

441
00:23:56,560 --> 00:23:59,680
Speaker 1: mode taking a nap, or playing cards, or playing guitar,

442
00:24:00,000 --> 00:24:03,280
Speaker 1: not air guitar, but playing guitar, uh, doing all kinds

443
00:24:03,320 --> 00:24:06,280
Speaker 1: of things. I mean just you know, facing sideways instead

444
00:24:06,280 --> 00:24:10,720
Speaker 1: of forward. Um, it's just it's I guess it's fun

445
00:24:10,760 --> 00:24:12,439
Speaker 1: to get online and kind of show what you can

446
00:24:12,480 --> 00:24:15,000
Speaker 1: do other than driving in rush hour traffic. That's that.

447
00:24:15,119 --> 00:24:16,879
Speaker 1: That's the point is is a lot of these videos

448
00:24:16,880 --> 00:24:19,080
Speaker 1: that a lot of these videos are making is that

449
00:24:19,119 --> 00:24:21,320
Speaker 1: look what I'm doing, I'm making use of my time

450
00:24:21,760 --> 00:24:24,640
Speaker 1: aside from just driving home my laptop out, I'm still

451
00:24:24,640 --> 00:24:31,480
Speaker 1: working or whatever the case might be, shopping. Unfortunately, Joshua

452
00:24:31,520 --> 00:24:33,399
Speaker 1: Brown also has some of these videos out there that

453
00:24:33,520 --> 00:24:36,600
Speaker 1: shows him him engaged in some of these activities. And

454
00:24:36,840 --> 00:24:39,760
Speaker 1: you know the thing is these cars, especially the Tesla,

455
00:24:39,880 --> 00:24:43,520
Speaker 1: they have of course engine control modules, lots of modules

456
00:24:43,520 --> 00:24:47,000
Speaker 1: in this one in the Tesla models. That's going to

457
00:24:47,080 --> 00:24:49,639
Speaker 1: tell the tale. That's going to say, it's kind of

458
00:24:49,640 --> 00:24:51,960
Speaker 1: like an event recorder at a black box, if you will,

459
00:24:52,040 --> 00:24:56,199
Speaker 1: for for autmobile, and it's going to, uh, let us

460
00:24:56,240 --> 00:24:59,680
Speaker 1: know what Speedy was traveling, exactly what happened before and

461
00:24:59,680 --> 00:25:03,000
Speaker 1: and after this accident immediately after. It's going to also

462
00:25:03,119 --> 00:25:05,320
Speaker 1: let us know when the last time there was any

463
00:25:05,400 --> 00:25:07,560
Speaker 1: kind of driver input into the system and all this

464
00:25:07,680 --> 00:25:10,439
Speaker 1: can be read via code, and if somebody was careful

465
00:25:10,520 --> 00:25:13,520
Speaker 1: enough to remove those modules properly, which I hope they were,

466
00:25:13,920 --> 00:25:15,639
Speaker 1: will be able to kind of piece this all together,

467
00:25:16,000 --> 00:25:18,720
Speaker 1: you know, in reverse order, and figure out what happened.

468
00:25:18,760 --> 00:25:21,480
Speaker 1: And it may be a while and or possibly even

469
00:25:21,520 --> 00:25:25,280
Speaker 1: never before we hear the details of that investigation, and

470
00:25:25,320 --> 00:25:28,160
Speaker 1: they may may keep that tight. Yeah. So, but it's

471
00:25:28,320 --> 00:25:32,280
Speaker 1: it's again, it's one of those things where we see people, uh,

472
00:25:32,320 --> 00:25:37,600
Speaker 1: disregarding Tesla's very clear message about what autopilot is intended

473
00:25:37,640 --> 00:25:40,240
Speaker 1: to be. I have said a few times this past

474
00:25:40,280 --> 00:25:44,200
Speaker 1: week when talking about the story that part of the

475
00:25:44,320 --> 00:25:47,760
Speaker 1: issue to me is calling the feature autopilot in the

476
00:25:47,800 --> 00:25:51,560
Speaker 1: first place. Not again not assigning blame to Tesla, but

477
00:25:51,680 --> 00:25:54,240
Speaker 1: just saying that I think some people are taking this

478
00:25:54,320 --> 00:25:58,359
Speaker 1: word autopilot and they're thinking it means more than what

479
00:25:58,480 --> 00:26:02,200
Speaker 1: it's supposed to do. We got to think that autopilot

480
00:26:02,400 --> 00:26:05,600
Speaker 1: is really a collection of systems. They're integrated, so it's

481
00:26:06,040 --> 00:26:09,679
Speaker 1: that's pretty cool, but they're they're systems that have been

482
00:26:09,720 --> 00:26:13,440
Speaker 1: found on on high end vehicles for the past few years, uh,

483
00:26:13,480 --> 00:26:15,960
Speaker 1: that are integrated in such a way that almost feels

484
00:26:16,000 --> 00:26:18,960
Speaker 1: like the cars really taking over. But it's not not

485
00:26:19,119 --> 00:26:21,880
Speaker 1: quite there yet, and Tesla has maintained like, no, it's

486
00:26:21,920 --> 00:26:24,280
Speaker 1: not an autonomous type of thing. It could do a

487
00:26:24,280 --> 00:26:27,320
Speaker 1: lot of stuff. And and if your hands are on

488
00:26:27,359 --> 00:26:29,960
Speaker 1: the wheel and you're paying attention, it may feel like

489
00:26:31,280 --> 00:26:33,359
Speaker 1: I don't even need to be here for this to work.

490
00:26:33,560 --> 00:26:35,720
Speaker 1: But but but the reality is that if you see

491
00:26:35,720 --> 00:26:39,119
Speaker 1: a semi trailer trailer in tractor crossing the road in

492
00:26:39,160 --> 00:26:41,439
Speaker 1: front of you, you're going to react by touching the

493
00:26:41,480 --> 00:26:44,239
Speaker 1: brake or swerving to avoid that that vehicle. You're going

494
00:26:44,240 --> 00:26:45,919
Speaker 1: to see it ahead of time. I I just have

495
00:26:46,240 --> 00:26:49,119
Speaker 1: a gut feeling that there were no eyes up in

496
00:26:49,119 --> 00:26:51,239
Speaker 1: this situation. There were no hands on the wheel at all.

497
00:26:51,280 --> 00:26:53,720
Speaker 1: There was probably no driver input for quite some time

498
00:26:54,200 --> 00:26:56,520
Speaker 1: in this case. And I just again just a gut feeling.

499
00:26:56,560 --> 00:26:59,080
Speaker 1: But I mean, just that the way the accident happened,

500
00:26:59,240 --> 00:27:01,439
Speaker 1: I can't see it the other way. And that's of

501
00:27:01,440 --> 00:27:04,280
Speaker 1: course this is all borrowing any kind of medical situation.

502
00:27:04,320 --> 00:27:05,880
Speaker 1: But I think, but I think we would have heard

503
00:27:05,920 --> 00:27:08,439
Speaker 1: that by now if that had been the case, if

504
00:27:08,440 --> 00:27:10,359
Speaker 1: there was like a heart attack or a stroke, or

505
00:27:10,560 --> 00:27:13,800
Speaker 1: a medical situation was so dire that he was unable

506
00:27:13,840 --> 00:27:17,120
Speaker 1: to interact with the vehicle in any way. I think

507
00:27:17,160 --> 00:27:19,879
Speaker 1: that we would have heard about that by now, but

508
00:27:20,200 --> 00:27:21,920
Speaker 1: you never know. I mean, we'll have to let all

509
00:27:21,960 --> 00:27:23,879
Speaker 1: that kind of come out in the wash, and it will.

510
00:27:24,640 --> 00:27:28,520
Speaker 1: But I mean, you gotta understand that this is going

511
00:27:28,560 --> 00:27:30,920
Speaker 1: to happen in other makes and models of cars as

512
00:27:30,920 --> 00:27:34,560
Speaker 1: well that have systems that are autonomous like or you know,

513
00:27:34,600 --> 00:27:38,280
Speaker 1: autopilot like, and that you know, um, the Mercedes that

514
00:27:38,320 --> 00:27:39,800
Speaker 1: will do the same thing, or the bmw s that

515
00:27:39,840 --> 00:27:41,960
Speaker 1: will do the same thing, or the Chryslers or any

516
00:27:42,000 --> 00:27:44,720
Speaker 1: of these cars that can drive themselves in traffic. People

517
00:27:44,720 --> 00:27:47,160
Speaker 1: are going to kind of take that to the extreme

518
00:27:47,240 --> 00:27:50,359
Speaker 1: and and not pay attention. It's like, um, you're just

519
00:27:50,440 --> 00:27:53,439
Speaker 1: taking advantage of the situation, right and the you know,

520
00:27:53,480 --> 00:27:56,160
Speaker 1: there's a very real fear in the technology sphere that

521
00:27:56,200 --> 00:28:00,680
Speaker 1: this will end up creating hurdles for auton amous vehicles

522
00:28:00,680 --> 00:28:05,159
Speaker 1: down down down the road. I didn't want to do

523
00:28:05,200 --> 00:28:07,240
Speaker 1: a pun there, but I wouldn't have did one anyway.

524
00:28:07,960 --> 00:28:10,280
Speaker 1: So but that, yeah, in the future, you're going to

525
00:28:10,280 --> 00:28:14,240
Speaker 1: see some hurdles in the way of autonomous vehicles because

526
00:28:14,280 --> 00:28:19,000
Speaker 1: of this. It's to me, it's it's apples and oranges,

527
00:28:19,040 --> 00:28:22,639
Speaker 1: because an autonomous vehicle, especially if you're taking the route

528
00:28:22,640 --> 00:28:26,240
Speaker 1: that Google is taking, there's no way to interact with

529
00:28:26,240 --> 00:28:28,879
Speaker 1: that vehicle apart from telling it where you want to

530
00:28:28,920 --> 00:28:31,879
Speaker 1: go it there's there are no controls, there's no break,

531
00:28:31,880 --> 00:28:35,399
Speaker 1: there's no accelerator, there's no steering wheel, there's nothing, and

532
00:28:35,480 --> 00:28:41,160
Speaker 1: that in that case, you have to really convincingly demonstrate

533
00:28:41,240 --> 00:28:44,480
Speaker 1: that the technology is robust and capable of responding to

534
00:28:45,360 --> 00:28:48,800
Speaker 1: a myriad of situations, well beyond just the typical traffic

535
00:28:49,000 --> 00:28:51,400
Speaker 1: situations you would run into on a day to day basis,

536
00:28:51,440 --> 00:28:55,360
Speaker 1: but some of the more extraordinary experiences that you could encounter,

537
00:28:55,520 --> 00:28:59,240
Speaker 1: because you know, one day may not be exactly the

538
00:28:59,280 --> 00:29:01,160
Speaker 1: same as the next. You may have one day where

539
00:29:01,160 --> 00:29:03,760
Speaker 1: you're driving to work and everything is pretty much normal,

540
00:29:03,800 --> 00:29:05,240
Speaker 1: and then maybe the next day you try to work

541
00:29:05,240 --> 00:29:08,120
Speaker 1: and who knows, a poultry truck has just spilled a

542
00:29:08,160 --> 00:29:10,960
Speaker 1: big load of chickens all across the road. That happens

543
00:29:11,000 --> 00:29:13,520
Speaker 1: here in Georgia. Let me tell you that happens two

544
00:29:13,600 --> 00:29:16,080
Speaker 1: or three times a year here in Georgia. Yeah, it's

545
00:29:16,120 --> 00:29:19,920
Speaker 1: not that uncommon. Yeah, it's uh. I grew up in Gainesville, Georgia,

546
00:29:19,960 --> 00:29:22,520
Speaker 1: poultry capital of the world. We've got a chicken statue

547
00:29:22,520 --> 00:29:25,640
Speaker 1: in the middle of town. That's not a lie. Um

548
00:29:25,680 --> 00:29:27,920
Speaker 1: and yeah, it happens. It's one of those things where

549
00:29:27,960 --> 00:29:29,400
Speaker 1: but but it's one of those things where you if

550
00:29:29,400 --> 00:29:33,520
Speaker 1: you're programming an autonomous vehicle out in Mountain View, California,

551
00:29:33,800 --> 00:29:39,000
Speaker 1: you're not necessarily thinking, Hey, what happens if chickens are everywhere?

552
00:29:40,000 --> 00:29:43,800
Speaker 1: Do we have a chicken plan? Yeah, that says, but

553
00:29:44,000 --> 00:29:45,960
Speaker 1: that's the source of stuff you have to take into account.

554
00:29:46,400 --> 00:29:49,640
Speaker 1: My my worry is that we're going to see a

555
00:29:49,760 --> 00:29:58,840
Speaker 1: disproportionate response from various UH agencies in in the wake

556
00:29:59,000 --> 00:30:03,000
Speaker 1: of this accident, which again tragic accident. We hate to

557
00:30:03,040 --> 00:30:07,480
Speaker 1: see this happen at all UH and and place a

558
00:30:07,600 --> 00:30:10,800
Speaker 1: level of responsibility that is not merited. Yeah, you know,

559
00:30:10,880 --> 00:30:13,200
Speaker 1: I think in the short term, my gut feeling is

560
00:30:13,200 --> 00:30:15,320
Speaker 1: that you're right. But I think that it won't be

561
00:30:15,360 --> 00:30:17,920
Speaker 1: too long before we get back on this full board

562
00:30:18,400 --> 00:30:21,600
Speaker 1: where a lot of people I mean sad to say

563
00:30:21,640 --> 00:30:24,840
Speaker 1: that they'll do this will become part of Tesla's history,

564
00:30:24,960 --> 00:30:27,080
Speaker 1: you know, and they'll say, well, yeah, that happened, but

565
00:30:27,160 --> 00:30:30,960
Speaker 1: we then ran two million tests in order you know,

566
00:30:30,960 --> 00:30:33,680
Speaker 1: electronic tests in order to prevent that from ever happening again.

567
00:30:33,800 --> 00:30:38,800
Speaker 1: And now that situation won't occur ever again in a Tesla. Now,

568
00:30:39,080 --> 00:30:42,000
Speaker 1: what's the next unusual set of circumstances that will lead

569
00:30:42,000 --> 00:30:44,000
Speaker 1: to a driver death? That's gonna that's gonna be the

570
00:30:44,000 --> 00:30:46,720
Speaker 1: next question. Because you can only do so much. You

571
00:30:46,760 --> 00:30:50,040
Speaker 1: can't foresee every single circumstances. Well, and I and that

572
00:30:50,120 --> 00:30:53,200
Speaker 1: also reminds me, in fact, I should have said this earlier.

573
00:30:53,240 --> 00:30:56,480
Speaker 1: But that's a good point in that the Tesla Autopilot

574
00:30:56,600 --> 00:31:00,400
Speaker 1: is a beta service. It's it's in beta, which means

575
00:31:00,440 --> 00:31:02,880
Speaker 1: that it's in testing mode, it's not the final product.

576
00:31:03,480 --> 00:31:07,840
Speaker 1: And in fact, Tesla takes the data gathered from people

577
00:31:07,920 --> 00:31:11,840
Speaker 1: driving an autopilot mode in order to tweak autopilot and

578
00:31:11,880 --> 00:31:14,160
Speaker 1: make it more effective. So, in other words, this is

579
00:31:14,240 --> 00:31:17,840
Speaker 1: a trial of a program that they're still not legally

580
00:31:17,880 --> 00:31:20,520
Speaker 1: ready to call even autonomous. Yet people are treating it

581
00:31:20,600 --> 00:31:22,880
Speaker 1: as it's autonomous. So it's like it's like two levels

582
00:31:22,920 --> 00:31:25,440
Speaker 1: below that. At least, yes, it's it's it's a level

583
00:31:25,480 --> 00:31:28,760
Speaker 1: where if you are treating it like it's an autonomous car,

584
00:31:29,840 --> 00:31:32,040
Speaker 1: you are you are behaving in a way that I

585
00:31:32,080 --> 00:31:37,680
Speaker 1: would argue as irresponsible. Um. And you know, I'm hopeful

586
00:31:37,840 --> 00:31:42,240
Speaker 1: that this will stories like this will become fewer and

587
00:31:42,280 --> 00:31:47,600
Speaker 1: fewer in number, simply because the improvements in technology have

588
00:31:47,800 --> 00:31:50,120
Speaker 1: us avoid some of these accidents where we never have

589
00:31:50,200 --> 00:31:54,200
Speaker 1: a story, right, Like, you're not gonna tell, hey, seventeen

590
00:31:54,360 --> 00:31:58,080
Speaker 1: more accidents didn't happen today. That's not a story, right

591
00:31:58,840 --> 00:32:01,120
Speaker 1: unless you had some sort of we're definitive proof of it,

592
00:32:01,160 --> 00:32:03,680
Speaker 1: you know who. I want to have autonomous systems or

593
00:32:03,760 --> 00:32:08,520
Speaker 1: autopilot systems every driver around me because recently, I don't

594
00:32:08,520 --> 00:32:10,640
Speaker 1: know if this is a thing everywhere. I would guess

595
00:32:10,640 --> 00:32:13,840
Speaker 1: that it is because it distracted driving with smartphones, et cetera.

596
00:32:14,320 --> 00:32:17,600
Speaker 1: Everything else. Uh, there have been several times in his

597
00:32:17,760 --> 00:32:22,640
Speaker 1: last five seven days, maybe I've had several occasions where

598
00:32:22,640 --> 00:32:25,840
Speaker 1: I've had to make an invasive mover at maneuver as

599
00:32:25,960 --> 00:32:28,239
Speaker 1: the vehicle in front of somebody who is coming up

600
00:32:28,240 --> 00:32:30,760
Speaker 1: and behind me with a device in their hand. And

601
00:32:31,240 --> 00:32:36,320
Speaker 1: it's it's just it's hard to try to maintain focus

602
00:32:36,320 --> 00:32:38,320
Speaker 1: on what I'm doing when I'm having to watch what

603
00:32:38,360 --> 00:32:39,520
Speaker 1: people in the lane next to me you are doing,

604
00:32:39,520 --> 00:32:41,719
Speaker 1: because they're they're constantly you know, two wheels over the

605
00:32:41,720 --> 00:32:44,080
Speaker 1: line or um, you know, someone behind me is just

606
00:32:44,280 --> 00:32:47,760
Speaker 1: every time late, breaking and nearly touching my bumper. It

607
00:32:47,880 --> 00:32:49,800
Speaker 1: drives me crazy. If they had an auto or an

608
00:32:49,800 --> 00:32:53,160
Speaker 1: autopilot system, or even an adaptive cruise control that was

609
00:32:53,200 --> 00:32:55,880
Speaker 1: capable of stopping the vehicle like Mercedes or you know

610
00:32:55,920 --> 00:32:59,959
Speaker 1: BMW have, I would love it myself. I would run,

611
00:33:00,160 --> 00:33:03,520
Speaker 1: remain in control. But for everybody else, I have a

612
00:33:03,560 --> 00:33:06,240
Speaker 1: feeling that what you've just described as something that is

613
00:33:06,360 --> 00:33:10,520
Speaker 1: universally wished for that everybody but me. I think that's

614
00:33:10,560 --> 00:33:13,200
Speaker 1: the way. I know how selfish. That's I'm of the

615
00:33:13,320 --> 00:33:18,240
Speaker 1: everybody period, But you know I don't try. Yeah, well,

616
00:33:18,840 --> 00:33:20,560
Speaker 1: there's something to be said for for that as well.

617
00:33:20,600 --> 00:33:23,200
Speaker 1: You know that. You know, if everybody had the system,

618
00:33:23,240 --> 00:33:24,880
Speaker 1: it would work out that way, right, And there's so

619
00:33:24,920 --> 00:33:28,000
Speaker 1: many that are like that. If if it was one implemented,

620
00:33:28,760 --> 00:33:31,640
Speaker 1: a lot of things would be a lot safer. Yeah,

621
00:33:31,680 --> 00:33:35,880
Speaker 1: a lot more boring boring, you say, but you could

622
00:33:35,960 --> 00:33:38,560
Speaker 1: get to where you're going with less traffic. Is it

623
00:33:38,680 --> 00:33:41,920
Speaker 1: more for yeah, well, you would be able to fill

624
00:33:41,960 --> 00:33:44,840
Speaker 1: your time with entertaining things. Is it more involve being

625
00:33:44,880 --> 00:33:47,680
Speaker 1: in a car more boring or boring. Er, it's more boring,

626
00:33:47,760 --> 00:33:50,080
Speaker 1: more boring. I can tell you what's more boring is

627
00:33:50,600 --> 00:33:55,200
Speaker 1: us debating the word. So yeah, we're gonna wrap that

628
00:33:55,320 --> 00:34:01,680
Speaker 1: part up the discussion. So ultimately, uh, I'm hopeful that

629
00:34:01,680 --> 00:34:06,000
Speaker 1: that Tesla autopilot can continue to evolve over time become better.

630
00:34:06,760 --> 00:34:09,959
Speaker 1: I'm hopeful that more people really take it to heart

631
00:34:10,120 --> 00:34:12,920
Speaker 1: that their level of responsibility has not changed when they

632
00:34:13,000 --> 00:34:17,280
Speaker 1: engage autopilot, that that system is there to improve their safety,

633
00:34:17,280 --> 00:34:20,760
Speaker 1: but not replace them as an actual operator of the vehicle,

634
00:34:20,920 --> 00:34:25,799
Speaker 1: not entirely at any rate. Remain vigilant behind the wheel. Yeah,

635
00:34:26,000 --> 00:34:32,400
Speaker 1: don't don't create more terrible stories like I feel for

636
00:34:32,640 --> 00:34:36,080
Speaker 1: Mr Brown's family and I am. I really wish that

637
00:34:36,120 --> 00:34:38,360
Speaker 1: this story had turned out a different way. And of

638
00:34:38,400 --> 00:34:40,479
Speaker 1: course there's a second story that we could talk about

639
00:34:40,640 --> 00:34:43,120
Speaker 1: that um, but we don't have all the details. There

640
00:34:43,160 --> 00:34:46,600
Speaker 1: was one another accident that happens to life first with

641
00:34:46,680 --> 00:34:49,040
Speaker 1: the Tesla Model X. Yeah, that's right, that's their newest

642
00:34:49,040 --> 00:34:51,840
Speaker 1: first that's the SUV looking vehicle, and that was on

643
00:34:52,600 --> 00:34:56,520
Speaker 1: the Pennsylvania Turnpike. Have you ever been on the turnpike? Yeah,

644
00:34:56,560 --> 00:34:59,279
Speaker 1: you know what it's like. Yeah, my wife's from Pennsylvania.

645
00:34:59,400 --> 00:35:02,640
Speaker 1: So I we've also been on it, and the story

646
00:35:02,680 --> 00:35:06,440
Speaker 1: in that case was that supposedly they had turned on autopilot,

647
00:35:06,719 --> 00:35:09,560
Speaker 1: the vehicle struck a guardrail on the right hand side.

648
00:35:09,640 --> 00:35:12,600
Speaker 1: The vehicle then veered across to the left hand side,

649
00:35:12,640 --> 00:35:14,520
Speaker 1: hit the concrete median in the middle of the road,

650
00:35:14,760 --> 00:35:17,160
Speaker 1: and then flipped over. Yeah, this is crazy because this

651
00:35:17,239 --> 00:35:20,279
Speaker 1: is not like, um, you just simply didn't detect something

652
00:35:20,440 --> 00:35:23,439
Speaker 1: above the road surface. This left the lane. Yeah, so

653
00:35:23,560 --> 00:35:25,320
Speaker 1: that's something totally different. We're going to find out a

654
00:35:25,360 --> 00:35:27,560
Speaker 1: lot more about this one, so we and we don't

655
00:35:27,600 --> 00:35:31,000
Speaker 1: have the details yet. We do know that Tesla initially

656
00:35:31,080 --> 00:35:34,960
Speaker 1: said it doesn't look like autopilot was necessarily involved in

657
00:35:35,000 --> 00:35:38,239
Speaker 1: this particular accident, and then they kind of revised that

658
00:35:38,280 --> 00:35:40,960
Speaker 1: to say that the data so far has not supported it,

659
00:35:41,000 --> 00:35:44,320
Speaker 1: but that they are investigating it. Um And to be honest,

660
00:35:44,360 --> 00:35:49,080
Speaker 1: we don't have the full information. Right, We've got the uh,

661
00:35:49,120 --> 00:35:51,640
Speaker 1: the first hand account of the driver. Both the driver

662
00:35:51,760 --> 00:35:55,840
Speaker 1: and his passenger escaped from They didn't I'm sure they

663
00:35:55,880 --> 00:35:59,120
Speaker 1: were injured, but they were They did not die. Maybe

664
00:35:59,120 --> 00:36:01,120
Speaker 1: cut some bruises or something like that. But yeah, it

665
00:36:01,160 --> 00:36:04,920
Speaker 1: wasn't as quite as a um traumatic I guess as

666
00:36:04,960 --> 00:36:07,600
Speaker 1: the other one was. Of course, so we we don't

667
00:36:07,640 --> 00:36:09,759
Speaker 1: have enough information on that at the time of this

668
00:36:09,880 --> 00:36:13,720
Speaker 1: recording to really dive into was this actually a failure

669
00:36:13,840 --> 00:36:17,720
Speaker 1: of autopilot? Because if this were a failure under totally

670
00:36:17,719 --> 00:36:21,120
Speaker 1: different circumstances, then you have to start asking some pretty

671
00:36:21,200 --> 00:36:24,520
Speaker 1: tough questions about Tesla's technology. Can I just make a guess,

672
00:36:24,600 --> 00:36:27,880
Speaker 1: I'll make a guess. An irresponsible guess is that somehow

673
00:36:27,880 --> 00:36:31,800
Speaker 1: the driver disengaged the system. Because the lane departure system

674
00:36:31,920 --> 00:36:33,560
Speaker 1: is something that's been around for so long and they've

675
00:36:33,600 --> 00:36:36,760
Speaker 1: got it so nailed down, really that something like that

676
00:36:36,760 --> 00:36:40,320
Speaker 1: that part of the of the autopilot system with everything

677
00:36:40,360 --> 00:36:42,680
Speaker 1: else that it has to to think about. If it

678
00:36:42,800 --> 00:36:45,040
Speaker 1: just simply left the lane because it couldn't detect the lane,

679
00:36:45,040 --> 00:36:47,680
Speaker 1: that's that's uh, I don't it seems like something is

680
00:36:47,840 --> 00:36:49,520
Speaker 1: up there? It would you would think it would give

681
00:36:49,560 --> 00:36:52,840
Speaker 1: an alert to the driver to say, like bing or

682
00:36:52,880 --> 00:36:55,560
Speaker 1: something like, hey, you know, make sure you're taking control,

683
00:36:55,600 --> 00:36:57,520
Speaker 1: because you know, I've heard of I've heard of these

684
00:36:57,520 --> 00:37:00,319
Speaker 1: systems having a little bit of trouble at it at

685
00:37:00,360 --> 00:37:04,040
Speaker 1: sunrise and sunset detecting detecting white lines on the road

686
00:37:04,080 --> 00:37:07,200
Speaker 1: because of the glare, and maybe that's a you know

687
00:37:08,040 --> 00:37:09,960
Speaker 1: factor in this. I would imagine such a thing like

688
00:37:10,120 --> 00:37:15,600
Speaker 1: here in Atlanta, if it's if it's been raining, uh,

689
00:37:15,640 --> 00:37:18,680
Speaker 1: and then you get sunshine. It is really hard to

690
00:37:18,680 --> 00:37:20,439
Speaker 1: see some of the lines on some of the roads

691
00:37:20,440 --> 00:37:23,319
Speaker 1: here in Atlanta. I do if I feel. Yeah, so

692
00:37:23,560 --> 00:37:27,440
Speaker 1: when you start feeling that bump of the reflector, Okay,

693
00:37:27,480 --> 00:37:31,160
Speaker 1: that's fair, I got you, all right, But again that's

694
00:37:31,160 --> 00:37:35,799
Speaker 1: all irresponsible speculation. Yeah, exactly. We we don't know. So

695
00:37:35,800 --> 00:37:39,160
Speaker 1: so while we're making some wild guesses here, by the

696
00:37:39,200 --> 00:37:41,799
Speaker 1: time this episode comes out, maybe there'll be more information

697
00:37:41,840 --> 00:37:45,560
Speaker 1: that will be available, and perhaps if if it actually

698
00:37:45,680 --> 00:37:50,399
Speaker 1: is ultimately an autopilot issue, we'll revisit this and talk

699
00:37:50,480 --> 00:37:53,920
Speaker 1: more about it. But for now we're going to transition

700
00:37:54,000 --> 00:37:56,560
Speaker 1: over to a different story. And this was one that

701
00:37:56,640 --> 00:37:59,600
Speaker 1: you brought to my attention, Scott. This um. This this

702
00:38:00,000 --> 00:38:04,480
Speaker 1: p posal that was part of the European parliament um

703
00:38:04,520 --> 00:38:08,000
Speaker 1: where they want to talk about robots and and part

704
00:38:08,040 --> 00:38:12,720
Speaker 1: of the proposal that's got the most attention is the

705
00:38:12,760 --> 00:38:16,520
Speaker 1: concept of electronic personhood for robots. Yeah, and that's what

706
00:38:16,560 --> 00:38:18,160
Speaker 1: I came to you with because I wasn't sure if

707
00:38:18,160 --> 00:38:20,000
Speaker 1: this is going to be in a podcast episode or not.

708
00:38:20,040 --> 00:38:21,960
Speaker 1: I just thought it was kind of an interesting little

709
00:38:22,040 --> 00:38:24,399
Speaker 1: news blurer. But I suppose and and I'll be I'll

710
00:38:24,400 --> 00:38:26,360
Speaker 1: be right up front with you when I say that

711
00:38:26,400 --> 00:38:29,840
Speaker 1: I have more questions about this than answers. Really well, Scott, thankfully,

712
00:38:29,880 --> 00:38:32,279
Speaker 1: I have printed out the entire report and I have

713
00:38:32,360 --> 00:38:34,640
Speaker 1: read it, so I am happy to answer some question.

714
00:38:34,680 --> 00:38:36,960
Speaker 1: Have you? Will you read it to us? Now? Slowly?

715
00:38:38,480 --> 00:38:41,040
Speaker 1: Twenty two pages? Let's begin. Actually, I do want to read.

716
00:38:41,160 --> 00:38:43,480
Speaker 1: I do want to read the first paragraph very briefly,

717
00:38:44,160 --> 00:38:48,000
Speaker 1: because I love it so much. So this is the

718
00:38:48,239 --> 00:38:54,520
Speaker 1: this is introduction section A. Whereas from Mary Shelley's Frankenstein's

719
00:38:54,560 --> 00:38:57,840
Speaker 1: Monster to the classical myth of Pygmalion, through the story

720
00:38:57,840 --> 00:39:01,600
Speaker 1: of Prague's Gulam to the robot of Carol Keepec, who

721
00:39:01,640 --> 00:39:05,400
Speaker 1: coined the word, people have fantasized about the possibility of

722
00:39:05,440 --> 00:39:09,879
Speaker 1: building intelligent machines, more often than not androids with human features.

723
00:39:11,400 --> 00:39:13,560
Speaker 1: I would never have expected that to make it into

724
00:39:13,600 --> 00:39:19,480
Speaker 1: this draft. Not only that's not the only fiction reference

725
00:39:19,560 --> 00:39:26,680
Speaker 1: made in this So a famous, famous fictional piece about

726
00:39:26,800 --> 00:39:32,680
Speaker 1: robotics is mentioned, which is, of course Asimov's Laws of robots.

727
00:39:32,719 --> 00:39:35,560
Speaker 1: Have you heard of Asimov's Laws of robotics? So basically,

728
00:39:35,600 --> 00:39:38,760
Speaker 1: in case you're not familiar, first law is, a robot

729
00:39:38,880 --> 00:39:41,440
Speaker 1: may not harm a human or through inaction, allow a

730
00:39:41,520 --> 00:39:44,680
Speaker 1: human to come to harm. Second law is a robot

731
00:39:44,840 --> 00:39:48,279
Speaker 1: must follow the commands of a human unless it were

732
00:39:48,360 --> 00:39:52,000
Speaker 1: to violate the first law. Third law is a robot

733
00:39:52,120 --> 00:39:55,080
Speaker 1: must protect itself from harm unless it would bring it

734
00:39:55,120 --> 00:39:57,839
Speaker 1: into conflict with one of the first two laws. And

735
00:39:57,920 --> 00:40:01,160
Speaker 1: these are critical in this argument because they're going to

736
00:40:01,200 --> 00:40:04,279
Speaker 1: treat them as if they're human with uh, not only

737
00:40:04,440 --> 00:40:07,759
Speaker 1: not only rights, but also responsibilities. They're going to hold

738
00:40:07,800 --> 00:40:12,560
Speaker 1: them accountable for their actions, which is their program by

739
00:40:12,680 --> 00:40:16,680
Speaker 1: humans at this point. So what what is this? Where

740
00:40:16,760 --> 00:40:19,880
Speaker 1: is this all sort out to here? Because the idea

741
00:40:19,960 --> 00:40:22,719
Speaker 1: that a robot that's in a factory creating a part

742
00:40:23,400 --> 00:40:26,200
Speaker 1: or you know, an automotive part, the idea that that

743
00:40:26,320 --> 00:40:30,839
Speaker 1: is soon to be considered an electronic person. Uh, it's

744
00:40:30,960 --> 00:40:34,160
Speaker 1: it's crazy too. It seems insane to me. But the

745
00:40:34,200 --> 00:40:37,360
Speaker 1: goal here, as we'll talk about in I guess a

746
00:40:37,400 --> 00:40:40,080
Speaker 1: little bit more in depth, is that they'll be taxed,

747
00:40:40,520 --> 00:40:43,120
Speaker 1: they will pay into a social security system that will

748
00:40:43,120 --> 00:40:49,040
Speaker 1: then be drawn from by humans, but not robots. Robots

749
00:40:49,080 --> 00:40:52,480
Speaker 1: don't get social security. Well, yeah, you get compensation, all right,

750
00:40:52,520 --> 00:40:54,840
Speaker 1: So these are these are it's true. I'll tell you

751
00:40:54,840 --> 00:40:59,799
Speaker 1: about they get compensation proposal. I'll tell you we're talking maintenance. No,

752
00:41:00,200 --> 00:41:05,080
Speaker 1: is that a compensation? Really? Know what it is? Their

753
00:41:05,120 --> 00:41:08,680
Speaker 1: Their compensation is money money they get paid. Well, okay,

754
00:41:08,719 --> 00:41:11,560
Speaker 1: so that goes to the owner of the robot. But

755
00:41:11,680 --> 00:41:13,480
Speaker 1: the owner of the robot is the one that's paying

756
00:41:13,520 --> 00:41:16,319
Speaker 1: into the social security right, the owner of the robot pays.

757
00:41:16,360 --> 00:41:18,480
Speaker 1: The owner of the robot pays the robot. Well, this

758
00:41:18,520 --> 00:41:21,280
Speaker 1: sounds like a real screw drive. You want to know why? Alright,

759
00:41:21,680 --> 00:41:25,279
Speaker 1: here's here's here's the reason why this gets so it

760
00:41:25,320 --> 00:41:27,880
Speaker 1: gets clear once you start to understand the logic, because

761
00:41:27,880 --> 00:41:29,600
Speaker 1: at first, on the face of it, you're like, this

762
00:41:29,640 --> 00:41:32,960
Speaker 1: is ridiculous. Why would an owner have to pay in

763
00:41:33,200 --> 00:41:35,719
Speaker 1: for social security for a robot which is never going

764
00:41:35,760 --> 00:41:38,359
Speaker 1: to draw on social Security? Yes, why would you ever

765
00:41:38,680 --> 00:41:42,479
Speaker 1: pay a salary to a robot? Well, the compensation fund.

766
00:41:42,480 --> 00:41:44,960
Speaker 1: First of all, I was being a little unfair. That

767
00:41:45,080 --> 00:41:49,000
Speaker 1: was one possible solution to a very real problem. That

768
00:41:49,200 --> 00:41:53,480
Speaker 1: very real problem being when you get to a situation

769
00:41:53,480 --> 00:41:58,320
Speaker 1: in which a robot causes damage or harm, who is

770
00:41:58,400 --> 00:42:01,759
Speaker 1: liable for that damage or harm? And how do you

771
00:42:02,239 --> 00:42:07,640
Speaker 1: compensate the harmed party? And so one of the arguments

772
00:42:07,719 --> 00:42:11,759
Speaker 1: was the more autonomous a robot is, the more it

773
00:42:11,880 --> 00:42:15,480
Speaker 1: is able to interact with its environment in new ways,

774
00:42:15,600 --> 00:42:18,640
Speaker 1: like use machine learning to adapt to its environment and

775
00:42:18,680 --> 00:42:22,520
Speaker 1: work within that environment, the less liable you can hold

776
00:42:22,880 --> 00:42:26,759
Speaker 1: the manufacturer of that robot if it's not something that

777
00:42:26,880 --> 00:42:30,120
Speaker 1: is directly tied to a very basic function of the robot.

778
00:42:30,760 --> 00:42:34,360
Speaker 1: You could argue, while the robot learned something, it learned

779
00:42:34,480 --> 00:42:38,160
Speaker 1: a poor way of doing that thing, someone or something

780
00:42:38,280 --> 00:42:41,240
Speaker 1: was harmed as a result of that. The robots actually

781
00:42:41,239 --> 00:42:44,840
Speaker 1: at fault, not the producer. This is a self aware issue,

782
00:42:45,680 --> 00:42:48,400
Speaker 1: not even self aware, but so much as like like so,

783
00:42:48,520 --> 00:42:51,480
Speaker 1: let's say there there's a famous thing in Stanford where

784
00:42:51,960 --> 00:42:55,680
Speaker 1: uh computer scientists set up a computer with artificial intelligence,

785
00:42:56,120 --> 00:42:58,680
Speaker 1: and they had to observe a pendulum as it swung

786
00:42:58,719 --> 00:43:02,400
Speaker 1: back and forth. Through observing the pendulum, the computer was

787
00:43:02,440 --> 00:43:07,520
Speaker 1: able to figure out the basic laws of motion just

788
00:43:07,640 --> 00:43:11,640
Speaker 1: by observing the behavior of the pendulum swinging without being

789
00:43:11,680 --> 00:43:15,319
Speaker 1: given any information about the laws emotion. Now extend that

790
00:43:15,400 --> 00:43:18,680
Speaker 1: to an idea for a an artificially intelligent device. It

791
00:43:18,680 --> 00:43:20,960
Speaker 1: doesn't have to be self aware. It just has to

792
00:43:21,000 --> 00:43:25,760
Speaker 1: be able to observe things within its environment and adapt

793
00:43:25,840 --> 00:43:28,440
Speaker 1: to them. Another great example would be let's say you've

794
00:43:28,440 --> 00:43:30,279
Speaker 1: made a robot, and you put a robot on a

795
00:43:30,480 --> 00:43:32,879
Speaker 1: on an assembly line, and the robot needs to put

796
00:43:32,920 --> 00:43:36,560
Speaker 1: together a certain thing. And then as the robot attempts

797
00:43:36,560 --> 00:43:39,480
Speaker 1: to put the thing together, at first, the robots pretty crappy.

798
00:43:39,760 --> 00:43:42,920
Speaker 1: It's not doing it very well. But each time it attempts,

799
00:43:42,920 --> 00:43:44,920
Speaker 1: it tries something a little bit different and starts to

800
00:43:45,000 --> 00:43:48,120
Speaker 1: learn what works better than what worked before. You're not

801
00:43:48,200 --> 00:43:50,799
Speaker 1: actually programming the robot to do this. The robot is

802
00:43:51,040 --> 00:43:54,400
Speaker 1: learning how to do this through trial and error, essentially,

803
00:43:54,440 --> 00:43:57,319
Speaker 1: just as a would on the same line exactly. And

804
00:43:58,160 --> 00:44:00,920
Speaker 1: most of the time the robots we talk about an industry,

805
00:44:01,000 --> 00:44:03,400
Speaker 1: they don't fall into that category. They've been designed to

806
00:44:03,440 --> 00:44:08,560
Speaker 1: do a very specific series of actions repeated repetitively, like

807
00:44:08,640 --> 00:44:10,759
Speaker 1: that's it, that's and that's all they do. They don't.

808
00:44:11,120 --> 00:44:14,120
Speaker 1: They don't start putting together a car and then turn

809
00:44:14,160 --> 00:44:17,799
Speaker 1: around and flip burgers that's not the purpose, the idea

810
00:44:17,880 --> 00:44:21,400
Speaker 1: being with these new robots, they'd be more adaptive and

811
00:44:21,440 --> 00:44:24,080
Speaker 1: could therefore change the way they do things in order

812
00:44:24,160 --> 00:44:27,000
Speaker 1: to try and experiment do something a little more effectively

813
00:44:28,040 --> 00:44:31,120
Speaker 1: if in fact, we get to a further stage where

814
00:44:31,200 --> 00:44:36,080
Speaker 1: robots are able to adapt even beyond that level. Because

815
00:44:36,239 --> 00:44:39,640
Speaker 1: really this proposal is more about we're heading down a

816
00:44:39,760 --> 00:44:43,760
Speaker 1: road eventually we're going to need to have legislation to

817
00:44:43,760 --> 00:44:46,200
Speaker 1: to handle these issues. It would be better for us

818
00:44:46,239 --> 00:44:49,839
Speaker 1: to think about it now than when it becomes necessary,

819
00:44:49,920 --> 00:44:52,640
Speaker 1: like like when a problem happens, we have to figure

820
00:44:52,640 --> 00:44:55,360
Speaker 1: out what to do about it. It's more about anticipating

821
00:44:55,400 --> 00:44:57,160
Speaker 1: those problems, all right, I see now. But then the

822
00:44:57,239 --> 00:44:59,480
Speaker 1: knee jerk reaction to this from a lot of people

823
00:44:59,680 --> 00:45:02,200
Speaker 1: is that, uh, you know, the robots are taking my

824
00:45:02,320 --> 00:45:06,160
Speaker 1: job and also they're they're not paying Social Security and

825
00:45:06,239 --> 00:45:08,520
Speaker 1: so so what's gonna happen in the in the end here,

826
00:45:08,560 --> 00:45:11,120
Speaker 1: we're gonna have a uh, we're gonna have an economy

827
00:45:11,160 --> 00:45:14,319
Speaker 1: collapse because it's gonna be all robotic workers in the

828
00:45:14,440 --> 00:45:16,759
Speaker 1: in the factory or going to have massive amounts of

829
00:45:16,840 --> 00:45:20,359
Speaker 1: unemployment across the board. Um, and no one's paying into

830
00:45:20,400 --> 00:45:23,239
Speaker 1: the system that already can't sustain the number of people

831
00:45:23,280 --> 00:45:26,560
Speaker 1: that have retired or are drawing from that right now,

832
00:45:27,360 --> 00:45:29,880
Speaker 1: what's gonna happen when I'm in that position, because it's

833
00:45:29,920 --> 00:45:32,239
Speaker 1: gonna be an entirely robotic work For this is like

834
00:45:32,280 --> 00:45:36,040
Speaker 1: the the the the just the idea that they have,

835
00:45:36,120 --> 00:45:38,120
Speaker 1: you know, it's not it's not the reality. Really. The

836
00:45:38,200 --> 00:45:41,480
Speaker 1: reality is that as the as the robots are slowly

837
00:45:41,560 --> 00:45:45,279
Speaker 1: being implemented in the workforce, humans are also increasing in

838
00:45:45,320 --> 00:45:48,840
Speaker 1: the workforce, but in different jobs. So the robots maybe

839
00:45:48,840 --> 00:45:52,799
Speaker 1: taking this position away slowly. And I've got a few

840
00:45:52,880 --> 00:45:55,720
Speaker 1: numbers here I can mention in a moment um slowly.

841
00:45:55,760 --> 00:45:59,400
Speaker 1: It's it's happening very slowly for for this um, you know,

842
00:45:59,440 --> 00:46:01,400
Speaker 1: like the end of the world scenario that they're thinking of.

843
00:46:01,560 --> 00:46:03,160
Speaker 1: You know that um not into the world, but you

844
00:46:03,200 --> 00:46:05,359
Speaker 1: know what I mean, Well, yeah, the idea, the idea

845
00:46:05,400 --> 00:46:08,959
Speaker 1: of capitalism folding in on itself. Yeah, And and they're

846
00:46:09,040 --> 00:46:12,480
Speaker 1: they're thinking of it happening almost immediately overnight. And if

847
00:46:12,520 --> 00:46:14,680
Speaker 1: it's something like that did happen overnight, that would be

848
00:46:14,719 --> 00:46:18,200
Speaker 1: horrific for the the economy would collapse almost immediately. It

849
00:46:18,200 --> 00:46:20,200
Speaker 1: would be really really difficult for for the system to

850
00:46:20,280 --> 00:46:23,839
Speaker 1: keep up. So it's a much more it's a it's

851
00:46:23,880 --> 00:46:27,239
Speaker 1: a much slower implementation than what people are thinking it is,

852
00:46:27,360 --> 00:46:30,880
Speaker 1: and and that humans are in fact finding jobs in

853
00:46:30,960 --> 00:46:33,680
Speaker 1: other positions and places that you know then keep them

854
00:46:33,680 --> 00:46:37,359
Speaker 1: gainfully employed. And it's just it's a balance. It's all

855
00:46:37,360 --> 00:46:40,080
Speaker 1: a balance right now. But if things were to happen

856
00:46:40,280 --> 00:46:43,000
Speaker 1: quickly as I said it would, it would throw things

857
00:46:43,000 --> 00:46:45,200
Speaker 1: into turmoil. But it's not happening that way. Yeah, it's

858
00:46:45,239 --> 00:46:47,759
Speaker 1: not happening that way right now. The question is will

859
00:46:47,800 --> 00:46:50,319
Speaker 1: it always be more of a gradual thing or will

860
00:46:50,400 --> 00:46:53,040
Speaker 1: will there be a tipping point? Right like will there

861
00:46:53,160 --> 00:46:57,239
Speaker 1: be a development in robotics and artificial intelligence where we

862
00:46:57,280 --> 00:47:00,000
Speaker 1: get to a tipping point where it's a rapid implement

863
00:47:00,000 --> 00:47:04,600
Speaker 1: intation and deployment of automated systems across multiple jobs. Like

864
00:47:04,640 --> 00:47:08,600
Speaker 1: we are seeing certain jobs get more automation involved in them,

865
00:47:08,640 --> 00:47:11,400
Speaker 1: not all of them stay that way. Sometimes a company

866
00:47:11,440 --> 00:47:15,680
Speaker 1: will go in with an automation strategy and then ultimately decide, hey,

867
00:47:15,719 --> 00:47:17,719
Speaker 1: this is not actually working out the way we thought

868
00:47:17,719 --> 00:47:20,240
Speaker 1: it would, and they back off of it. In other cases,

869
00:47:20,280 --> 00:47:22,560
Speaker 1: we see more and more automation go into that space.

870
00:47:22,960 --> 00:47:25,279
Speaker 1: And so what what this proposal is trying to do

871
00:47:26,120 --> 00:47:30,759
Speaker 1: is say, in the case where perhaps we see more

872
00:47:30,840 --> 00:47:34,200
Speaker 1: jobs going away from automation than our generative through automation,

873
00:47:34,239 --> 00:47:36,879
Speaker 1: we would need to have a method to respond to that.

874
00:47:37,239 --> 00:47:40,120
Speaker 1: Here are some potential ways we could do that. But

875
00:47:40,160 --> 00:47:44,160
Speaker 1: they also actually acknowledge this is not necessarily the case.

876
00:47:44,160 --> 00:47:46,880
Speaker 1: In fact, one of the proposals in the draft is,

877
00:47:47,239 --> 00:47:48,799
Speaker 1: by the way, the draft report, I should read out

878
00:47:48,800 --> 00:47:52,560
Speaker 1: the title Draft Report with recommendations to the Commission on

879
00:47:52,560 --> 00:47:56,560
Speaker 1: Civil Law Rules on Robotics in case you were wondering

880
00:47:56,560 --> 00:47:59,680
Speaker 1: if there were such a thing, uh they in the

881
00:47:59,760 --> 00:48:02,640
Speaker 1: draft if they actually state that one of the things

882
00:48:02,680 --> 00:48:05,919
Speaker 1: that should happen is the European Union should create an

883
00:48:05,960 --> 00:48:11,840
Speaker 1: agency or or a department that actually monitors the number

884
00:48:11,880 --> 00:48:16,520
Speaker 1: of jobs in the EU that are quote unquote taken

885
00:48:16,640 --> 00:48:19,600
Speaker 1: by robots and the number of jobs that are created.

886
00:48:19,640 --> 00:48:21,960
Speaker 1: Because they said, we we lack this right now, we

887
00:48:22,040 --> 00:48:26,440
Speaker 1: don't actually have a dedicated group of people that are

888
00:48:26,480 --> 00:48:29,160
Speaker 1: monitoring these job trends. And and in fact, if we

889
00:48:29,239 --> 00:48:32,800
Speaker 1: continue to see more jobs created than are being taken,

890
00:48:33,280 --> 00:48:36,200
Speaker 1: that becomes a non issue, right like, but we don't

891
00:48:36,280 --> 00:48:38,960
Speaker 1: know is the problem I found those numbers. If you

892
00:48:39,000 --> 00:48:41,040
Speaker 1: want to hear what I want to hear, So well,

893
00:48:41,160 --> 00:48:43,879
Speaker 1: just I guess a precursor to this is that, Um,

894
00:48:43,960 --> 00:48:46,560
Speaker 1: in that same draft you're talking about, the European Parliament draft,

895
00:48:46,800 --> 00:48:49,440
Speaker 1: the Committee on Legal Affairs said that organizations should have

896
00:48:49,480 --> 00:48:52,919
Speaker 1: to declare savings they've made in also savings they made

897
00:48:52,920 --> 00:48:56,200
Speaker 1: in social security contributions by using robotics instead of people

898
00:48:56,480 --> 00:48:59,360
Speaker 1: for tax purposes. So that amount then is going to

899
00:48:59,400 --> 00:49:01,600
Speaker 1: be applied to tax. They're will be taxed on that amount.

900
00:49:02,080 --> 00:49:05,319
Speaker 1: So um, well, okay, anyway, let's move on to this.

901
00:49:05,719 --> 00:49:10,600
Speaker 1: There's no proven correlation between increasing robot density and unemployment.

902
00:49:10,640 --> 00:49:12,360
Speaker 1: That's what I was getting at before, is that By

903
00:49:12,440 --> 00:49:14,759
Speaker 1: pointed out that there's a number of employees in the

904
00:49:14,800 --> 00:49:18,879
Speaker 1: German automotive industry. Um that, in fact, well, the whole

905
00:49:18,880 --> 00:49:22,000
Speaker 1: German automoive industry by rose by thirteen percent between two

906
00:49:22,040 --> 00:49:26,800
Speaker 1: thousand and ten, and rather well, industrial robots stock in

907
00:49:26,840 --> 00:49:30,640
Speaker 1: the industry rose seventeen percent and in the same time period, So, um,

908
00:49:30,840 --> 00:49:34,200
Speaker 1: humans up thirteen percent, robots up seventeen percent. Sure, but

909
00:49:34,239 --> 00:49:37,400
Speaker 1: they're they're right now coexisting. Yeah, and it's not like

910
00:49:37,480 --> 00:49:40,319
Speaker 1: ones declining, the other ones increasing, you know, exponentially. It's

911
00:49:40,360 --> 00:49:42,319
Speaker 1: just they're they're kind of growing at the same rate.

912
00:49:42,320 --> 00:49:44,640
Speaker 1: It's just getting bigger, that's all. And I think I

913
00:49:44,640 --> 00:49:46,600
Speaker 1: think part of the issue here is that some people

914
00:49:46,640 --> 00:49:49,719
Speaker 1: who are commenting on the on the report have probably

915
00:49:49,760 --> 00:49:51,799
Speaker 1: not read it, at least not all the way through,

916
00:49:51,840 --> 00:49:54,600
Speaker 1: because I when I read it, the feeling I got

917
00:49:54,760 --> 00:49:58,160
Speaker 1: was not so much that they were saying, Uh, here's

918
00:49:58,200 --> 00:50:00,160
Speaker 1: the problem that we need to solve right now. It

919
00:50:00,239 --> 00:50:03,680
Speaker 1: was more like people have talked about the possibility of

920
00:50:03,719 --> 00:50:08,120
Speaker 1: this thing happening. If this thing in fact happens, we're

921
00:50:08,200 --> 00:50:12,440
Speaker 1: going to be in trouble unless we plan for that eventuality.

922
00:50:12,520 --> 00:50:14,400
Speaker 1: And they say, here are some ways that we could

923
00:50:14,719 --> 00:50:18,560
Speaker 1: at least have have systems in place to to make

924
00:50:18,600 --> 00:50:22,279
Speaker 1: that transition less painful. And the framework of time that

925
00:50:22,280 --> 00:50:26,160
Speaker 1: they're talking about here is a decade or even decades

926
00:50:26,200 --> 00:50:28,840
Speaker 1: in in reality, because they said it's not going to

927
00:50:28,880 --> 00:50:31,799
Speaker 1: happen overnight like we've been saying. And the other they're

928
00:50:31,800 --> 00:50:33,279
Speaker 1: The quick thing that I want to mention is that,

929
00:50:33,840 --> 00:50:35,840
Speaker 1: as you said, most people haven't really read the draft

930
00:50:35,920 --> 00:50:39,040
Speaker 1: or what it read it really entails, but a lot

931
00:50:39,040 --> 00:50:41,240
Speaker 1: of people right away. Will say, oh, well, my toaster

932
00:50:41,320 --> 00:50:43,799
Speaker 1: can make make toast on its own, and my refrigerator

933
00:50:43,800 --> 00:50:45,200
Speaker 1: knows when I need to stock it because it's a

934
00:50:45,239 --> 00:50:47,960
Speaker 1: smart refrigerator. You're gonna charge my smartphone like that's a

935
00:50:48,040 --> 00:50:50,239
Speaker 1: person as well? Are you gonna you know, tax me

936
00:50:50,320 --> 00:50:53,520
Speaker 1: for uh you know all the devices, my my thermostat

937
00:50:53,800 --> 00:50:56,120
Speaker 1: that knows when I'm coming home and makes it warmer. Uh,

938
00:50:56,160 --> 00:50:58,880
Speaker 1: you know, where does this end? What's the what's the ends?

939
00:50:59,000 --> 00:51:01,600
Speaker 1: Like who's going to be tax and why? Yeah, it's

940
00:51:01,719 --> 00:51:05,399
Speaker 1: it's interesting. They actually in the report mentioned that there

941
00:51:05,440 --> 00:51:09,080
Speaker 1: needs to be a new classification either either the whole

942
00:51:09,080 --> 00:51:13,960
Speaker 1: electronic personhood thing. They said that, uh, once we see

943
00:51:14,040 --> 00:51:18,360
Speaker 1: robots received reach a certain level of sophistication, they have

944
00:51:18,440 --> 00:51:21,560
Speaker 1: gone beyond what we would typically refer to as an

945
00:51:21,600 --> 00:51:24,880
Speaker 1: object like a toaster. They say, yeah, a toaster is

946
00:51:24,880 --> 00:51:28,160
Speaker 1: an object. You're not gonna treat a toaster like it's

947
00:51:28,160 --> 00:51:32,120
Speaker 1: a sentient creature or even even a robot with advanced

948
00:51:32,480 --> 00:51:36,480
Speaker 1: uh you know, artificial intelligence doesn't necessarily mean it's sentient.

949
00:51:36,640 --> 00:51:39,200
Speaker 1: And they aren't even saying we should treat them as sentient.

950
00:51:39,239 --> 00:51:41,279
Speaker 1: They just say we might need to come up with

951
00:51:41,320 --> 00:51:45,480
Speaker 1: a new classification, something that's not human or animal, but

952
00:51:45,600 --> 00:51:50,239
Speaker 1: something with a legal classification to deal with robotics in

953
00:51:50,360 --> 00:51:55,200
Speaker 1: a manner that is consistent with just legal proceedings, because

954
00:51:55,200 --> 00:51:57,719
Speaker 1: there's not anything yet well, and one of those parameters

955
00:51:57,760 --> 00:52:01,040
Speaker 1: would have to be something that directly takes the place

956
00:52:01,080 --> 00:52:03,719
Speaker 1: of a human on an assembly line or in a

957
00:52:03,760 --> 00:52:06,000
Speaker 1: fast food restaurant or you know, wherever it would be.

958
00:52:06,520 --> 00:52:08,640
Speaker 1: It's not it's not something that it's not like a

959
00:52:08,640 --> 00:52:11,319
Speaker 1: device that you use would not be considered like you're

960
00:52:11,360 --> 00:52:13,560
Speaker 1: taking a job away from somebody. You don't have somebody

961
00:52:13,640 --> 00:52:15,839
Speaker 1: your house that makes toast for you, and and now

962
00:52:15,840 --> 00:52:18,919
Speaker 1: the toaster is automatic and it's taking away the toaster's job.

963
00:52:19,440 --> 00:52:21,840
Speaker 1: It's not that way. It's it's it's a device that

964
00:52:21,920 --> 00:52:24,560
Speaker 1: you normally would have to operate manually, but now it's

965
00:52:24,600 --> 00:52:26,319
Speaker 1: just a smart device and it does that for you.

966
00:52:26,360 --> 00:52:28,759
Speaker 1: It's just a it's a handy thing. But it's not

967
00:52:28,840 --> 00:52:32,400
Speaker 1: it's not taking away employment from somebody. Yeah, it's uh,

968
00:52:32,480 --> 00:52:38,759
Speaker 1: I mean, I think that particular idea is one that uh,

969
00:52:39,040 --> 00:52:42,880
Speaker 1: some technologists have really been bandying about as a possibility

970
00:52:43,080 --> 00:52:46,880
Speaker 1: like a decade or two decades out. You know, something

971
00:52:46,880 --> 00:52:49,960
Speaker 1: where we actually see more and more automation and artificial

972
00:52:49,960 --> 00:52:52,560
Speaker 1: intelligence taking the place of jobs and fewer and fewer

973
00:52:52,600 --> 00:52:56,160
Speaker 1: opportunities for humans. It's not like it's something that is

974
00:52:56,880 --> 00:53:01,560
Speaker 1: um imperative right now. But again, I think that the

975
00:53:01,640 --> 00:53:04,000
Speaker 1: people who drafted the report are doing it in a

976
00:53:04,040 --> 00:53:07,160
Speaker 1: way that is fairly responsible in the idea that there

977
00:53:07,160 --> 00:53:09,560
Speaker 1: they are looking ahead, like they actually talk about how

978
00:53:10,400 --> 00:53:13,240
Speaker 1: there they need to look ahead ten to fifteen years

979
00:53:13,840 --> 00:53:17,320
Speaker 1: and and anticipate what skills are going to be needed

980
00:53:17,320 --> 00:53:19,560
Speaker 1: in the job, which is really hard to do. Oh

981
00:53:19,600 --> 00:53:21,719
Speaker 1: my gosh, I was just gonna say this is impossible.

982
00:53:22,160 --> 00:53:24,719
Speaker 1: But because look back fifteen years ago and see where

983
00:53:24,760 --> 00:53:26,839
Speaker 1: we were, it was entirely I mean not not maybe

984
00:53:26,880 --> 00:53:29,080
Speaker 1: fifteen years ago. It looked back thirty years ago, it's

985
00:53:29,200 --> 00:53:31,560
Speaker 1: entirely humans doing all that stuff. And it wasn't and

986
00:53:31,560 --> 00:53:34,360
Speaker 1: there were there may be a few simple, simple robots

987
00:53:34,360 --> 00:53:36,719
Speaker 1: doing things. But look at an automation or you know,

988
00:53:36,960 --> 00:53:40,080
Speaker 1: an automated assembly line now in an auto plant. It's

989
00:53:40,160 --> 00:53:42,920
Speaker 1: unbelievable what they're capable of and the number of robots

990
00:53:43,000 --> 00:53:47,160
Speaker 1: versus the number of humans doing that. But I one

991
00:53:47,200 --> 00:53:48,880
Speaker 1: thing I gotta get out of here is this is

992
00:53:48,920 --> 00:53:52,319
Speaker 1: this This opens up so many boxes of worms in

993
00:53:52,360 --> 00:53:55,560
Speaker 1: that we're not talking about many other things here. This

994
00:53:55,600 --> 00:53:58,960
Speaker 1: is strictly this issue. But uh, there's gonna be the

995
00:53:59,000 --> 00:54:01,120
Speaker 1: whole you know, what are we paying these people to

996
00:54:01,120 --> 00:54:03,080
Speaker 1: do the jobs versus what we pay the robots to

997
00:54:03,080 --> 00:54:05,040
Speaker 1: do the job, which is not really paying them. It's

998
00:54:05,160 --> 00:54:07,920
Speaker 1: it's more like paying for the robot to do the

999
00:54:08,000 --> 00:54:11,160
Speaker 1: job and programming and then maintaining it. And then what

1000
00:54:11,200 --> 00:54:12,920
Speaker 1: does it due to the price of the product? And

1001
00:54:12,960 --> 00:54:14,640
Speaker 1: why is that in there in the first place, Because

1002
00:54:14,640 --> 00:54:16,200
Speaker 1: they want to lower the price of the product by

1003
00:54:16,200 --> 00:54:18,399
Speaker 1: not having that human that they're gonna have to pay

1004
00:54:18,480 --> 00:54:22,880
Speaker 1: Social Security to for thirty years after retirement. The robots

1005
00:54:22,880 --> 00:54:26,160
Speaker 1: don't do that. So there's there's so many layers of

1006
00:54:26,160 --> 00:54:27,560
Speaker 1: this that we can argue back and forth, and it

1007
00:54:27,600 --> 00:54:30,400
Speaker 1: gets very political, it really does. Yeah, I I imagine

1008
00:54:30,440 --> 00:54:32,680
Speaker 1: at least that some of the arguments they've made for

1009
00:54:32,800 --> 00:54:35,360
Speaker 1: things like the compensation fund for the robot, that's really

1010
00:54:35,760 --> 00:54:40,400
Speaker 1: more of a break glass in case robot goes berserk fund.

1011
00:54:40,960 --> 00:54:43,080
Speaker 1: I mean that's really what it's meant for. The compensation

1012
00:54:43,120 --> 00:54:45,800
Speaker 1: fund is really meant for a Hey, if your robot

1013
00:54:45,800 --> 00:54:49,040
Speaker 1: happens to go biserk one day and rampage down the

1014
00:54:49,040 --> 00:54:52,520
Speaker 1: street and slap kids around. You're gonna be paying some money. Well,

1015
00:54:52,560 --> 00:54:55,080
Speaker 1: this is this is the safety fund for that. What if?

1016
00:54:55,440 --> 00:54:59,080
Speaker 1: So here's here's maybe imagine this scenario. Let's say robot

1017
00:54:59,120 --> 00:55:01,400
Speaker 1: that's supposed to in stall the front window, you know,

1018
00:55:01,400 --> 00:55:03,600
Speaker 1: the glass in the vehicle. What if it just starts

1019
00:55:03,600 --> 00:55:06,320
Speaker 1: flinging glass all over the over the factory and and

1020
00:55:06,440 --> 00:55:09,560
Speaker 1: does injure human or it does, you know, slice through

1021
00:55:09,560 --> 00:55:13,719
Speaker 1: several lines of hydraulic lines for the other robots. How

1022
00:55:13,719 --> 00:55:15,439
Speaker 1: do you come for that damage? That's well, let's see

1023
00:55:15,440 --> 00:55:17,600
Speaker 1: and and it's one bad apple. One of the things

1024
00:55:17,600 --> 00:55:20,239
Speaker 1: they mentioned was that so the conversation fun, like they said,

1025
00:55:20,320 --> 00:55:24,080
Speaker 1: was a possible approach. The other thing they talked about

1026
00:55:24,080 --> 00:55:27,160
Speaker 1: was obligatory insurance, similar to what you would have with

1027
00:55:27,160 --> 00:55:29,759
Speaker 1: car insurance, only in this case it would be the

1028
00:55:29,760 --> 00:55:33,240
Speaker 1: company's producing the robots that would be responsible for paying

1029
00:55:33,280 --> 00:55:36,440
Speaker 1: that insurance, not the people who purchase or own the robot.

1030
00:55:36,440 --> 00:55:38,279
Speaker 1: All right, I know that was a ridiculous example, but

1031
00:55:38,320 --> 00:55:43,880
Speaker 1: it's perfectly it's it's perfectly a reasonable example. It goes berserk. Yeah,

1032
00:55:44,040 --> 00:55:46,839
Speaker 1: you know what happens. They're saying, like, you know, as

1033
00:55:46,880 --> 00:55:50,279
Speaker 1: we get to a point where robots are in are

1034
00:55:50,360 --> 00:55:54,239
Speaker 1: able to act more autonomously within an environment, they're going

1035
00:55:54,280 --> 00:55:57,440
Speaker 1: to encounter variables that you could not This goes back

1036
00:55:57,480 --> 00:56:00,719
Speaker 1: to Tesla Autopilot. They're going to encounter variables that you

1037
00:56:00,800 --> 00:56:05,920
Speaker 1: would not have expected when you were first programming office politics. Yeah,

1038
00:56:06,080 --> 00:56:10,319
Speaker 1: you know, maybe maybe something uh simple, maybe it's something

1039
00:56:10,360 --> 00:56:14,200
Speaker 1: that is that hardly ever happens, but happens at this

1040
00:56:14,280 --> 00:56:18,480
Speaker 1: one point, And how does the robot behave in that situation?

1041
00:56:18,600 --> 00:56:21,319
Speaker 1: And and there's their argument is that they're going to

1042
00:56:21,360 --> 00:56:25,280
Speaker 1: be cases where it is truly unpredictable that you cannot,

1043
00:56:25,480 --> 00:56:29,840
Speaker 1: just from its basic programming, necessarily anticipate what the robot's

1044
00:56:29,880 --> 00:56:32,680
Speaker 1: going to do. Like say, like it's after work and

1045
00:56:32,800 --> 00:56:35,439
Speaker 1: one robot hits on the other robot's girlfriend at the bar,

1046
00:56:36,320 --> 00:56:38,360
Speaker 1: and then Monday, there's a lot of tension between the

1047
00:56:38,400 --> 00:56:39,719
Speaker 1: two of them. Yet at work it's going to be

1048
00:56:39,719 --> 00:56:42,080
Speaker 1: a difficult situation where they are standing around the w

1049
00:56:42,200 --> 00:56:45,319
Speaker 1: D forty cooler. That's something you just can't account for

1050
00:56:45,480 --> 00:56:48,720
Speaker 1: right now. Well, I mean, like like specifically, the the

1051
00:56:48,760 --> 00:56:51,080
Speaker 1: passage that I keep kind of referring to here, it

1052
00:56:51,160 --> 00:56:55,400
Speaker 1: says considers that in principle, once the ultimately responsible parties

1053
00:56:55,440 --> 00:56:59,000
Speaker 1: have been identified, their liability would be proportionate to the

1054
00:56:59,000 --> 00:57:01,920
Speaker 1: actual level of structions given to the robot and of

1055
00:57:01,960 --> 00:57:05,840
Speaker 1: its autonomy, so that the greater robots learning capability or

1056
00:57:05,840 --> 00:57:10,319
Speaker 1: autonomy is, the lower other parties responsibility should be, and

1057
00:57:10,360 --> 00:57:14,760
Speaker 1: the longer robots education has lasted, the greater the responsibility

1058
00:57:14,800 --> 00:57:17,800
Speaker 1: of its teacher should be. All right, so this should

1059
00:57:17,840 --> 00:57:20,800
Speaker 1: have a lot of robot programmers shaking in their boots

1060
00:57:20,880 --> 00:57:24,120
Speaker 1: right now, because that really ups their responsibility for this

1061
00:57:24,320 --> 00:57:27,480
Speaker 1: this machine, which is already great, but that will extend

1062
00:57:28,240 --> 00:57:31,480
Speaker 1: into if it learns how to adapt to the job.

1063
00:57:31,680 --> 00:57:35,240
Speaker 1: And then but then then it's the robot's fault as

1064
00:57:35,280 --> 00:57:36,680
Speaker 1: long as there as long as you can make the

1065
00:57:36,760 --> 00:57:40,360
Speaker 1: robot autonomous enough, then you're like, I'm free. This is

1066
00:57:40,400 --> 00:57:43,080
Speaker 1: like having a kid. I'm responsible for my kid up

1067
00:57:43,080 --> 00:57:44,760
Speaker 1: to a certain age, and then after that it's the

1068
00:57:44,840 --> 00:57:47,600
Speaker 1: kid's fault. I guess. So I mean it's like, well,

1069
00:57:47,640 --> 00:57:50,120
Speaker 1: when when the robot turns eighteen, maybe that's that's when.

1070
00:57:50,240 --> 00:57:51,920
Speaker 1: But that's the idea, that's the basic idea of the

1071
00:57:51,960 --> 00:57:54,160
Speaker 1: idea is that if if you program a robot so

1072
00:57:54,200 --> 00:57:59,320
Speaker 1: that it has very little autonomy, then you would argue,

1073
00:57:59,400 --> 00:58:02,600
Speaker 1: logically speaking, the person liable for any damage to the

1074
00:58:02,680 --> 00:58:04,720
Speaker 1: robot makes is the person who programmed it or built

1075
00:58:04,720 --> 00:58:07,800
Speaker 1: it or whatever, because the robot can only follow the

1076
00:58:07,840 --> 00:58:10,480
Speaker 1: instructions that were given to it. If you have a

1077
00:58:10,560 --> 00:58:13,959
Speaker 1: robot that has more autonomy and more ability to extrapolate

1078
00:58:14,000 --> 00:58:17,200
Speaker 1: from its environment, then it goes beyond the basics that

1079
00:58:17,320 --> 00:58:20,240
Speaker 1: you gave the that you equipped the robot with, and

1080
00:58:20,280 --> 00:58:22,720
Speaker 1: so it becomes harder to say, oh, it's your fault

1081
00:58:22,720 --> 00:58:26,000
Speaker 1: because your robot did this thing. So it's kind of

1082
00:58:26,000 --> 00:58:28,120
Speaker 1: like having a kid and a kid growing up and

1083
00:58:28,160 --> 00:58:32,240
Speaker 1: you're saying, you know, why can't you control your kid, Like, well,

1084
00:58:32,280 --> 00:58:34,760
Speaker 1: my kid is becoming a person, and I mean I can.

1085
00:58:34,880 --> 00:58:37,919
Speaker 1: I'm doing the best I can and educating and and

1086
00:58:38,000 --> 00:58:40,760
Speaker 1: giving discipline and giving guidance to my kid. But my

1087
00:58:40,840 --> 00:58:43,240
Speaker 1: kid is a person. So occasionally my kid chooses to

1088
00:58:43,240 --> 00:58:45,480
Speaker 1: do something that you know, we have to have a

1089
00:58:45,480 --> 00:58:48,160
Speaker 1: discussion and say, listen, this was a bad choice you made.

1090
00:58:48,440 --> 00:58:50,560
Speaker 1: Here's why it was. That kind of all part of

1091
00:58:50,600 --> 00:58:53,840
Speaker 1: free will, it is. Yeah, So it's an interesting argument

1092
00:58:53,840 --> 00:58:55,360
Speaker 1: though that it's like it's like a child that you

1093
00:58:55,400 --> 00:58:58,080
Speaker 1: have to maintain control over for a certain amount of time.

1094
00:58:58,560 --> 00:59:01,880
Speaker 1: And then there's a point where you say this is

1095
00:59:01,920 --> 00:59:04,160
Speaker 1: operating on its own at this point, responsible for its

1096
00:59:04,160 --> 00:59:07,240
Speaker 1: own actions. And and again the report is very clear

1097
00:59:07,400 --> 00:59:11,320
Speaker 1: to say that this is stuff that we're talking about

1098
00:59:11,320 --> 00:59:13,680
Speaker 1: that decades out. It's not like it's not like this

1099
00:59:13,760 --> 00:59:16,000
Speaker 1: is It's not like we're gonna wake up tomorrow and

1100
00:59:16,840 --> 00:59:19,640
Speaker 1: our our little remote controlled robot is suddenly gonna be

1101
00:59:19,800 --> 00:59:22,800
Speaker 1: no back talking us, no no on the lions right now.

1102
00:59:22,840 --> 00:59:24,800
Speaker 1: It's like you pick up part A and put it

1103
00:59:24,800 --> 00:59:27,560
Speaker 1: in slot B and then do it again and again

1104
00:59:27,640 --> 00:59:29,920
Speaker 1: and again for eighteen hours a day. And and the

1105
00:59:29,960 --> 00:59:32,600
Speaker 1: report is essentially saying like we just want to make

1106
00:59:32,680 --> 00:59:38,960
Speaker 1: sure that we're not unprepared when when technologology advances beyond

1107
00:59:39,200 --> 00:59:42,240
Speaker 1: the stage where at now I mean, Scott, we've seen

1108
00:59:42,360 --> 00:59:45,160
Speaker 1: in the past, like with other types of technology, how

1109
00:59:45,760 --> 00:59:50,200
Speaker 1: technological development far outpaces the law, and then we have

1110
00:59:50,320 --> 00:59:53,360
Speaker 1: to figure out, okay, wait, what does this actually mean

1111
00:59:53,480 --> 00:59:56,800
Speaker 1: legally speaking? What what this draft is trying to do

1112
00:59:56,960 --> 00:59:58,600
Speaker 1: is get ahead of that and just to say like,

1113
00:59:58,680 --> 01:00:02,360
Speaker 1: let's set up the legal frame now, so that we

1114
01:00:02,560 --> 01:00:06,280
Speaker 1: aren't caught in that situation when it ultimately happens. Well,

1115
01:00:06,320 --> 01:00:08,600
Speaker 1: but it does. It does lead to some jokes. Yeah,

1116
01:00:08,640 --> 01:00:10,760
Speaker 1: and what gets everybody's dander up is that you know,

1117
01:00:10,760 --> 01:00:13,360
Speaker 1: they're gonna they're they're calling them electronic persons, and that

1118
01:00:13,520 --> 01:00:17,000
Speaker 1: right away is is kind of a I don't know,

1119
01:00:17,040 --> 01:00:19,439
Speaker 1: it's a is it not. It's not like bully move,

1120
01:00:19,520 --> 01:00:22,800
Speaker 1: but it's more like, um, this is definitely something that's

1121
01:00:22,840 --> 01:00:25,400
Speaker 1: replacing you as as a person in the or at

1122
01:00:25,440 --> 01:00:29,960
Speaker 1: least joining you. Yeah, at the minimum, let's say, is

1123
01:00:30,000 --> 01:00:32,880
Speaker 1: joining you. And then the other thing is that I

1124
01:00:32,920 --> 01:00:34,800
Speaker 1: think a lot of people feel this way too, is

1125
01:00:34,840 --> 01:00:37,280
Speaker 1: that wait a minute, you're gonna tax them as if

1126
01:00:37,280 --> 01:00:40,800
Speaker 1: it's a human. Isn't that The reason why they may

1127
01:00:40,800 --> 01:00:44,000
Speaker 1: be replaced that human with a machine is that they

1128
01:00:44,000 --> 01:00:47,560
Speaker 1: didn't have to be responsible for it after that point,

1129
01:00:47,680 --> 01:00:49,960
Speaker 1: like once it retires, because these robots don't retire, they

1130
01:00:49,960 --> 01:00:52,880
Speaker 1: don't die, they have you know, they have mechanical issues

1131
01:00:52,920 --> 01:00:55,560
Speaker 1: in there, and they are I guess retired in a way,

1132
01:00:55,560 --> 01:00:58,120
Speaker 1: and that they're replaced by another one. But you don't

1133
01:00:58,120 --> 01:01:00,360
Speaker 1: go on, you don't continue paying for it with a

1134
01:01:00,480 --> 01:01:03,120
Speaker 1: pension for the rest of the robots, you know, air

1135
01:01:03,200 --> 01:01:07,960
Speaker 1: quote life, you know, um, it's it's it's strange that

1136
01:01:08,200 --> 01:01:11,280
Speaker 1: people people want to extrapolate this too, like like it's

1137
01:01:11,280 --> 01:01:13,640
Speaker 1: a human and that you'll have to care for it

1138
01:01:13,960 --> 01:01:17,000
Speaker 1: for the rest of its its mechanical life as if

1139
01:01:17,040 --> 01:01:18,360
Speaker 1: it's a human. But that's not the way it is.

1140
01:01:18,400 --> 01:01:20,520
Speaker 1: Really No, I think it's different. I think really what

1141
01:01:20,600 --> 01:01:23,360
Speaker 1: they're what they were trying to get at, is that

1142
01:01:23,400 --> 01:01:26,320
Speaker 1: there are certain systems we have in place as human

1143
01:01:26,360 --> 01:01:30,720
Speaker 1: beings that depend entirely or at least in large part

1144
01:01:30,800 --> 01:01:34,520
Speaker 1: on employment taxes, and that if we, in fact do

1145
01:01:34,680 --> 01:01:39,800
Speaker 1: reach a future which is still not not necessarily the

1146
01:01:39,800 --> 01:01:41,560
Speaker 1: the thing that's going to happen, but if we reach

1147
01:01:41,600 --> 01:01:46,200
Speaker 1: a future where automation really has taken over to a

1148
01:01:46,280 --> 01:01:49,680
Speaker 1: great extent, Uh, what do we do to protect those

1149
01:01:49,680 --> 01:01:52,560
Speaker 1: systems for the people who are still dependent upon them

1150
01:01:52,680 --> 01:01:56,600
Speaker 1: until we reach a point where those systems are moot

1151
01:01:56,600 --> 01:01:58,480
Speaker 1: because we've moved on to something else. In fact, one

1152
01:01:58,480 --> 01:02:00,840
Speaker 1: of the other things they actually mentioned is that the

1153
01:02:00,840 --> 01:02:04,640
Speaker 1: Member States could all start to consider the possibility of

1154
01:02:04,680 --> 01:02:08,200
Speaker 1: a general basic income, which you know, we've got a

1155
01:02:08,280 --> 01:02:10,520
Speaker 1: couple of countries out there in Europe that are are

1156
01:02:10,600 --> 01:02:14,560
Speaker 1: experimenting with this to some degree. Uh. I did an

1157
01:02:14,600 --> 01:02:17,640
Speaker 1: episode of Forward Thinking's audio podcast where I talked about this,

1158
01:02:17,720 --> 01:02:21,320
Speaker 1: and I said, I would use that as a way

1159
01:02:21,320 --> 01:02:24,320
Speaker 1: of just seeing what happens. And then, you know, do

1160
01:02:24,440 --> 01:02:26,960
Speaker 1: you say, all right, it works, let's try it, or

1161
01:02:27,000 --> 01:02:29,360
Speaker 1: do you say, oh boy, that didn't work at all,

1162
01:02:29,440 --> 01:02:31,760
Speaker 1: let's not do that. I think we didn't implement it everywhere,

1163
01:02:31,800 --> 01:02:34,360
Speaker 1: and I think I think automo manufacturers are gonna hate this.

1164
01:02:34,640 --> 01:02:37,120
Speaker 1: I think it's gonna be bad for their business. I

1165
01:02:37,160 --> 01:02:38,920
Speaker 1: really do. They're they're not gonna like this at all,

1166
01:02:39,120 --> 01:02:42,920
Speaker 1: because that was the benefit to them of having something

1167
01:02:42,920 --> 01:02:45,160
Speaker 1: that robot. It doesn't it doesn't have the complaints, it

1168
01:02:45,200 --> 01:02:47,720
Speaker 1: doesn't have the you know, the same concerns. Right, you

1169
01:02:47,760 --> 01:02:49,800
Speaker 1: have the you have the upfront cost, and you have

1170
01:02:49,840 --> 01:02:52,320
Speaker 1: the maintenance cost of the robots, but you don't have

1171
01:02:52,400 --> 01:02:55,760
Speaker 1: these ongoing you don't have a salary, you don't have benefits,

1172
01:02:55,760 --> 01:02:59,840
Speaker 1: you don't have these other taxes and other elements that

1173
01:02:59,840 --> 01:03:01,800
Speaker 1: you would have to have if it were a human workforce.

1174
01:03:01,960 --> 01:03:04,520
Speaker 1: And if you start adding all these things that they

1175
01:03:04,520 --> 01:03:06,240
Speaker 1: have to pay on the on the end of the

1176
01:03:06,280 --> 01:03:08,600
Speaker 1: owner of the robot, you know, the founder of the

1177
01:03:08,600 --> 01:03:10,960
Speaker 1: company or the the owner of the company, whoever that

1178
01:03:11,040 --> 01:03:13,000
Speaker 1: may be. It maybe a group of people. If they

1179
01:03:13,000 --> 01:03:15,400
Speaker 1: have to pay more for that, you better believe that

1180
01:03:15,400 --> 01:03:17,000
Speaker 1: that price is going to be passed on to the

1181
01:03:17,040 --> 01:03:19,360
Speaker 1: consumer and the price of the price of your automobile

1182
01:03:19,440 --> 01:03:22,800
Speaker 1: is going to go skyrocketing. And it just leads to

1183
01:03:22,880 --> 01:03:25,320
Speaker 1: a lot of trouble. I mean, there's gonna be um,

1184
01:03:25,400 --> 01:03:27,200
Speaker 1: there's gonna be some issues. I think if they if

1185
01:03:27,200 --> 01:03:29,560
Speaker 1: they really do go forward with this, and it's just

1186
01:03:29,600 --> 01:03:31,320
Speaker 1: a draft now it's an I said, it's kind of

1187
01:03:31,320 --> 01:03:33,840
Speaker 1: getting it in people's heads that it may happen. But

1188
01:03:33,920 --> 01:03:36,479
Speaker 1: if they really do go forward with this, it's gonna

1189
01:03:36,520 --> 01:03:38,480
Speaker 1: cause a lot of trouble. I think. The I think

1190
01:03:38,520 --> 01:03:41,200
Speaker 1: the important thing to remember is it's not just that

1191
01:03:41,240 --> 01:03:43,080
Speaker 1: it's a draft report, but also that this is a

1192
01:03:43,120 --> 01:03:46,520
Speaker 1: report that's giving recommendations, and most of those recommendations really

1193
01:03:46,560 --> 01:03:51,840
Speaker 1: boiled down to let's form an actual official European agency

1194
01:03:52,040 --> 01:03:54,880
Speaker 1: that is going to oversee this stuff and staff it

1195
01:03:55,040 --> 01:03:57,520
Speaker 1: with people who are experts in their field, not just

1196
01:03:57,600 --> 01:04:03,840
Speaker 1: technical experts, but regulatory experts, ethical experts, to do to

1197
01:04:03,840 --> 01:04:06,080
Speaker 1: to do a lot of the legwork, to make sure

1198
01:04:06,800 --> 01:04:11,520
Speaker 1: that if any legislative aims are are created, that they

1199
01:04:11,520 --> 01:04:15,320
Speaker 1: are done so with the most information from the most

1200
01:04:15,600 --> 01:04:20,600
Speaker 1: the most diverse perspective possible, rather than you know, this

1201
01:04:20,680 --> 01:04:22,680
Speaker 1: seems like we should try this, let's do this, let's

1202
01:04:22,720 --> 01:04:25,520
Speaker 1: go this way. You be ready, but take your time

1203
01:04:25,560 --> 01:04:28,120
Speaker 1: getting there. And I think that that ultimately is the

1204
01:04:28,120 --> 01:04:30,880
Speaker 1: message of the report. It is a little whimsical in places,

1205
01:04:31,080 --> 01:04:34,400
Speaker 1: the fact that it references fiction. UH you think I

1206
01:04:34,520 --> 01:04:36,680
Speaker 1: love it. I think it's great. It was one of

1207
01:04:36,720 --> 01:04:37,960
Speaker 1: the things that you can word to me and said,

1208
01:04:38,080 --> 01:04:40,840
Speaker 1: can you believe this? You will see to me this

1209
01:04:40,880 --> 01:04:43,120
Speaker 1: is you're excited. But I do a show called forward

1210
01:04:43,120 --> 01:04:46,120
Speaker 1: Thinking you know where, and so I know how hard

1211
01:04:46,200 --> 01:04:48,080
Speaker 1: it is to try and predict the future. I have

1212
01:04:48,200 --> 01:04:52,960
Speaker 1: to do it every week fw Cohen thinking it's a

1213
01:04:52,960 --> 01:04:55,360
Speaker 1: sponsored by Toyota, this show is not, but forward Thinking

1214
01:04:55,440 --> 01:05:00,360
Speaker 1: is anyway the UH doing. Doing the show air, we

1215
01:05:00,400 --> 01:05:05,160
Speaker 1: talked about how it's funny because in those episodes when

1216
01:05:05,160 --> 01:05:08,240
Speaker 1: we talk about these big, big ideas, things like what

1217
01:05:08,400 --> 01:05:13,560
Speaker 1: happens if a UH an artificial intelligence becomes sophisticated enough

1218
01:05:13,600 --> 01:05:18,280
Speaker 1: that it is difficult too to differentiate it from an

1219
01:05:18,280 --> 01:05:23,000
Speaker 1: intelligent creature to the point maybe it has some form

1220
01:05:23,000 --> 01:05:25,640
Speaker 1: of self awareness. Maybe it doesn't, but maybe it seems

1221
01:05:25,720 --> 01:05:29,320
Speaker 1: like it does. Like, that's a difficult question. What happens

1222
01:05:29,440 --> 01:05:33,520
Speaker 1: if automation were to take over more jobs than it

1223
01:05:33,560 --> 01:05:36,280
Speaker 1: could create? What if we were you know, the all

1224
01:05:36,320 --> 01:05:40,800
Speaker 1: these different what if questions We always conclude with, you know,

1225
01:05:40,840 --> 01:05:43,160
Speaker 1: we don't really know, but it really is important that

1226
01:05:43,160 --> 01:05:46,800
Speaker 1: we talk about it and discuss it and start thinking,

1227
01:05:47,080 --> 01:05:49,840
Speaker 1: you know what, how would this actually impact us now,

1228
01:05:50,440 --> 01:05:52,680
Speaker 1: so that we're not caught by surprise what happens later on.

1229
01:05:52,760 --> 01:05:55,760
Speaker 1: And I feel like this draft report is exactly what

1230
01:05:55,840 --> 01:05:58,960
Speaker 1: I keep saying and so wild. Part of it is

1231
01:05:59,000 --> 01:06:02,520
Speaker 1: funny and and you can joke about like your toaster

1232
01:06:02,640 --> 01:06:05,560
Speaker 1: becoming a person, uh, and I would make the same joke.

1233
01:06:06,400 --> 01:06:09,440
Speaker 1: I'm also I also admire that someone has taken the

1234
01:06:09,520 --> 01:06:12,680
Speaker 1: time to do what I keep asking people to do

1235
01:06:12,720 --> 01:06:15,000
Speaker 1: it every episode. I'm like, well, I can't make too

1236
01:06:15,080 --> 01:06:17,080
Speaker 1: much fun of them. I've been this is exactly what

1237
01:06:17,120 --> 01:06:21,880
Speaker 1: I've been calling for for four years. It's become a reality. Yeah.

1238
01:06:21,920 --> 01:06:25,320
Speaker 1: Now I'm just like amused that it happened. Also, I'll

1239
01:06:25,320 --> 01:06:27,240
Speaker 1: go ahead and tell you guys, like this report is

1240
01:06:27,480 --> 01:06:30,240
Speaker 1: very easy to read. Uh is twenty two pages long,

1241
01:06:30,360 --> 01:06:32,400
Speaker 1: and that's after you get past the first couple of

1242
01:06:32,440 --> 01:06:34,920
Speaker 1: pages of it's less than twenty pages because the first

1243
01:06:34,960 --> 01:06:37,240
Speaker 1: couple of pages are like a cover and a table

1244
01:06:37,320 --> 01:06:40,440
Speaker 1: contents and stuff. It's a piec cake. It's it's it's

1245
01:06:40,440 --> 01:06:44,160
Speaker 1: actually you know, I mean, it's written in a little

1246
01:06:44,200 --> 01:06:47,280
Speaker 1: bit of legal language, but not terrible. Like it's nowhere

1247
01:06:47,440 --> 01:06:49,360
Speaker 1: near as dense as some of the stuff I used

1248
01:06:49,360 --> 01:06:51,400
Speaker 1: to have to read when I worked in a law firm.

1249
01:06:51,840 --> 01:06:55,880
Speaker 1: Those were dark times, my friend. Um, but it's it's

1250
01:06:55,920 --> 01:06:58,160
Speaker 1: a pretty easy read. It's a little scattered because you

1251
01:06:58,200 --> 01:07:01,640
Speaker 1: feel like some of the sections are repeated, but it's

1252
01:07:01,640 --> 01:07:05,240
Speaker 1: worth checking out. And it's all available online and PDF form,

1253
01:07:05,280 --> 01:07:07,960
Speaker 1: so you can go and pull down this read it

1254
01:07:08,360 --> 01:07:11,200
Speaker 1: see if you you know, think like, is this is

1255
01:07:11,240 --> 01:07:15,880
Speaker 1: this prudent? Is it? Is it really proactive? Is it reactionary?

1256
01:07:15,960 --> 01:07:18,360
Speaker 1: Is it unnecessary? Should we not worry about this for

1257
01:07:18,360 --> 01:07:22,480
Speaker 1: another ten years? I mean it certainly could spark at

1258
01:07:22,560 --> 01:07:25,600
Speaker 1: least a conversation. We should also keep in mind, nothing

1259
01:07:25,600 --> 01:07:29,520
Speaker 1: in the report is like would become legally binding. It's

1260
01:07:29,560 --> 01:07:32,200
Speaker 1: really just a bunch of suggestions. It's just just kind

1261
01:07:32,200 --> 01:07:36,400
Speaker 1: of a prep work work framework, I guess for them

1262
01:07:36,400 --> 01:07:38,600
Speaker 1: to adhere to later. It's right, it's not even not

1263
01:07:38,680 --> 01:07:41,520
Speaker 1: even so much as like a legal proposal as it is. Hey, guys,

1264
01:07:41,520 --> 01:07:42,720
Speaker 1: I think it'd be a really good idea if we've

1265
01:07:42,720 --> 01:07:44,480
Speaker 1: got some smart people to think about this stuff for

1266
01:07:44,520 --> 01:07:47,360
Speaker 1: a while. That's when it comes down to start with

1267
01:07:47,400 --> 01:07:49,439
Speaker 1: this and see what you come up with. Yeah, yeah,

1268
01:07:49,560 --> 01:07:51,560
Speaker 1: pretty much. And they you know the fact that they say,

1269
01:07:51,600 --> 01:07:55,520
Speaker 1: like this is one possible solution. Uh, there they are

1270
01:07:56,120 --> 01:07:59,920
Speaker 1: implying at least that they don't know all the answers

1271
01:08:00,160 --> 01:08:02,640
Speaker 1: and therefore they're just kind of throwing something out there,

1272
01:08:02,680 --> 01:08:05,040
Speaker 1: and maybe there's a much better way of approaching it

1273
01:08:05,080 --> 01:08:07,000
Speaker 1: than the way that they have suggested. You know. So

1274
01:08:07,040 --> 01:08:08,880
Speaker 1: European Parliament is a lot like us. They have more

1275
01:08:08,960 --> 01:08:11,200
Speaker 1: questions than answers on this one. Yeah, but at least

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01:08:11,200 --> 01:08:14,760
Speaker 1: they're asking to get you know, the top thinkers together

1277
01:08:14,840 --> 01:08:16,880
Speaker 1: to try and answer some of these questions. That's true,

1278
01:08:16,920 --> 01:08:18,800
Speaker 1: just like how stuff works did with us here to

1279
01:08:19,000 --> 01:08:21,639
Speaker 1: That's right. That's that's why we make the big bucks

1280
01:08:22,040 --> 01:08:27,480
Speaker 1: Scott get the top thinkers together in the podcast studio. Okay,

1281
01:08:27,479 --> 01:08:29,960
Speaker 1: that's a little that's too much. We were really only

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01:08:30,040 --> 01:08:34,080
Speaker 1: the only two people here, so it was just there's

1283
01:08:34,080 --> 01:08:37,240
Speaker 1: no other option. Really, that's right, there's an empty room,

1284
01:08:37,479 --> 01:08:39,400
Speaker 1: and I just happened to raise my hand. Yeah. Yeah,

1285
01:08:39,520 --> 01:08:42,200
Speaker 1: it's either going to be you know you are Matt

1286
01:08:42,200 --> 01:08:45,640
Speaker 1: Frederick and man, I can't stand that guy. So I

1287
01:08:45,680 --> 01:08:47,840
Speaker 1: said that just as he was literally passing the window

1288
01:08:47,880 --> 01:08:49,639
Speaker 1: I was looking at that. I love Matt. He'll never

1289
01:08:49,680 --> 01:08:51,559
Speaker 1: he'll never listen to this. I don't have to worry

1290
01:08:51,560 --> 01:08:53,040
Speaker 1: about I'm listening to this, but just in case, I

1291
01:08:53,280 --> 01:08:56,760
Speaker 1: love you, Matt. All right. So guys, first of all,

1292
01:08:56,880 --> 01:08:58,920
Speaker 1: I gotta thank Scott again for joining me on this

1293
01:08:58,960 --> 01:09:03,960
Speaker 1: show and bringing bringing the European Parliament proposal to my

1294
01:09:04,000 --> 01:09:06,839
Speaker 1: attention because it has given me a wealth of material.

1295
01:09:07,040 --> 01:09:09,080
Speaker 1: Thank you for asking me into the studio again. With you,

1296
01:09:09,120 --> 01:09:10,760
Speaker 1: I always have a good time and it's always a

1297
01:09:10,760 --> 01:09:13,960
Speaker 1: fun conversation. It's a good back and forth between us. Yeah, yeah,

1298
01:09:14,080 --> 01:09:17,160
Speaker 1: we we have a blast. There's certain people that I

1299
01:09:17,560 --> 01:09:21,200
Speaker 1: just feel a fun rapport with and I'm happy to say,

1300
01:09:21,760 --> 01:09:24,840
Speaker 1: at no time during this particular conversation did I set

1301
01:09:24,840 --> 01:09:26,800
Speaker 1: out to break your heart. Yeah you didn't. You didn't

1302
01:09:26,840 --> 01:09:30,639
Speaker 1: even he didn't physically strike me either way. Sometimes do it? Sometimes?

1303
01:09:30,680 --> 01:09:33,839
Speaker 1: I mean, you know, it's it's a Friday and I'm tired. Honestly,

1304
01:09:34,080 --> 01:09:35,800
Speaker 1: I don't think I have the energy. Yeah, you know what,

1305
01:09:35,880 --> 01:09:39,000
Speaker 1: usually you do? You too, tried to you try to try,

1306
01:09:39,080 --> 01:09:41,680
Speaker 1: try to sing me with you. Usually I usually withhold

1307
01:09:41,800 --> 01:09:45,599
Speaker 1: one important piece of information and then see see how

1308
01:09:45,680 --> 01:09:48,200
Speaker 1: you react. It normally has something to do with people

1309
01:09:48,240 --> 01:09:51,679
Speaker 1: not driving anymore, but I'm not doing that today. So

1310
01:09:51,920 --> 01:09:55,000
Speaker 1: of course you can go check out car Stuff. Scott

1311
01:09:55,080 --> 01:09:56,920
Speaker 1: is a co host on car Stuff along with Ben

1312
01:09:56,960 --> 01:10:00,519
Speaker 1: Bolan and Uh. He does amazing work here at how

1313
01:10:00,600 --> 01:10:03,920
Speaker 1: stuff Works, so check that out. And guys, if you

1314
01:10:03,920 --> 01:10:07,840
Speaker 1: have any questions or suggestions for future episodes, write me

1315
01:10:07,920 --> 01:10:11,120
Speaker 1: my email addresses tech stuff at how stuff works dot

1316
01:10:11,160 --> 01:10:14,720
Speaker 1: com or drop me a line on Twitter or Facebook.

1317
01:10:14,760 --> 01:10:17,080
Speaker 1: The handle at both of those is text stuff H

1318
01:10:17,400 --> 01:10:20,679
Speaker 1: s W and I will talk to you again. Really

1319
01:10:20,720 --> 01:10:27,879
Speaker 1: see for more on this and thousands of other topics,

1320
01:10:28,120 --> 01:10:39,240
Speaker 1: is a how stuff Works dot com