Publikation
Computational Sustainability and Technology (CST)
Daniel Sonntag
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Technical Note, 2025.
Zusammenfassung
Computational sustainability (CS) is the scientific field that aims to balance societal, economic, and environmental
resources using methods from computer science and artificial intelligence (AI). AI models, e.g., machine learning
models, enrich models of computational sustainability. Research in interactive machine learning can make important
contributions to help address key challenges of sustainability (AI for CS). Computational sustainability questions enrich
AI research, not only by providing problems that involve uncertainty or vagueness, thus generating compelling new AI
challenges, but also by providing a requirement framework for resource-bounded computation (CS for AI).
The research department Interactive machine learning of the German Research Center for Artificial Intelligence hosts
“Computational Sustainability & Technology”; we use applied artificial intelligence methods in the areas of machine
learning, knowledge representation, and intelligent user interfaces to help achieve more sustainable systems (AI for CS)
or to build more sustainable AI systems (CS for AI).
Using the power of, for example, deep learning computers, we can process large quantities of information and
allocate resources based on real-time information. On the other hand, we have to decide when to regulate the power
consumption of such AI systems. Applications are widespread. For example, smart grids implement renewable resources
and storage capabilities to control the production and expenditure of energy. In the project Seadash, we work on
integrating machine learning methods for event detection and classification of underwater signals to preserve marine
fauna. Further, together with edge computing (the new distributed computing paradigm that brings computation and
data storage closer to the location where it is needed) we do not only improve response times and save bandwidth, but
also reduce energy consumption (Mobile AI Lab).
The theory of computational sustainability includes aspects from game theory, machine learning theory and human
computer interaction theory. For example, climate change, pollution, and other environmental crises can be explained by
theories of human psychology (e.g., the individual in a social world) and can hence be computed by (machine learning)
models with computational models of the Prisoner’s Dilemma. More is More? More computation is not always more,
as unsustainable consumption of energy should be avoided. There are already interesting approaches in the machine
learning community, e.g., towards the systematic reporting of the energy and carbon footprints of machine learning or
looking at methodological issues related to training on big data and large web corpora where billions or even trillions
of parameters are tuned. Humans, on the contrary, can do such “training” with only a few examples or from simple
instructions (cf. interactive machine learning, https://www.dfki.de/iml/).
AI for CS and CS for AI and the application domains bring us back to the main challenges of artificial intelligence
research and applied research in the area of CS technology: (1) Incompleteness, (2) vagueness, (3) uncertainty and
reasoning (in deep learning), and (4) resource-bounded computation and learning. In our projects, we tackle these
theoretical challenges and focus on imitation learning, learning with small datasets, transfer learning, long term
autonomy of sustainable AI systems, never ending learning, hybrid teams, IoT, multi-sensor streams for small interaction devices, mobile computing platforms (Mobile AI Lab), and the efficient use of big deep learning clusters.