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On Tue, 24 Dec, 12:03 AM UTC
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[1]
Computer models are vital for studying everything. Here's how AI could make them even better
Here's one definition of science: it's essentially an iterative process of building models with ever-greater explanatory power. A model is just an approximation or simplification of how we think the world works. In the past, these models could be very simple, as simple in fact as a mathematical formula. But over time, they have evolved and scientists have built increasingly sophisticated simulations of the world as new data has become available. A computer model of the Earth's climate can show us temperatures will rise as we continue to release greenhouse gases into the atmosphere. Models can also predict how infectious disease will spread in a population, for example. Computer models can be rejected if experimental evidence does not support them. So there's a kind of arms race to keep models competitive as new data appears. And the revolution occurring in the field of artificial intelligence (AI) could make these vital tools even better. Take weather and climate forecasting. The numerical models used to predict weather are large, complex and demanding in terms of the amount of computing power needed to run them. They are also unable to learn from past weather patterns. However, methods based around artificial intelligence, including a subset of AI known as machine learning, have shown huge potential to improve on what we currently have. Machine learning involves creating algorithms (sets of mathematical rules to perform particular tasks) that can learn from data and apply these lessons to unseen data. But until recently, weather models that incorporated machine learning techniques weren't considered suitable for what's called ensemble forecasting, a set of forecasts that shows the range of possible future weather conditions. Nor were they useful for weather and climate simulations over the longer term, as opposed to near-term forecasts. However, a recent study published in Nature showed that a machine learning model called NeuralGCM produces ensemble forecasts that were just as good as the leading models. It could also produce realistic long-term forecasts of changes in the climate. Machine learning models have to be "trained" by feeding them lots of data, from which they learn and improve at what they do. The training process is expensive and requires a lot of computer power. However, after a model is trained, using it to make predictions is comparatively fast and cheap. The results show that AI can enhance the large-scale physical simulations that are essential for understanding and predicting the climate system. Big data As the British statistician George E.P. Box said, "All models are wrong but some models are useful." We must also remember that all measurements are wrong. There is always some noise present in our data and it is not an entirely accurate reflection of the state of the world. But models employing machine learning are enabled by "big data." Vast stores of information and measurements can be used to train these models, giving them ever-greater predictive power. In general, big data is characterized by the three v's: volume, velocity and variety. Data now arrives in bigger volumes, at greater velocity, and with increased variety. This is partly due to the way that different electronic devices can be connected, via what's called the "Internet of Things." Improving our understanding of how the Earth's climate system will evolve in coming decades will be vital for informing efforts to tackle greenhouse emissions. It will also help us adapt to the effects of global warming. Models using machine learning and another AI-based approach called deep learning have been used to detect and track COVID-19. Researchers have developed machine learning models that incorporate clinical, genetic and lifestyle factors to predict an individual's risk of developing cardiovascular disease. Scientists have also used the AI technique of deep reinforcement learning to develop tools that allow them to control the hot plasma necessary to generate nuclear fusion reactions. In the past, AI was a fairly narrow field with very specific applications, such as playing chess. With the dawn of generative AI, its uses are much broader, with the technology able to create new content such as text, images and video. This moves us closer to the goal of general artificial intelligence, where the technology becomes capable of carrying out any task a human could do. Building computer-based models of the world with the help of artificial intelligence represents another major milestone. The world of science is starting to recognize the power of AI, as seen in the award this year of two Nobel prizes for work involving artificial intelligence. Maybe we are not that far away from a Nobel prize being awarded to an AI -- or even a situation where a machine decides who to award the prizes to.
[2]
Computer models are vital for studying everything from climate change to disease - here's how AI could make them even better
Loughborough University provides funding as a member of The Conversation UK. Here's one definition of science: it's essentially an iterative process of building models with ever-greater explanatory power. A model is just an approximation or simplification of how we think the world works. In the past, these models could be very simple, as simple in fact as a mathematical formula. But over time, they have evolved and scientists have built increasingly sophisticated simulations of the world as new data has become available. A computer model of the Earth's climate can show us temperatures will rise as we continue to release greenhouse gases into the atmosphere. Models can also predict how infectious disease will spread in a population, for example. Computer models can be rejected if experimental evidence does not support them. So there's a kind of arms race to keep models competitive as new data appears. And the revolution occurring in the field of artificial intelligence (AI) could make these vital tools even better. Take weather and climate forecasting. The numerical models used to predict weather are large, complex and demanding in terms of the amount of computing power needed to run them. They are also unable to learn from past weather patterns. However, methods based around artificial intelligence, including a subset of AI known as machine learning, have shown huge potential to improve on what we currently have. Machine learning involves creating algorithms (sets of mathematical rules to perform particular tasks) that can learn from data and apply these lessons to unseen data. But until recently, weather models that incorporated machine learning techniques weren't considered suitable for what's called ensemble forecasting, a set of forecasts that shows the range of possible future weather conditions. Nor were they useful for weather and climate simulations over the longer term, as opposed to near-term forecasts. However, a recent study published in Nature showed that a machine learning model called NeuralGCM produces ensemble forecasts that were just as good as the leading models. It could also produce realistic long-term forecasts of changes in the climate. Machine learning models have to be "trained" by feeding them lots of data, from which they learn and improve at what they do. The training process is expensive and requires a lot of computer power. However, after a model is trained, using it to make predictions is comparatively fast and cheap. The results show that AI can enhance the large-scale physical simulations that are essential for understanding and predicting the climate system. Big data As the British statistician George E.P. Box said, "All models are wrong but some models are useful." We must also remember that all measurements are wrong. There is always some noise present in our data and it is not an entirely accurate reflection of the state of the world. But models employing machine learning are enabled by "big data". Vast stores of information and measurements can be used to train these models, giving them ever-greater predictive power. In general, big data is characterised by the three v's: volume, velocity and variety. Data now arrives in bigger volumes, at greater velocity, and with increased variety. This is partly due to the way that different electronic devices can be connected, via what's called the "Internet of Things". Improving our understanding of how the Earth's climate system will evolve in coming decades will be vital for informing efforts to tackle greenhouse emissions. It will also help us adapt to the effects of global warming. Models using machine learning and another AI-based approach called deep learning have been used to detect and track COVID-19. Researchers have developed machine learning models that incorporate clinical, genetic and lifestyle factors to predict an individual's risk of developing cardiovascular disease. Scientists have also used the AI technique of deep reinforcement learning to develop tools that allow them to control the hot plasma necessary to generate nuclear fusion reactions. In the past, AI was a fairly narrow field with very specific applications, such as playing chess. With the dawn of generative AI, its uses are much broader, with the technology able to create new content such as text, images and video. This moves us closer to the goal of general artificial intelligence, where the technology becomes capable of carrying out any task a human could do. Building computer-based models of the world with the help of artificial intelligence represents another major milestone. The world of science is starting to recognise the power of AI, as seen in the award this year of two Nobel prizes for work involving artificial intelligence. Maybe we are not that far away from a Nobel prize being awarded to an AI - or even a situation where a machine decides who to award the prizes to.
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AI and machine learning are transforming computer modeling, improving predictions in climate science, disease tracking, and other fields. This advancement could lead to more accurate forecasts and better understanding of complex systems.
Science can be defined as an iterative process of building models with increasing explanatory power. These models, which are approximations or simplifications of how we understand the world, have evolved from simple mathematical formulas to sophisticated computer simulations 1. As new data becomes available, scientists continually refine and improve these models to better represent complex systems such as climate change and disease spread.
Artificial Intelligence (AI) is poised to revolutionize the field of computer modeling, making these vital tools even more powerful and accurate. Machine learning, a subset of AI, has shown tremendous potential in enhancing various aspects of scientific research and prediction 2.
One area where AI is making significant strides is in weather and climate forecasting. Traditional numerical models used for weather prediction are complex and computationally demanding. However, machine learning methods are now being applied to improve these models:
The effectiveness of AI models is largely due to the availability of "big data." Characterized by volume, velocity, and variety, big data provides vast amounts of information to train these models, enhancing their predictive power 1. The Internet of Things contributes to this data influx, connecting various electronic devices and expanding the pool of available information.
AI's impact extends beyond climate science:
As AI continues to evolve, its applications in scientific modeling are expanding. The emergence of generative AI, capable of creating new content, brings us closer to general artificial intelligence 1. This progress is reflected in the scientific community's recognition of AI's importance, as evidenced by two Nobel prizes awarded for AI-related work 2.
While AI offers immense potential, it's important to remember that all models and measurements have limitations. As statistician George E.P. Box noted, "All models are wrong, but some models are useful" 1. Scientists must continue to validate AI-enhanced models against experimental evidence and refine them as new data becomes available.
As AI's role in scientific research grows, it raises intriguing questions about the future of scientific discovery and recognition. Some speculate that we may not be far from seeing an AI system awarded a Nobel prize or even determining prize recipients 2.
Google's new AI-driven weather prediction model, GraphCast, outperforms traditional forecasting methods, promising more accurate and efficient weather predictions. This breakthrough could transform meteorology and climate science.
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The European Centre for Medium-range Weather Forecasts (ECMWF) has launched a new AI-powered weather forecasting system that outperforms conventional methods, offering more accurate predictions up to 15 days ahead.
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Google DeepMind's new AI model, GenCast, outperforms traditional weather forecasting systems with unprecedented accuracy, potentially transforming meteorology and disaster preparedness.
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A new study highlights how artificial intelligence can revolutionize infectious disease research and outbreak management, emphasizing the need for ethical considerations and data accessibility.
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