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A major interdisciplinary initiative has been launched that aims to meet the challenges and opportunities of new technologies as they emerge, today and far into the future.

Given the frenetic pace of AI development, the international consensus demonstrated at the AI Summit is much-needed progress, says AI expert Dr Seán Ó hÉigeartaigh. 

Over 100 letters sent to French sailors, but never delivered, have been read for the first time since they were written in 1757-8. The letters include heart-breaking love letters and evidence of family quarrels. The letters were seized by Britain’s Royal Navy during the Seven Years’ War and forgotten about until historian Renaud Morieux studied them.

‘Hard-to-decarbonize’ (HtD) houses are responsible for over a quarter of all direct housing emissions – a major obstacle to achieving net zero – but are rarely identified or targeted for improvement.

Now a new ‘deep learning’ model trained by researchers from Cambridge University’s Department of Architecture promises to make it far easier, faster and cheaper to identify these high priority problem properties and develop strategies to improve their green credentials.

Houses can be ‘hard to decarbonize’ for various reasons including their age, structure, location, social-economic barriers and availability of data. Policymakers have tended to focus mostly on generic buildings or specific hard-to-decarbonise technologies but the study, published in the journal Sustainable Cities and Society, could help to change this.

Maoran Sun, an urban researcher and data scientist, and his PhD supervisor Dr Ronita Bardhan (Selwyn College), who leads Cambridge’s Sustainable Design Group, show that their AI model can classify HtD houses with 90% precision and expect this to rise as they add more data, work which is already underway.

Dr Bardhan said: “This is the first time that AI has been trained to identify hard-to-decarbonize buildings using open source data to achieve this.

“Policymakers need to know how many houses they have to decarbonize, but they often lack the resources to perform detail audits on every house. Our model can direct them to high priority houses, saving them precious time and resources.”

The model also helps authorities to understand the geographical distribution of HtD houses, enabling them to efficiently target and deploy interventions efficiently.

The researchers trained their AI model using data for their home city of Cambridge, in the United Kingdom. They fed in data from Energy Performance Certificates (EPCs) as well as data from street view images, aerial view images, land surface temperature and building stock. In total, their model identified 700 HtD houses and 635 non-HtD houses. All of the data used was open source.

Maoran Sun said: “We trained our model using the limited EPC data which was available. Now the model can predict for the city’s other houses without the need for any EPC data.”

Bardhan added: “This data is available freely and our model can even be used in countries where datasets are very patchy. The framework enables users to feed in multi-source datasets for identification of HtD houses.”

Sun and Bardhan are now working on an even more advanced framework which will bring additional data layers relating to factors including energy use, poverty levels and thermal images of building facades. They expect this to increase the model’s accuracy but also to provide even more detailed information.

The model is already capable of identifying specific parts of buildings, such as roofs and windows, which are losing most heat, and whether a building is old or modern. But the researchers are confident they can significantly increase detail and accuracy.

They are already training AI models based on other UK cities using thermal images of buildings, and are collaborating with a space products-based organisation to benefit from higher resolution thermal images from new satellites. Bardhan has been part of the NSIP – UK Space Agency program where she collaborated with the Department of Astronomy and Cambridge Zero on using high resolution thermal infrared space telescopes for globally monitoring the energy efficiency of buildings.

Sun said: “Our models will increasingly help residents and authorities to target retrofitting interventions to particular building features like walls, windows and other elements.”

Bardhan explains that, until now, decarbonization policy decisions have been based on evidence derived from limited datasets, but is optimistic about AI’s power to change this.

“We can now deal with far larger datasets. Moving forward with climate change, we need adaptation strategies based on evidence of the kind provided by our model. Even very simple street view photographs can offer a wealth of information without putting anyone at risk.”

The researchers argue that by making data more visible and accessible to the public, it will become much easier to build consensus around efforts to achieve net zero.

“Empowering people with their own data makes it much easier for them to negotiate for support,” Bardhan said.

She added: “There is a lot of talk about the need for specialised skills to achieve decarbonisation but these are simple data sets and we can make this model very user friendly and accessible for the authorities and individual residents.”

Cambridge as a study site

Cambridge is an atypical city but informative site on which to base the initial model. Bardhan notes that Cambridge is relatively affluent meaning that there is a greater willingness and financial ability to decarbonize houses.

“Cambridge isn’t ‘hard to reach’ for decarbonisation in that sense,” Bardhan said. “But the city’s housing stock is quite old and building bylaws prevent retrofitting and the use of modern materials in some of the more historically important properties. So it faces interesting challenges.”

The researchers will discuss their findings with Cambridge City Council. Bardhan previously worked with the Council to assess council houses for heat loss. They will also continue to work with colleagues at Cambridge Zero and the University’s Decarbonisation Network.

Reference

M. Sun & R. Bardhan, ‘Identifying Hard-to-Decarbonize houses from multi-source data in Cambridge, UK’, Sustainable Cities and Society (2023). DOI: 10.1016/j.scs.2023.105015

First of its kind AI-model can help policymakers efficiently identify and prioritize houses for retrofitting and other decarbonizing measures.

This is the first time that AI has been trained to identify hard-to-decarbonize buildingsRonita BardhanRonita BardhanStreet view images of houses in Cambridge, UK, identifying building features. Red represents region contributing most to the 'Hard-to-decarbonize' identification. Blue represents low contribution.

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The British government should offer tax breaks for businesses developing AI-powered products and services, or applying AI to their existing operations, to “unlock the UK’s potential for augmented productivity”, according to a new University of Cambridge report.

Researchers argue that the UK currently lacks the computing capacity and capital required to build “generative” machine learning models fast enough to compete with US companies such as Google, Microsoft or Open AI.

Instead, they call for a UK focus on leveraging these new AI systems for real-world applications – such as developing new diagnostic products and addressing the shortage of software engineers, for example – which could provide a major boost to the British economy.

However, the researchers caution that without new legislation to ensure the UK has solid legal and ethical AI regulation, such plans could falter. British industries and the public may struggle to trust emerging AI platforms such as ChatGPT enough to invest time and money into skilling up.

The policy report is a collaboration between Cambridge’s Minderoo Centre for Technology and Democracy, Bennett Institute for Public Policy, and ai@cam: the University’s flagship initiative on artificial intelligence.

“Generative AI will change the nature of how things are produced, just as what occurred with factory assembly lines in the 1910s or globalised supply chains at the turn of the millennium,” said Dame Diane Coyle, Bennett Professor of Public Policy. “The UK can become a global leader in actually plugging these AI technologies into the economy.”

Prof Gina Neff, Executive Director of the Minderoo Centre for Technology and Democracy, said: “A new Bill that fosters confidence in AI by legislating for data protection, intellectual property and product safety is vital groundwork for using this technology to increase UK productivity.”

Generative AI uses algorithms trained on giant datasets to output original high-quality text, images, audio, or video at ferocious speed and scale. The text-based ChatGPT dominated headlines this year. Other examples include Midjourney, which can conjure imagery in any different style in seconds.

Networked grids – or clusters – of computing hardware called Graphics Processing Units (GPU) are required to handle the vast quantities of data that hone these machine-learning models. For example, ChatGPT is estimated to cost $40 million a month in computing alone. In the spring of this year, the UK chancellor announced £100 million for a “Frontier AI Taskforce” to scope out the creation of home-grown AI to rival the likes of Google Bard.

However, the report points out that the supercomputer announced by the UK chancellor is unlikely to be online until 2026, while none of the big three US tech companies – Amazon, Microsoft or Google – have GPU clusters in the UK.

“The UK has no companies big enough to invest meaningfully in foundation model development,” said report co-author Sam Gilbert. “State spending on technology is modest compared to China and the US, as we have seen in the UK chip industry.”

As such, the UK should use its strengths in fin-tech, cybersecurity and health-tech to build software – the apps, tools and interfaces – that harnesses AI for everyday use, says the report.

“Generative AI has been shown to speed up coding by some 55%, which could help with the UK’s chronic developer shortage,” said Gilbert. “In fact, this type of AI can even help non-programmers to build sophisticated software.”

Moreover, the UK has world-class research universities that could drive progress in tackling AI stumbling blocks: from the cooling of data centres to the detection of AI-generated misinformation.

At the moment, however, UK organisations lack incentives to comply with responsible AI. “The UK’s current approach to regulating generative AI is based on a set of vague and voluntary principles that nod at security and transparency,” said report co-author Dr Ann Kristin Glenster.

“The UK will only be able to realise the economic benefits of AI if the technology can be trusted, and that can only be ensured through meaningful legislation and regulation.”

Along with new AI laws, the report suggests a series of tax incentives, such as an enhanced Seed Enterprise Investment Scheme, to increase the supply of capital to AI start-ups, as well as tax credits for all businesses including generative AI in their operations. Challenge prizes could be launched to identify bottom-up uses of generative AI from within organisations.

Legislation regulating AI safety and transparency is needed, say researchers, so British industry and education can put resources into AI development with confidence.

The UK can become a global leader in actually plugging these AI technologies into the economyDiane Coyle Getty/BlackJack3DData Tunnel

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Despite changes to the HM Treasury Green Book to encourage forms of valuation other than economic, local authorities are struggling to capture social, environmental and cultural value in a way that feeds into their systems and processes. This new project aims to make this easy by spatialising data so that it can be used as a basis for targeted hyperlocal action for a green transition.

Prof Flora Samuel said: “Climate change cannot be addressed without revealing and tackling the inequalities within society and where they are happening. Only when we know what is happening where, and how people are adapting to climate change can we make well informed decisions.”

“The aim of this pragmatic project is to create a Community Open Map Platform that will bring together multiple layers of spatial information to give a social, environmental, cultural and economic picture of what is happening in a neighbourhood, area, local authority, region or nation.”

Green Transition Ecosystems (GTEs) are large-scale projects that focus on translating the best design-led research into real-world benefits. Capitalising on clusters of design excellence, GTEs will address distinct challenges posed by the climate crisis including, but not limited to, realising net zero goals.

GTEs are the flagship funding strand of the £25m Future Observatory: Design the Green Transition programme, funded by the AHRC and delivered in partnership with the Design Museum.

The COMP will address the following overarching aims of the Green Transitions Ecosystem call: measurable, green transition-supportive behaviour change across sectors and publics; design that fosters positive behaviour change in support of green transition goals, including strategy and policy; region-focused solutions for example the infrastructure supporting rural communities and, lastly, designing for diversity.

To meet these aims the COMP will deliver a baseline model mapping platform for decision making with communities for use by Local Authorities (LoAs) across the UK and beyond. To do this a pilot COMP will be made for the Isle of Anglesey to help the LoA measure its progress towards a green transition and fulfilment of the Future Generations Wales Act in a transparent and inclusive way.

The Isle of Anglesey/Ynys Môn in North Wales was chosen as the case study for this project largely because it is a discrete geographical place that is rural, disconnected and in decline with a local authority that has high ambitions to reinvent itself as a centre of sustainable innovation, to be an 'Energy Island’ at the centre of low carbon energy research and development. The bilingual context of Anglesey provides a particular opportunity to explore issues around multilingual engagement, inclusion and culture, a UK wide challenge.

The project, a collaboration with the Wales Institute of Social and Economic Research and Data (Wiserd) at Cardiff University and Wrexham Glyndwr University as well as several other partners is supported by the Welsh Government and the Future Generations Commission in Wales who are investigating ways to measure, and spatialise, attainment against the Well-being of Future Generations (Wales) Act (2015), a world-leading piece of sustainability legislation.

The Community Open Map Platform (COMP) will offer a range of well designed and accessible information to communities, local authorities and policy makers alike, as well as opportunities to contribute to the maps. The map layers will constantly grow with information and sophistication, reconfigured according to local policy and boundaries. And crucially, they will be developed and monitored with and by a representative cross section of the local community.

An accessible website will be designed as data repository tailored to a range of audiences, scalable for use across the UK. Social, cultural and environmental map layers will be co-created with children and young people to show, for instance, where people connect, engage with cultural activities and do small things to adapt to climate change.

The community-made data will be overlaid onto existing census and administrative data sets to build a baseline Future Generations map of the Isle of Anglesey. The layers can be clustered together to measure the island’s progress against the Act but can also be reconfigured to other kinds of measurement schema. In this way the project will offer a model for inclusive, transparent and evidence based planning, offering lessons for the rest of the UK and beyond.

This award is part of the Future Observatory: Design the Green Transition programme, the largest publicly funded design research and innovation programme in the UK. Funded by AHRC in partnership with Future Observatory at the Design Museum, this £25m multimodal investment aims to bring design researchers, universities, and businesses together to catalyse the transition to net zero and a green economy.

Christopher Smith, Executive Chair of the Arts and Humanities Research Council said:

“Design is a critical bridge between research and innovation. Placing the individual act of production or consumption within the context of an wider system of social and economic behaviour is critical to productivity, development and sustainability.

"That’s why design is the essential tool for us to confront and chart a path through our current global and local predicaments, and that’s why AHRC has placed design at the heart of its strategy for collaboration within UKRI.

"From health systems to energy efficiency to sustainability, these four Green Transition Ecosystem projects the UK are at the cutting edge of design, offering models for problem solving, and will touch on lives right across the UK.”

A team led by Prof Flora Samuel from Cambridge’s Department of Architecture has been awarded one of four new £4.625 million Green Transition Ecosystem grants by the Arts and Humanities Research Council (AHRC) to create a Community Open Map Platform (COMP) for Future Generations to chart the green transition on the Isle of Anglesey/Ynys Môn.

Climate change cannot be addressed without revealing and tackling the inequalities within society and where they are happeningFlora SamuelEllena McGuinness on UnsplashAnglesey beach crowded with people

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