The heart of the transformer model is its “attention mechanism”, which enables it to weigh the importance of different parts of an input sequence when processing each element of the output sequence. Our paper “ Earthformer: Exploring space-time transformers for Earth system forecasting”, published at NeurIPS 2022, suggests a novel attention mechanism we call cuboid attention, which enables transformers to process large-scale, multidimensional data much more efficiently.Īnd in “ PreDiff: Precipitation nowcasting with latent diffusion models”, to appear at NeurIPS 2023, we show that diffusion models can both enable probabilistic forecasts and impose constraints on model outputs, making them much more consistent with both the historical record and the laws of physics. In recent work, our team at Amazon Web Services has tackled all these challenges. And finally, typical machine learning models don’t have guardrails imposed by physical laws or historical precedents and can produce outputs that are unlikely or even impossible. Sometimes, however, it may be more important to know that there’s a 10% chance of an extreme weather event than to know the general averages across a range of possible outcomes. Most existing machine-learning-based Earth systems models also output single, point forecasts, which are often averages across wide ranges of possible outcomes. Foremost among these is the high dimensionality of Earth system data: naively applying the transformer’s quadratic-complexity attention mechanism is too computationally expensive. But these efforts have encountered several major challenges. The success of transformer-based models in other AI domains has led researchers to attempt applying them to Earth system forecasting, too. Precise and timely forecasting of these variabilities can help people take necessary precautions to avoid crises or better utilize natural resources such as wind and solar energy. Variabilities ranging from regular events like temperature fluctuations to extreme events like drought, hailstorms, and the El Niño–Southern Oscillation (ENSO) phenomenon can influence crop yields, delay airline flights, and cause floods and forest fires. Can seeking happiness make people unhappy? Paradoxical effects of valuing happiness. Mauss IB, Tamir M, Anderson CL, Savino NS. Procedia - Social and Behavioral Sciences. The effect of perceived social support on subjective well-being. Whillans AV, Dunn EW, Smeets P, Bekkers R, Norton MI. Psychological well-being revisited: advances in the science and practice of eudaimonia. Positive psychology and gratitude interventions: a randomized clinical trial. A systematic review of the relationship between physical activity and happiness. Pursuing happiness: The architecture of sustainable change. Mood and cytokine response to influenza virus in older adults. L ink between healthy lifestyle and psychological well-being in Lithuanian adults aged 45-72: a cross-sectional study. Sapranaviciute-Zabazlajeva L, Luksiene D, Virviciute D, Bobak M, Tamosiunas A. Positive affect and biological function in everyday life. Emotional experience improves with age: evidence based on over 10 years of experience sampling. The keys to happiness: Associations between personal values regarding core life domains and happiness in South Korea. Washington (DC): National Academies Press (US). ![]() Subjective Well-Being: Measuring Happiness, Suffering, and Other Dimensions of Experience. Panel on Measuring Subjective Well-Being in a Policy-Relevant Framework Committee on National Statistics Division on Behavioral and Social Sciences and Education National Research Council Stone AA, Mackie C, editors. The neuroscience of happiness and pleasure. Ideal levels of prosocial involvement in relation to momentary affect and eudaimonia: Exploring the golden mean.
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