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Dr. Didem Gürdür Broo个人简介

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发表于 2021-12-15 06:13:23 | 显示全部楼层 |阅读模式
第48期系统工程沙龙演讲嘉宾简介
Research Background
Didem cares about the future of the world and nature. She is a computer scientist with a Ph.D. in mechatronics, which can give you an idea about how much she loves to talk about the future and emerging technologies. She is a data person, always finds a way to talk about how important it is to know your data, use it to make decisions and at some point expect her to talk about art, visualizations and visual analytics. Didem is a person who does not hesitate to talk about inequalities and point out her ethical concerns. She dreams of a better world and actively works on improving inequalities regardless of their nature. She is an analytical thinker with a passion for design thinking, a researcher with a future perspective, an engineer who likes problems more than solutions and a teacher who likes to play during lectures. She is a good reader, sailor, divemaster, photographer and drone pilot.

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CDBB projects involved with
Digital twins for smart infrastructure
Future built environments
Research ambitions for CDBB
  • To provide guidelines and strategies for the built environment industry that will enable to create a sustainable future world.
  • To understand the role of digital twins and data in this rapidly changing world and provide a framework to build resilient, interoperable and sustainable smart infrastructure.
  • To identify the current needs of the existing infrastructure but also imagining what their role will be in an integrated, data-driven, digitalised and intelligent future world



[size=1.1em]BiographyResearchPublicationsKey publications:



Other publications:





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 楼主| 发表于 2021-12-15 06:14:32 | 显示全部楼层




Artificial intelligence is already powering much of the technology helping to drive the modern economy. AI is now an essential part of how we use the internet but can also be found in stock exchanges, advanced factories and automated warehouses. It is starting to drive our cars and even vacuum our floors. And yet only a fraction of companies which stand to significantly benefit from AI are exploiting this approach to help deliver their products and services.
One important reason for this is a lack of high-quality data. Technology giants such as Google, Microsoft and Amazon have been able to make great strides in AI – developing software to answer our questions and identify what’s in our photos – because of their vast data-gathering operations. But many established industries that could benefit from AI and advanced robotics are struggling to gather, manage and use data in a helpful way.
Having high-quality and trustworthy data is key to helping companies to better understand their markets and customers and enable automated decision making. At an infrastructure level, data can guide planners and developers and help optimise the use and maintenance of buildings, roads and railways. This could also help reduce carbon emissions by making our infrastructure last longer and work more efficiently, helping to reduce wasted energy and unnecessary traffic.
Foundation of AI
Data is, simply, the foundation of artificial intelligence. To train AI to perform a specific task, you typically need to run sample data through its progressive learning algorithms so that it can adapt and improve its ability to recognise patterns and respond accordingly. Some AI can then automate the repetitive process of discovering useful information from new data and even become better at spotting patterns than humans or identify things we never could. In some cases, the more data that AI processes, the better it learns to function.

However, despite the potential benefits, research shows that in some sectors as little as 10% of companies have unlocked these kind of advanced analytics approaches. Industries such as telecoms, automotive and financial services are trying to catch up with the tech giants. But many sectors, including health-care, education, government and construction, are still not close to reaching the full potential of using data and AI.
For instance, speeding up medical diagnosis and making it more accurate could save US$400 billion in the US healthcare sector alone. But the right rules and incentives to encourage enough people to share their medical data with AI developers aren’t yet in place and so the sector has yet to realise this potential.

AI could speed up and improve medical diagnosis. Sergey Nivens/Shutterstock
So how can more companies start gathering the data that will help them make the most of AI? There are typically several key problems that can hold companies back. The data needed may not exist, it may be inaccessible (for example because it is private), it may exist in too many locations, sources or formats to be useful. It can also be of limited quality or not collected for use with AI and so not have the right information.
There might also be be too much of it. We often hear about the value of “big data”, very large data sets from which patterns and other useful insights can be drawn. But collecting more data does not always lead to better analytics results and sometimes can be unnecessarily complicated and resource-intensive.
These problems can often occur because companies don’t have the right strategy or expertise. Research shows many companies still lack dedicated data teams to make sure the right data is gathered, managed and then correctly used. However, my colleagues and I have recently conducted research showing technology companies with fewer than 50 employees often use data analytics heavily. This suggests innovative start-ups can be more aware of the value of data and agile enough to use it effectively compared to traditional large companies.
If the traditional companies and other organisations that could benefit most from data and AI want to be able to compete, profit and build a sustainable world, they must start embracing data. AI solutions can only be as good as the quality of data they are built on. This means hiring the right people and putting in place the required policies to gather the correct data, make it accessible, assess the quality and then put it to use to develop AI solutions. Only in this way will these organisations be in a position to truly take advantage of the next industrial revolution.


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 楼主| 发表于 2021-12-15 06:16:53 | 显示全部楼层
Let Covid crisis be catalyst to change construction for good



Didem Gürdür Broo and Jennifer Schooling
29 April 2020





Topics
Construction and coronavirus




[size=1.6em]As industry comes to terms with the coronavirus pandemic, there is opportunity to bring positive change. Didem Gürdür Broo and Jennifer Schooling from the Centre for Smart Infrastructure and Construction ask: when would be a better time to start this shift than now?
[size=1.2em]While the coronavirus pandemic is at the core of our lives, it is very difficult not to remember American politician Rahm Emanuel’s famous quote from the 2008 financial crash: “You never let a serious crisis go to waste. And what I mean by that it's an opportunity to do things you think you could not do before,” he said.
[size=1.2em]Now, we face another crisis. It has been predicted that many private-sector markets will not return to growth due to the impacts of the Covid pandemic until at least 2022. Most of us are still coming to terms with the effects of the coronavirus pandemic on our organisations. So, is it possible to consider this crisis from another perspective and identify opportunities for our industry to learn, to improve, and to better prepare for the future?
[size=1.2em]What if we applied future thinking, scenario building, and foresight activities to imagine and understand the future ahead of us? What if we proactively sought to adopt emerging technologies, and re-evaluate processes, organisational structures, and projects from a systematic perspective? Could this pandemic be a tipping point to a more sustainable future?
[size=1.2em]Future thinking
[size=1.2em]Future thinking is the systematic exploration of probable futures, including different factors from emerging technological and social changes. It uses a set of tools to track, analyse, imagine, decide and act on changes. It recognises opportunities and potential threats before they evolve and identifies future needs by revealing cognitive knowns and unknowns. Having the ability to imagine the future in a methodological way can prepare us by offering different perspectives on future phenomena years in advance.
[size=1.2em]In times of uncertainty, using future thinking methods such as scenario planning can help organisations to prepare for the post-pandemic world. It enables us to craft scenarios that could be important elements of the future. Both scenario planning and resulting scenarios are useful analytical exercises for developing a better understanding of the change, as well as the production and distribution of knowledge on change.
[size=1.2em]
[size=1.2em]Future thinking brings the ability to look beyond one’s own disciplinary boundaries, follow changes in other domains, consider different outcomes of trends and uncertainties, imagine future scenarios and develop strategies for these different futures. This is extremely helpful for not only developing a common language among and across organisations to discuss the future and the potential effects of change, but also prepare for radical change. Shifting our mindset from today to the future enables us to consider time and uncertainty when strategising. When would be a better time to start this shift than now?
[size=1.2em]Smart infrastructure
[size=1.2em]Smart infrastructure has the potential to revolutionise how infrastructure is delivered, managed and automatically operated. Data, the Internet of Things, digital twins, artificial intelligence applications and robotics are opportunities that can help the industry to deliver smart infrastructure solutions faster and better. Organisations need to create environments to discuss change and enable their workforce to develop new skills. It is now time to adopt an agile and continuous learning mindset to ensure a future response that is (re-)calibrated to the circumstances at hand. When combined with future scenarios, these technology advancements can underpin organisational strategies and objectives. The industry needs to understand how data can improve productivity and increase the value and efficiency of our infrastructure and the services it delivers.
[size=1.2em]Yes, we face uncertain times. However, after the pandemic, the industry will need to recover. The question is, will we take this opportunity to adopt new technologies, tools and methodologies to help us to overcome current and future challenges in the industry? It is time to learn, discuss, compare, understand and test.
[size=1.2em]Sustainable built environment
[size=1.2em]The 21st century is characterised by rapid change, uncertainty and increasing interconnectedness. The grand sustainability challenges of this century, including climate change, air pollution, deforestation and rising inequalities, are complex issues encompassing interdependent systems. This complexity results in a pluralism of values within societies, and understanding this complexity requires a holistic view secured by a multidisciplinary collaboration to develop a systems perspective beyond our individual profession and domain.
[size=1.2em]This pandemic is causing unprecedented changes. Just this week the price of a barrel of oil slipped below $20, its lowest level since 2002. There has been a global drop in air pollution due to reductions in traffic and industrial activity. This shows what environmental improvements could be achieved if we work towards sustainable low carbon solutions. But how do we do it?
[size=1.2em]
[size=1.2em]What if we use this time to understand different perspectives on sustainable development goals and engage all stakeholders, for instance, in net zero carbon discussions? To fully understand our new post-pandemic role, we should think in systems, shift from disconnections to interconnectedness, from linear to circular ways of thinking, from silos to emergence, from parts to wholes, and from isolation to relationships.
[size=1.2em]Let’s not forget, technology, new needs and urgency made it possible for organisations to work effectively remotely – what we all would have thought impossible just a matter of weeks ago. When we need to, we can overcome challenges and act fast. Post-pandemic future and climate change will require this action too. We’ve seen the possible and now we have to once again make the seemingly impossible possible.
[size=1.2em]Didem Gürdür Broo is a research associate at the Centre for Smart Infrastructure and Construction and Laing O'Rourke Centre for Construction Engineering and Technology, Department of Engineering, University of Cambridge, Cambridge, UK. Jennifer Schooling is the Director of CSIC.

[size=1.2em]原帖:http://www.infrastructure-intelligence.com/article/apr-2020/let-covid-crisis-be-catalyst-change-construction-good








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 楼主| 发表于 2021-12-16 06:41:20 | 显示全部楼层

本期沙龙主题:Human-centred Cyber-physical Systems: How can we build better intelligent systems?


Abstract:
High quality, trustworthy data can help organizations build strategies, capture value, increase the potential of automation and enable insightful and fast decision-making. Data could change the cities we inhabit through real-time solutions to challenges such as traffic congestion, air quality, energy distribution and monitoring. It can help us to build better ecosystems that support human flourishing. Data could enable us to be more effective, efficient, and sustainable. And it is already changing the world one industry at a time.

At the same time, data is often referred to as “the new oil”. We are, on one hand, benefitted from the revolutions that oil fuelled. Yet, on the other hand, one of the biggest threats that the earth is facing today is the unanticipated results of this revolution – climate change. Now that we are at the beginning of another new era, which many call the fourth industrial revolution, it is vital to understand how data-related decisions of today can affect the future and we focus on understanding the cause-effect relationships of our design decisions. Therefore, it is essential to not only focus on the opportunities of data but also understand the challenges around it.

This talk aims to provide an overview of the important characteristics of cyber-physical systems, common challenges related to data usage in cyber-physical systems. We will discuss strategies to deal with these challenges and have a brief introduction to three mindsets that can help us to do not only design intelligent systems but also doing this in a human-centred and sustainable way.
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