Page 42 - AI Standards for Global Impact: From Governance to Action

AI Standards for Global Impact: From Governance to Action

reinvention driving progress through successive generations of machine learning
technologies over the past 15 years.

Figure 28: Left to right: Francois E� Guichard, Focal Point, Intelligent Transport Systems
and Automated Driving, United Nations Economic Commission for Europe (UNECE);
Helen Pan, General Manager, Baidu Apollo; Vincent Vanhoucke, Distinguished
Engineer, Waymo

5�10 AI and energy

Gitta Kutyniok, Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence, at
Ludwig-Maximilians Universität München, and Qi Shuguang, Vice Deputy Engineer at the China
Academy of Information and Communications Technology (CAICT); shared the key outcomes
of the summit's workshop on "Navigating the Intersect of AI, Environment and Energy for
a Sustainable Future," highlighting the progress in the area of energy efficiency for AI and
upcoming challenges in the future where standards would be needed.

AI can play a significant role in climate monitoring and reducing environmental impacts, including
energy and water consumption and GHG emissions. It can also help improve sectoral systems
such as power grids, agriculture, waste management, biodiversity conservation, and transport
and mobility. For example, in the manufacturing and energy sectors, AI contributes to energy
savings and supports green transitions through device control, process optimization, recycling,
IoT integration, and deep learning technologies.

The key points discussed are summarised below:

a) Challenges for energy efficiency for AI
o The environmental impacts of AI – including significant energy consumption, CO2
emissions, and health risks from PM 2.5 near hyperscale data centres – is growing
exponentially with advancements like generative AI and cloud computing, which rely
on massive data centres.

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