Machine learning and AI implementation are reducing carbon footprints

It has become clear, in recent years, that machine learning and AI are transforming how the defence and cyber security sector works. The possibilities with these powerful concepts are promising and endless – in fact, it is important to recognise that AI experts do not know the extent of change that machine learning and AI could potentially bring.

Used in the right way, machine learning and AI together can be used to drive procurement to become far more innovative, sustainable and collaborative than it has ever been before, particularly in areas that are causing global concern, like the climate change crisis.

Machine learning in context

Machine learning holds a host of powerful opportunities for businesses across all sectors, because of what it is at its very core: a tool that counters cyber threats and controls enormous volumes of data by programming a machine to react, take commands and make decisions.

Machine learning is a crucial tool for detecting cyber threats. Previously, trying to find any evidence of a cyber attack was like looking for a needle in a haystack. Deception technology, driven by machine learning and AI, effectively hands the needle over to the IT security team, by scanning millions of data points in a matter of seconds.

In addition to this, machine learning is already becoming integrated into people’s everyday lives. Smartphones are an obvious example, being essentially small computing systems with extremely powerful chips that constantly process data and instructions. The algorithms that run machine learning help people with everyday tasks like their online shopping, and soon machine learning, combined with AI, will support major cities in the European Union to reduce their carbon footprint.

Machine learning, AI and the environment

In early 2020 a new project, ‘AI4Cities’, was launched. The concept is to use AI to help cities in the EU reduce their carbon footprint by addressing issues of traffic congestion, inefficient transport systems and poor energy use in buildings. The project is EU-funded and will last for three years, working with six cities across the EU – Helsinki, Finland; Amsterdam, Netherlands; Copenhagen, Denmark; the Paris region in France; Stavanger, Norway; and Tallinn, Estonia.

The project will be divided into five phases:

  • a preparatory phase
  • three pre-commercial procurement (PCP) phases, including design, prototyping and testing
  • an impact assessment and follow-up phase

This project is a remarkable milestone in the relationship between machine learning and the environment. The European Union is collectively the third biggest producer of carbon emissions in the world. Seventy per cent of EU citizens live in urban areas, meaning that most of those emissions are concentrated around cities in the EU. Should the AI4Cities project prove to be successful, the role that machine learning and AI play will only evolve further, becoming an essential procurement tool for all sectors, not only defence.

ICLEI – Local Governments for Sustainability, a global network of around two thousand local and regional governments that have made a commitment to sustainable development, will be leading the way on providing procurement guidelines for organisations going forward.

Speaking on the AI4Cities project, Phillip Tepper, Coordinator of Sustainable Economy and Procurement for ICLEI, said:

“AI4Cities is a living example [of] how sustainability and innovation go hand in hand, providing practical answers to the challenges ahead.”

Stay updated on developments in the AI4Cities project with DCI.

Learn more about machine learning and AI

If you want to find out more about the issues around machine learning and AI, our other DCI blogs AI and Machine Learning in Defence and How the MOD Achieves Sustainable Procurement are great places to start.

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