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Deep Learning in the Fight Against Climate Change · Artificial Intelligence Exposed

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  Speaker 1: Welcome to the deep dive. Today, we are tackling what
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  Speaker 1: is well arguably humanity's most complex and urgent undertaking, navigating
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  Speaker 1: the global crisis of climate change. For many of us,
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  Speaker 1: the sheer scale of this problem, you know, the way
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  Speaker 1: whether policy, energy, all connect it can feel frankly paralyzing.
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  Speaker 1: So our mission today is to cut through some of
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  Speaker 1: that complexity. We're doing a deep dive looking at source
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  Speaker 1: material that explores how advanced AI, specifically deep learning, isn't
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  Speaker 1: just analyzing the problem, it's actually becoming a tool to
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  Speaker 1: actively fight it. We really wanted to still the essential,
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  Speaker 1: maybe game changing ways this tech is being used on
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  Speaker 1: the climate front lines.
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  Speaker 2: Yeah, and if you think about climate change fundamentally, it's
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  Speaker 2: a massive data problem, isn't. You've got everything changing all
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  Speaker 2: at once, ocean's, the atmosphere, energy markets, it's incredibly complex.
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  Speaker 2: Deep learning DL it's almost tailor made for this level
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  Speaker 2: of complexity. Your traditional models they're good, but they often
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  Speaker 2: can't handle the sheer volume of environmental data or crucially
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  Speaker 2: detect the really intricate nonlinear patterns. Deep neural networks excel
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  Speaker 2: there So DL acts like this ultra powerful pattern recognition
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  Speaker 2: engine and it's giving scientists, policy makers, industries these critical
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  Speaker 2: actionable insights. It's really fundamentally restructuring how we approach sustainability.
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  Speaker 1: Okay, that makes sense. An engine for complexity. Let's unpack that.
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  Speaker 1: If DL is this engine, where is it heading the
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  Speaker 1: ground running first? Where are we seeing the most immediate,
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  Speaker 1: sort of tangible impact on understanding our shifting climate. The
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  Speaker 1: starting line has to be prediction, right, client modeling, short
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  Speaker 1: term weather forecasting. These seem like primary uses. But why
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  Speaker 1: is DL needed? I mean, the traditional models they're built
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  Speaker 1: on complex physics.
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  Speaker 2: They are, and they're brilliant pieces of science, but they
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  Speaker 2: hit a wall. It's the atmosphere's chaotic variability, that inherent randomness.
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  Speaker 2: They're also incredibly computationally expensive. Yeah, and that chaos often
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  Speaker 2: means they struggle to capture the fast moving, really localized
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  Speaker 2: details that lead to extreme weather.
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  Speaker 1: Events, right, the things that cause the most immediate damage.
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  Speaker 2: Precisely, So think about it this way. Traditional models are
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  Speaker 2: trying to solve these complex physics equations over and over.
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  Speaker 2: DL takes a different approach. It learns through observation. Essentially,
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  Speaker 2: we train these deep neural networks on well decades of
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  Speaker 2: historical climate data that we pair that with real time
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  Speaker 2: satellite observations, ground sensor readings, just vast amounts of information,
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  Speaker 2: and this massive training exercise, let's the network find hidden
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  Speaker 2: correlations things in temperature, precipitation, wind dynamics, patterns of human
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  Speaker 2: scientists might just never spot in the noise.
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  Speaker 1: So it's seen connections.
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  Speaker 2: We miss in many cases. Yes, and the output shift
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  Speaker 2: is transformative. Instead of maybe getting a regional warning about
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  Speaker 2: a potential heat wave, we're starting to get highly accurate,
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  Speaker 2: very localized forecasts, sometimes weeks in advance. Weeks. Wow. And
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  Speaker 2: this is the crucial takeaway for you listening. These improved
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  Speaker 2: predictions allow governments, communities to move from just react after a.
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  Speaker 1: Disaster hits like a hurricane or a flood exactly.
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  Speaker 2: To proactive preparation that means saving lives, securing infrastructure, and
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  Speaker 2: minimizing those huge multi billion dollar economic damages from climate shocks.
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  Speaker 1: So it's really about building resilience before the storm, so to.
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  Speaker 2: Speak, precisely before the wave hits.
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  Speaker 1: That shift from reaction to preparation that feels absolutely vital.
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  Speaker 1: The climate change isn't only about predicting the future, right,
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  Speaker 1: it's also knowing what's happening now to our physical environment
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  Speaker 1: or ecosystems? And this is where I think it gets
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  Speaker 1: really interesting, this idea of automating the process of while
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  Speaker 1: watching the planet twenty four to seven. How does AI
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  Speaker 1: become these eyes on the ecosystem from space without needing
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  Speaker 1: an army of analysts glued to screens?
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  Speaker 2: Right? Well, we rely very heavily on satellite imagery drown
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  Speaker 2: imagery too, and to process that sheer scale of visual data,
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  Speaker 2: we use specialized networks. They're called convolutional neural networks or
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  Speaker 2: cnnsn NS. Think of a CNN as a sort of
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  Speaker 2: specialized digital eye. Unlike simpler algorithms that might just look
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  Speaker 2: at say pixel colors, CNN's process images by seeing features
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  Speaker 2: in layers, edges, texture shapes. This lists to identify specific
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  Speaker 2: things like a certain type of tree indicating disease, or
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  Speaker 2: even a specific model of illegal fishing boat. And it
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  Speaker 2: can do this regardless of cloud cover sometimes or different
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  Speaker 2: lighting conditions. Learns the important features.
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  Speaker 1: So hang on, it's not just recognizing a patch of
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  Speaker 1: green as forest, it's recognizing a specific pattern within that
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  Speaker 1: green that indicates, say, illegal logging is happening. Even in
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  Speaker 1: some huge remote jungle exactly that.
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  Speaker 2: And the really crucial insight here is a speed factor.
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  Speaker 2: Think about monitoring deforestation or tracking how fast glacier is retreating.
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  Speaker 2: Traditionally that needed human teams reviewing satellite images, sometimes over
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  Speaker 2: months to get an accurate picture.
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  Speaker 1: Right, a very slow process, very.
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  Speaker 2: Slow DL can deliver an instant quantified assessment like right now.
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  Speaker 2: This capacity lets us monitor environment health in well mere
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  Speaker 2: real time. We're seeing examples of DL tracking precise glacier
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  Speaker 2: melt rates day by day, identifying illegal mining operations almost
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  Speaker 2: as they start instantly spotting unauthorized fishing vessels inside protected
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  Speaker 2: marine zones.
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  Speaker 1: That near real time capability. That must be the game changer,
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  Speaker 1: because if you can spot an environmental grim in minutes.
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  Speaker 2: Not much, you can intervene immediately, you can actually stop
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  Speaker 2: it exactly.
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  Speaker 1: So AI provides this constant quantified health report on ecosystems
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  Speaker 1: and on the pace of change, which has to accelerate
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  Speaker 1: conservation efforts globally right significantly.
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  Speaker 2: Yes, it supports those international efforts with hard fast data.
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  Speaker 1: Okay, so we've got prediction, we've got monitoring. Let's shift
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  Speaker 1: gears moving from protecting nature to powering our civilization energy.
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  Speaker 1: You mentioned the challenge with renewables wind and solar. The
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  Speaker 1: big one is intermittency. Right, supply goes up and down.
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  Speaker 2: HM dramatically sometimes.
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  Speaker 1: So how does deep learning take something inherently unreliable, like
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  Speaker 1: say win power on a gusty day, and make it
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  Speaker 1: reliable enough to power a modern city.
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  Speaker 2: It's a huge challenge. The modern power grid needs constant,
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  Speaker 2: split second balancing. If the energy generation doesn't perfectly match
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  Speaker 2: the demand at any given moment, you risk brownouts or even.
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  Speaker 1: Blackouts right delicate balance arry.
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  Speaker 2: To solve this, we often turn to another type of
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  Speaker 2: specialized network, recurrent neural networks RNNs, sometimes combined with something
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  Speaker 2: called reinforcement learning rnn's.
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  Speaker 1: Okay, how are they different.
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  Speaker 2: Well, rn n's are special because they have a sort
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  Speaker 2: of memory. They're designed to understand sequences and time dependencies.
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  Speaker 2: So they don't just look at the current snapshot of
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  Speaker 2: the weather right now. They analyze the trend of wind
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  Speaker 2: speed over the last few hours, the trajectory of cloud cover,
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  Speaker 2: historical patterns of energy use at this time of day.
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  Speaker 1: Ah, So it looks at the past to predict the
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  Speaker 1: immediate future exactly.
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  Speaker 2: By predicting both the supply side, what the renewables will
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  Speaker 2: likely generate, and the demand side what consumers will need.
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  Speaker 2: With really high accuracy, these systems can instantly optimize operations
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  Speaker 2: shifting power, storing energy, maybe retailing demand slightly.
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  Speaker 1: And that instant optimization that makes renewable stable.
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  Speaker 2: It dramatically improves great efficiency and stability. Yes, it makes
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  Speaker 2: renewables a much more predictable, reliable partner in the overall
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  Speaker 2: energy mix. Reduces waste too.
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  Speaker 1: That's a fascinating It's like giving the grid a sort
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  Speaker 1: of intelligence to anticipate the weather's.
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  Speaker 2: Impact in a way.
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  Speaker 1: Yes, but this isn't just about the power station itself,
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  Speaker 1: is it. Let's talk about the big energy users transportation logistics.
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  Speaker 1: We're talking massive container ships, global trucking fleets, huge sources
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  Speaker 1: of greenhouse gases. Now optimizing traffic That sounds maybe small scale.
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  Speaker 1: How does a slightly faster shipping route really offset the
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  Speaker 1: massive carbon footprint of global logistics?
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  Speaker 2: Yeah, it sounds small, but the key is the sheer
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  Speaker 2: volume and the power of marginal gains when you're dealing
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  Speaker 2: with literally thousands of ships or millions of trucks globally,
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  Speaker 2: small percentage improvements in fuel efficiency add up incredibly quickly.
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  Speaker 2: They translate into massive global emission reductions.
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  Speaker 1: Okay, the scale makes the small changes significant exactly.
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  Speaker 2: AI models integrate these enormous, constantly changing data sets. We're
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  Speaker 2: talking GPS from vehicles, real time traffic patterns, fuel consumption rates,
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  Speaker 2: even weather forecasts along the route, right, and they use
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  Speaker 2: this to achieve two main things. First, optimizing shipping routes
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  Speaker 2: not just for speed but for fuel efficiency, avoiding heavy weather,
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  Speaker 2: unimizing distance. Second, within cities, they can dynamically manage traffic flow,
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  Speaker 2: reducing engine idling time, minimizing stop star traffic, which burns
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  Speaker 2: a lot of fuel.
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  Speaker 1: So it's not just finding the quickest route, it's finding
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  Speaker 1: the most fuel efficient route based on all that real
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  Speaker 1: time data, changing hour by hour.
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  Speaker 2: Precisely, and optimizing the whole system, not just one truck
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  Speaker 2: or one ship in isolation.
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  Speaker 1: That really does add up globally. Then, okay, let's pivot again.
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  Speaker 1: Another huge sector, agriculture. It's a sector heavily affected by
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  Speaker 1: climate change, but it also contributes significant MIMS itself. What
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  Speaker 1: specific kind of granular data does DL analyze here to
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  Speaker 1: make farming more well, genuinely sustainable, not just efficient.
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  Speaker 2: Yeah, agriculture is a critical piece here. DL is enabling
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  Speaker 2: what's often called precision agriculture. The networks analyze an incredible
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  Speaker 2: range of localized variables, things like soil health indicators, moisture
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  Speaker 2: levels down to specific patches of a field, also the
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  Speaker 2: specific growth stages of the crops, combined with highly localized
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  Speaker 2: microweather forecasts.
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  Speaker 1: Microweather like for one part of a farm versus another.
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  Speaker 2: Potentially yes, or just much more accurate forecasts for that
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  Speaker 2: specific farm's location. By synthesizing all this information, the system
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  Speaker 2: provides recommendations that lead to well hyper sustainable farming practices.
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  Speaker 2: The really critical intervention is optimizing resource use water and fertilizer. Mainly,
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  Speaker 2: it means recommending the precise absolute minimum amount of water
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  Speaker 2: needed for the crops current stage and conditions, and the
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  Speaker 2: exact spatial application of fertilizer, sometimes to the level of
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  Speaker 2: individual plants. Potentially wow. This is a huge dual benefit. First,
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  Speaker 2: it drastically reduces the amount of precious water needed to
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  Speaker 2: grow food, and second, crucially, it lowers the greenhouse gas
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  Speaker 2: emissions associated with producing nitrogen fertilizers and from excess fertilizer
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  Speaker 2: use on.
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  Speaker 1: Fields, so cleaner farming and more resilient crops.
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  Speaker 2: Yes, it helps stabilize crop yields against stresses like drought,
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  Speaker 2: while making the whole process cleaner.
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  Speaker 1: Okay, so beyond growing food better, we also have to
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  Speaker 1: deal with the carbon that's already in the atmosphere. That
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  Speaker 1: means accelerating R and D right, carbon capture tech, future
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  Speaker 1: sustainable materials. How does deep learning speed up this search?
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  Speaker 1: Is this just a small improvement or is it something
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  Speaker 1: more transformative?
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  Speaker 2: It's potentially an exponential leap. Think about traditional material science.
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  Speaker 2: It involves a lot of slow, lab based trial and error,
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  Speaker 2: very painstaking.
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  Speaker 1: Yeah, testing one compound at a time.
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  Speaker 2: Right when you're looking for new materials, maybe for better
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  Speaker 2: solar panels, more durable ones, or a subject that can
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  Speaker 2: efficiently grab Sea two from the air, you're facing a
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  Speaker 2: staggering number of possibilities, almost infinite chemical and physical combinations.
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  Speaker 1: An impossible search space for humans alone.
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  Speaker 2: Pretty much. Deep learning is used here to sift through
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  Speaker 2: these massive theoretical and experimental data sets. It simulates and
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  Speaker 2: predicts the performance of millions, maybe billions, of potential compounds
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  Speaker 2: before anyone.
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  Speaker 1: Makes them in a lap, so it filters the possibilities
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  Speaker 1: down drastically by orders of magnitude.
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  Speaker 2: Yes, this process speeds up the discovery the validation of
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  Speaker 2: new technologies for carbon capture storage utilization. It can compress
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  Speaker 2: years of research into weeks or months. It gives us
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  Speaker 2: a much faster path to potentially removing CO two from
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  Speaker 2: the atmosphere at the scale we actually need. Okay, so
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  Speaker 2: we've covered prediction monitoring, grid optimization, agriculture R and D acceleration.
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  Speaker 2: It paints a compelling picture. Deal seems like an absolutely
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  Speaker 2: necessary ally in this fight. But you mentioned earlier it's
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  Speaker 2: not a silver bullet. What are the major roadblocks, the
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  Speaker 2: structural challenges raised in the sources that could limit how
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  Speaker 2: effective this tech is or how equitably it gets rolled out.
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  Speaker 1: Yeah, there are significant challenges, and we run into a
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  Speaker 1: kind of paradox of its tower almost immediately, the energy
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  Speaker 1: consumption dilemma.
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  Speaker 2: Ah right, Yeah, Training these models takes power, a lot
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  Speaker 2: of power.
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  Speaker 1: Training these huge neural networks, especially the ones doing global
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  Speaker 1: climate modeling or materials discovery. It demands significant computational resources,
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  Speaker 1: and that energy use itself contributes to greenhouse gas emissions,
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  Speaker 1: so you risk undermining the very climate benefit the AI
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  Speaker 1: is trying to achieve.
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  Speaker 2: That's a serious contradiction, it is.
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  Speaker 1: And research is frantically focused on this, developing more energy
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  Speaker 1: efficient network architectures, new types of AI chips, and implementing
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  Speaker 1: green computing methods globally using renewable energy to power the
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  Speaker 1: data centers. But that's a big infrastructure shift, and what
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  Speaker 1: else beyond energy?
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  Speaker 2: The second major challenge, often overlooked is data quality and importantly,
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  Speaker 2: data accessibility. If the climate related data used to train
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  Speaker 2: these models is biased or incomplete, or simply unavailable in
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  Speaker 2: certain often vulnerable reasons of the world, then.
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  Speaker 1: The AI's predictions won't be accurate or fair for those areas.
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  Speaker 2: Exactly, It fundamentally limits the accuracy in the fairness of
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  Speaker 2: the AI's output. If you don't have good granular soil
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  Speaker 2: data from smallholder farms in rural Africa, for example, you
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  Speaker 2: simply cannot optimize farming there using these advanced AI techniques.
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  Speaker 2: The solution's risk becoming sequestered only in data rich, often
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  Speaker 2: wealthier parts of the world.
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  Speaker 1: That day, equality and access issue seems critical for making
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  Speaker 1: sure solutions are truly global and equitable. So pulling this together,
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  Speaker 1: what does it all mean for maximizing deals positive impact globally.
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  Speaker 1: What's the sort of blueprint for moving forward ensuring this
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  Speaker 1: powerful technology benefits everyone, especially the most vulnerable.
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  Speaker 2: Well, the sources we looked at are pretty unequivocal on this.
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  Speaker 2: Collaboration is absolutely non negotiable. This means scientists, governments, the
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  Speaker 2: private sector, international bodies. They must work together aggressively, how practically,
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  Speaker 2: through things like open data initiatives, pooling and standardizing environmental
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  Speaker 2: data globally, making it accessible. Sharing computing resources is another
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  Speaker 2: big one, democratizing access to the massive power needed for
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  Speaker 2: training these models and fostering robust international partnerships. The goal
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  Speaker 2: has to be ensuring that these sophisticated, high power AI
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  Speaker 2: solutions are accessible and actually deployed in all regions, particularly
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  Speaker 2: those most vulnerable to climate impacts, rather than just remaining
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  Speaker 2: the domain of a few wealthy nations or corporations.
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  Speaker 1: So this deep dive really shows that deep learning isn't
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  Speaker 1: just interesting tech. It's becoming a critical, maybe transformative tool.
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  Speaker 1: It seems to be empowering humanity to respond to this
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  Speaker 1: crisis with a degree of precision and maybe speed we
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  Speaker 1: just couldn't achieve even a decade ago. Enhancing our ability
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  Speaker 1: to predict protect ecosystems, optimize energy, reduce emissions. It's quite remarkable.
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  Speaker 2: I think we've moved past treating AI in this space
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  Speaker 2: as just a novelty or a research project. It's rapidly
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  Speaker 2: becoming necessary infrastructure in the fight for a sustainable and
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  Speaker 2: hopefully resilient future. The intelligent ethical use of these tools
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  Speaker 2: really is one of the most promising paths we currently have.
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  Speaker 1: Indeed a powerful tool, but as we discussed, one with
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  Speaker 1: its own costs, and that very power, the power of
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  Speaker 1: these massive computing networks needed for the highest impact applications.
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  Speaker 1: It brings us right back to that core energy challenge.
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  Speaker 1: If training these essential AI tools requires huge amounts of
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  Speaker 1: energy energy grids that themselves need decarbonizing over potentially decades,
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  Speaker 1: how will the global community prioritize which environmental problems are
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  Speaker 1: deemed urgent enough to justify the immediate carbon cost of
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  Speaker 1: running those vast neural networks. That's something for you to
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  Speaker 1: think about as you consider the true cost and the
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  Speaker 1: complex choices behind climate intelligence.