vtt/episode_018_small.vtt

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 The following is a conversation with Elon Musk.

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 He's the CEO of Tesla, SpaceX, Neuralink,

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 and a cofounder of several other companies.

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 This conversation is part

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 of the artificial intelligence podcast.

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 The series includes leading researchers

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 in academia and industry, including CEOs and CTOs

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 of automotive, robotics, AI, and technology companies.

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 This conversation happened after the release of the paper

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 from our group at MIT on driver functional vigilance

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 during use of Tesla's autopilot.

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 The Tesla team reached out to me,

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 offering a podcast conversation with Mr. Musk.

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 I accepted with full control of questions I could ask

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 and the choice of what is released publicly.

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 I ended up editing out nothing of substance.

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 I've never spoken with Elon before this conversation,

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 publicly or privately.

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 Neither he nor his companies have any influence

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 on my opinion, nor on the rigor and integrity

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 of the scientific method that I practice

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 in my position at MIT.

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 Tesla has never financially supported my research

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 and I've never owned a Tesla vehicle.

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 I've never owned Tesla stock.

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 This podcast is not a scientific paper.

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 It is a conversation.

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 I respect Elon as I do all other leaders

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 and engineers I've spoken with.

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 We agree on some things and disagree on others.

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 My goal is always with these conversations

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 is to understand the way the guest sees the world.

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 One particular point of this agreement

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 in this conversation was the extent

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 to which camera based driver monitoring

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 will improve outcomes and for how long

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 it will remain relevant for AI assisted driving.

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 As someone who works on and is fascinated

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 by human centered artificial intelligence,

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 I believe that if implemented and integrated effectively,

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 camera based driver monitoring is likely to be of benefit

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 in both the short term and the long term.

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 In contrast, Elon and Tesla's focus

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 is on the improvement of autopilot

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 such that its statistical safety benefits

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 override any concern of human behavior and psychology.

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 Elon and I may not agree on everything

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 but I deeply respect the engineering

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 and innovation behind the efforts that he leads.

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 My goal here is to catalyze a rigorous, nuanced

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 and objective discussion in industry and academia

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 on AI assisted driving,

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 one that ultimately makes for a safer and better world.

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 And now here's my conversation with Elon Musk.

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 What was the vision, the dream of autopilot

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 when in the beginning the big picture system level

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 when it was first conceived

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 and started being installed in 2014

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 in the hardware and the cars?

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 What was the vision, the dream?

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 I would characterize the vision or dream

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 simply that there are obviously two

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 massive revolutions in the automobile industry.

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 One is the transition to electrification

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 and then the other is autonomy.

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 And it became obvious to me that in the future

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 any car that does not have autonomy

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 I would be about as useful as a horse.

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 Which is not to say that there's no use, it's just rare

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 and somewhat idiosyncratic

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 if somebody has a horse at this point.

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 It's just obvious that cars will drive themselves completely.

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 It's just a question of time

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 and if we did not participate in the autonomy revolution

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 then our cars would not be useful to people

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 relative to cars that are autonomous.

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 I mean an autonomous car is arguably worth

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 five to 10 times more than a car that which is not autonomous.

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 In the long term.

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 Turns out what you mean by long term,

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 but let's say at least for the next five years

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 perhaps 10 years.

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 So there are a lot of very interesting design choices

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 with autopilot early on.

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 First is showing on the instrument cluster

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 or in the Model 3 on the center stack display

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 what the combined sensor suite sees.

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 What was the thinking behind that choice?

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 Was there a debate?

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 What was the process?

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 The whole point of the display is to provide a health check

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 on the vehicle's perception of reality.

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 So the vehicle's taking information for a bunch of sensors

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 primarily cameras, but also radar and ultrasonics,

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 GPS and so forth.

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 And then that information is then rendered into vector space

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 and that with a bunch of objects with properties

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 like lane lines and traffic lights and other cars.

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 And then in vector space that is re rendered onto a display

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 so you can confirm whether the car knows

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 what's going on or not by looking out the window.

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 Right, I think that's an extremely powerful thing

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 for people to get an understanding

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 to become one with the system

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 and understanding what the system is capable of.

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 Now, have you considered showing more?

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 So if we look at the computer vision,

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 you know, like road segmentation, lane detection,

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 vehicle detection, object detection, underlying the system,

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 there is at the edges some uncertainty.

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 Have you considered revealing the parts

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 that the uncertainty in the system, the sort of problem

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 these associated with say image recognition

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 or something like that?

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 Yeah, so right now it shows like the vehicles

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 and the vicinity of very clean crisp image

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 and people do confirm that there's a car in front of me

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 and the system sees there's a car in front of me

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 but to help people build an intuition

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 of what computer vision is by showing some of the uncertainty.

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 Well, I think it's, in my car,

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 I always look at the sort of the debug view

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 and there's two debug views.

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 One is augmented vision, which I'm sure you've seen

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 where it's basically, we draw boxes and labels

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 around objects that are recognized.

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 And then there's what we call the visualizer,

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 which is basically a vector space representation

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 summing up the input from all sensors.

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 That does not show any pictures,

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 but it shows all of the,

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 it basically shows the cause view of the world in vector space.

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 But I think this is very difficult for normal people to understand.

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 They would not know what they're looking at.

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 So it's almost an HMI challenge.

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 The current things that are being displayed

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 is optimized for the general public understanding

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 of what the system is capable of.

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 It's like if you have no idea how computer vision works

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 or anything, you can still look at the screen

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 and see if the car knows what's going on.

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 And then if you're a development engineer

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 or if you have the development build like I do,

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 then you can see all the debug information.

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 But those would just be total diverse to most people.

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 What's your view on how to best distribute effort?

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 So there's three, I would say, technical aspects of autopilot

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 that are really important.

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 So it's the underlying algorithms,

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 like the neural network architecture.

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 There's the data that's trained on

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 and then there's the hardware development.

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 There may be others.

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 But so look, algorithm, data, hardware.

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 You only have so much money, only have so much time.

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 What do you think is the most important thing

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 to allocate resources to?

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 Do you see it as pretty evenly distributed

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 between those three?

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 We automatically get fast amounts of data

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 because all of our cars have

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 eight external facing cameras and radar

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 and usually 12 ultrasonic sensors, GPS, obviously,

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 and IMU.

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 And so we basically have a fleet that has,

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 we've got about 400,000 cars on the road

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 that have that level of data.

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 I think you keep quite close track of it, actually.

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 Yes.

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 So we're approaching half a million cars

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 on the road that have the full sensor suite.

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 So this is, I'm not sure how many other cars

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 on the road have this sensor suite,

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 but I'd be surprised if it's more than 5,000,

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 which means that we have 99% of all the data.

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 So there's this huge inflow of data.

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 Absolutely, massive inflow of data.

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 And then it's taken about three years,

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 but now we've finally developed our full self driving computer,

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 which can process

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 an order of magnitude as much as the NVIDIA system

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 that we currently have in the cars.

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 And it's really just to use it,

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 you unplug the NVIDIA computer and plug the Tesla computer in.

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 And that's it.

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 And it's, in fact, we're not even,

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 we're still exploring the boundaries of its capabilities,

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 but we're able to run the cameras at full frame rate,

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 full resolution, not even crop of the images,

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 and it's still got headroom, even on one of the systems.

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 The full self driving computer is really two computers,

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 two systems on a chip that are fully redundant.

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 So you could put a bolt through basically any part of that system

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 and it still works.

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 The redundancy, are they perfect copies of each other?

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 Or also it's purely for redundancy

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 as opposed to an arguing machine kind of architecture

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 where they're both making decisions.

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 This is purely for redundancy.

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 I think it's more like, if you have a twin engine aircraft,

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 commercial aircraft,

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 this system will operate best if both systems are operating,

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 but it's capable of operating safely on one.

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 So, but as it is right now, we can just run,

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 we haven't even hit the edge of performance,

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 so there's no need to actually distribute

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 functionality across both SoCs.

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 We can actually just run a full duplicate on each one.

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 You haven't really explored or hit the limit of the system?

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 Not yet, hit the limit now.

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 So the magic of deep learning is that it gets better with data.

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 You said there's a huge inflow of data,

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 but the thing about driving the really valuable data

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 to learn from is the edge cases.

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 So how do you, I mean, I've heard you talk somewhere about

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 autopilot disengagement as being an important moment of time to use.

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 Is there other edge cases or perhaps can you speak to those edge cases,

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 what aspects of them might be valuable,

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 or if you have other ideas,

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 how to discover more and more and more edge cases in driving?

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 Well, there's a lot of things that I learned.

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 There are certainly edge cases where I say somebody's on autopilot

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 and they take over.

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 And then, okay, that's a trigger that goes to a system that says,

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 okay, do they take over for convenience

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 or do they take over because the autopilot wasn't working properly?

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 There's also, like let's say we're trying to figure out

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 what is the optimal spline for traversing an intersection.

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 Then the ones where there are no interventions

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 and are the right ones.

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 So you then say, okay, when it looks like this, do the following.

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 And then you get the optimal spline for a complex,

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 now getting a complex intersection.

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 So that's for, there's kind of the common case.

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 You're trying to capture a huge amount of samples

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 of a particular intersection, how one thing went right.

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 And then there's the edge case where, as you said,

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 not for convenience, but something didn't go exactly right.

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 Somebody took over, somebody asserted manual control from autopilot.

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 And really, like the way to look at this is view all input is error.

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 If the user had to do input, it does something.

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 All input is error.

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 That's a powerful line to think of it that way,

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 because it may very well be error.

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 But if you want to exit the highway,

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 or if you want to, it's a navigation decision

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 that all autopilot is not currently designed to do,

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 then the driver takes over.

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 How do you know the difference?

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 Yeah, that's going to change with navigate and autopilot,

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 which we've just released, and without stall confirm.

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 So the navigation, like lane change based,

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 like asserting control in order to do a lane change,

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 or exit a freeway, or doing highway interchange,

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 the vast majority of that will go away with the release

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 that just went out.

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 Yeah, I don't think people quite understand

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 how big of a step that is.

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 Yeah, they don't.

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 If you drive the car, then you do.

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 So you still have to keep your hands on the steering wheel

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 currently when it does the automatic lane change?

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 What are...

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 So there's these big leaps through the development of autopilot

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 through its history,

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 and what stands out to you as the big leaps?

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 I would say this one,

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 navigate and autopilot without having to confirm,

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 is a huge leap.

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 It is a huge leap.

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 It also automatically overtakes slow cars.

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 So it's both navigation and seeking the fastest lane.

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 So it'll overtake a slow cause and exit the freeway

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 and take highway interchanges.

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 And then we have traffic light recognition,

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 which is introduced initially as a warning.

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 I mean, on the development version that I'm driving,

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 the car fully stops and goes at traffic lights.

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 So those are the steps, right?

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 You just mentioned something sort of

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 including a step towards full autonomy.

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 What would you say are the biggest technological roadblocks

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 to full cell driving?

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 Actually, I don't think...

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 I think we just...

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 the full cell driving computer that we just...

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 that has a...

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 what we call the FSD computer.

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 That's now in production.

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 So if you order any Model SRX or any Model 3

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 that has the full cell driving package,

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 you'll get the FSD computer.

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 That's important to have enough base computation.

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 Then refining the neural net and the control software.

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 But all of that can just be provided as an over there update.

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 The thing that's really profound,

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 and where I'll be emphasizing at the...

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 that investor day that we're having focused on autonomy,

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 is that the cars currently being produced,

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 or the hardware currently being produced,

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 is capable of full cell driving.

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 But capable is an interesting word because...

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 Like the hardware is.

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 And as we refine the software,

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 the capabilities will increase dramatically

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 and then the reliability will increase dramatically

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 and then it will receive regulatory approval.

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 So essentially buying a car today is an investment in the future.

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 You're essentially buying...

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 I think the most profound thing is that

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 if you buy a Tesla today,

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 I believe you are buying an appreciating asset,

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 not a depreciating asset.

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 So that's a really important statement there

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 because if hardware is capable enough,

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 that's the hard thing to upgrade usually.

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 Exactly.

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 So then the rest is a software problem.

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 Yes. Software has no marginal cost, really.

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 But what's your intuition on the software side?

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 How hard are the remaining steps

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 to get it to where...

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 you know, the experience,

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 not just the safety, but the full experience

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 is something that people would enjoy.

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 I think people would enjoy it very much on the highways.

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 It's a total game changer for quality of life,

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 for using Tesla autopilot on the highways.

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 So it's really just extending that functionality to city streets,

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 adding in the traffic light recognition,

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 navigating complex intersections,

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 and then being able to navigate complicated parking lots

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 so the car can exit a parking space

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 and come and find you even if it's in a complete maze of a parking lot.

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 And then you can just drop you off and find a parking spot by itself.

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 Yeah, in terms of enjoyability

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 and something that people would actually find a lot of use from,

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 the parking lot is a really...

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 it's rich of annoyance when you have to do it manually,

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 so there's a lot of benefit to be gained from automation there.

17:07.840 --> 17:11.840
 So let me start injecting the human into this discussion a little bit.

17:11.840 --> 17:14.840
 So let's talk about full autonomy.

17:14.840 --> 17:17.840
 If you look at the current level four vehicles,

17:17.840 --> 17:19.840
 being Tesla and road like Waymo and so on,

17:19.840 --> 17:22.840
 they're only technically autonomous.

17:22.840 --> 17:25.840
 They're really level two systems

17:25.840 --> 17:28.840
 with just a different design philosophy

17:28.840 --> 17:31.840
 because there's always a safety driver in almost all cases

17:31.840 --> 17:33.840
 and they're monitoring the system.

17:33.840 --> 17:37.840
 Maybe Tesla's full self driving

17:37.840 --> 17:41.840
 is still for a time to come,

17:41.840 --> 17:44.840
 requiring supervision of the human being.

17:44.840 --> 17:47.840
 So its capabilities are powerful enough to drive,

17:47.840 --> 17:50.840
 but nevertheless requires the human to still be supervising

17:50.840 --> 17:56.840
 just like a safety driver is in a other fully autonomous vehicles.

17:56.840 --> 18:01.840
 I think it will require detecting hands on wheel

18:01.840 --> 18:08.840
 or at least six months or something like that from here.

18:08.840 --> 18:11.840
 Really it's a question of like,

18:11.840 --> 18:15.840
 from a regulatory standpoint,

18:15.840 --> 18:19.840
 how much safer than a person does autopilot need to be

18:19.840 --> 18:24.840
 for it to be okay to not monitor the car?

18:24.840 --> 18:27.840
 And this is a debate that one can have.

18:27.840 --> 18:31.840
 But you need a large amount of data

18:31.840 --> 18:34.840
 so you can prove with high confidence,

18:34.840 --> 18:36.840
 statistically speaking,

18:36.840 --> 18:39.840
 that the car is dramatically safer than a person

18:39.840 --> 18:42.840
 and that adding in the person monitoring

18:42.840 --> 18:45.840
 does not materially affect the safety.

18:45.840 --> 18:49.840
 So it might need to be like two or three hundred percent safer than a person.

18:49.840 --> 18:51.840
 And how do you prove that?

18:51.840 --> 18:53.840
 Incidence per mile.

18:53.840 --> 18:56.840
 So crashes and fatalities.

18:56.840 --> 18:58.840
 Yeah, fatalities would be a factor,

18:58.840 --> 19:00.840
 but there are just not enough fatalities

19:00.840 --> 19:03.840
 to be statistically significant at scale.

19:03.840 --> 19:06.840
 But there are enough crashes,

19:06.840 --> 19:10.840
 there are far more crashes than there are fatalities.

19:10.840 --> 19:15.840
 So you can assess what is the probability of a crash,

19:15.840 --> 19:19.840
 then there's another step which probability of injury

19:19.840 --> 19:21.840
 and probability of permanent injury

19:21.840 --> 19:23.840
 and probability of death.

19:23.840 --> 19:27.840
 And all of those need to be much better than a person

19:27.840 --> 19:32.840
 by at least perhaps two hundred percent.

19:32.840 --> 19:36.840
 And you think there's the ability to have a healthy discourse

19:36.840 --> 19:39.840
 with the regulatory bodies on this topic?

19:39.840 --> 19:43.840
 I mean, there's no question that regulators pay

19:43.840 --> 19:48.840
 disproportionate amount of attention to that which generates press.

19:48.840 --> 19:50.840
 This is just an objective fact.

19:50.840 --> 19:52.840
 And Tesla generates a lot of press.

19:52.840 --> 19:56.840
 So that, you know, in the United States,

19:56.840 --> 20:00.840
 there's I think almost 40,000 automotive deaths per year.

20:00.840 --> 20:03.840
 But if there are four in Tesla,

20:03.840 --> 20:06.840
 they'll probably receive a thousand times more press

20:06.840 --> 20:08.840
 than anyone else.

20:08.840 --> 20:10.840
 So the psychology of that is actually fascinating.

20:10.840 --> 20:12.840
 I don't think we'll have enough time to talk about that,

20:12.840 --> 20:16.840
 but I have to talk to you about the human side of things.

20:16.840 --> 20:20.840
 So myself and our team at MIT recently released a paper

20:20.840 --> 20:24.840
 on functional vigilance of drivers while using autopilot.

20:24.840 --> 20:27.840
 This is work we've been doing since autopilot was first

20:27.840 --> 20:30.840
 released publicly over three years ago,

20:30.840 --> 20:34.840
 collecting video driver faces and driver body.

20:34.840 --> 20:38.840
 So I saw that you tweeted a quote from the abstract

20:38.840 --> 20:43.840
 so I can at least guess that you've glanced at it.

20:43.840 --> 20:46.840
 Can I talk you through what we found?

20:46.840 --> 20:51.840
 Okay, so it appears that in the data that we've collected

20:51.840 --> 20:54.840
 that drivers are maintaining functional vigilance

20:54.840 --> 20:57.840
 such that we're looking at 18,000 disengagement

20:57.840 --> 21:02.840
 from autopilot, 18,900 and annotating were they able

21:02.840 --> 21:05.840
 to take over control in a timely manner?

21:05.840 --> 21:07.840
 So they were there present looking at the road

21:07.840 --> 21:09.840
 to take over control.

21:09.840 --> 21:14.840
 Okay, so this goes against what many would predict

21:14.840 --> 21:18.840
 from the body of literature on vigilance with automation.

21:18.840 --> 21:21.840
 Now the question is, do you think these results

21:21.840 --> 21:23.840
 hold across the broader population?

21:23.840 --> 21:26.840
 So ours is just a small subset.

21:26.840 --> 21:30.840
 Do you think one of the criticism is that there's

21:30.840 --> 21:34.840
 a small minority of drivers that may be highly responsible

21:34.840 --> 21:37.840
 where their vigilance decrement would increase

21:37.840 --> 21:39.840
 with autopilot use?

21:39.840 --> 21:41.840
 I think this is all really going to be swept.

21:41.840 --> 21:46.840
 I mean, the system's improving so much so fast

21:46.840 --> 21:50.840
 that this is going to be a mood point very soon

21:50.840 --> 21:56.840
 where vigilance is, if something's many times safer

21:56.840 --> 22:00.840
 than a person, then adding a person does,

22:00.840 --> 22:04.840
 the effect on safety is limited.

22:04.840 --> 22:09.840
 And in fact, it could be negative.

22:09.840 --> 22:11.840
 That's really interesting.

22:11.840 --> 22:16.840
 So the fact that a human may, some percent of the population

22:16.840 --> 22:20.840
 may exhibit a vigilance decrement will not affect

22:20.840 --> 22:22.840
 overall statistics numbers of safety.

22:22.840 --> 22:27.840
 No, in fact, I think it will become very, very quickly,

22:27.840 --> 22:29.840
 maybe even towards the end of this year,

22:29.840 --> 22:32.840
 but I'd say I'd be shocked if it's not next year,

22:32.840 --> 22:36.840
 at the latest, that having a human intervene

22:36.840 --> 22:39.840
 will increase safety.

22:39.840 --> 22:40.840
 Decrease.

22:40.840 --> 22:42.840
 I can imagine if you're an elevator.

22:42.840 --> 22:45.840
 Now, it used to be that there were elevator operators

22:45.840 --> 22:47.840
 and you couldn't go on an elevator by yourself

22:47.840 --> 22:51.840
 and work the lever to move between floors.

22:51.840 --> 22:56.840
 And now, nobody wants an elevator operator

22:56.840 --> 23:00.840
 because the automated elevator that stops the floors

23:00.840 --> 23:03.840
 is much safer than the elevator operator.

23:03.840 --> 23:05.840
 And in fact, it would be quite dangerous

23:05.840 --> 23:07.840
 if someone with a lever that can move

23:07.840 --> 23:09.840
 the elevator between floors.

23:09.840 --> 23:12.840
 So that's a really powerful statement

23:12.840 --> 23:14.840
 and a really interesting one.

23:14.840 --> 23:16.840
 But I also have to ask, from a user experience

23:16.840 --> 23:18.840
 and from a safety perspective,

23:18.840 --> 23:20.840
 one of the passions for me algorithmically

23:20.840 --> 23:25.840
 is camera based detection of sensing the human,

23:25.840 --> 23:27.840
 but detecting what the driver is looking at,

23:27.840 --> 23:29.840
 cognitive load, body pose.

23:29.840 --> 23:31.840
 On the computer vision side, that's a fascinating problem,

23:31.840 --> 23:34.840
 but there's many in industry who believe

23:34.840 --> 23:37.840
 you have to have camera based driver monitoring.

23:37.840 --> 23:39.840
 Do you think this could be benefit gained

23:39.840 --> 23:41.840
 from driver monitoring?

23:41.840 --> 23:45.840
 If you have a system that's out or below

23:45.840 --> 23:49.840
 human level reliability, then driver monitoring makes sense.

23:49.840 --> 23:51.840
 But if your system is dramatically better,

23:51.840 --> 23:53.840
 more reliable than a human,

23:53.840 --> 23:58.840
 then driver monitoring is not help much.

23:58.840 --> 24:03.840
 And like I said, you wouldn't want someone into...

24:03.840 --> 24:05.840
 You wouldn't want someone in the elevator.

24:05.840 --> 24:07.840
 If you're in an elevator, do you really want someone

24:07.840 --> 24:09.840
 with a big lever, some random person operating

24:09.840 --> 24:11.840
 in the elevator between floors?

24:11.840 --> 24:13.840
 I wouldn't trust that.

24:13.840 --> 24:16.840
 I would rather have the buttons.

24:16.840 --> 24:19.840
 Okay, you're optimistic about the pace

24:19.840 --> 24:21.840
 of improvement of the system.

24:21.840 --> 24:23.840
 From what you've seen with the full self driving car,

24:23.840 --> 24:25.840
 computer.

24:25.840 --> 24:27.840
 The rate of improvement is exponential.

24:27.840 --> 24:30.840
 So one of the other very interesting design choices

24:30.840 --> 24:34.840
 early on that connects to this is the operational

24:34.840 --> 24:37.840
 design domain of autopilot.

24:37.840 --> 24:41.840
 So where autopilot is able to be turned on.

24:41.840 --> 24:46.840
 So contrast another vehicle system that we're studying

24:46.840 --> 24:48.840
 is the Cadillac SuperCrew system.

24:48.840 --> 24:51.840
 That's in terms of ODD, very constrained to this particular

24:51.840 --> 24:54.840
 kinds of highways, well mapped, tested,

24:54.840 --> 24:58.840
 but it's much narrower than the ODD of Tesla vehicles.

24:58.840 --> 25:00.840
 What's...

25:00.840 --> 25:02.840
 It's like ADD.

25:02.840 --> 25:04.840
 Yeah.

25:04.840 --> 25:07.840
 That's good. That's a good line.

25:07.840 --> 25:10.840
 What was the design decision

25:10.840 --> 25:13.840
 in that different philosophy of thinking where...

25:13.840 --> 25:15.840
 There's pros and cons.

25:15.840 --> 25:20.840
 What we see with a wide ODD is Tesla drivers are able

25:20.840 --> 25:23.840
 to explore more the limitations of the system,

25:23.840 --> 25:26.840
 at least early on, and they understand together

25:26.840 --> 25:28.840
 the instrument cluster display.

25:28.840 --> 25:30.840
 They start to understand what are the capabilities.

25:30.840 --> 25:32.840
 So that's a benefit.

25:32.840 --> 25:37.840
 The con is you're letting drivers use it basically anywhere.

25:37.840 --> 25:41.840
 Well, anyways, I could detect lanes with confidence.

25:41.840 --> 25:46.840
 Was there a philosophy design decisions that were challenging

25:46.840 --> 25:48.840
 that were being made there?

25:48.840 --> 25:53.840
 Or from the very beginning, was that done on purpose

25:53.840 --> 25:55.840
 with intent?

25:55.840 --> 25:58.840
 Frankly, it's pretty crazy letting people drive

25:58.840 --> 26:02.840
 a two ton death machine manually.

26:02.840 --> 26:04.840
 That's crazy.

26:04.840 --> 26:06.840
 In the future, people will be like,

26:06.840 --> 26:09.840
 I can't believe anyone was just allowed to drive

26:09.840 --> 26:12.840
 one of these two ton death machines

26:12.840 --> 26:14.840
 and they just drive wherever they wanted,

26:14.840 --> 26:16.840
 just like elevators.

26:16.840 --> 26:18.840
 You just move the elevator with the lever wherever you want.

26:18.840 --> 26:21.840
 It can stop at halfway between floors if you want.

26:21.840 --> 26:24.840
 It's pretty crazy.

26:24.840 --> 26:29.840
 So it's going to seem like a mad thing in the future

26:29.840 --> 26:32.840
 that people were driving cars.

26:32.840 --> 26:35.840
 So I have a bunch of questions about the human psychology,

26:35.840 --> 26:37.840
 about behavior and so on.

26:37.840 --> 26:39.840
 I don't know.

26:39.840 --> 26:45.840
 Because you have faith in the AI system,

26:45.840 --> 26:50.840
 not faith, but both on the hardware side

26:50.840 --> 26:52.840
 and the deep learning approach of learning from data

26:52.840 --> 26:55.840
 will make it just far safer than humans.

26:55.840 --> 26:57.840
 Yeah, exactly.

26:57.840 --> 27:00.840
 Recently, there are a few hackers who tricked autopilot

27:00.840 --> 27:03.840
 to act in unexpected ways with adversarial examples.

27:03.840 --> 27:06.840
 So we all know that neural network systems

27:06.840 --> 27:08.840
 are very sensitive to minor disturbances

27:08.840 --> 27:10.840
 to these adversarial examples on input.

27:10.840 --> 27:13.840
 Do you think it's possible to defend against something like this

27:13.840 --> 27:15.840
 for the industry?

27:15.840 --> 27:17.840
 Sure.

27:17.840 --> 27:22.840
 Can you elaborate on the confidence behind that answer?

27:22.840 --> 27:27.840
 Well, a neural net is just like a basic bunch of matrix math.

27:27.840 --> 27:30.840
 You have to be like a very sophisticated,

27:30.840 --> 27:32.840
 somebody who really understands neural nets

27:32.840 --> 27:37.840
 and basically reverse engineer how the matrix is being built

27:37.840 --> 27:42.840
 and then create a little thing that just exactly causes

27:42.840 --> 27:44.840
 the matrix math to be slightly off.

27:44.840 --> 27:48.840
 But it's very easy to then block that by having

27:48.840 --> 27:51.840
 basically anti negative recognition.

27:51.840 --> 27:55.840
 It's like if the system sees something that looks like a matrix hack

27:55.840 --> 28:01.840
 excluded, it's such an easy thing to do.

28:01.840 --> 28:05.840
 So learn both on the valid data and the invalid data.

28:05.840 --> 28:07.840
 So basically learn on the adversarial examples

28:07.840 --> 28:09.840
 to be able to exclude them.

28:09.840 --> 28:12.840
 Yeah, you basically want to both know what is a car

28:12.840 --> 28:15.840
 and what is definitely not a car.

28:15.840 --> 28:18.840
 You train for this is a car and this is definitely not a car.

28:18.840 --> 28:20.840
 Those are two different things.

28:20.840 --> 28:23.840
 People have no idea neural nets really.

28:23.840 --> 28:25.840
 They probably think neural nets involves like, you know,

28:25.840 --> 28:28.840
 fishing net or something.

28:28.840 --> 28:35.840
 So as you know, taking a step beyond just Tesla and autopilot,

28:35.840 --> 28:39.840
 current deep learning approaches still seem in some ways

28:39.840 --> 28:44.840
 to be far from general intelligence systems.

28:44.840 --> 28:49.840
 Do you think the current approaches will take us to general intelligence

28:49.840 --> 28:55.840
 or do totally new ideas need to be invented?

28:55.840 --> 28:59.840
 I think we're missing a few key ideas for general intelligence,

28:59.840 --> 29:04.840
 general, artificial general intelligence.

29:04.840 --> 29:08.840
 But it's going to be upon us very quickly

29:08.840 --> 29:11.840
 and then we'll need to figure out what shall we do

29:11.840 --> 29:15.840
 if we even have that choice.

29:15.840 --> 29:18.840
 But it's amazing how people can't differentiate between, say,

29:18.840 --> 29:22.840
 the narrow AI that, you know, allows a car to figure out

29:22.840 --> 29:25.840
 what a lane line is and, you know,

29:25.840 --> 29:29.840
 and navigate streets versus general intelligence.

29:29.840 --> 29:32.840
 Like these are just very different things.

29:32.840 --> 29:35.840
 Like your toaster and your computer are both machines,

29:35.840 --> 29:38.840
 but one's much more sophisticated than another.

29:38.840 --> 29:43.840
 You're confident with Tesla you can create the world's best toaster.

29:43.840 --> 29:45.840
 The world's best toaster, yes.

29:45.840 --> 29:48.840
 The world's best self driving.

29:48.840 --> 29:51.840
 I'm, yes.

29:51.840 --> 29:54.840
 To me, right now, this seems game set match.

29:54.840 --> 29:57.840
 I don't, I mean, that's, I don't want to be complacent or overconfident,

29:57.840 --> 29:59.840
 but that's what it appears.

29:59.840 --> 30:02.840
 That is just literally what it, how it appears right now.

30:02.840 --> 30:06.840
 It could be wrong, but it appears to be the case

30:06.840 --> 30:10.840
 that Tesla is vastly ahead of everyone.

30:10.840 --> 30:13.840
 Do you think we will ever create an AI system

30:13.840 --> 30:17.840
 that we can love and loves us back in a deep meaningful way

30:17.840 --> 30:20.840
 like in the movie, Her?

30:20.840 --> 30:23.840
 I think AI will be capable of convincing you

30:23.840 --> 30:25.840
 to fall in love with it very well.

30:25.840 --> 30:28.840
 And that's different than us humans?

30:28.840 --> 30:31.840
 You know, we start getting into a metaphysical question

30:31.840 --> 30:35.840
 and do emotions and thoughts exist in a different realm than the physical.

30:35.840 --> 30:37.840
 And maybe they do, maybe they don't.

30:37.840 --> 30:39.840
 I don't know, but from a physics standpoint,

30:39.840 --> 30:43.840
 I tend to think of things, you know,

30:43.840 --> 30:47.840
 like physics was my main sort of training.

30:47.840 --> 30:50.840
 And from a physics standpoint,

30:50.840 --> 30:52.840
 essentially, if it loves you in a way

30:52.840 --> 30:57.840
 that you can't tell whether it's real or not, it is real.

30:57.840 --> 30:59.840
 That's a physics view of love.

30:59.840 --> 31:04.840
 If you cannot prove that it does not,

31:04.840 --> 31:07.840
 if there's no test that you can apply

31:07.840 --> 31:14.840
 that would make it allow you to tell the difference,

31:14.840 --> 31:16.840
 then there is no difference.

31:16.840 --> 31:20.840
 And it's similar to seeing our world as simulation.

31:20.840 --> 31:22.840
 There may not be a test to tell the difference

31:22.840 --> 31:24.840
 between what the real world and the simulation.

31:24.840 --> 31:26.840
 And therefore, from a physics perspective,

31:26.840 --> 31:28.840
 it might as well be the same thing.

31:28.840 --> 31:29.840
 Yes.

31:29.840 --> 31:32.840
 There may be ways to test whether it's a simulation.

31:32.840 --> 31:35.840
 There might be, I'm not saying there aren't,

31:35.840 --> 31:38.840
 but you could certainly imagine that a simulation could correct

31:38.840 --> 31:40.840
 that once an entity in the simulation

31:40.840 --> 31:42.840
 found a way to detect the simulation,

31:42.840 --> 31:44.840
 it could either restart, you know,

31:44.840 --> 31:47.840
 pause the simulation, start a new simulation,

31:47.840 --> 31:52.840
 or do one of many other things that then corrects for that error.

31:52.840 --> 31:58.840
 So when maybe you or somebody else creates an AGI system

31:58.840 --> 32:02.840
 and you get to ask her one question,

32:02.840 --> 32:16.840
 what would that question be?

32:16.840 --> 32:21.840
 What's outside the simulation?

32:21.840 --> 32:23.840
 Milan, thank you so much for talking today.

32:23.840 --> 32:52.840
 All right, thank you.