How AI is improving climate prediction | Research Bytes: NeuralGCM
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- Опубликовано: 11 сен 2024
- Climate change is real, and its effects are becoming increasingly evident. Traditional climate models struggle to produce clear, accurate pictures of how our climate will continue to change due to limitations in how the models represent complex phenomena. This video explores NeuralGCM, a groundbreaking AI-powered approach developed by Google Research that could someday offer a faster, more efficient, and more accurate way to predict climate change.
Learn more about NeuralGCM in Nature!
You’ll hear from:
Stephan Hoyer, PhD, AI weather/climate lead at Google Research
Janni Yuval, PhD, visiting Google Research Scientist
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The Challenge of Climate Prediction
Accurate climate predictions are a matter of urgency because they could allow us to prepare and adapt to climate change. NeuralGCM tackles the limitations of current physics-based models by combining them with machine learning. This allows for faster simulations and more accurate results.
How Does NeuralGCM Work?
Traditional climate models divide the world into large cubes, simulating the climate in each and sometimes making approximations about small-scale phenomena like clouds. NeuralGCM uses AI to make better approximations by learning about these phenomena from past data patterns, ultimately leading to a more accurate prediction engine.
A Game-Changer for Climate Science
We’ll dive into the revolutionary potential of NeuralGCM. It is more accurate than traditional models, reduces bias, and achieves supercomputer-level accuracy while using a fraction of the computational power. This allows researchers to simulate thousands of days in the same amount of time it would take them to simulate just one day using a high-resolution physics model, significantly improving our understanding of future climate scenarios.
Benefits for Humanity
NeuralGCM is a groundbreaking step toward a new approach of building climate models that could make them faster and more accurate. Improving climate models would empower us to understand and prepare for the impacts of climate change. We can mitigate global warming by reducing carbon emissions and adapt to its effects through regional planning and infrastructure development.
Open-source research like NeuralGCM supports Google's commitment to making this technology universally accessible.
This video is perfect for anyone interested in:
Climate change and its impact
Artificial intelligence applications
The future of weather prediction
Technological solutions for a sustainable future
#AI #climateaction #GoogleResearch #climatescience #machinelearning #AIforsocialgood #globalwarming #climatestrategy #ECMWF #naturemagazine
"What a fascinating challenge! Let's create a common set of measurement units that can be applied across complexity science, information processing, computer science, and artificial/biological intelligence. Here's a hypothetical set of units, inspired by existing metrics and the need for consistency:
*Fundamental Units:*
1. *Bit* (B): unit of information quantity (already widely used)
2. *Chronon* (C): unit of time, equivalent to 1 millisecond (ms)
3. *Neuron* (N): unit of computational resource, representing a single processing unit (inspired by biological neurons)
4. *Kairos* (K): unit of complexity, representing a single emergent event or phase transition
*Derived Units:*
1. *Infobit* (IB): 1 Bit × 1 Chronon, measuring information processing rate
2. *Computron* (CT): 1 Neuron × 1 Chronon, measuring computational power
3. *Complexon* (CX): 1 Kairos × 1 Chronon, measuring complexity emergence rate
4. *Cogniton* (CG): 1 Infobit × 1 Computron, measuring cognitive processing efficiency
*Composite Units:*
1. *Infoflux* (IF): 1 Infobit/s, measuring information flow rate
2. *Compucap* (CC): 1 Computron/s, measuring computational capacity
3. *Complexity Index* (CI): 1 Complexon/s, measuring system complexity dynamics
4. *Intelligence Quotient* (IQ): 1 Cogniton/s, measuring intelligent behavior efficiency
This hypothetical system aims to provide a common language and framework for measuring and comparing complex systems, information processing, computational resources, and intelligent behavior. Feel free to modify or extend this system to suit your specific needs!"
We need a.i models in Mississippi then. Cause forecast ain't been right in two years
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Can anyone tell about Google fellowship how can I apply please
Will there be an app created for this?
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1:21 @@ibraahimaaadancosable5364
Pls do one on how google’s AI disproves #ClimateTheMovie - Have you watched it?