Oliver Zielinski

Artificial Intelligence can contribute to climate protection in a variety of ways. In which areas do you see the greatest potential of the technology in the fight against climate change?

Oliver Zielinski: AI is a tool for generating knowledge, increasing efficiency and automating processes. In addition to mobility, energy supply, industry and the circular economy, the technology can unleash its great potential for climate- and environment-friendly innovations in building management and agriculture in particular. These sectors have a higher degree of digitalization maturity. This "digital readiness" is important if the opportunities offered by AI are to be effectively exploited quickly, as Artificial Intelligence requires both a technological superstructure and a corresponding willingness on the part of those involved.

In general, the important question is: How much efficiency can AI actually achieve within these areas? Experts assume 10 to 30 percent here, for example by improving processes, shortening routes and optimizing the use of raw materials. One area where I see the possibility of making significant progress in the long term is the circular economy. Successes to date here have been rather modest in terms of global material flows, partly because the effort required for end-to-end reuse of products and raw materials is not economically worthwhile. AI can support these processes at all levels, provide targeted information about products, improve their reusability and automate labor-intensive processes - despite the high complexity of the tasks.

The high energy consumption of AI systems is much discussed. What does "green AI" look like?

Oliver Zielinski: We need more sustainability through AI and more sustainability in AI. The term "green AI" is based on the work on "Green AI" published by Roy Schwartz and co-authors in 2020 and the claim formulated there that green AI reduces its own ecological footprint AND promotes inclusion. Resource efficiency is the key concept, which means using as little material and energy as possible, throughout the lifecycle of AI methods. This also includes the open provision of annotated data and pre-trained or pre-optimized models to promote their high reusability and enable participation by actors with poorer access to knowledge and computing infrastructures. With the increasing use of smart devices, AI models are also increasingly being run directly on users' premises. Due to the very high volume of such "AI-of-Things" (AIoT) systems, the individual energy and raw material requirements of each system, its useful life, and reusability must now also be considered. Green AI thus goes beyond pure energy consumption and differs from the discussion about operating large data centers with regenerative energies.

What needs to be done to ensure that the ecological balance of AI deployment on behalf of climate protection is positive?

Oliver Zielinski: Some things are already being done, but that is not enough. In research funding in particular, various federal ministries (BMUV, BMBF, BMWK) have developed and published programs that lay the foundations and translate them into exemplary applications. These projects are often referred to as lighthouses. That's a start, but we need a whole sea of lights: Successful approaches must be brought into the mainstream. This requires social acceptance, a legal framework and, last but not least, investors. Broader acceptance is achievable through participation and transparency, also and especially in the often emotionally driven AI debate. Legal frameworks and standards help to make environmental sustainability the standard, which in turn also provides positive impetus for the financing of new business models and ventures. In the end, Green AI can thus become a seal of quality for climate-friendly AI technologies and, at the same time, an ecological competitive advantage for Europe.