Speaker 1: Welcome to the Sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: Imagine for a second that you are trying to predict
Speaker 2: the weather of a specific city, but like, not just
Speaker 2: tomorrow's weather. We are talking thirty or even forty five
Speaker 2: days from today.
Speaker 3: Right, which is incredibly difficult.
Speaker 2: Exactly, and your only tool to accomplish this impossible pask
Speaker 2: is just a simple list of daily temperatures leading up
Speaker 2: to right now.
Speaker 3: Yeah, you are dealing with a totally inherently chaotic system there.
Speaker 3: I mean, the atmosphere is notoriously noisy. There are countless
Speaker 3: invisible variables interacting at all times.
Speaker 2: It is the classic butterfly effect in action. Yeah, a tiny,
Speaker 2: almost imperceptible shift in the wind, or a fractional different
Speaker 2: it's a measurement early on just cascades into massive, completely
Speaker 2: unpredictable differences a month later.
Speaker 3: Oh.
Speaker 2: Absolutely, you are effectively trying to guess the twist ending
Speaker 2: of an incredibly complex three hour movie based entirely on
Speaker 2: a single blurry frame from the first ten minutes.
Speaker 3: That is a really great way to put it. And
Speaker 3: what's fascinating here is that the way scientists currently tackle
Speaker 3: this kind of chaos relies on a highly clever architecture
Speaker 3: called reservoir computing. Specifically, they use these things called echo
Speaker 3: state networks. But what makes this field so compelling at
Speaker 3: this exact moment is a recent test of that architecture.
Speaker 2: Right, the breakthrough test.
Speaker 3: Yeah, researchers took a system of just nine single atoms
Speaker 3: nine and they put them head to head with a
Speaker 3: massive state of the art classical computing setup of ten
Speaker 3: thousand artificial neurons, and the atoms one they completely outperformed
Speaker 3: the massive.
Speaker 2: Network, nine individual atoms beating ten thousand artificial neurals. I mean,
Speaker 2: to truly appreciate the sheer scale of what that tiny
Speaker 2: system achieved, we really have to look at the massive
Speaker 2: classical goliath it was up against. First we do, Yeah,
Speaker 2: we need to understand the workhourse of chaotic prediction, which
Speaker 2: is the echo state network or ESN.
Speaker 3: So if you look at standard neural networks, they traditionally
Speaker 3: struggle with time series data.
Speaker 2: That is information that unfolds chronologically right.
Speaker 3: Exactly like weather patterns or fluctuating energy grids or the
Speaker 3: stock market. With a regular neural network, if you want
Speaker 3: it to learn how a temperature drop on Tuesday affects
Speaker 3: the storm front the following Thursday, you have to adjust
Speaker 3: every single mathematical weight and connection inside the.
Speaker 2: Network during the training phase, right during training, because the
Speaker 2: network has to essentially learn the flow of time step
Speaker 2: by step, which I imagine requires massive computing power huge amounts.
Speaker 3: The algorithm has to go all the way to the
Speaker 3: end of the data, realize it made a mistake, and
Speaker 3: then mathematically crawl backward through time to fix every single connection.
Speaker 3: Oh wow, Yeah, so the training gets bogged down or
Speaker 3: it just gets stuck in a loop and stops learning entirely.
Speaker 2: That sounds incredibly inefficient.
Speaker 3: It really is. But echo state networks bypass that entirely
Speaker 3: by creating what they call a reservoir.
Speaker 2: A reservoir, yeah, I think of.
Speaker 3: A massive hidden layer made up of hundreds or thousands
Speaker 3: of artificial neurons.
Speaker 2: Okay, let's unpack this. Let me try to visualize the
Speaker 2: mechanics here. If we think of this ESN reservoir as
Speaker 2: a large, perfectly still pond.
Speaker 3: I like where this is going, right, So.
Speaker 2: Your input data, let's say today's temperature in our city
Speaker 2: is a pebble. You drop that pebble into the pond.
Speaker 2: Immediately it starts creating ripples. Yes, those ripples bounce off
Speaker 2: the edges, they cross over each other, and they mix
Speaker 2: together in highly complex, unpredictable ways.
Speaker 3: And then when tomorrow's temperature comes in, that is another
Speaker 3: pebble dropped into the water, right, but it does not
Speaker 3: hit a flat, empty surface. It hits a pond that
Speaker 3: is already rippling from yesterday's pebble. So the new ripples
Speaker 3: physically interact with the old ripples.
Speaker 2: Oh. I see, the.
Speaker 3: Surface of the pond affect carries a running memory of
Speaker 3: everything that has been dropped into it right up to
Speaker 3: that moment.
Speaker 2: So older hipples naturally fade out over time, because if
Speaker 2: they didn't fade, the pond would just become a chaotic,
Speaker 2: turbulent mess of endless waves where you couldn't read a
Speaker 2: single distinct pattern exactly.
Speaker 3: But if they faded immediately, the pond would have no
Speaker 3: memory of the past week's weather at all.
Speaker 2: That makes sense, So that balance of fading ripples is
Speaker 2: what allows the system to hold a usable history.
Speaker 3: Yeah, and the defining feature of the echostate network is
Speaker 3: how it is built to handle those ripples. When engineers
Speaker 3: first set up the network, those hundreds or thousands of
Speaker 3: artificial neurons inside the reservoir are connected to each other
Speaker 3: completely at random, completely random yep, And then nobody ever
Speaker 3: touches them again. The connections inside the reservoir stay permanently
Speaker 3: fixed during the entire training process.
Speaker 2: Wow. Really, that fixed nature is the echo state property.
Speaker 3: Then you got it. The data comes in and every
Speaker 3: neuron updates its state using a really simple activation function.
Speaker 3: Usually they use the ton function Palm. Yeah, it is
Speaker 3: just a mathematical way of squeezing any number down so
Speaker 3: it fits neatly between minus one and positive one.
Speaker 2: Oh, I get it. That way, the math doesn't explode
Speaker 2: into infinity as the ripples keep bouncing.
Speaker 3: Around, exactly so, as the days go by in the
Speaker 3: weather data, the states of all these thousands of neurons evolve. Together.
Speaker 3: They turn a simple one dimensional string of temperature numbers
Speaker 3: into an incredibly rich, high dimensional map of patterns.
Speaker 2: It is transforming a boring list of numbers into a
Speaker 2: vast complex.
Speaker 3: Topography, beautifully said.
Speaker 2: But wait, if we never train or adjust those thousands
Speaker 2: of connections inside the reservoir, how does the network actually
Speaker 2: learn to predict the storm next month.
Speaker 3: Well, that happens at the very end of the line
Speaker 3: in the readout layer vat out layer. Right. It is
Speaker 3: usually just a basic linear equation, and it is the
Speaker 3: only part of the entire system that ever gets trained. Oh, okay,
Speaker 3: you feed your historical weather data into the reservoir. Let
Speaker 3: it generate all those rich, complex ripples and simply collect
Speaker 3: the states of the neurons. Then you use very fast,
Speaker 3: straightforward math to figure out the best weights, the kind
Speaker 3: of math like ridge regression or at least squares, just
Speaker 3: basic stuff to map those rippling reservoir states to the
Speaker 3: actual future temperatures you want to predict.
Speaker 2: Go back to our pond analogy. It's like we aren't
Speaker 2: trying to track every single drop of water or calculate
Speaker 2: the physics of how the pebble hit the surface. Right,
Speaker 2: We just set up a camera pointing at the surface
Speaker 2: of the pond, and we train a simple algorithm to realize, Oh,
Speaker 2: when the waves look exactly like this specific pattern, it
Speaker 2: means a storm is coming in five days.
Speaker 3: That is a perfect analogy. And because you skip the
Speaker 3: agonizingly slow process of training that massive central reservoir, the
Speaker 3: whole process is incredibly fast.
Speaker 2: Which is huge.
Speaker 3: It is you don't need giant supercomputer clusters. These echo
Speaker 3: state networks can run on ordinary everyday computers. Really Yeah,
Speaker 3: And scientists use them to handle fundamentally chaotic systems all
Speaker 3: the time. They use them to model the Lorenza tractor,
Speaker 3: which maps atmospheric convection.
Speaker 2: Wow.
Speaker 3: They use them for high frequency financial forecasting, processing human speech,
Speaker 3: and even controlling the complex movements of robotics.
Speaker 2: So for weather forecasting, they just feed in historical daily
Speaker 2: climate data like maximum daily temperatures over several years, and
Speaker 2: train that simple camera at the end the readout layer
Speaker 2: to recognize the seasonal cycles and random variations and.
Speaker 3: The slow long term trends. Yeah.
Speaker 2: But naturally, because this is science, I assume the immediate
Speaker 2: thought is to just build a bigger pond. I mean,
Speaker 2: if five hundred neurons create a good wave pattern, ten
Speaker 2: thousand neurons should create a perfect.
Speaker 3: One, right, you would think, So researchers consistently scale them up.
Speaker 3: They move from five hundred nodes to one thousand than
Speaker 3: five thousand, and frequently push them up to ten thousand
Speaker 3: no's or more.
Speaker 2: Because a bigger reservoir means a higher dimensional space.
Speaker 3: Exactly, there is more room for diverse dynamic patterns to form,
Speaker 3: giving that readout layer a much richer map of ripples
Speaker 3: to pull features from.
Speaker 2: So if a five thousand node network gives you a
Speaker 2: great prediction, a ten thousand node network should just completely
Speaker 2: obliterate it in terms of accuracy.
Speaker 3: Well, that is the theory.
Speaker 2: But from what you're saying, that isn't what happens. Why
Speaker 2: not do the ripples just turn to white noise? Is
Speaker 2: a massive pond just too chaotic for the camera to read?
Speaker 3: The issue is a hit, a hard wall of diminishing returns.
Speaker 2: Diminishing returns.
Speaker 3: Yeah, When scientists actually run these massive networks on weather data,
Speaker 3: the initial gains are great. One thousand nodes easily beats
Speaker 3: five hundred. Five thousand is better still, But when you
Speaker 3: jump from five thousand to ten thousand nodes, the improvement
Speaker 3: suddenly flattens out. It just flattens completely. The extra classical
Speaker 3: nodes stop providing truly new information. The dynamic patterns inside
Speaker 3: the reservoir start to overlap and repeat themselves. You stop
Speaker 3: getting distinct wave patterns and just start generating redundant echoes.
Speaker 2: So it's just wasting computing power at that point.
Speaker 3: Essentially, yes, and beyond just the redundancy, making the network
Speaker 3: larger dramatically increases the frigility of the system. The hyper
Speaker 3: parameter tuning becomes a total nightmare.
Speaker 2: Tuning, Like what kind of parameters?
Speaker 3: Well, to keep a ten thousand node network stable, you
Speaker 3: have to perfectly tune parameters with names like the spectral radius,
Speaker 3: the leaking rate, the input scaling, and the sparsity of
Speaker 3: the reservoir.
Speaker 2: Hold on, let's ground those terms in the pond analogy.
Speaker 2: What is a spectral radius in physical terms?
Speaker 3: Good question. Think of the spectral radius as how aggressively
Speaker 3: the ripples bounce off the edges of the pond. Okay,
Speaker 3: if you set it too high, the waves amplify each
Speaker 3: other uncontrollably, and the whole system becomes wildly unstable. It
Speaker 3: just starts spitting out pure garbage.
Speaker 2: And what about sparsity.
Speaker 3: Sparsity would be like placing large rocks in the pond
Speaker 3: to block certain ripples from traveling everywhere. And the leaking
Speaker 3: rate is the viscosity of the water itself.
Speaker 2: Oh, I see. So if you set the leaking rate
Speaker 2: just a fraction too high, making the water too thick,
Speaker 2: the memory fades too fast, the reservoir goes quiet les instantly,
Speaker 2: and you lose the historical data entirely.
Speaker 3: It is agonizingly delicate. Scientists knew classical echo state networks
Speaker 3: were hitting this wall. They knew just making the classical
Speaker 3: pond bigger wasn't the answer because they were entirely bound
Speaker 3: by classical math.
Speaker 2: Because it's still just sequential processing right.
Speaker 3: Inside that classical reservoir. It is still just single numbers
Speaker 3: being added, multiplied, and passed through functions sequentially. There is
Speaker 3: no built in native way to explore multiple possibilities simultaneously.
Speaker 2: What did they do.
Speaker 3: They set up a direct, brutal head to head battle,
Speaker 3: the ultimate stress test. They took actual, real world daily
Speaker 3: weather records from the city of.
Speaker 2: Delhi, real weather data.
Speaker 3: Yes, and they didn't just ask the networks to predict
Speaker 3: to Mars temperature. They task the systems with forecasting the
Speaker 3: chaotic temperature trends fifteen days out, thirty days out, and
Speaker 3: a massive forty five days out.
Speaker 2: Wow, forty five days.
Speaker 3: Yeah, in the classical corner, they lined up the heavyweights,
Speaker 3: carefully tuned, optimized echo state network sized at five hundred,
Speaker 3: one thousand, five thousand and ten thousand nodes.
Speaker 2: And in the other corner of the challenger a quantum reservoir.
Speaker 3: Exactly. But this wasn't some warehouse sized, billion dollar quantum
Speaker 3: supercomputer chilled absolute zero. No, it was built from just
Speaker 3: nine interacting atomic spins, nine individual atoms inside.
Speaker 2: A molecule, and the result.
Speaker 3: The result was staggering. The tiny nine spin quantum system
Speaker 3: didn't just compete, It captured the temperature trends accurately across
Speaker 3: those fifteen thirty and forty five day horizons, beating the
Speaker 3: massive ten thousand node classical network at its own game.
Speaker 2: Here's where it gets really interesting. It's like a single
Speaker 2: person with a multitool outbuilding an entire construction crew of
Speaker 2: ten thousand workers. Yeah, it's hard to wrap your mind
Speaker 2: around that.
Speaker 3: It is very unintuitive.
Speaker 2: We are so trained to assume that bigger is always better.
Speaker 2: You know that more nodes automatically equal more intelligence. How
Speaker 2: does a microscoptic clump of nine atoms hold more more
Speaker 2: complexity than ten thousand artificial neurons.
Speaker 3: Well, it comes down to quantum mechanics.
Speaker 2: Right, So if classical nodes are locked into processing one
Speaker 2: number at a time, are these atoms doing that quantum
Speaker 2: trick where they exist in multiple states at once to
Speaker 2: process more data?
Speaker 3: That is exactly the first mechanism at play superposition superposition. Yes,
Speaker 3: in a classical network, a node is in one specific
Speaker 3: state at one specific time. It is a one or
Speaker 3: a zero, a positive or a negative. But quantum superposition
Speaker 3: allows those nine atomic spins to explore a vast number
Speaker 3: of states simultaneously.
Speaker 2: Like they're doing all the math at the same time.
Speaker 3: Basically, they exist in a fluid cloud of probabilities, exploring
Speaker 3: many dynamics at once. Even though there are only nine
Speaker 3: physical pieces, the mathematical space they are exploring is exponentially
Speaker 3: larger than what nine classical nodes could ever reach.
Speaker 2: That is incredible. And then there's the entanglement factor. Right
Speaker 2: in the classical pond, we artificially wire the nodes together
Speaker 2: and hope the mathematical ripples create useful relationships, But entanglement
Speaker 2: is a physical property exactly.
Speaker 3: It creates deep fundamental correlations between the atomic spins. They
Speaker 3: share information in a profound, non local way.
Speaker 2: So to bring it back to the pond analogy, entanglement
Speaker 2: would be like dropping a pedal on the left side
Speaker 2: of the pond and it instantly creates perfectly mirrored ripples
Speaker 2: on the far right side of the pond, without the
Speaker 2: water having to physically travel across the middle.
Speaker 3: The connection is instantaneous and deeply linked. Yes, the richness
Speaker 3: of those entangled physical connections completely dwarfs the random mathematical
Speaker 3: wiring of the classical network unbelievable. But perhaps the most
Speaker 3: elegant part of the whole quantum system is how it
Speaker 3: handles the fading memory we talked about earlier.
Speaker 2: Oh right, the leaking rate.
Speaker 3: Yeah, In a classical network, engineers have to agonize over
Speaker 3: the leaking rate. Fading memory is mathematical fiction. They have
Speaker 3: to carefully code and manually tuned so the network doesn't
Speaker 3: forget the past too fast.
Speaker 2: But the quantum system uses natural dissipation.
Speaker 3: Exactly in the physical quantum system. Natural dissipation happens on
Speaker 3: its own. As time passes, the excited atomic spins naturally
Speaker 3: relax back to their base.
Speaker 2: State, so nature essentially comes pre installed with the exact
Speaker 2: fading memory algorithm that classical engineers spend months trying to code.
Speaker 3: That's a brilliant way to phrase it. The physical, natural
Speaker 3: way these atoms relax over time perfectly matches the exact
Speaker 3: requirement to process time series data.
Speaker 2: The researchers don't have to perfectly tune a mathematical leaking
Speaker 2: rate because the physics the molecule does it for them automatically.
Speaker 3: Yes, if we connect this to the bigger picture, the
Speaker 3: quantum system isn't running a program to simulate the math
Speaker 3: of a reservoir. It physically is the reservoir. It uses
Speaker 3: the native rules of the universe to do profoundly more
Speaker 3: with exponentially less.
Speaker 2: Okay, I follow the physics of why it works better,
Speaker 2: but I keep getting stuck on the mechanics of the input.
Speaker 2: I mean, I can wrap my head around typing a
Speaker 2: temperature number into a computer program, but how do you
Speaker 2: feed the daily temperature of Deli into nine microscopic atoms?
Speaker 3: Ah? They use nuclear magnetic resonance or NMR.
Speaker 2: NMR like the technology behind an MRI machine at a hospital.
Speaker 2: We're putting nine atoms into a medical scanner.
Speaker 3: The underlying principle is very similar. Yeah, you use highly
Speaker 3: precise magnetic fields and radio frequency pulses to physically interact
Speaker 3: with the natural spin spin couplings of the atoms.
Speaker 2: Okay, so how does the temperature data translate to that?
Speaker 3: Well, if the daily temperature in Deli is ninety five degrees,
Speaker 3: the researchers translate that number into a radio pulse tuned
Speaker 3: to a very specific frequency, almost like tuning a guitar string.
Speaker 2: So they fire that specific frequency at the molecule and
Speaker 2: it makes the atoms vibrate or spin in a way
Speaker 2: that physically represents that exact temperature.
Speaker 3: You've got it. It is literally dropping the physical pebble into
Speaker 3: a quantum pond that is just wild. Then you let
Speaker 3: those natural quantum dynamics, the superposition, the entanglement, the dissipation
Speaker 3: play out as the ripples interact, and then you use
Speaker 3: the nmr AG again to measure the resulting physical states of.
Speaker 2: The molecule, and that gives you the prediction.
Speaker 3: Mostly yes, But to be completely transparent about the architecture,
Speaker 3: the quantum system does occasionally get a tiny bit of
Speaker 3: help at the very end. In that might outlayer, we
Speaker 3: discuss the camera watching the pawn. Right, the researchers found
Speaker 3: that pairing the nine spin reservoir with a slightly more
Speaker 3: sophisticated nonlinear redoubt helped translate those deeply complex quantum states
Speaker 3: into the final temperature predictions with incredible accuracy.
Speaker 2: That kind of readout.
Speaker 3: Specifically, they use support vector regression with a radial basis
Speaker 3: function kernel.
Speaker 2: Wait planning GUS please a radial basis function kernel that
Speaker 2: basically just means the camera is capable of drawing curve
Speaker 2: lines instead of just straight ones to find a patter
Speaker 2: right yep, it can match more complex circular groupings of data.
Speaker 3: That is a great way to summarize it. It allows
Speaker 3: the readout layer to handle more complex nonlinear relationships.
Speaker 2: Makes sense.
Speaker 3: But even with that tiny mathematical assist at the readout layer,
Speaker 3: the heavy lifting, the full processing of the chaotic time
Speaker 3: series data, the memory, the pattern recognition is being done
Speaker 3: entirely by the natural physics of those nine spins.
Speaker 2: So why does this specific battle between ten thousand artificial
Speaker 2: neurons and nine atomic spins matter to you listening right now, Well,
Speaker 2: because it completely reframes the timeline of quantum technology.
Speaker 3: It really does.
Speaker 2: When we usually hear about quantum computing, the narrative is
Speaker 2: almost always about the distant future. We are told we
Speaker 2: have to wait decades for massive, flawlessly error corrected, fault
Speaker 2: tolerance Sci Fi supercomputers before quantum technology actually touches our
Speaker 2: daily lives.
Speaker 3: But this experiment proves that we do not have to wait.
Speaker 3: Classical echostate networks are brilliant feats of engineering. They have
Speaker 3: pushed the limits of what we can do with scale,
Speaker 3: hyper parameter tuning and brute force math. But nature's native
Speaker 3: quantum physics can effortlessly outmash years of our best classical engineering. Today.
Speaker 2: It proves that practical world changing of advantages aren't locked
Speaker 2: away in the twenty fifties. We can use today's near
Speaker 2: term hardware, these imperfect, small scale, noisy quantum systems to
Speaker 2: drastically improve everyday forecasting. Right now, we are talking about
Speaker 2: better predictions for balancing the energy grid during heat waves,
Speaker 2: or more accurate modeling of volatile financial markets, and knowing
Speaker 2: exactly what the weather's going to do forty five days
Speaker 2: from now so agriculture and shipping can actually prepare.
Speaker 3: It is a profound paradigm shift. This raises an important question.
Speaker 3: You know, we are no longer trying to brute force
Speaker 3: the math of the universe to simulate chaos. We are
Speaker 3: finally letting the universe do the math for us.
Speaker 2: Which brings us back to that blurry frame of a
Speaker 2: movie we talked about at the very beginning, trying to
Speaker 2: predict the complex twist ending from almost nothing is an
Speaker 2: incredibly complex, naturally dissipating system of just nine atomic spins
Speaker 2: can accurately model the chaotic cascading weather patterns of an
Speaker 2: entire massive city for a month and a half when
Speaker 2: entirely new, seemingly unpredictable human systems. Might we be able
Speaker 2: to perfectly map if we scaled.
Speaker 3: This approach up, Yeah, that is the real question.
Speaker 2: I mean, not to ten thousand atoms, but just one
Speaker 2: hundred or maybe one thousand quantum spins. Could we model
Speaker 2: the hidden cascading ripple effects of the global economy? Could
Speaker 2: we perfectly predict the exact spread and mutation timeline of
Speaker 2: a virus across continents? If just nine atoms can map
Speaker 2: out the storms of tomorrow, what on Earth or one
Speaker 2: thousand atoms is going to show us about the future