Objectives
- Compare effects of climate and land use on fluxes within the same climate zone and among the mesic and semi-arid regions
- Combine multi-scale observations (satellite, flux sites, inventories, tall towers) in neural networks to determine how current climate, land-use and land cover influence processes
- Modify CLM to reduce uncertainties in simulated effects of land use and land cover on biogeochemical and biophysical processes (crops, poplar)
- Investigate future climate variability, and effects of changes in land use and land cover on terrestrial processes
Deep learning has renewed interest in computational creativity. Can machines be creative? In which sense? And why this would be useful? We argue current creative AI systems are stuck: they explore combination, analogy or random, but the value of the objects are provided by the system designer.
The only way to creative AI is to develop agents building their own value.
We also argue: the generative potential of deep learning is understudied.
Current focus is on likelihood - whereas creativity is unlikely.
We present an implementation of these ideas on the MNIST handwritten digits dataset - to create symbols that could have been digits (e.g. in an imaginary culture) but that are not.
Objectives
- Compare effects of climate and land use on fluxes within the same climate zone and among the mesic and semi-arid regions
- Combine multi-scale observations (satellite, flux sites, inventories, tall towers) in neural networks to determine how current climate, land-use and land cover influence processes
- Modify CLM to reduce uncertainties in simulated effects of land use and land cover on biogeochemical and biophysical processes (crops, poplar)
- Investigate future climate variability, and effects of changes in land use and land cover on terrestrial processes
Deep learning has renewed interest in computational creativity. Can machines be creative? In which sense? And why this would be useful? We argue current creative AI systems are stuck: they explore combination, analogy or random, but the value of the objects are provided by the system designer.
The only way to creative AI is to develop agents building their own value.
We also argue: the generative potential of deep learning is understudied.
Current focus is on likelihood - whereas creativity is unlikely.
We present an implementation of these ideas on the MNIST handwritten digits dataset - to create symbols that could have been digits (e.g. in an imaginary culture) but that are not.
Objectives:
- Determine how soil moisture and nutrients regulate microbial C-use efficiency (CUE)
- Develop mathematical functions that can be incorporated into earth system models
- Improve our ability to predict the impact of climate change on soil C-sequestration in agricultural systems
Objectives
- Understand, model and predict greenhouse gases emissions from grasslands and winter wheat croplands under changing microbes, climate, livestock and manure use across the scales of field, farm and watershed
- Broaden STEM education for K-12 and college students and teachers, and engage farmers, ranchers, decision makers, and citizen scientists to participate in in-situ data collection and analyses
Die Predictive Analytics World ist die führende Fachkonferenz für anwendungsorientierte Predictive Analytics. Anwender, Entscheider und Experten von Predictive Analytics treffen sich in Berlin, um sich über die neuesten Erkenntnisse und Fortschritte zu informieren, sich untereinander fachlich und persönlich auszutauschen und sich von den Erfolgen inspirieren zu lassen.
Perspektiven und Trends: + Wo prägt Big Data den digitalen Wandel? + Welche Technologien helfen?
Big Data Lab: + Wie gelingt der Schnellstart in die neue Informationsökonomie?
Praxiserfahrungen: + Welche Erfahrungen machen andere? + Wo steht Ihre Branche?
Objectives:
- Determine how soil moisture and nutrients regulate microbial C-use efficiency (CUE)
- Develop mathematical functions that can be incorporated into earth system models
- Improve our ability to predict the impact of climate change on soil C-sequestration in agricultural systems
Objectives
- Understand, model and predict greenhouse gases emissions from grasslands and winter wheat croplands under changing microbes, climate, livestock and manure use across the scales of field, farm and watershed
- Broaden STEM education for K-12 and college students and teachers, and engage farmers, ranchers, decision makers, and citizen scientists to participate in in-situ data collection and analyses
Die Predictive Analytics World ist die führende Fachkonferenz für anwendungsorientierte Predictive Analytics. Anwender, Entscheider und Experten von Predictive Analytics treffen sich in Berlin, um sich über die neuesten Erkenntnisse und Fortschritte zu informieren, sich untereinander fachlich und persönlich auszutauschen und sich von den Erfolgen inspirieren zu lassen.
Perspektiven und Trends: + Wo prägt Big Data den digitalen Wandel? + Welche Technologien helfen?
Big Data Lab: + Wie gelingt der Schnellstart in die neue Informationsökonomie?
Praxiserfahrungen: + Welche Erfahrungen machen andere? + Wo steht Ihre Branche?
DaF-Community - Präsentation anlässlich der IDT 2009 in Jena
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