Speaker 1: Welcome to Bedtime Astronomy. Explore the wonders of the cosmos
Speaker 1: with our soothing Bedtime Astronomie podcast. Each episode offers a
Speaker 1: gentle journey through the stars, planets, and beyond, perfect for
Speaker 1: unwinding after a long day. Let's travel through the mysteries
Speaker 1: of the universe as you drift off into a peaceful
Speaker 1: slumber under the night sky.
Speaker 2: Welcome back to the show. It is Monday, February second,
Speaker 2: twenty twenty six, and I have to say, looking at
Speaker 2: the date today, it feels like we are finally living
Speaker 2: in that future we were always promised and the you know,
Speaker 2: the paperback sci fi novels of the nineties.
Speaker 3: It certainly does. Especially when you look at the news
Speaker 3: we're unpacking today, it feels less like a standard press release, yeah,
Speaker 3: and more like a lost chapter from an Isaac Asimov
Speaker 3: book exactly.
Speaker 2: I mean. Down here on Earth, we've become pretty jaded
Speaker 2: about artificial intelligence over the last few years. We see
Speaker 2: the headlines about self driving taxis navigating San Francisco.
Speaker 3: Fog or AI writing generic marketing emails that all sound.
Speaker 2: The same, right, or generating that weird, slightly hallucinated art
Speaker 2: with seven fingers. On one hand, it's almost become background noise.
Speaker 2: We sort of take it for granted. But the milestone
Speaker 2: we are discussing today takes that technology and well literally
Speaker 2: puts it on a completely different.
Speaker 3: Planet is a massive shift in context. We aren't talking
Speaker 3: about a chatbot helping you organize your calendar. We're talking
Speaker 3: about a nuclear powered explorer navigating the most hostile, unforgiving
Speaker 3: terrain imaginable entirely on its own.
Speaker 2: And that's the core news event. It's something that frankly
Speaker 2: changes the game for space exploration. NASA's Perseverance Rover, which
Speaker 2: has been roaming the Red Planet for years now, has
Speaker 2: successively completed its first ever drives planned entirely by artificial intelligence.
Speaker 2: And I don't mean assisted driving where a human is
Speaker 2: sort of hovering over a brake pedal. I mean the
Speaker 2: AI took the wheel right.
Speaker 3: And just to be clear from the outset, because I
Speaker 3: think people hear AI and rover and they immediately think
Speaker 3: of the old collision avoidance systems.
Speaker 2: We've had for a while, sure, like the sensors in
Speaker 2: your car.
Speaker 3: Exactly, This isn't the rover just avoiding a sharp rock
Speaker 3: while a human holds the steering wheel. This is the
Speaker 3: AI planning the route, making the decisions, and then executing
Speaker 3: the drive without a human looking over its shoulder in
Speaker 3: real time.
Speaker 2: That's the hook for me. I want you to imagine
Speaker 2: driving a car, but the steering wheel is one hundred
Speaker 2: and forty million miles away, and instead of trying to
Speaker 2: steer it yourself with the twenty minute delay, which is
Speaker 2: physically impossible, you just hand the keys to the onboard
Speaker 2: computer and say you figure it out, get me to
Speaker 2: that mountain range.
Speaker 3: And that's what NASA has effectively just done. It's incredible,
Speaker 3: it is yeah, and it's a very active way to
Speaker 3: put it. To guide us through this. We're looking at
Speaker 3: a comprehensive report that was released today February two from
Speaker 3: NASA and the Jet Propulsion Laboratory or JPL.
Speaker 2: So this is the real deal. This isn't just speculation,
Speaker 2: Oh no, this is it.
Speaker 3: This comes straight from the Rover Operation Center. We're analyzing
Speaker 3: data that's been verified by the Mars Connissance orbiter and
Speaker 3: there are details on a specific collaboration with the AI
Speaker 3: company Anthropic, So we have a lot of credible, high
Speaker 3: level data to sift through. This isn't a roadmap. This
Speaker 3: is operational history being made right now.
Speaker 2: Okay, so our mission for this discussion is pretty clear.
Speaker 2: We need to unpack how we got from generative AI
Speaker 2: writing bad poetry on the Internet to navigating bedrock and
Speaker 2: craters on Mars. We need to understand the technical leap here.
Speaker 3: We need to analyze that delta because for the last
Speaker 3: twenty eight years of Mars exploration, humans have been very
Speaker 3: very much in the loop. This represents a move from
Speaker 3: human joysticking, or at least the illusion of joysticking, to
Speaker 3: true robotic agency.
Speaker 2: And we definitely need to talk about the safety aspect
Speaker 2: because if my computer crash is here in the studio,
Speaker 2: I reboot it and maybe lose a word document. If
Speaker 2: a rover crash is on Mars, that's a billion dollar
Speaker 2: mistake that you can't undo. Right, So we're going to
Speaker 2: talk about the digital twin technology that keeps it safe,
Speaker 2: which is a fascinating concept in itself.
Speaker 3: It's a fascinating layer of protection. Honestly, it's probably the
Speaker 3: only reason the engineers at JPL can sleep at night.
Speaker 2: All right, let's get into the milestone itself. Let's start
Speaker 2: by decoupling the human driver from the machine.
Speaker 3: Right, So let's look at this specific event. This all
Speaker 3: happened very recently. The breakthrough drives occurred on December eighth
Speaker 3: and December tenth, twenty twenty five.
Speaker 2: Okay, so just a couple of months ago. And to
Speaker 2: set the scene visually, where exactly is Perseverance located? Right now?
Speaker 2: It's situated at the rim of Jezuro Crater, which, from
Speaker 2: what I understand, is not exactly a paved parking lot.
Speaker 2: It's not the Bonneville Salt Flats where you can just
Speaker 2: you know, throttle down and go.
Speaker 3: Far from it.
Speaker 1: No.
Speaker 3: Jezro is scientifically fascinating, but logistically it's a nightmare. You
Speaker 3: have steep slopes, you have patches of loose sand that
Speaker 3: act like traps. You have shirt bedrock outcrops that can
Speaker 3: chew up the wheels. It is a complex, treacherous environment
Speaker 3: for a human to navigate, let alone a machine doing
Speaker 3: it completely solo.
Speaker 2: I love the analogy of a child taking their first steps,
Speaker 2: you know that feeling you let go of their hand
Speaker 2: and they wobble a bit and you just hold your breath,
Speaker 2: hoping they don't face plant. Except in this case, the
Speaker 2: child is a one ton nuclear powered robot and the
Speaker 2: parents are millions of miles away. Looking at a twenty
Speaker 2: minute delayed video feed.
Speaker 3: That's the tension exactly, and the steps it took were significant.
Speaker 3: This wasn't just inching forward to test the waters.
Speaker 2: This wasn't a baby step.
Speaker 3: Not at all. On that first drive on December eighth,
Speaker 3: Perseverance drove six hundred and eighty nine feet that's about
Speaker 3: two hundred and ten meters.
Speaker 2: That's more than two American football fields on its own
Speaker 2: in one gup exactly.
Speaker 3: And then just two days later, on December tenth, it
Speaker 3: beat that record. It drove eight hundred and seven feet,
Speaker 3: which is two hundred and forty six meters.
Speaker 2: So why are those numbers so significant? Because I can
Speaker 2: hear a listener saying, okay, eight hundred feet. My commute
Speaker 2: is twenty miles, My roomb is eight hundred feet in
Speaker 2: my living room. Why is this such a big deal
Speaker 2: for MASA.
Speaker 3: You have to look at it in the context of
Speaker 3: Martian exploration history. These aren't just inches. In the past,
Speaker 3: Every single meter, every foot had to be meticulously planned
Speaker 3: by a human to have the rover cover that kind
Speaker 3: of distance autonomously in complex terrain. It's a quantum leap.
Speaker 2: Quantum leap.
Speaker 3: It proves the system isn't just cautious, it's capable. It's
Speaker 3: traversing distances that would typically take days, sometimes weeks, of
Speaker 3: back and forth communication and planning. It's a fundamental shift
Speaker 3: in the operational tempo.
Speaker 2: Okay, so before we get too deep into the how,
Speaker 2: I think we need to bust a myth I alluded
Speaker 2: to it earlier, the status quo. I think when most
Speaker 2: people picture NASA driving a rover, they picture a guy
Speaker 2: in a blue polo shirt with a headset and a joystick.
Speaker 3: Oh yeah, video game image.
Speaker 2: Right, watching a screen turning left and right in real
Speaker 2: time like they're playing a video game.
Speaker 3: That is the classic Hollywood image, and it is completely
Speaker 3: fundamentally wrong. It is physically impossible.
Speaker 2: Because of the speed of light. Right, it's the ultimate
Speaker 2: speed limit. You just can't get around it.
Speaker 3: Precisely, Mars is on average about one hundred and forty
Speaker 3: million miles away. That's one hundred and twenty five million kilometers.
Speaker 3: Even at the speed of light, radio signals take anywhere
Speaker 3: from five to twenty minutes to get there, and then
Speaker 3: another five to twenty minutes to get back. It varies
Speaker 3: depending on where the planets are in their orbits.
Speaker 2: So let's play that out. If you're the driver and
Speaker 2: you saw a cliff coming up on your screen and
Speaker 2: you hit the brakes.
Speaker 3: The rover would have fallen off that clip twenty minutes ago.
Speaker 3: You cannot drive live. It's like trying to drive a
Speaker 3: car while looking at a photo of the road that
Speaker 3: was taken ten minutes ago. If you see a pedestrian
Speaker 3: in the photo, well, you've already hit them.
Speaker 2: So how have we been doing it for the last
Speaker 2: twenty eight years since Sojourner back in the nineties.
Speaker 3: It's a painstaking process. We call it the human in
Speaker 3: the loop workflow. Typically, human rover planners sitting at JPL
Speaker 3: in California download all the latest images from the rover
Speaker 3: and from satellites. They analyze the terrain manually. They look
Speaker 3: at the rocks, the sand, traps, the slopes. They put
Speaker 3: on three D glasses literally stereoscopic glasses, and stare at
Speaker 3: serial images to judge depth and distance.
Speaker 2: They're basically staring at foot photos and drawing a line
Speaker 2: on a map like go here, then turn left thirty degrees,
Speaker 2: then stop.
Speaker 3: Essentially, yes, they sketch a route using waypoints. These are
Speaker 3: specific coordinates the rover travels to, and because humans are
Speaker 3: cautious and because they can only see so much from
Speaker 3: static images, these waypoints are usually spaced pretty close together.
Speaker 2: How close are we talking.
Speaker 3: Usually know more than about one hundred feet to maybe
Speaker 3: three hundred and thirty feet apart on a really good day,
Speaker 3: with clear terrain, that's thirty to one hundred meters. They
Speaker 3: have to be short mops to ensure they aren't sending
Speaker 3: the rover into a hazard they didn't catch in the photos.
Speaker 2: So the old way is move a little bit, stop,
Speaker 2: take a picture, send it all the way to Earth,
Speaker 2: wait for a whole team of humans to look at it,
Speaker 2: sleep on it, draw new lines, send the command back,
Speaker 2: and then it moves a little bit more. It sounds
Speaker 2: excruciatingly slow it is.
Speaker 3: It's incredibly stop and go. It relies entirely on the
Speaker 3: Earth based cycle. If the humans are sleeping, the rover
Speaker 3: is sleeping. If the data takes a while to download
Speaker 3: via the deep space network, the rover sits there. The
Speaker 3: rover spends far more time I'm waiting than it does driving.
Speaker 2: And when you have a limited mission lifespan, that's just
Speaker 2: lost time, wasted opportunity.
Speaker 3: It's terribly inefficient. And this new system, this new AI,
Speaker 3: it blows that limitation completely out of the water.
Speaker 2: It decouples the driving from the human schedule.
Speaker 3: It decouples the rover's progress from Earth's rotation. The rover
Speaker 3: can make decisions on the fly. It doesn't need to
Speaker 3: ask for permission for every meter. It just needs a destination.
Speaker 2: Let's get into the technology, because this is the part
Speaker 2: that I think will really surprise people. We aren't just
Speaker 2: talking about a basic collision avoidance system like you have
Speaker 2: in a modern car.
Speaker 3: Right.
Speaker 2: This isn't just a sensor beeping if you get too
Speaker 2: close to a wall.
Speaker 3: No, no, this is much more sophisticated. The report identifies
Speaker 3: the specific technology as generative AI using vision language models.
Speaker 2: Okay, stop right there, vision language models. When I hear
Speaker 2: generative AI, I think of chatbots. I think of asking
Speaker 2: a computer to write me a recipe for lasagna or
Speaker 2: a sonnet about a toaster.
Speaker 3: Right, And that's what most of us think of. It's
Speaker 3: the same underlying architecture, it's the same kind of neural network,
Speaker 3: but it's applied in a completely different way.
Speaker 2: So how does a language model drive a rover. Is
Speaker 2: it talking to the rocks? Hello?
Speaker 4: Rock?
Speaker 2: Are you friendly? What's happening here?
Speaker 3: It's a great question, and it creates a bit of
Speaker 3: cognitive dissonance. But think about what those models are actually doing.
Speaker 3: They're processing vast amounts of information. In the case of
Speaker 3: a chatbot, that's text and finding patterns.
Speaker 2: Okay.
Speaker 3: In this case, the initiative was led by JPL's Rover
Speaker 3: Operations Center, the ROC, in collaboration with Anthropic.
Speaker 2: The makers of claud Ai.
Speaker 3: Correct, they're using claude Ai models. Now, a vision language
Speaker 3: model doesn't just process text. It processes images as if
Speaker 3: they were a language. You can look at a photo
Speaker 3: and understand the context of what is in it.
Speaker 2: So it's not just seeing pixels. It's seeing rock, sand cliff.
Speaker 2: It's assigning meaning to the visual data exactly.
Speaker 3: It analyzes imagery. And the crucial part here is that
Speaker 3: it uses the exact same visual data that the human
Speaker 3: planners use. It's not using some secret new sensor that
Speaker 3: we don't know about. It's looking at the same maps,
Speaker 3: the same photos and deciding independently where to put the
Speaker 3: wait points.
Speaker 2: You used to term there semantic understanding explain that distinction
Speaker 2: for us because that sounds important.
Speaker 3: It's the key difference. Traditional computer vision, like in older systems,
Speaker 3: looks at geometry. It sees a bump, it sees a drop,
Speaker 3: it sees an obstacle, but it doesn't necessarily know what
Speaker 3: that obstacle is. Is it a soft bush or a
Speaker 3: granite boulder. A vision language model can look at a
Speaker 3: patch of ground and say that is a sand ribble.
Speaker 3: Sand ripples are dangerous avoid, or that is bedrock. Bedrock
Speaker 3: is stable and safe to drive on drive. It brings
Speaker 3: a layer of reasoning, of geologic understanding to the image processing.
Speaker 2: It's a huge difference. It's the difference between seeing a
Speaker 2: shape and knowing it's a stop sign, and you have
Speaker 2: to break let's break down the inputs. What is the
Speaker 2: AI actually looking at to make these decisions.
Speaker 3: There are three main buckets of data that report highlights. First,
Speaker 3: it's using high resolution orbital image. This comes from the
Speaker 3: high rise camera.
Speaker 2: That's on the Mars reconnaissance orbiter flying overhead right so.
Speaker 3: It has the bird's eye view the macromap. It sees
Speaker 3: the layout of the land from space, the big picture. Second,
Speaker 3: it uses terrain slope data from digital elevation models, so
Speaker 3: it knows the three D shape of the ground, where
Speaker 3: it's flat, where it's deep, where the cliffs.
Speaker 2: Are, got it the lay of the land.
Speaker 3: And the third is JPL's own surface mission data set.
Speaker 3: This is the historical knowledge, the context of the mission itself.
Speaker 3: It's effectively the memory of where the rover has been,
Speaker 3: what kind of terrain it has encountered before, and what
Speaker 3: the overall mission parameters are.
Speaker 2: So it takes all that data, the map from above,
Speaker 2: the three D model of the ground, and its own
Speaker 2: memories and does what.
Speaker 3: It makes decisions. The report specifically lists the features the
Speaker 3: AI is trained to identify. It looks for bedrock, it
Speaker 3: looks for outcrops, it looks for hazardous boulder fields, and
Speaker 3: very importantly, it looks for sand ripples.
Speaker 2: Sand ripples sounds so harmless, but on Mars they are
Speaker 2: basically quicksand traps for rovers right, they are deadly.
Speaker 3: The Spirit rover, one of the previous generation, eventually met
Speaker 3: its end, effectively getting stuck in soft soil that looks solid.
Speaker 3: If you get stuck in a sandtrap on Mars mission over,
Speaker 3: you can't call a tow truck wow. So the AI
Speaker 3: identifies all these features, weighs the risks, and then generates
Speaker 3: a continuous path. It places those wait points, those fixed
Speaker 3: locations where the rover can stop for new instructions completely
Speaker 3: on its own.
Speaker 2: That is just wild. It's basically doing the job of
Speaker 2: a highly trained, very cautious NASA engineer, but it's doing
Speaker 2: it right there on the processor in real time.
Speaker 3: Well, the processing is fascinating. Yeah, but I know what
Speaker 3: you're thinking, and I know what the listener is probably
Speaker 3: thinking right now.
Speaker 2: That trust oh absolutely. I mean we've all seen AI
Speaker 2: make mistakes. We've seen them hallucinate facts. We've seen chatbots
Speaker 2: and vent court cases that never happen. What if this
Speaker 2: AI hallucinates a road where there's actually a crater.
Speaker 3: That is the billion dollar question. It's the thing that
Speaker 3: keeps engineers up at night. And this brings us to
Speaker 3: the next critical piece, the safety net.
Speaker 2: Right because NASA isn't known for being reckless. They don't
Speaker 2: just upload code and hope for the best. The Silicon
Speaker 2: Valley motto of move fast and break things doesn't really
Speaker 2: work when breaking things ends the entire space program.
Speaker 3: No, they are incredibly and rightly risk averse. The solution
Speaker 3: they developed is something they call the digital twin.
Speaker 2: I love this concept. It sounds very cyberpunk. Explain what
Speaker 2: a digital twin is in this context.
Speaker 3: It's exactly what it sounds like. Yeah, it is a
Speaker 3: virtual replica of the Perseverance Rover, but it lives inside
Speaker 3: a supercomputer at JPL in California. It is a physics
Speaker 3: perfect simulation of the rover and the Martian environment it's in.
Speaker 2: So before the real rover moves an inch on Mars, the.
Speaker 3: AI generates the plan. It says I want to drive here, here,
Speaker 3: and here. But instead of just beaming that command straight
Speaker 3: to Mars, the engineers feed it into the digital twin.
Speaker 2: First they simulate the drive.
Speaker 3: They do more than just simulate it, They stress test
Speaker 3: it to an unbelievable degree. The report states they verify
Speaker 3: over five hundred thousand telemetry.
Speaker 2: Variables five hundred thousand half.
Speaker 3: A million data points for every single proposed drive. They
Speaker 3: are checking everything wheel traction, suspension tilt, the power being
Speaker 3: drawn by each motor, the thermal limits on the electronics,
Speaker 3: the currents. They run the AI's plan through this digital gauntlet.
Speaker 2: So if the AI says, drive over that sketchy looking rock,
Speaker 2: the digital twin simulates it and flags a warning like
Speaker 2: warning suspension damage is ninety percent likely or warning tilt
Speaker 2: exceeds safe threshold by fifteen degrees.
Speaker 3: Precisely. It catches the hallucinations, it catches the risky maneuvers,
Speaker 3: it catches the small miscalculations, and only after the digital
Speaker 3: twin confirms the drive is safe green lights Across all
Speaker 3: half a million variables are the plans packaged up and
Speaker 3: sent via NASA's Deep space network to the physical rover
Speaker 3: on Mars.
Speaker 2: So it's a hybrid model. It's not fully autonomous in
Speaker 2: the sense that the AI can do whatever it wants.
Speaker 3: It is. It's AI planning, digital verification by humans and
Speaker 3: their simulators, and then physical execution. It's not blind trust.
Speaker 3: It's trust, but verify on a massive, massive scale.
Speaker 2: That makes me feel a lot better about the whole thing.
Speaker 2: It's not just a robot running wild. It's a robot
Speaker 2: proposing a plan and a very sophisticated simulation proving it
Speaker 2: works before anything real.
Speaker 3: Happens, and that verification step is what allows them to
Speaker 3: be bold. It allows them to let the AI try things,
Speaker 3: to plan these longer, more aggressive routes, knowing the safety
Speaker 3: net will catch a bad decision before it becomes a disaster.
Speaker 3: It essentially lets the rover think creatively about a path
Speaker 3: while the digital twin acts as its conscience.
Speaker 2: That's a great way to put it. So we know
Speaker 2: how it works. But let's talk about the why. Why
Speaker 2: do we even need this? Is it just because it's
Speaker 2: cool tech? Or is there a more practical reason for
Speaker 2: this push towards autonomy.
Speaker 3: There is a very practical, very urgent reason. It all
Speaker 3: comes down to two things, efficiency and scientific return.
Speaker 2: Okay, we have a quote here from the NASA administrator,
Speaker 2: Jared Isaacman.
Speaker 3: Yes, he said this broad how we will explore other worlds.
Speaker 3: He pointed out that autonomous technologies are absolutely essential for
Speaker 3: operating more efficiently and for responding to challenging terrain without
Speaker 3: waiting for.
Speaker 2: Earth responding to challenging terrain. That's an interesting phrase because
Speaker 2: right now, if the terrain gets tough, the humans on
Speaker 2: Earth slow everything way way down. They take smaller steps,
Speaker 2: they get more cautious.
Speaker 3: Exactly, and that slows down the science. If you have
Speaker 3: to wait twenty four hours for every thirty meters of
Speaker 3: progress because the ground is tricky, it takes years to
Speaker 3: get to the interesting geology over the next ridge. The
Speaker 3: rover has a finite lifespan. It's nuclear power source, the
Speaker 3: RTG slowly decays over time, every day wasted waiting for
Speaker 3: commands as a day of science loss.
Speaker 2: Forever Vandy Verma, who is a space roboticist at JPL,
Speaker 2: she broke this down into three pillars. I think this
Speaker 2: is a great way to visualize what the AI is
Speaker 2: actually doing for the mission.
Speaker 3: Yes, she outlined the three core functions of off planet
Speaker 3: driving that AI enhances. First, there is perception seeing the world,
Speaker 3: seeing the rocks and riffles, but not just taking a picture,
Speaker 3: understanding what is in the picture. This is where that
Speaker 3: vision language model shines. It perceives the environment more like
Speaker 3: a human geologist would with context.
Speaker 2: Okay, that's pillar One.
Speaker 3: Second is localization, which is knowing where you are, knowing
Speaker 3: exactly where you are on the map. And that sounds simple,
Speaker 3: but you have to remember on Mars you don't have GPS.
Speaker 3: There are no satellites pinging your phone. The rover has
Speaker 3: to figure out where it is by looking at the
Speaker 3: landmarks around it, the hills, the craters, and matching them
Speaker 3: to the orbital maps. It's an incredibly complex calculation.
Speaker 2: That is a good point. I never even thought about
Speaker 2: the lack of GPS. It's true dead reckoning old school navigation,
Speaker 2: it is.
Speaker 3: And the third pillar she mentions is planning in control.
Speaker 3: That's deciding the safest, most efficient path and then executing
Speaker 3: the commands to follow it. Firma's point is that AI
Speaker 3: streamlines all three of these pillars simultaneously. It perceives faster,
Speaker 3: it localizes more accurately, and it plans more aggressive paths
Speaker 3: more safely than the old stopping go method.
Speaker 2: And there's a bonus here too. It's not just about driving, right.
Speaker 2: The report mentions that the AI is also helping with
Speaker 2: the science itself.
Speaker 3: This is where it gets really exciting for the researchers
Speaker 3: back on Earth. The rover takes thousands upon thousands of images.
Speaker 3: A whole team of humans can't possibly look at every
Speaker 3: pixel of every image with the same level of scrutiny.
Speaker 2: We get tired, we get distracted, we miss things, we
Speaker 2: need coffee breaks, we blink exactly.
Speaker 3: But the AI doesn't get tired. The report says it
Speaker 3: can scour huge volumes of rover images. It can flag
Speaker 3: interesting surface features, maybe a strange rock formation or a
Speaker 3: discolored patch of soil that might indicate a specific mineral
Speaker 3: deposit that a human might have just scrolled past.
Speaker 2: So the AI acts as a scout, a science scout.
Speaker 3: It acts as a primary filter. It filters all the
Speaker 3: noise so the human scientists can focus on the discoveries.
Speaker 3: It basically taps them on the shoulder and says, hey,
Speaker 3: you should really look at this weird rock over here.
Speaker 2: That changes the dynamic completely. Instead of humans telling the
Speaker 2: rover what to look at, the rover is not telling
Speaker 2: the humans what they should be looking at.
Speaker 3: It's a partnership. It's a genuine collaboration, and it makes
Speaker 3: the science return per day, per dollar much much higher.
Speaker 3: You aren't wasting time looking at boring dust. You are
Speaker 3: jumping straight to the anomalies.
Speaker 2: So let's look to the future beyond Jesero Crater. Because
Speaker 2: if this works here, surely they aren't going to stop
Speaker 2: with just one rover. This has to be the plan
Speaker 2: for everything going forward.
Speaker 3: Oh absolutely, this is just the proof of concept. The
Speaker 3: vision is to scale this up significantly.
Speaker 2: We talk about driving two hundred maybe two hundred and
Speaker 2: fifty meters what's the next goal. What are they aiming for?
Speaker 3: Kilometer scale drives. Vanni Varma talks about minimizing operator workloads
Speaker 3: so the rovers can handle long all distances completely on
Speaker 3: their own. Imagine a rover that you tell go to
Speaker 3: that mountain range five miles away, and you don't talk
Speaker 3: to it again for a week. It just reports in
Speaker 3: when it gets there.
Speaker 2: That would exponentially increase the amount of Mars we can explore,
Speaker 2: cover entire regions, not just single craters in a single mission.
Speaker 3: It would, but it's not just for rovers. Matt Wallace,
Speaker 3: who is the manager of JPL's Exploration System's office, talks
Speaker 3: about edge applications.
Speaker 2: Edge applications, what does that mean?
Speaker 3: Computing at the edge, meaning right there on the device itself,
Speaker 3: not in a server back on Earth. He's talking about
Speaker 3: expanding this tech to helicopters, to drones, and to other
Speaker 3: surface elements.
Speaker 2: We've seen the Ingenuity helicopter, which was absolutely amazing, but
Speaker 2: imagine a whole swarm of autonomous drones mapping a canyon
Speaker 2: without any human input.
Speaker 3: Precisely flying on Mars is incredibly difficult. You don't have
Speaker 3: time for a human pilot to correct for a gust
Speaker 3: of wind. The atmosphere is less than one percent as
Speaker 3: dense as Earth's, the winds are unpredictable. The AI needs
Speaker 3: to handle stability and navigation instantly. You need that split
Speaker 3: second reaction time that only an onboard AI can provide.
Speaker 2: And Wallace brings up this beautiful concept. He calls it
Speaker 2: collective wisdom.
Speaker 3: This is fascinating. He talks about t t's training these
Speaker 3: AI systems with the knowledge of NASA's best engineers, scientists,
Speaker 3: and even astronauts.
Speaker 2: What does that mean in practice? How do you do that?
Speaker 3: You are essentially taking the brain power, the experience, the
Speaker 3: intuition of the best human explorers and baking it directly
Speaker 3: into the AI model. You feed the model every drive
Speaker 3: decision ever made by a human planner. You show it
Speaker 3: every rock a geologist ever flagged is interesting. You teach
Speaker 3: it what a safe slope looks like according to the
Speaker 3: most experienced driver.
Speaker 2: So the AI isn't starting from scratch. It's standing on
Speaker 2: the shoulders of well human giants.
Speaker 3: It creates a system where the rover drives with the
Speaker 3: caution of a veteran engineer and the curiosity of a
Speaker 3: lead scientist. It democratizes that expertise and puts it inside
Speaker 3: the robot. It's preserving the institutional knowledge of NASA and
Speaker 3: exporting it to another planet.
Speaker 2: And the ultimate goal of all this where does this
Speaker 2: road lead.
Speaker 3: It leads to establishing the infrastructure for a permanent human
Speaker 3: presence beyond.
Speaker 2: Earth, first on the Moon with the Artemis.
Speaker 3: Program on the Moon, and eventually taking the US to
Speaker 3: Mars and beyond.
Speaker 2: This is the key takeaway for me. This AI isn't
Speaker 2: about replacing humans, It's about building the road for us
Speaker 2: to get there exactly.
Speaker 3: We can't send humans to Mars safely if we don't
Speaker 3: have autonomous systems that can maintain the habitat, scout the terrain,
Speaker 3: and ensure safety when we aren't looking. That communication lag
Speaker 3: makes manual control not just inefficient, but dangerous for life
Speaker 3: support systems. If an oxygen generator fails, you need an
Speaker 3: AI to fix it now, not twenty minutes from now,
Speaker 3: when the alarm finally reaches Houston.
Speaker 2: We need machines that can think for themselves to keep
Speaker 2: us safe.
Speaker 3: That's the only way it works.
Speaker 2: So let's wrap this up. We've covered a lot of
Speaker 2: ground pun absolutely intended we have. Let's just summarize the
Speaker 2: key points for everyone listening.
Speaker 3: First, the headline Perseverance has successfully used generative AI, specifically
Speaker 3: anthropics claud to navigate the Jazuro Crater on Mars completely
Speaker 3: on its own.
Speaker 2: It drove over fourteen hundred feet total across two in
Speaker 2: December twenty twenty five. This is a massive leap from
Speaker 2: the inch by inch crawls of past missions.
Speaker 3: And it did it safely. The entire system is backstock
Speaker 3: by a digital twin at JPL that verifies over five
Speaker 3: hundred thousand variables before a single wheel turns on the
Speaker 3: Red planet.
Speaker 2: This effectively signals the end of the joystick era, which
Speaker 2: was really a myth anyway, and the beginning of true,
Speaker 2: meaningful autonomy in deep space exploration.
Speaker 3: It's a turning point. We are no longer micromanaging our
Speaker 3: robox from one hundred and forty million miles away. We
Speaker 3: are empowering them to be our proxies.
Speaker 2: And that leads me to my final thought. And I
Speaker 2: want you to chew on this, and I want everyone
Speaker 2: listening to chew on this as well.
Speaker 3: Oh fork.
Speaker 2: We talked about that concept of collective wisdom. We talked
Speaker 2: about the rover flagging its own science targets. So if
Speaker 2: the rover can now perceive the world, localize itself, plan
Speaker 2: its own path, and decide for itself what is scientifically
Speaker 2: interesting enough to show us? At what point does it
Speaker 2: stop being just a tool?
Speaker 3: That is the question, isn't it?
Speaker 2: At what point does the rovers start being a remote
Speaker 2: controlled car and start being a partner exploring with us?
Speaker 2: And if it flags a discovery, if it's the one
Speaker 2: that finds the fossil or the evidence of past water,
Speaker 2: or you know, life, whose discovery is it? Is it
Speaker 2: the scientist back in Pasadena who looks at the photo,
Speaker 2: or is it Perseverance's discovery?
Speaker 3: It blurs the line between creator and creation. As the
Speaker 3: machines get smarter, the credit gets harder to assign. But perhaps,
Speaker 3: you know, maybe that's the point. We are extending our
Speaker 3: consciousness to another world. We are building our successors, our
Speaker 3: partners in expiation.
Speaker 2: It's a fascinating time to be alive and a fascinating
Speaker 2: time to be watching the stars. Thank you for joining
Speaker 2: us on this exploration of the future of Mars.
Speaker 3: It was a pleasure.
Speaker 2: We'll see on the next one.
Speaker 4: Keep looking up.
Speaker 3: The school system