Speaker 1: Welcome to Bedtime Astronomy. Explore the wonders of the cosmos
Speaker 1: with our soothing Bedtime Astronomy 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: Okay, let's unpack this. We are basically building the smartest
Speaker 2: machines in human history, right to unlock the secrets.
Speaker 3: Of the universe, right, that is the goal.
Speaker 2: But it turns out we're accidentally teaching them to be
Speaker 2: completely blind.
Speaker 3: Yeah. It is really the ultimate irony of modern science. Honestly.
Speaker 3: I mean, we are not dealing with a hardware failure here, right,
Speaker 3: you know, a glitch in the code. What we are
Speaker 3: looking at is a fundamental flaw in the actual philosophy
Speaker 3: of how a machine learns to understand reality.
Speaker 2: Which is wild to think about, because I mean, think
Speaker 2: about the expectation of precision. We carry into something like
Speaker 2: a medical diagnosis. Oh, absolutely, Like you fall, you break
Speaker 2: your arm, You go to the hospital, the doctor takes
Speaker 2: an X ray, clips it to the lightboard, and there
Speaker 2: it is.
Speaker 3: It's right in front of you exactly, just.
Speaker 2: A jagged white line cutting across the dark bone. It's binary,
Speaker 2: you know, broken or not broken.
Speaker 3: And that stark visual evidence is so comforting because it
Speaker 3: maps perfectly onto our desire for reality to be easily categorized.
Speaker 2: Yes, we want the universe to be just as clean
Speaker 2: as that X ray.
Speaker 3: We really do.
Speaker 2: We want to point these massive, powerful telescopes into the dark,
Speaker 2: take a cosmic X ray, and just have an AI
Speaker 2: point to a jagged white line and say, boom, there
Speaker 2: is the new law of physics.
Speaker 3: But the cosmos violently refuses to be categorized that easily.
Speaker 3: It really does the phenomena that cosmologists are hunting for
Speaker 3: right now. They do not leave clean jagged lines. They
Speaker 3: leave whispers.
Speaker 2: Whispers. I like that.
Speaker 3: Yeah, they leave incredibly subtle statistic variations and the way
Speaker 3: galaxies cluster together over billions of years. And to find
Speaker 3: those whispers, we have essentially handed the mathematical keys to
Speaker 3: the universe over to artificial neural.
Speaker 2: Networks, which makes sense on paper, right because they can
Speaker 2: process so much data.
Speaker 3: It makes total sense. But the massive often overlook challenge
Speaker 3: here is that the prior knowledge we feed into those networks.
Speaker 2: The baseline rules we teach them exactly.
Speaker 3: The foundational rules we give them just so they can function.
Speaker 3: That can actually become the very obstacle that prevents them
Speaker 3: from recognizing a revolutionary discovery.
Speaker 2: Okay, So imagine someone hands you a pair of highly
Speaker 2: advanced glasses, right, Okay, and they tell you these glasses
Speaker 2: will let you see the true, unvarnished architecture of a
Speaker 2: completely alien planet.
Speaker 3: Sounds great so far.
Speaker 2: Right, But there is a hidden catch. The lenses have
Speaker 2: been perfectly tinted, perfectly calibrated by your guide to only
Speaker 2: highlight shapes and colors that already exist in your own backyard.
Speaker 3: Ah. I see where you're going with this.
Speaker 2: So you step onto this completely alien landscape, a place
Speaker 2: with entirely new physics and weird new structures. But everywhere
Speaker 2: you look, the glass is aggressively filled to the light.
Speaker 3: So all you see are what familiar oak trees and
Speaker 3: picket fences.
Speaker 2: Exactly, you are staring directly at the unknown, but your
Speaker 2: tools are forcing you to see the known.
Speaker 3: That is precisely the trap we are inadvertently building for
Speaker 3: our most advanced AI.
Speaker 2: It's terrifying when you would like that. Yeah, So to
Speaker 2: grasp the sheer scale of this trap, I feel like
Speaker 2: we have to do so.
Speaker 3: Right now, the blueprint of the universe is dominated by
Speaker 3: the Standard Model of.
Speaker 2: Cosmology, which has a very catchy name, right Oh.
Speaker 3: Yeah, LAMB to CDM, very clunky LAMB to CDM.
Speaker 2: But it is the raining heavyweight champion of physics right now.
Speaker 3: It absolutely is. It tells the mathematical story of how
Speaker 3: we got from the literal dawn of time to the vast,
Speaker 3: complex cosmic web we observe in the night sky today.
Speaker 2: And it does a really good job at certain things,
Speaker 2: doesn't it.
Speaker 3: It does a brilliant job. Actually. It explains the rate
Speaker 3: at which the universes expanding, for one, right, and it
Speaker 3: accounts for the existence of cold dark matter, which is
Speaker 3: basically the invisible scaffolding that holds galaxies together.
Speaker 2: Okay, so lamba CDM is this monumental achievement of human intellect.
Speaker 3: It works, It provides a highly accurate description of the
Speaker 3: large scale structure of the.
Speaker 2: Cosmos, and there's always a button always.
Speaker 3: Every physicist knows it is an incomplete story, ye it is.
Speaker 3: It's a brilliant rough.
Speaker 2: Draft, right because the closer we look the glorier that
Speaker 2: initial cosmic X ray becomes.
Speaker 3: Exactly, observations have started pointing toward anomalies that simply do
Speaker 3: not fit neatly into that lambda CDM framework.
Speaker 2: So scientists are desperate to find what they broadly just
Speaker 2: call new physics.
Speaker 3: Right, new physics. We are talking about universe altering variables.
Speaker 2: Here, Like, what's an example of that?
Speaker 3: Well, for instance, the effects of massive neutrinos okay, or
Speaker 3: modified theories of gravity that suggest our fundamental understanding of
Speaker 3: how attracts mass might need a really massive overhaul.
Speaker 2: Wow, So changing gravity itself.
Speaker 3: Yeah. And there is even the tantalizing possibility of an
Speaker 3: evolving dark.
Speaker 2: Energy meaning it changes, right, a.
Speaker 3: Repulsive force that doesn't just stay constant, but actually changes
Speaker 3: its behavior and strength over a vast cosmic time scale.
Speaker 2: That is mind bending. But exploring those possibilities requires a
Speaker 2: completely different approach to science, doesn't it.
Speaker 3: It really does. I mean, think about how normal science works.
Speaker 3: If a biologist wants to test a new variable, they
Speaker 3: just set up a controlled experiment in a Petrie dish.
Speaker 2: Right, they had a chemical see what the bacteria do exactly?
Speaker 3: Or if an engineer wants to test a new aerodynamic shape.
Speaker 3: They build a model and they put it in a
Speaker 3: wind tunnel.
Speaker 2: But you can't exactly put the universe in a Petrie dish.
Speaker 3: No you cannot. You can't just tweak the dial on
Speaker 3: the gravitational constant of the real universe and observe what
Speaker 3: happens to a galaxy cluster.
Speaker 2: I mean, I hope not.
Speaker 3: That sounds dangerous, right, So the only option left is
Speaker 3: to build a fake universe.
Speaker 2: You literally have to construct a digital cosmos from scratch.
Speaker 3: Which it brings us to the reliance on massive, extraordinarily
Speaker 3: detailed computer simulations.
Speaker 2: Like the Quixote simulations.
Speaker 3: Yes, the Keyote simulations are a huge cornerstone of this
Speaker 3: kind of research. The methodology here is just fascinating.
Speaker 2: How does it actually work?
Speaker 3: Well? Researchers take a perfectly identical region of a virtual universe,
Speaker 3: just a massive cube of simulated space, and they run
Speaker 3: it through time over and over again. Okay, but each
Speaker 3: time they run it they change the underlying mathematical laws
Speaker 3: of physics.
Speaker 2: So they are essentially running an ab test on reality.
Speaker 3: That is exactly what it is, an ab test on
Speaker 3: the universe. They take the standard lambda CDM model and
Speaker 3: they hit.
Speaker 2: Play and they just watch what happens.
Speaker 3: Yeah, they watch how the dark matter and the gas
Speaker 3: coalesce into galaxies over billions of simulated years.
Speaker 2: Okay, that's the a test, right.
Speaker 3: Then they take that exact same starting configuration, that same
Speaker 3: digital cube, but this time they inject the new physics.
Speaker 2: So they sprinkle in some massive neutrinos, or.
Speaker 3: They tweak the laws of gravity exactly, and then they
Speaker 3: hit play.
Speaker 2: Again and then comes the comparison.
Speaker 3: Yes, yes, they lay the final snapshots of these two
Speaker 3: virtual universes side by.
Speaker 2: Side, and I imagine they aren't looking for like giant
Speaker 2: neon signs saying gravity changed here.
Speaker 3: No, not at all. What they're looking for are not
Speaker 3: massive glaring differences, but incredibly subtle structural shift.
Speaker 2: What give me a.
Speaker 3: Visual like, did a massive dark matter halo stretch may
Speaker 3: be a fraction of a percent more in the modified universe?
Speaker 2: Wow, fraction of a percent?
Speaker 3: Or did the cosmic web, you know, the massive filaments
Speaker 3: of matter that connect galaxy clusters, did they clump together differently?
Speaker 2: Okay, So if you are listening to this, you might
Speaker 2: be thinking what I'm thinking right now, which is wait
Speaker 2: a second, mike smartphone can process billions of calculations a second. Right,
Speaker 2: we have server farms that span entire city blocks. Why
Speaker 2: is running a computer simulation such a bottlemex.
Speaker 3: It's a fair question.
Speaker 2: Why can't we just generate a million of these virtual
Speaker 2: universes by next Tuesday and just comb through the.
Speaker 3: Data, Because that intuition completely underestimates the sheer computational violence
Speaker 3: of an n body universe simulation.
Speaker 2: Computational violence. I love that phrase.
Speaker 3: It really is. You are not rendering a static image.
Speaker 3: You are calculating the gravitational pull of billions upon billions
Speaker 3: of individual particles of matter.
Speaker 2: Let's try to ground this in a visual for everyone. Okay, yeah,
Speaker 2: imagine rendering a highly detailed four K open world video
Speaker 2: game environment.
Speaker 3: Oh, like a massive RPG exactly.
Speaker 2: The computer has to calculate the lighting, the shadows, the
Speaker 2: physics of the wind moving through the leaves on a tree.
Speaker 3: The reflections in a puddle on the ground.
Speaker 2: Right, and if you blow up a building in the game,
Speaker 2: the physics engine has to track the trajectory of every
Speaker 2: single piece of debris.
Speaker 3: Which takes a lot of processing power.
Speaker 2: Even with the best graphics cards on Earth. If there's
Speaker 2: too much happening on screen, the game lags. The processor
Speaker 2: simply cannot do the math fast enough.
Speaker 3: It drops frames.
Speaker 2: Right now, take that video game environment, but instead of
Speaker 2: a forest or a city block, you have to render
Speaker 2: every single massive structure, every galaxy, every dark matter halo
Speaker 2: in a swath of the cosmos, millions of light years across.
Speaker 3: And crucially, and this is the part that breaks supercomputers,
Speaker 3: every single particle in that simulation is interacting with every
Speaker 3: other particle simultaneously.
Speaker 2: Wait, really, every single one.
Speaker 3: Yes, because gravity has infinite range. If you move one
Speaker 3: particle a millimeter, it's gravitational pull on a particle a
Speaker 3: billion light years away technically changes Oh my god. And
Speaker 3: so you have to recalculate the entire system.
Speaker 2: Every time anything moves.
Speaker 3: And you have to do this frame by frame, step
Speaker 3: by microscopic timestep over a timeline of fourteen billion years.
Speaker 2: That is just I can't even wrap my head around
Speaker 2: that math.
Speaker 3: The sheer volume of mathematical integration required is unfathomable. It
Speaker 3: literally brings the most powerful supercomputers on the planet to
Speaker 3: their knees.
Speaker 2: But those simulations are non negoti right, Like we have
Speaker 2: to do them.
Speaker 3: They are the only way forward. Those tiny variations in
Speaker 3: how madd clusters in a Quixote simulation compared to a
Speaker 3: standard model simulation, those are the exact breadcrumbs scientists are
Speaker 3: desperately looking for.
Speaker 2: Right if we want to find the faint whispers of
Speaker 2: new physics hidden in the grand structure of the cosmos,
Speaker 2: we cannot afford to cut corners on the physics engine itself.
Speaker 3: We can't. But the computational cost in terms of time,
Speaker 3: electricity and just hardware or wear and tearor is simply unsustainable.
Speaker 2: So researchers hit a wall.
Speaker 3: A massive wall brute force computing. Just building a bigger, hotter,
Speaker 3: more expensive supercomputer was no longer a viable.
Speaker 2: Path because it just scales too poorly.
Speaker 3: Exactly. They had to find a radically smarter way to
Speaker 3: process the data. They couldn't change the complexity of the universe,
Speaker 3: so they had to change the efficiency of the observer.
Speaker 2: And this is where we see this brilliant pivot.
Speaker 3: Yes, a total paradigm shift.
Speaker 2: Researchers like Vienna Krishnaraj and Adrian Bayer from Princeton University
Speaker 2: in the Flatirn Institute, they turned to a specific machine
Speaker 2: learning technique to basically bypass this.
Speaker 3: Bottleneck, and it's called transfer learning.
Speaker 2: Transfer learning. Oh okay, So instead of taking a completely
Speaker 2: blank artificial neural network and just forcing it to learn
Speaker 2: from the most complex, expensive new physics simulations right out.
Speaker 3: Of the gate, which would take forever, right.
Speaker 2: They completely changed the curriculum.
Speaker 3: They utilized pre training. They took the AI and they
Speaker 3: first trained it exclusively on the simpler, vastly cheaper simulations, the.
Speaker 2: Ones based entirely on the standard LAMB to CDM model.
Speaker 3: Exactly. They allowed the AI to build a baseline mathematical
Speaker 3: understanding of how a standard unexceptional universe operates.
Speaker 2: So they taught at the basics first right.
Speaker 3: Only after the neural network had established its foundational weights
Speaker 3: and biases. Only after it had mastered the basic rules
Speaker 3: of gravity and clustering, did they introduce the sophisticated, expensive models, the.
Speaker 2: Ones containing the modified gravity or the massive neutrinos.
Speaker 3: Yes, Adrian Bayer had perfect analogy for this.
Speaker 2: Oh, I love a good analogy, let's hear it.
Speaker 3: He compares this approach to the way humans learn from textbooks.
Speaker 3: You do not hand a first year medical student the
Speaker 3: most advanced cutting Edge Journal on Experimental Neurosurgery on their
Speaker 3: first day of class.
Speaker 2: No, they would just cry. It would be entirely incomprehensible.
Speaker 3: Right, You give them a basic anatomy textbook. You have
Speaker 3: the memorize the standard placement of bones, organs and arteries,
Speaker 3: the foundation. They learn the basic book to grasp the
Speaker 3: foundational concepts, and once that architecture is mapped in their mind,
Speaker 3: they can move on to the really complicated book that.
Speaker 2: Makes total sense. Vina Krishniaj, the lead author of the study.
Speaker 2: She pointed out that this phased strategy prevents the AI
Speaker 2: from having to digest everything.
Speaker 3: All at once, exactly, because when a neural network starts
Speaker 3: from scratch, it doesn't know what a galaxy is.
Speaker 2: It doesn't know what empty space is.
Speaker 3: Right, It is just looking at raw numbers. But by
Speaker 3: pre training it on the cheaper simulations, it learns to
Speaker 3: recognize the broad, easily calculable rules of the standard model.
Speaker 2: So then when it's finally confronted with the complex new physics,
Speaker 2: it doesn't have to relearn what gravity is from scratch exactly.
Speaker 3: It only has to fine tune its existing knowledge to
Speaker 3: account for the subtle anomalies.
Speaker 2: Here's where it gets really interesting to me, because it's
Speaker 2: exactly like teaching a kid to ride a bike. Oh
Speaker 2: how so, Well, you don't take a six year old,
Speaker 2: put them on a twenty one speed carbon fiber mountain
Speaker 2: bike and just shove them down a steep, rocky trail.
Speaker 2: I mean, I wouldn't you start them on a simple
Speaker 2: tricycle on a flat driveway. Right. You let their brain
Speaker 2: build the fundamental muscle memory of balance, of pedaling, of
Speaker 2: how turning the handlebars correlates with changing direction, the basics.
Speaker 2: The tricycle is the simple LAMB to CDM simulation, and
Speaker 2: once their nervous system has mapped those basic physical relationships,
Speaker 2: transferring those skills to the complex mountain bike is infinitely
Speaker 2: faster and easier than if they had started on the
Speaker 2: mountain bike on day one.
Speaker 3: That is a brilliant way to put it, and the
Speaker 3: results of applying this textbook strategy to cosmology were absolutely staggering.
Speaker 2: By employing transfer learning, the researchers were able to reduce
Speaker 2: the need for those incredibly expensive complex simulations by a
Speaker 2: factor of more than ten.
Speaker 3: A factor of ten. We really need to sit with
Speaker 3: that for a second.
Speaker 2: Yeah, that's not just a small improvement, It.
Speaker 3: Is a revolution and workflow. A project that would have
Speaker 3: required a decade of constant supercomputer time, consuming millions of
Speaker 3: dollars in electricity and hardware, wear and tear, can suddenly
Speaker 3: be completed in a single year.
Speaker 2: It completely shifts the paradigm of what is actually possible
Speaker 2: in astronomical research.
Speaker 3: It does, and the underlying mechanism here mirrors the exact
Speaker 3: techniques driving the current explosion in generative AI and large
Speaker 3: language models.
Speaker 2: Right like the chatbots everyone uses.
Speaker 3: Now exactly The entire modern artificial intelligence industry relies on
Speaker 3: foundation models.
Speaker 2: Where you pre train a massive model on billions of
Speaker 2: pages of basic human text so fundament understands grammar and context,
Speaker 2: and then.
Speaker 3: You fine tune it for a specific task like writing
Speaker 3: code or drafting legal documents.
Speaker 2: Which dramatically speeds up inference and slashes training costs.
Speaker 3: The researcher successfully took this cornerstone of computer science and
Speaker 3: applied it to the structural analysis of the universe itself.
Speaker 2: So saving ten times the computing power sounds like the
Speaker 2: absolute ultimate triumph.
Speaker 3: It really does.
Speaker 2: It feels like the moment in the movie where the
Speaker 2: scientists cracked the code. They popped the champagne and the
Speaker 2: credits role it would be.
Speaker 3: If the story ended with computational efficiency. Ah, but speed
Speaker 3: comes at a psychological cost.
Speaker 2: Because learning from a basic textbook creates a very specific,
Speaker 2: incredibly dangerous kind of blindness. Yes, this is where the
Speaker 2: triumph of transfer learning takes a really sharp turn into
Speaker 2: the psychological pitfalls of artificial intelligence.
Speaker 3: The researchers uncovered a severe downside to their brilliant shortcut.
Speaker 3: It's a phenomenon known as negative transfer.
Speaker 2: Negative transfer it sounds what exactly is it?
Speaker 3: Negative transfer is exactly what it sounds like. It happens
Speaker 3: when an AI relies so heavily on the familiar foundational
Speaker 3: patterns that learned during its pre training phase that it
Speaker 3: completely misses or misinterprets the evidence of something genuinely new.
Speaker 2: So the AI becomes heavily biased by its own baseline education.
Speaker 3: Yes, the pre trained neural network is no longer a
Speaker 3: blank slate. Its internal parameters have been deeply, deeply shaped
Speaker 3: by the standard model.
Speaker 2: It is internalized the assumption that LAMB to CDM is
Speaker 2: the definitive truth.
Speaker 3: Exactly, So, when the researchers finally introduce the complex simulations
Speaker 3: containing the new physics, the massive neutrinos or the modified gravity.
Speaker 3: The AI does not view this new data objectively.
Speaker 2: It interprets the unfamiliar information entirely through the rigid, tinted
Speaker 2: lens of what it already knows.
Speaker 3: To really understand the mechanics of this danger, we need
Speaker 3: to extend that medical diagnosis analogy you brought up earlier.
Speaker 2: Okay, let's do it.
Speaker 3: Imagine that medical student, learning from the introductory textbook. They
Speaker 3: spend months memorizing the symptoms of the common cold, A
Speaker 3: running nose, a slight fever, lethargy.
Speaker 2: Very standard stuff.
Speaker 3: Right, Those patterns are neurologically ingrained. The heuristic is set
Speaker 3: in their brain. So years later they are a practicing
Speaker 3: physician and a patient walks into the clinic with a
Speaker 3: running nose, a slight fever, and lethargy, and.
Speaker 2: The doctor's brain, seeking efficiency, immediately fires off the established
Speaker 2: pattern common cold.
Speaker 3: The cognitive path of least resistance exactly. But what the
Speaker 3: doctor fails to realize because they are so blinded by
Speaker 3: the familiarity of the symptoms, is that the patient actually
Speaker 3: has a vanishingly rare, highly lethal autoimmune disease that just
Speaker 3: happens to share those exact early symptoms.
Speaker 2: Wow. So in this scenario, the doctor's existing knowledge, the
Speaker 2: foundational education from the introductory textbook, actively causes harm.
Speaker 3: It acts as a set of cognitive blinders.
Speaker 2: Because they have seen this symptom cluster a thousand times before,
Speaker 2: their brain forcefully encourages the wrong, familiar conclusion.
Speaker 3: Yes, if that doctor had been a completely blank slate,
Speaker 3: or if they hadn't relied on the heuristic shortcut, they
Speaker 3: might have looked closer.
Speaker 2: They might have ordered bloodwork, they might have.
Speaker 3: Noticed a subtle anomaly in the white blood cell count
Speaker 3: and made the correct diagnosis.
Speaker 2: But their prior knowledge actively pushed them away from the truth.
Speaker 3: And that cognitive trap is negative transfer, and the AI
Speaker 3: systems observing the simulated universes fell right into it.
Speaker 2: That is wild.
Speaker 3: In some of the simulations, the mathematical signatures left behind
Speaker 3: by the new physics produced patterns that superficially resembled the
Speaker 3: patterns the AI had already associated with the standard cosmological model.
Speaker 2: So is the AI actually failing in these moments or
Speaker 2: is it just doing exactly what we trained it to do?
Speaker 3: That is the big question.
Speaker 2: I mean, we built the neural network to find patterns,
Speaker 2: to optimize for efficiency, and to rely on its prior
Speaker 2: experience to minimize its error rate. Can we really blame
Speaker 2: the machine for being an exceptional student of a flawed curriculum.
Speaker 3: No, we can't. It is entirely a philosophical failure on
Speaker 3: our part, not a computational one.
Speaker 2: The algorithm is executing its mathematical gradient dissent flawlessly.
Speaker 3: Flawlessly. The mistake belongs entirely to the architects of the
Speaker 3: learning process. By pre training the AI heavily on the
Speaker 3: lambda CDM model, we implicitly taught the machine that the
Speaker 3: standard universe is the default state of reality.
Speaker 2: We hard coded a conservative bias into its worldview.
Speaker 3: We did so. When the AI encounters a slight deviation
Speaker 3: in the cosmic web, its first mathematical instinct is not,
Speaker 3: oh wow, this is a revolutionary discovery of new physics.
Speaker 2: It's more like, how can I slightly adjust my standard
Speaker 2: model parameters to explain away this weird data?
Speaker 3: Exactly. It's the dark side of efficiency. We desperately wanted
Speaker 3: the AI to process the universe faster, and the price
Speaker 3: we paid for that computational speed was the machine's open mindedness.
Speaker 2: It makes the assumption that the familiar answer is always
Speaker 2: the most likely answer.
Speaker 3: And when your entire scientific mission is to hunt for
Speaker 3: the unknown, to map the fringes of uncharted physics, an
Speaker 3: assumption of familiarity is absolutely fatal, fatal.
Speaker 2: So to see just how fatal this negative transfer can be,
Speaker 2: we have to look at the specific collision point in
Speaker 2: the researcher study, the.
Speaker 3: Exact moment the simulated universe through a curveball that the
Speaker 3: brilliantly trained AI completely misread.
Speaker 2: Right, And this blindness became incredibly apparent when the researchers
Speaker 2: fed the AI the simulations that included massive neutrinos.
Speaker 3: Now, massive neutrinos are fascinating.
Speaker 2: Tell me about them.
Speaker 3: They are fundamental particles. They are incredibly abundant, incredibly tiny,
Speaker 3: and they barely interact with normal matter at all.
Speaker 2: Right, Like, trillions of them are passing through your body
Speaker 2: right now as we speak, exactly.
Speaker 3: But crucially, they do have a tiny bit of mass,
Speaker 3: and because they travel so incredibly fast, they essentially act
Speaker 3: as a cosmic smoothing agent.
Speaker 2: A smoothing agent.
Speaker 3: Yeah, they zip through the universe, and their gravity subtly
Speaker 3: smears out the dense clustering of the cosmic web.
Speaker 2: So the researchers wanted to see if the AI could
Speaker 2: detect this subtle smearing effect.
Speaker 3: Yes, but the AI walked directly into a trap because
Speaker 3: of the pre training. Right, the observational signatures of a
Speaker 3: massive neutrino, the specific way they alter the distribution of matter,
Speaker 3: look almost indistinguishable from the changes caused by a completely
Speaker 3: standard mundane parameter in the LAMB to CDM model.
Speaker 2: The parameter known simply as sigma eight sigma eight, which
Speaker 2: is basically the universe's clumping factor exactly.
Speaker 3: It is the measure of how strongly matter groups together
Speaker 3: over time. If you turn up the sigma eight dial,
Speaker 3: galaxies cluster more tightly.
Speaker 2: If you turn it down, the universe looks smoother and
Speaker 2: more spread out.
Speaker 3: So consider the AI's perspective here. It is looking at
Speaker 3: a universe where the presence of massive neutrinos has slightly
Speaker 3: smoothed out the galaxy clusters.
Speaker 2: But the AI doesn't know about massive neutrinos.
Speaker 3: Yet, It has no idea. It only knows its basic textbook,
Speaker 3: so it looks at the smoothed out cosmic web and includes, Oh,
Speaker 3: the universe isn't filled with exotic new particles.
Speaker 2: This is just a standard universe where the sigma eight
Speaker 2: clumping factor happens to be dialed down a bit.
Speaker 3: Precisely, the visual mathematical end result of both of these
Speaker 3: distinct physical forces looks nearly identical to the algorithm.
Speaker 2: The AI looked at a paradigm shifting particle and confidently
Speaker 2: misidentified it as a standard variable it did.
Speaker 3: Vina Krishnaraj summarized the mechanism behind this failure brilliantly.
Speaker 2: What'd you say?
Speaker 3: She noted that the negative transfer is not just some
Speaker 3: random glitch in the code. It is driven by what
Speaker 3: she calls underlying physical degeneracies in the model.
Speaker 2: Physical degeneracies. Yes, that is a dense, intimidating phrase, very dense, but.
Speaker 3: It represents one of the most terrifying concepts in all
Speaker 3: of physics.
Speaker 2: Okay, let's break that down. What exactly does a degeneracy mean?
Speaker 2: When we were talking about the laws of nature.
Speaker 3: Degeneracy occurs when two completely separate, fundamentally different physical process
Speaker 3: is produce the exact same observable signature.
Speaker 2: So you have cause A and you have cause B.
Speaker 3: They operate on entirely different mechanical principles, but the final result,
Speaker 3: the physical fingerprint left behind at the scene of the crime,
Speaker 3: is completely indistinguishable.
Speaker 2: Oh, I've got a great way to picture this. Think
Speaker 2: of it like a forensic investigation in your own front yard.
Speaker 2: You wake up in the morning, you look out your
Speaker 2: window and you see wet pavement on your driveway. You
Speaker 2: are observing a physical signature water on concrete. But how
Speaker 2: did it get there? Did a brief localized rainstorm pass
Speaker 2: through ten minutes before you woke up, that's cause A.
Speaker 2: Or did your neighbor leave their high powered lawn sprinkler
Speaker 2: on overnight and the water overshot onto your property that's
Speaker 2: caused B.
Speaker 3: And the end result, the physical reality of the wet
Speaker 3: concrete is identical in both scenarios exactly.
Speaker 2: So, if you are trapped inside your house and all
Speaker 2: you have is that one visual observation of the wet pavement,
Speaker 2: how do you determine the truth?
Speaker 3: The answer there is you have to look for secondary clues.
Speaker 2: Right. You look to see if the leaves on the
Speaker 2: top branches of the trees are wet, which would indicate rain.
Speaker 3: Or you look to see if the neighbor's hose is
Speaker 3: currently warm or pressurized.
Speaker 2: But and here is the kicker. If you have been
Speaker 2: trained your entire life to believe that rain doesn't exist,
Speaker 2: Oh wow, the only way water ever gets on a
Speaker 2: driveways from a sprinkler. You won't even bother looking for
Speaker 2: the secondary clues.
Speaker 3: Your brain will see the wet pavement, declare sprinkler, and
Speaker 3: completely stop thinking.
Speaker 2: And this is precisely why physical degeneracies force us to
Speaker 2: deeply question the limitations of both our artificial intelligence and
Speaker 2: our own observational tools.
Speaker 3: The universe is presenting a wet driveway. Is it massive
Speaker 3: neutrinos or is it a lower sigma eight slumping factor?
Speaker 2: If two massive cosmic realities look identical to an AI,
Speaker 2: it exposes a severe vulnerability in our methodology.
Speaker 3: It demands rigorous critical thinking. We have to step back
Speaker 3: and ask how many other fundamental truths of the universe
Speaker 3: are currently hiding behind these physicals degeneracies.
Speaker 2: How many times has our AI or even generations of
Speaker 2: human astronomers looked at the cosmic pavement, assumed it was
Speaker 2: just a standard sprinkler and completely missed the torrential rainstorm
Speaker 2: of new physics.
Speaker 3: It is a genuinely unsettling thought, isn't it. We are
Speaker 3: spending billions of dollars in decades of human capital building
Speaker 3: these unfathomably complex machines to find the ultimate truths of reality, and.
Speaker 2: They're getting tripped up by the cosmic equivalent of wet pavement.
Speaker 3: Because the negative transfer, the AI's blindness isn't a bug,
Speaker 3: as krishnararjs pointed out, it is driven by the degeneracies.
Speaker 3: The AI is actively being drawn into a trap that
Speaker 3: was set by the physical laws of the universe themselves.
Speaker 2: The critical challenge now is figuring out how to break
Speaker 2: the degeneracy.
Speaker 3: Because if the AI remains locked into its pre trained mindset,
Speaker 3: it will always default to the sigma eight explanation.
Speaker 2: It will always assume the sprinkler. Because the introductory textbook
Speaker 2: had studied spent fifty pages detailing the mechanics of sprinklers
Speaker 2: and never once mentioned the possibility of rain.
Speaker 3: It requires teaching the machine to value uncertainty, which is
Speaker 3: incredibly hard to program.
Speaker 2: So let's bring this back down to earth. Why does
Speaker 2: this matter right now? Why does this specific vulnerability in
Speaker 2: a simulated environment matter outside the walls of the flatirn Institute.
Speaker 3: Why should you listening to this care that a neural
Speaker 3: network got confused by some fake neutrinos. Exactly, It matters
Speaker 3: intensely because we are standing right on the precipice of
Speaker 3: a fundamentally new era in astronomy.
Speaker 2: Right the research demonstrating this negative transfer trap has so
Speaker 2: far only been tested on simulations.
Speaker 3: It was contained within the safe, controlled environment of a
Speaker 3: virtual cosmos.
Speaker 2: But the immediate next step for the scientific community is
Speaker 2: taking this transfer learning architecture and unleashing it on real
Speaker 2: astronomical observations.
Speaker 3: We are about to hand these AI systems the most
Speaker 3: important massive data sets in human history.
Speaker 2: We are entering an era of upcoming cosmological surveys that
Speaker 2: will collect unprecedented amounts high precision data about the actual universe.
Speaker 3: We are talking about fleets of advanced telescopes and satellites
Speaker 3: mapping billions of galaxies in three dimensions.
Speaker 2: Staring deeper into the past than ever before.
Speaker 3: And beaming back exabates of raw information. It is a
Speaker 3: tsunami of cosmic data that human minds simply do not
Speaker 3: have the capacity to process on their own.
Speaker 2: AI is no longer just a helpful tool. It is
Speaker 2: becoming the mandatory gatekeeper of human discovery.
Speaker 3: We have no choice but to use it. It will
Speaker 3: be the primary filter through which humanity perceives the cosmos.
Speaker 2: And that is exactly why this cognitive trap is so
Speaker 2: critical to understand. If the artificial intelligence acting as our
Speaker 2: ultimate gatekeeper is fundamentally biased toward old ideas, if.
Speaker 3: It is suffering from negative.
Speaker 2: Transfer, and assuming every single anomaly in the data is
Speaker 2: just a variation of a standard textbook variable, then humanity's
Speaker 2: scientific progress could literally stall out.
Speaker 3: We could have the definitive evidence for new reality breaking
Speaker 3: physics sitting right there on our hart drives.
Speaker 2: Proof of evolving dark energy, proof of modified gravity, just
Speaker 2: buried in the data from these massive new surveys.
Speaker 3: And the AI will just quietly categorize it as normal,
Speaker 3: file it away under land of CDM and move on.
Speaker 2: We would be completely tragically blind to our own discoveries.
Speaker 3: The researchers concluded their study with a stark warning, what
Speaker 3: was it? They stress that the scientific community must be
Speaker 3: acutely aware of this negative transfer and actively engineer ways
Speaker 3: to mitigate it, because while pre training undeniably speeds up
Speaker 3: inference and solves the computational bottleneck.
Speaker 2: It also, in their words, may hinder learning new physics.
Speaker 3: We are staring at a profound paradox in modern science.
Speaker 2: The paradox are the smart tool we engineer, a solution
Speaker 2: to overcome our own physical limitations, and the solution inherits
Speaker 2: our cognitive biases.
Speaker 3: The tools are getting faster, they're getting infinitely more capable
Speaker 3: of ingesting vast sums of information, But that very reliance
Speaker 3: on foundational models makes them inherently conservative.
Speaker 2: Right part of the AI gets at deeply understanding the
Speaker 2: standard model. The better it gets at passing the introductory test,
Speaker 2: the less capable it becomes of imagining a reality that
Speaker 2: breaks those rules.
Speaker 3: We are at risk of engineering a digital genius that
Speaker 3: entirely lacks imagination.
Speaker 2: We started this exploration talking about the desire for a
Speaker 2: clean diagnosis.
Speaker 3: Yes, the comforting certainty of the jagged white line on
Speaker 3: an X ray telling us exactly what the truth is.
Speaker 2: And we looked at how cosmologists built the lambda CDM
Speaker 2: model to serve as that initial comforting X ray of
Speaker 2: the universe's structure.
Speaker 3: But when the anomalies began to mount and that standard
Speaker 3: model proved incomplete, we had to build the incredibly complex
Speaker 3: virtual universes the keyhote simulations to test for the hidden
Speaker 3: variables of new physics.
Speaker 2: And because rendering the gravitational dance of a billion particles
Speaker 2: over cosmic time was too computationally exhausting, we devised a
Speaker 2: brilliant shortcut.
Speaker 3: We utilized transfer learning.
Speaker 2: We forced the artificial intelligence to learn on a tricycle
Speaker 2: before we handed the keys to the mountain bike.
Speaker 3: We gave it an introductory textbook to establish its baseline understanding, and.
Speaker 2: We achieved a monumental factor of ten reduction and computing costs.
Speaker 2: But in the pursuit of efficiency, we walked right into
Speaker 2: the trap of negative transfer.
Speaker 3: We taught the artificial mind to prioritize the familiar, We
Speaker 3: inadvertently allowed it to mistake the profound universe altering signatures
Speaker 3: of massive neutrinos for the mundane standard clustering of matter.
Speaker 2: We stumbled headfirst into the terrifying reality of physical degeneracies,
Speaker 2: where completely different causes were the exact same visual mask.
Speaker 3: And now, as we prepare to unleash these algorithms on
Speaker 3: the real universe, on the tidal wave of data from
Speaker 3: the upcoming surveys, we have to figure out how to
Speaker 3: UnBias the machine.
Speaker 2: We have to somehow teach an artificial brain how to
Speaker 2: unlearned its own core assumptions.
Speaker 3: We have to teach it to look for the secondary clues,
Speaker 3: to search for the wet leaves.
Speaker 2: Otherwise we might never see the rain. We will only
Speaker 2: ever see the sprinkler.
Speaker 3: It is going to require an incredibly delicate, intricate balance
Speaker 3: in the years to come.
Speaker 2: Pushing for computational speed and efficiency on one side.
Speaker 3: While aggressively guarding the capacity for true open minded discovery
Speaker 3: on the other.
Speaker 2: Which leaves us with one final, deeply mind expanding thought
Speaker 2: to carry with you.
Speaker 3: Okay, I'm ready.
Speaker 2: We have spent a lot of time breaking down the
Speaker 2: concept of physical degeneracies. The AI struggled because two radically
Speaker 2: different laws of physics resulted in the exact same visual
Speaker 2: clustering of matter.
Speaker 3: Right, it was a failure of the algorithm's interpretive logic.
Speaker 2: But what if the ultimate barrier isn't the AI's learning
Speaker 2: process at all?
Speaker 3: What do you mean?
Speaker 2: What if the universe itself has fundamental, insurmountable limits on
Speaker 2: what can be observed from our tiny vantage point here
Speaker 2: on Earth. Oh wow, if the cosmos intentionally hides the
Speaker 2: deepest truths of reality behind identical physical fingerprints.
Speaker 3: If entirely different fundamental laws cast the exact same shadow
Speaker 3: on our cave.
Speaker 2: Wall, exactly, how will we, or any super advanced AI
Speaker 2: we ever managed to build, ever truly know which set
Speaker 2: of laws governs reality.
Speaker 3: That is the beautiful, terrifying frontier of cosmology. We are
Speaker 3: attempting to translate a cosmic text where multiple completely different
Speaker 3: physical languages result in the exact same sentence.
Speaker 2: It is going to take a lot more than an
Speaker 2: introductory textbook to figure that out. Thank you so much
Speaker 2: for joining the conversation today and for exploring the furtless
Speaker 2: edges of the known universe with us. Keep questioning the
Speaker 2: X rays and never assume you know exactly why the
Speaker 2: pavement is wet.