What to expect:
Artificial intelligence – especially machine learning (ML) – can combat climate change, but it can also amplify it. In this webinar, we discuss environmental impacts of ML, but also countermeasures to ensure that ML is not part of the problem, but part of the solution.
There are many possible applications of ML with positive environmental impact: early detection of forest fires, optimization of traffic flows, or resource savings through data-based maintenance. These and other measures help to minimize human impact on the environment and save CO2, for example.
At the same time, the resource requirements of ML models themselves are steadily increasing. Training and operating ML models – especially in data centers – requires large amounts of space, hardware and, not least, electricity. Despite efficiency improvements, the trend is increasing, a trend that will certainly continue for some years to come. This makes it all the more important to be aware of the environmental impact of ML and to minimize it directly during application. How big these environmental impacts are and how you can minimize them, you will learn in this webinar.
Speakers:
- Alexander Niltop (statworx)
- Dr. Maria Börner (Flower)
This event is organized by the statworx initiative AI & Environment.
Register for the webinar here: https://statworx.clickmeeting.com/ai-sustainability/register