Aurora is a company working on self-driving technology. Chris Hermsen is a leader there, and the hosts are talking about what he said at a big industry event.
The MIT mobility forum is a conference where people talk about transportation technology. Here, it’s where Aurora’s CEO was interviewed or discussed ideas.
The trolley problem is a famous “what would you do?” ethics puzzle. People use it to talk about what self-driving cars should do in scary, unavoidable situations.
This is about how often the car’s computer checks what’s happening around it. If it updates more times per second, it can respond faster to changes than a person can.
Concept
360
“360” means the system is trying to see all around the car, not just straight ahead. The idea is that the computer can track multiple directions at the same time.
Concept
enabling the next AI revolution
They’re talking about AI changing in a big way—like a new approach that could make AI much more capable. The host connects that idea to Jan LeCun’s talk at Princeton.
They mention Facebook because LeCun worked there and helped run AI work. It’s a clue that his ideas have been tested in a real, large company environment.
AMI (Advanced Machine Intelligence) is described as Jan LeCun’s new AI venture. The name and description suggest a focused effort to pursue AI in a “different way,” implying a distinct research and product strategy versus mainstream approaches.
Concept
worldview, the context
They’re saying that what AI “understands” depends on the situation it’s in—its context. If the assumptions don’t match reality, the AI can give the wrong answer while sounding confident.
Google is referenced in a discussion about reported AI accuracy (e.g., “not 85% correct”). This matters because AI evaluation depends on the dataset, task definition, and error types—so headline accuracy percentages often hide important details.
The New York Times is mentioned in relation to a claim about Google’s accuracy. This is relevant as an example of how AI accuracy is discussed publicly, and how “percent correct” framing can be misleading without defining what’s being measured.
A robo-taxi is a taxi that drives itself. Instead of a human driver, the car handles the driving, and the company still has to prove it’s safe and get permission to operate.
Concept
Lane Herzberg
Lane Herzberg is mentioned in connection with a tragic incident during autonomous vehicle testing. When people talk about it, they’re usually discussing how self-driving companies handle safety and what they learn after serious mistakes.
Driverless trucks are trucks that can drive themselves using sensors and software. Even when they’re “driverless,” companies may still keep a person in the cab at first to watch and take over if needed. The big question is how safe and practical that is, and what the law requires.
An “attendant” in autonomous trucking is a human onboard who monitors the system and can intervene if something goes wrong. The transcript distinguishes this role from a traditional “driver,” which matters legally and operationally. This is tied to whether the attendant is subject to driver-specific regulations.
A “business case” is the argument that something is worth doing because it makes sense financially. Here, they’re saying it’s not enough for the technology to work—you also have to show it can be run safely and profitably. Risk and value are part of that calculation.
Hours of service rules are laws that limit how long truck drivers can work before they must rest. The goal is to prevent fatigue. The discussion is about whether an onboard safety person counts as a driver under those rules, which affects how the operation is run.
JB Hunt is a major U.S. trucking and logistics company often referenced in discussions about freight operations and long-haul trucking. In this segment, it’s used as an example of the kind of employer whose drivers face hours-of-service limits. That context helps listeners connect regulations to real industry operations.
That phrase means staying centered in your lane. The idea is that technology can help the truck not drift out of its lane, so the driver doesn’t have to constantly correct it.
That “triangle” is a warning sign you put out on the road when you’re stopped. It helps other drivers see you from far away so they can slow down and avoid you.
They’re saying the technology can do more of the driving for you, so the driver doesn’t have to stay tense the whole time. If the car handles key safety tasks, the driver can focus less on constant reactions.
“Long haul trucking” refers to transporting freight over long distances, usually on highways, often with strict scheduling and cost-per-mile targets. In AI/automation discussions, long-haul is a key use case because predictable routes and high utilization can make automation economics more compelling.
Uber is the ride-hailing company, but in this conversation it’s also being talked about as if it’s trying to use AI for driving-related goals. The hosts connect that to how the market valued Uber before and after major events.
Concept
1400 mile halls
They’re talking about very long trips—around 1,400 miles. Longer routes mean the truck is working more hours, so improvements in driving efficiency or automation can add up.
Class 8 trucks are the biggest commercial trucks used for long-distance hauling. They’re the kind of vehicles you see moving freight across states, and they’re expensive enough that improving efficiency can matter a lot.
Elaine Herzberg is mentioned as a serious real-world incident tied to self-driving vehicle testing. The hosts bring it up to mark how long it’s been since that event and to highlight how safety concerns shaped the conversation.
“On call 24 hours” means someone is available all day and night if something goes wrong. For self-driving ride services, that can mean a person is ready to help or take over when the system needs it.
Tesla makes cars and also works on self-driving-related technology. In this conversation, Tesla is mentioned as one of the major players in the ride-hailing/self-driving space.
Waymo is Google’s self-driving car company. They run ride services in certain places, and the point here is that they’ve focused on particular cities/conditions instead of trying to serve every kind of trip.
Supply and demand just means there are more people who want rides than there are rides available. The point is that getting the technology right isn’t enough—you also have to have enough service available where and when people need it.
Concept
capitalism
They’re talking about how competition between companies is supposed to benefit customers. But they’re also suggesting that competition can eventually lead to one company dominating.
Data visualization is the practice of turning raw information into charts or interactive views so people can understand patterns. In autonomous driving and smart mobility, visualization helps engineers and researchers inspect sensor data, model outputs, and system behavior.
They’re talking about how many homes don’t have a car for every person. If a household has only one car, the other person has to use other options like walking or getting rides.
A jalopy is basically a very old, run-down car. The point here is that even if a car is “cheap,” if it’s not safe or legal (like a broken tail light), you can end up paying fines and dealing with bigger problems.
A tail light is the red light at the back of your car. If it’s broken, other drivers can’t see you as well, and you can get ticketed until it’s fixed.
LIVE
Welcome back to the smart driving cars podcast. Thank you for spending time with us once again.
I'm Fred Fishkin, along with the faculty chair of autonomous vehicle engineering at Princeton
University, Alan Cornhouser. Hi, Alan. Hey, good afternoon. Good Friday afternoon. My goodness,
it's almost, it must be spring here in Jersey. We had a great day, you know, I guess,
done or something, right? And a weekend ahead where it's not going to be quite as hot, so.
Well, it's better than the snow and whatever. It seemed like just yesterday, whatever.
It almost was, it was freezing last week. That's true too. Starting on top in the latest smart
driving car newsletter, the MIT mobility forum and chat with Aurora CEO Chris Hermsen.
Yeah, so and once again, the MIT forum was really good and Chris was was really forthright.
And it was just, it was another really good one. So, and meanwhile, congratulations on
another good one. And my goodness, just think everybody should take a look at it.
You should take a look at some of the chat as it's going on too. There are some good questions.
And then there was just those, you know, really bad question. I mean, we had to discuss that trolley
problem again. I mean, are you kidding? Whatever. Well, Chris is one of these guys who's really
making things happen and doing it in a responsible way, right? Yeah, I mean, if anybody's ever really
thought about the trolley problem, it says two things are happening simultaneously. Guess what?
When I'm in the car, I can't see two things simultaneously. Because one thing's probably
over here. And one thing's probably over here. And my poor head has to go like this and like this.
So of course, these things as Chris properly mentioned, they look simultaneously
360. Just because they do that, they do it 30 times a second and fast and fast faster than I
could. My cognitive cycles aren't 30 times a second. Cut it out. Oh, I mean,
why don't you look at the value proposition here? And my goodness, you know,
sure, there's risk. There's risk in everything. At least I tell my students there's risk in
everything. Because my goodness, if you know something that has value and no risk,
whoa, what do I want all that? Are you joking? Cut it out, guys. I mean,
whoops, I've gotten wornary. I guess I'm finally getting old. I'm finally beyond 39. I'm still 39.
I've just gotten so wornary. I can't take it anymore. Oh, watch out. This guy might be falling.
From my view, stay home. Stay home. If I stay home, meteorites gonna come through the roof and
kill me. Oh, I should have gone. Taking the risk. Cut it out. All right. So from MIT,
everybody, we have no, the viewers just left. That's good. Now we can have a good time for it.
I think they enjoy that. No, they don't. From MIT, we'll head south to Princeton,
where there was a talk by Jan LeCun right there, enabling the next AI revolution.
He's the co-founder of a pretty unique AI venture now. He was at Facebook running the whole AI
industry. Yeah. Well, you know, pork, I got Buddha from Facebook. I guess he was too good.
So his new venture is called AMI Advanced Machine Intelligence,
and they're trying to do things in a different way, it seems. Well, yeah. It was just so great
having LeCun there. I mean, he was so good and really enjoyed, of course, a little chitchat we had
in the office. And then, of course, Vinner, and what a treat. What a treat, because
this is tough stuff. And of course, I guess we've graded some great context is essentially
everything almost. And unless you have the worldview, the context, and that is at least
your worldview. I mean, we don't even know what the correct worldview is. And I think I put in
even the, then I put it in, or maybe I'll put it in if I may have forgotten. What was the thing in
the New York Times a couple of days ago? Oh, Google is not 85% correct.
Well, I mean, what?
It's only 80, the first 80% of trivial.
Next 20 years, what were the words? Next 15 or not too tough. Next five, who?
And what the heck is correct? And what's the source?
What you training on? Was that correct? Oh, no, we don't need to train and on correct stuff.
Some suggest what? Well, really? You better check if your idea assumption is really correct.
Whatever. Oh, and he was great. He actually, he excused himself a couple of times for using his
French to basically, you know, tell some people that, you know, you're not looking at this in
the right way. But anyway, it was just great. He was, I think he surprised a lot of the, a lot of
the stuff. Yeah. And, you know, it's a shame that I wasn't able to get the, and that John
Montfield wasn't able to make it. But, you know, it's just the problem is, is you get somebody
really good in a crowd and it just, the crowd just overwhelms you. And so no wonder people get ushered
out the back door and don't interact. Because if you interact, I mean, it's, I mean, a lot of students
are really enthusiastic about asking their question. And as they should be. But, but boy,
it's tough. It's tough to keep it separated. But it was, it was really great having you on it,
on campus. And thank you, John. And good luck with your, hey,
sounds good. And speaking of John Hopfield, you highlight a book, Why Machines Learn the
Elegant Math Behind Modern AI by Anil Anandthaswamy. And there's a tie in to John here.
Yeah, you know, it just, it just, I mean, and he wrote it before, before John got,
got his Nobel. So he wasn't just kissing up or whatever.
But really, I mean, you know, John especially, I mean, he, he changed the world. And that's who
some officer tried and
I'll be as successful as we are because of our own faults. But anyway, yeah, it's just,
just really nice knowing people like that. You know, it's just really nice.
Terrific. From the Financial Times, Uber commits $10 billion to Robo Taxi's in strategy shift.
Yeah, I mean, you know, if you, if you go back, you go back to sort of the beginning of Uber,
then I mean, they were going to do, I don't know, at least from my perspective, they were going to
involved in Lane Herzberg
having an end of life occurrence. And I don't know, I haven't dug deeply into it, but you know,
well, there were some things they shouldn't have done. They should have done better. But again,
you know, it's easy. It's trivial on hindsight and foresight. If you take that and you learn that,
I think as Chris pointed out in his talk, they're working very hard, you know, at trying to be
exceedingly careful. And he was also asked the tough question, you have driverless trucks out
there running, is there anybody in the cab? And, and he said, there is an attendant in there,
but they're going to go to a driverless thing soon. But of course, he's going to have an attendant
in there. Because what he's still doing is demonstrating that the technology works. He
isn't out there really doing the business case and executing the business case. He's, he's still
trying to prove to himself, especially, and maybe everybody and also everybody else,
that the risk that's involved in this is a tolerable risk compared to the value proposition
that it will bring. And the value proposition is indeed
removing the driver. Or as, as I've been sort of suggesting lately,
you can actually deliver a substantial value proposition in a specific market
that says, my goodness, you could do it today and capture that value proposition.
If I put in a question that wasn't answered, and maybe hopefully somebody can answer it,
if in fact, the attendant on board is not considered to be a driver,
but is considered to be an overseer or something like that. Or which someone hopefully can argue
enough lawyers that that attendant is not subject to the hours of service regulation for drivers.
Because if you take a class A truck, or you take, you know, the owner operators
that are out there, they commit themselves to the job of moving freight for 24 hours.
Yet the hours of service regulations only say you can move freight, which is really your job,
for only whatever it is, 10 or 11 hours. And you're going to have this break and that break
and do da da da. Which means, you know, in this, in this occupation of being a truck driver,
in the long haul market, you devote, call it 10 or 11 hours to keeping the truck between
two white lines and not dying. But you also have to devote the other 13, 14 hours a day to the man
who's whatever the trucking company, the warners, whatever hunt, JB Hunt,
you don't get to go home. You don't get to go see your, your wife, your, your spouse,
your partner. You don't get to cut the grass. You don't get to do the whatever's that one
does around that. You're stuck. You're stuck either in your cab or in a truck stop,
or along a road. But there isn't even enough parking across the United States for these things
because they must rest. And of course they must rest.
But my goodness, if they're not the ones, if they're not really responsible for keeping that
truck between two white lines and it works. And the only reason you really have men there is
in case you pull over and you have to take your triangle out as, as, as, as some regulations say
and put a little triangle out there so people don't hit it. Because of course that's the best way
for what, isn't that fine? You can do that. You can be on call to do those little things.
And that truck can move 12 hours or 22 hours a day.
And it can be productive 22 hours a day as opposed to 10 or 11. That's double the productivity of
that asset. It's also double the productivity of whatever it's hauling because whatever it's
hauling is really valuable. Otherwise it'd be going by choo-choo.
It's so valuable that it's put into a truck.
And in fact, boy, if you, if you have hauls that are, let's say, 1,000 miles or 1,400 miles,
that you can now hit in one day because you're just in a tendon and you could make a nice living
area in the, in the back as, as, as was pointed out with, with, I guess, Tesla's doing with their
truck. It's a nice place. I mean, not as nice as our homes. And you could relax and you couldn't be
there. Where's the two white lines and how the, oh my goodness, how do I not crash into something?
And the technology does that for you. The value proposition that that entity,
the driver, which of course Chris pointed out is true. I mean, what is it that, I think,
depending on which metric you take, you know, truck driving is more dangerous than being a coal
miner. I mean, all of a sudden, you're not that dangerous. You can relax. You don't have to be
stressed every second because technology does it. You don't have, your head doesn't have to be on a,
on, on a swivel in, in, in, in, in case a trolley problem shows up because you got to see it at the
same time. You probably don't have to take as many medications and so on and so forth that you
put in your body to be able to do that. And you double the productivity of the truck.
And in some sense, in terms of the, the thing that's back there, you double its productivity too,
because it's moving instead of sitting. And the stump that's being moved is being moved for a
reason. Got to be moved so that it can go from production to consumption and not be sitting
around because that costs money. And somehow, I don't know, nobody's picking up on that fundamental.
So it seems to me, Chris could be out there, you know, moving freight with his stuff,
with a driver, being twice as productive, maybe sharing some of that productivity
to let the, the attendant make a few more bucks. And maybe the attendant can also
do something for the, for the going concern. I don't know. Do some customer service that AI
can't possibly do or whatever you have to communicate. You have the communications,
all the sun.
If you operated, you're beating learner. You're, you're being beating all the existing
long haul trucking companies that have conventional technology. My goodness, Chris, go do it.
Don't let somebody else do it. You'll become even richer. I don't, I don't know what and,
and, and deservedly so. Because guess what? You'll do it cheaper, better because you'll be
that much more productive than what the thing is out there and still have the person in there.
Wow. I don't know. Whatever. But they didn't, they didn't take my question and I didn't get to
give the answer. So I gave myself the question and I bored you with the answer. Never mind.
We started out talking about Uber is committing. Is that how we got there? And we ended up talking
about class eight trucks on, you know, in the marketplace of, you know, 1400 mile halls or
I was just thinking it's, it's just hard to believe that it's been eight years now since that
Elaine Herzberg tragedy eight years already. So yeah, that was a big one. I mean, you know,
I tell the students, I don't know if I'm right. I think goldmine, whatever, valued Uber at 120
before that and they went public at 60. And I had to say nothing really happened between their,
at least what they were putting out there is the valuation of 120 and
that one hurt. It hurt Uber. It hurt. It hurt. And so everybody that's in this is very careful.
And of course, Chris has to be very careful. But Mike, the beauty of his technology right now,
as long as they don't say the attendant has to do these goofy hours of service things,
the attendant that's with the truck 24 hours today, but is only allowed to move it for 10 or 11
or whatever the number is. If he or she's not involved in there and just need to do the oversight,
well, you know, I think you can be on call. A lot of people are on call. Doctors are on call 24.
I guess they don't make house calls anymore. I don't know. CEOs are on call 24 hours.
Well, they make this a million dollars. So they should be, I don't know, whatever.
Moving on. And Uber is involved in this one too. Axios reports and Mobility's new big three
are Tesla, Waymo and Uber. I guess you can read my comment, but I threw in there a few
problem is none of those guys are dealing with people who really need a ride.
And if there's a way, I think there's a heck of a market for that one.
Okay. And I guess handy rides will do that one because that's our mission statement.
So that pretty soon, at some point, maybe might go live to see a Fred probably not Axios forward.
Lean forward. Let everybody see your hat. No, no, I don't know. That's merch.
No, I mean, handy. We're gonna do it. Hell, nobody else seems to want to do it.
Although we may just, you know, I may say, Hey, Chris, you know, really, I'll do the truck thing.
If you don't want to do it. I don't know. Just there are distributions of markets.
Things are distributions. Nothing is good for everything.
But there are some good things for some markets.
And once just at the beginning, all of this stuff, one doesn't really even need a really
big market. I mean, for instance, the demand is so much greater than the supply.
Geez, guys, get some supply out there.
For instance, Waymo has wrapped up a New York City wrong market, right?
They're not I know, I'm not you're not going to go me into saying something nice about
Waymo. Okay, not going to go there.
I don't I don't mean not nice. It's just that people have so many options for getting from
here to there. Well, but you can go in there and compete in places. I mean, that's that's
that's capitalism. We're supposed to have lots of competitors in every market.
Although I also tell the students the objective of capitalism is to create a monopoly for yourself.
Never mind.
I guess I mean, I mean, if you look at I mean, really, I guess that's maybe maybe they
maybe that's why our or fees. I guess for in case we have any listeners of my former students,
we're not triple digit and class and size 102 class of 2029 signed up to be or fees.
Oh my goodness, we have like two faculty. I mean,
come on, University, give us some resources.
Alan, you highlight a piece from how to AI called the new Claude charts.
Yeah, I like that because because I guess I guess I spent a lot of time last century and
part of this century with with visualization of data and interactive visualization of data.
And I guess one of the things that I used to promote or used to preach was
was that was really interactive computer graphics because what it does is allows you to
learn and then probe and then search and then find and then question and critique and then
whatever. And this is this is the opportunity to to to learn through visualization of data.
And I guess, I don't know, I guess that's on that side of the brain guy as opposed to the other
side of the brain guy. And actually everyone we've started, I think every one of my classes this
spring, we like us because I don't I mean, I mentioned it here, I changed the name of our
course, my course this spring, the day before the first class, because I was finally finalizing my
syllabus. So I changed the name, changed the name. What was it before e-commerce and the
analytics of competitive engagement, I think is the title that I had, the competitive engagement
list, you know, sports analytics, but you can't just call it sports analytics, you got to give
it a you got to give it an Ivy Lake name, you know, competitive engagements or something like that.
And of course, inspired by that by Dr. Otis Jennings, one of my former students who
taught that course, maybe maybe in the first one to teach that kind of course at
that Duke University graduate school, I don't know, 10 years ago or whatever. And
but that's the name change and I decided to change the name to was instead of e-commerce,
blah, blah, blah, blah, blah, I changed it to AI commerce. Well,
there's some seriousness in that also, because if you look at going from brick and mortar to e-commerce,
you know, it's really having the computer as the interface here with respect to transactions. And
I have something and I don't want it. But I want something else. And you've read really want us,
I would not know why now you would really want us. And you don't care about the thing that I want.
I can't see. I mean, you must be really stupid. And so, all of a sudden, there's a transaction
between us. Because I get rid of what I don't want. And I get what I want. And you get rid of what
you don't want or what you want. Now, if we thought the same, that situation can occur.
So it's a good thing we think differently. You got it. I mean, otherwise, what think the same?
Oh, I'm gonna, we're both gonna want this. And we're not what the other stuff.
As you've said before, that would make for a pretty boring world if we were all,
I think, but yeah, and well, for other reasons that pretty, pretty boring word, because all the
guys would love one gal and all the gals would love one guy. And Fred, you'd be the lucky one. And
I wouldn't, you know, I mean, that's not good. But that's, but
And to wrap things up on a more serious note, you include a New York Times piece headlined
The Choking of Hormuz. Yeah. And one of the things that the New York Times has done very well over
the last at least 10 years or so is really put in some very good graphics. And of course,
as part of Bob Vanderbine, I did and Bob Vanderbine, you know, do purple America and all that all
sorts of things like that. And we started every class by bringing up the map of the market,
but before we display it, because class starts at 1040. And, you know, the market's been out
there for an hour. And of course, then, you know, it's guess is going to be red or is it going to
be blue or green. And so I have to guess. And then they all choose the opposite because they know
that the winning investment strategy is the inverse courthouse or because I told them that I
all I do is buy high and sell low and somehow who we know that. And so they, and I think,
although I think on Wednesday, there was first time that I was right. So I'll pick red, say it's
red, you know, because I think the world is coming to an end and damn things green. You know,
and they've all picked green. Or I think, oh my goodness, somehow Trump people apologize for
something. I guess that never happened. So I guess I never thought it was a bit green and
they pick red. And it's, I mean, I think I was over. I was over until until Wednesday. I think I
finally, the only other one I won was the one day that was basically gray. It was neutral. Nobody
picked neutral. But what's beautiful about about the map of the market is it gives you the fundamental
overview of what the whole darn thing is, and allows you as an individual to go focus on what
you're interested in. It allows you to click and go dig here and there and then go back up and dig
here and there and dig here. It is, at least to me, one of the best interactive graphic
presentations in terms of trying to understand what's going on. And what the New York Times has
done really well is also do other graphics well. They didn't do that one, but like what they did
with the Strait of Urmuz. But the problem with the graphics with the state of Urmuz or their map
of the Strait of Urmuz is you could see from where things were coming and their the percentage
contribution. And you could see where the where the flow was going in percentage contribution.
But you did not have what the implication of that was on those entities. Because you
knew that whatever it was, the 27% or the really not all that big amount that was going to Japan,
I think represented numbers. I don't recall the numbers like 74% of their whole oil
import. Whereas, you know, there may have been more going to some place else. But the percentage
of that was much less. So a couple of my students that took that extended it to make it, you know,
put that nuance onto the graphic along with a lot of other interactive pieces. And I think they used,
you know, the tools that are now available on in AI, so called AI, to really make better interactive
graphics that really take data and transform it into visual information. And I think they did a
really great job. So I put the link them on there. And that is one of the really good things of
some of the AI tools out there is their ability to basically allow you to take data and display it
without necessarily being the greatest, you know, C++ coder, you know, whatever. And it's really
valuable. And if you go back, we go back to the Chris Ermson presentation, he even admitted
that he goes out there now and uses some of these things to actually write some code.
Only Mac or Chris are back out there right code again. I mean, you know, I thought you gave
that up 20 years. I mean, I gave mine up. I was, I've actually been out there, you know,
and it is kind of fun. Whatever. Sorry. There's one other time. I didn't know if you wanted to
comment on the other time's piece, Alan, where did all the affordable cars go?
Yeah, well, where do the all the affordable cars go?
People, you know, people think that at least the people that have it think that everybody
have it has it. Everybody has a car. Everybody can get around. It's not the case. And Trenton's
61%, 60 some percent of the households have access to one or fewer, one or zero.
Now, if you're one person household, you have one car, you're in great shape.
If you're one, if you're a two person household, you have one car, what's the other person do?
They're walking or being slept and doing it the options. And if you if Uber and Lyft aren't
really cheap for you, then and now, you know, even even the the Jalopy and it's it's even worse than
that because it really, it really wasn't affordable to have a Jalopy because if you had a broken tail
light, then you got a ticket. And if you didn't pay the ticket because you've preferred to eat,
then guess what? There's a warrant out for your arrest. And then you happen to drive your Jalopy
through Princeton and our Princeton cops then arrested you. How do you get out from under that?
So, you know, having a having a Jalopy with a broken tail light doesn't solve it either.
And having a Jalopy without a broken tail light is not is not that cheap anymore.
And having a non Jalopy have have decent car, which is what this thing points out is really
challenging. So, we really need to get to a point in which we can provide high quality
affordable mobility to people who'd really need it. And hey, that's what Elizabeth and I are
are absolutely laser focused on doing. And, you know, chances of our successes, you know,
not really good because, you know, we're gonna get, you know, oh, my goodness, the sky is falling.
We've got to worry about the trolley problem. Cut it out.
Oh, I lost all my credibility, Fred. I am my own worst enemy, but I don't care. Whatever.
Well, that's going to wrap up this episode, Alan. You can find us at smart driving car.com.
Find all the links there that for the newsletter. My tech reports were at textination.com.
Thanks for watching or listening. Stay safe. Have a great weekend.
Hey, absolutely. It's like spring or something. Whoa.
It is spring, Alan.
It is spring or we pass that date. Really. Jesus. It was 28 degrees here. I thought we
lost our pansies. We bought all these pansies and put them on the porch. And I think they survived.
Anyway, have a great weekend, folks. Enjoy it.
About this episode
Chris Hermsen of Aurora gets discussed after an MIT Mobility Forum chat focused on responsible autonomy and the “trolley problem,” with the hosts arguing perception and risk are handled differently by 360° sensing at high update rates. Princeton’s Alan Cornhouser then highlights a talk by Yann LeCun on enabling the next AI revolution via AMI and the importance of context/worldviews. News coverage spans Uber’s $10B robo-taxi push, attendant-vs-driver legal/regulatory nuances, and why Tesla/Waymo/Uber may miss real rider needs. The episode also touches AI-assisted data visualization and affordable mobility gaps.
It's episode 411 of Smart Driving Cars with Princeton's Alain Kornhauser and co-host Fred Fishkin. In this edition, Aurora's Chris Urmson fields questions at the MIT Mobility Forum, AMI's Yann LeCun on campus in Princeton, Uber commits 10 billion to robotaxis, Axios reports mobility's new big three and more. Join Alain and Fred for the latest and subscribe!
