Professor Ong Yew Soon and Dr Lim Keng Hui provide their insights on how artificial intelligence technologies can be applied to meet sustainability demand
Copyright: sustainabilitymag.com – “Sustainability applications for artificial intelligence”
Artificial intelligence (AI) systems today are already transforming industries and becoming an indispensable part of our daily lives. Such systems, which leverage machines to process and analyse large amounts of data, have vastly changed how humans work and play, and are being used today in many sectors, from banking to energy to agriculture. But AI systems can be energy-intensive, and there is a pressing need for those working in the field of AI to address the potentially large environmental impacts. This is especially as demand for data and intelligent devices continues to proliferate. Singapore has committed itself to environmental sustainability, underlined by its ratification of the Paris Agreement and recent plans to reach net-zero by or around mid-century. Environmental criteria have also become a crucial performance measure for companies and industries. Rising interest in sustainable investing is a strong signal that corporations – including AI-dependent ones – will soon be held accountable for their greenhouse gas emissions.
The environmental impact of AI
The field of AI is progressing by leaps and bounds, driven by advances in hardware and an exponential increase in computing power. But the massive computation required to obtain these impressive technological feats comes at a price. Training AI models can incur substantial financial and environmental costs due to the energy needed to perform such computations. On top of the monetary costs of hardware, electricity and cloud compute time, powering such hardware for weeks and months at a time could also leave a huge carbon footprint.
Studies have found that Google’s AlphaGo Zero – the AI that plays the game of Go against itself to self-learn – generated a massive 96 tonnes of carbon dioxide over 40 days of research training. That is comparable to 1,000 hours of air travel, as well as the carbon footprint of about 10 average Singaporeans over an entire year.
In Singapore – a popular regional hub for data centres – there is growing concern over the increasing power consumption and widening carbon footprint of its data centre industry. A typical 20MW data centre on the island consumes the same amount of electricity a day as around 60,000 households – or about the energy usage size of Yishun town in Singapore. With an estimated 60 data centres here accounting for about 7 per cent of the country’s total electricity consumption in 2020, the carbon footprint is significant. […]
Read more: www.sustainabilitymag.com
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Professor Ong Yew Soon and Dr Lim Keng Hui provide their insights on how artificial intelligence technologies can be applied to meet sustainability demand
Copyright: sustainabilitymag.com – “Sustainability applications for artificial intelligence”
Artificial intelligence (AI) systems today are already transforming industries and becoming an indispensable part of our daily lives. Such systems, which leverage machines to process and analyse large amounts of data, have vastly changed how humans work and play, and are being used today in many sectors, from banking to energy to agriculture. But AI systems can be energy-intensive, and there is a pressing need for those working in the field of AI to address the potentially large environmental impacts. This is especially as demand for data and intelligent devices continues to proliferate. Singapore has committed itself to environmental sustainability, underlined by its ratification of the Paris Agreement and recent plans to reach net-zero by or around mid-century. Environmental criteria have also become a crucial performance measure for companies and industries. Rising interest in sustainable investing is a strong signal that corporations – including AI-dependent ones – will soon be held accountable for their greenhouse gas emissions.
The environmental impact of AI
The field of AI is progressing by leaps and bounds, driven by advances in hardware and an exponential increase in computing power. But the massive computation required to obtain these impressive technological feats comes at a price. Training AI models can incur substantial financial and environmental costs due to the energy needed to perform such computations. On top of the monetary costs of hardware, electricity and cloud compute time, powering such hardware for weeks and months at a time could also leave a huge carbon footprint.
Studies have found that Google’s AlphaGo Zero – the AI that plays the game of Go against itself to self-learn – generated a massive 96 tonnes of carbon dioxide over 40 days of research training. That is comparable to 1,000 hours of air travel, as well as the carbon footprint of about 10 average Singaporeans over an entire year.
In Singapore – a popular regional hub for data centres – there is growing concern over the increasing power consumption and widening carbon footprint of its data centre industry. A typical 20MW data centre on the island consumes the same amount of electricity a day as around 60,000 households – or about the energy usage size of Yishun town in Singapore. With an estimated 60 data centres here accounting for about 7 per cent of the country’s total electricity consumption in 2020, the carbon footprint is significant. […]
Read more: www.sustainabilitymag.com
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
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