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Seven years have passed since world leaders met in New York and agreed 17 Sustainable Development Goals (SDGs) to resolve major challenges including poverty, hunger, inequality, climate change and health.

The pandemic undoubtedly diverted attention from some of these issues in the past couple of years. But even before COVID-19, the UN was warning that progress to meet the SDGs was not advancing at the speed or on the scale needed. Meeting them by 2030 will be tough.

Yet I remain optimistic. The pandemic demonstrated like nothing else the power of working collaboratively, across borders, for the benefit of society. It concentrated minds, funding and policy to accelerate research into virus detection, disease treatments, vaccines and manufacturing platforms.

It was a truly remarkable effort from the global community to develop effective vaccines within a year of the virus first being detected, and these and other treatments have dramatically reduced the viruss fatality rate. This can be attributed to the brilliance, perseverance and creativity of scientists across the world. But they were not working alone: Artificial intelligence (AI) also played a key part.

The US company Moderna was among the first to release an effective COVID-19 vaccine. One reason it was able to make this breakthrough so quickly was the use of AI to speed up development. Modernas Chief Data and Artificial Intelligence Officer Dave Johnson explains that AI algorithms and robotic automation helped them move from manually producing around 30 mRNAs (a molecule fundamental to the vaccine) each month, to being able to produce around 1,000 a month.

Moderna is also using artificial intelligence to help their mRNA sequence design. Its co-founder Noubar Afeyan recently predicted during a visit to Imperial College London that immune medicine will see large advances in the coming years, and we can look forward to a future where medicine is more pre-emptive than reactionary.

If we can catch disease early and delay it, at a minimum, we could have a lot more impact at a lot less cost, he said. This is a great example of how AI can free up time for scientists to accelerate discovery and dedicate efforts to solving big challenges.

We are also seeing examples of AI technology driving improvements in other areas of healthcare, such as disease screening for cancer and malaria. Researchers from Google Health, DeepMind, the NHS, Northwestern University and colleagues at Imperial have designed and trained an AI model to spot breast cancer from X-ray images.

The computer algorithm, which was trained using mammography images from almost 29,000 women, was shown to be as effective as human radiologists in spotting cancer. At a time when health services around the world are stretched as they deal with long backlogs of patients following the pandemic, this sort of technology can help ease bottlenecks and improve treatment.

For malaria, a handheld lab-on-a-chip molecular diagnostics systems developed with AI could revolutionize how the disease is detected in remote parts of Africa. The project, which is led by the Digital Diagnostics for Africa Network, brings together collaborators such as MinoHealth AI Labs in Ghana and Imperial College Londons Global Development Hub. This technology could help pave the way for universal health coverage and push us towards achieving SDG3.

There are numerous other examples of how advances in AI could support our understanding of climate change (SDG13), enable our transition to sustainable transport systems (SDG11), and accelerate agri-tech to help farmers end food poverty and malnutrition (SDG2) among many benefits to the other SDGs too.

For example, the Alan Turing Institute, the UKs national centre for data science and artificial intelligence, are using machine learning to better understand the complex interactions between climate and Arctic sea ice.

With an expanding global population, we face challenges around food demand and production not only how to reduce malnourishment but the impact on the planet too, such as deforestation, emissions and biodiversity loss. To meet these needs, the use of artificial intelligence in agriculture is growing rapidly and is enabling farmers to enhance crop production, direct machinery to carry out tasks autonomously, and identify pest infestations before they occur.

Smart sensing technology is also helping farmers use fertilizer more effectively and reduce environmental damage. An exciting research project, funded by the EPSRC, Innovate UK and Cytiva, will help growers optimize timing and amount of fertilizer to use on their crops, taking into account factors like the weather and soil condition. This will reduce the expense and damaging effects of over-fertilizing soil.

Developing sustainable and smart transport systems will also be vital as cities and countries look to reduce the impact of air pollution and improve infrastructure. In the last decade, AI has powered a revolution in transport and mobility, from autonomous vehicles to ride-sharing apps and route-planners. AI is also being used to make public transport systems more efficient, reduce traffic congestion and pollution, and improve safety.

Despite its benefits to research and medicine, integrating AI into society and innovation is not always smooth sailing. Recent controversies on facial recognition, automated decision-making and COVID-related tracking, have led to some caution and suspicion. We need to ensure that AI is employed in ways that are trusted, transparent and inclusive. We need to make sure that there is an internationally coordinated, collaborative approach, just as there was in the pandemic.

The World Economic Forums Global AI Action Alliance brings together more than 100 leading companies, governments, international organizations, non-profits and academics united in a commitment to maximize AI's societal benefits while minimizing its risks.

Artificial intelligence (AI) is impacting all aspects of society homes, businesses, schools and even public spaces. But as the technology rapidly advances, multistakeholder collaboration is required to optimize accountability, transparency, privacy and impartiality.

The World Economic Forum's Platform for Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning is bringing together diverse perspectives to drive innovation and create trust.

Contact us for more information on how to get involved.

It is imperative that we put good processes and practices in place to ensure AI is developed in a positive and ethical way to see it adopted and used to its fullest by citizens and governments.

We must now work together to ensure that artificial intelligence can accelerate progress of the Sustainable Development Goals and help us get back on track to reaching them by 2030.

Written by

Alice Gast, President, Imperial College London

The views expressed in this article are those of the author alone and not the World Economic Forum.

Read more here:
Why artificial intelligence is vital in the race to meet the SDGs - World Economic Forum

I last sat down in 2005," Geoffrey Hinton often says, and it was a mistake." In the 17 years since, Hinton has never sat down; his severe back problems prevent him from doing so. He travels only by train or car, so he can sprawl across the seats. He cannot fly commercial, since airlines insist on being seated for take-off or landing. He eats like a monk on the altar", using a foam cushion to kneel at a table. With his trademark wry British humour, he talks of his back being a long-standing problem". In these 17 years, Hinton, working from the University of Toronto, has also transformed artificial intelligence (AI). He rescued neural networks back from an AI winter, invented deep learning, tutored a bevy of geniuses now at the bleeding edge of AI, and won the fabled Turing Award while he was at it.

I first came across the legend of Hinton in a fabulous book by Cade Metz called Genius Makers, where he detailed the lives of those who shaped AI, foremost among them being Hinton. After studying psychology at Cambridge and AI at the University of Edinburgh, Hinton went back to something which had fascinated him even as a child: How the human brain stored memories, and how it worked. He was one of the first researchers who started working on mimicking the human brain with computer hardware and software, thus constructing a newer and purer form of AI, which we now call deep learning. He started doing this in the 1980s, along with an intrepid bunch of students. His PhD thesis, titled Deep Neural Networks for Acoustic Modelling in Speech Recognition, demonstrated how deep neural networks outclassed older machine learning models like Hidden Markovs and Gaussian Mixtures at identifying speech patterns. He literally invented backpropagation, which was reportedly one of the concepts that inspired Googles BackRub search algorithm, the core of its exemplary service.

I get very excited when we discover a way of making neural networks betterand when thats closely related to how the brain works," says Hinton. By mimicking the brain, he sought to get rid of traditional machine learning techniques, where humans would label pictures, words and objects; instead, his work copied the brains self-learning techniques. He and his team built artificial neurons from interconnected layers of software modelled after the columns of neurons in the brains cortex. These neural nets can gather information, react to it, build an understanding of what something looks or sounds like" (bit.ly/3LRJwWo ). The AI community did not trust this new approach; Hinton told Sky News that it was an idea that almost no one on Earth believed in at that pointit was pretty much a dead idea, even among AI researchers".

Well, that sentiment has changed. Deep Learning has been harnessed by Google, Meta, Microsoft, DeepMind, Baidu and almost every other tech firm to build driverless cars, predict protein folding and beating humans at Go. Of Hintons students, Yann LeCun now leads Metas AI efforts, Yoshua Bengio is doing seminal work at University of Montreal, Ilya Sutskevar co-founded OpenAI, famous for GPT-3. Hinton himself works part time for Google, the result of a frenzied bidding war between Google, Microsoft and Baidu, where he auctioned his company (and his services) to Google for $44 millionthe stuff of legend in itself. Deep learning is now considered one of the most exciting developments in AI. It is regarded as the surest bet that AI will achieve artificial general intelligence, or AGI. As Hinton put it: We ceased to be the lunatic fringe. Were now the lunatic core."

Hinton comes from a formidably intellectual and academic family. His mother used to tell him to be an academic or be a failure". His great-great grandfather was George Boole, who invented Boolean logic and algebra, the foundation of modern computers. Georges wife Mary was a well-known teacher of algebra and logic. Marys uncle was George Everest, and as the Surveyor General of India, had the worlds highest peak named after him. Geoffreys great grandfather, a renowned mathematician, created the concept of the fourth dimension, and first drew the tesseract, and his cousin, Joan, a nuclear physicist was one of the few women to work on the Manhattan Project. His father, Howard Hinton, a formidable entomologist and a fellow of the Royal Society, often told him, Work really hard and maybe when youre twice as old as me, youll be half as good." Geoffrey did work hard, became the godfather of deep learning, a Turing Award winner and a fellow of the Royal Society. And he is not sitting on his laurels.

Jaspreet Bindra is founder of Tech Whisperer Ltd, a digital transformation and technology advisory practice.

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Artificial Intelligence And What It Owes A Man Who Never Sits Down | Mint - Mint

During one of the many live collaboration panels of MUSCs 2022 Innovation Week, an interesting discussion ensued, mirroring a common debate in health care and that is: How does artificial intelligence (AI) fit in?

Last week, as several clinicians and key members of the Clemson-MUSC AI Hub which was formed in 2021 were on hand at the Gazes Cardiac Research Institute, it became quickly evident that AI is gaining traction throughout the world of heath care. But equally evident was the fact that theres still some skepticism from the mainstream when it comes to the best ways to use it.

For congenital cardiologist G. Hamilton Baker, M.D., associate professor of pediatrics, AI remains a tremendous untapped resource.

AI is such a blanket term, he said in an interview right after the formation of the Clemson-MUSC AI Hub last year. Were leveraging data science and wrangling those giant databases with appropriately applied machine learning methods.

Baker has been utilizing AI in his work for several years now, working on a number of different AI+Biomedical projects ranging from congenital heart disease to diabetic eye disease.

I feel very strongly about education on AI. The goal is to teach clinicians how to understand and utilize AI. We arent asking people to learn how to code, we simply want them to learn how AI can work for them, Baker said.

At the Gazes, the topic quickly centered on AI and bias. Some clinicians believe the most elegant aspect of AI is that it removes unintended biases by letting the computers which are inherently without bias because theyre metal and silicone do the data crunching and leaving the treatment to the physicians.

When two clinicians might disagree on something, AI can help uncover unknown biases and dispel others, said MUSC Public Health Sciences assistant professor Paul Heider, Ph.D. AI just looks at the data and makes decisions that are based on that alone.

However, others argued that those AI programs were written by humans, and those inadvertent biases almost certainly were sprinkled in.

Trustworthiness is a key word that we need to be focusing on here, said Brian Dean, Ph.D., chairman of the Division of Computer Science at Clemson University. Because the AI system is becoming less of a smart sensor that provides input to the medical decision-making process and more of a teammate. So we have to be super careful because, after all, AI was trained based on human expert opinion, which is biased.

Dean agreed that AI is an extremely valuable tool for the medical field, cautioning all to simply be judicious with its use.

Jihad Obeid, M.D., co-director of the Biomedical Informatics Center at MUSC, agreed. If you use it as a decision aid, rather than a decision-maker, he said, AI can be a real asset.

Regardless of the differences of opinion in the room, panel members agreed that AI has unlimited potential for researchers and clinicians alike.

When it comes to AI in health care, its so tempting to talk about the hype, all the big stuff it can do, Baker said. But the truth of the matter is there are plenty of easy, smart projects where AI could really make a significant difference, and we just need more people on board.

According to MUSC provost Lisa K. Saladin, PT, Ph.D., MUSC is already using AI to develop techniques that can help to diagnose and treat a range of ills, including cancer, Alzheimers disease, substance abuse, child abuse, epilepsy, aphasia, inflammatory skin conditions and cardiac issues.

Baker said that clinicians who are interested in implementing AI into their research or practice should look into the AI Hub, as it offers a host of resources, including funding for AI. During this years Innovation Week, the Clemson-MUSC AI Hub gave out $100,000 worth of grants to five worthy projects.

We want people to know about this, he said. I know there are lots of people out there who could really use our help. We want to accelerate the adoption of AI for those who are interested."

More here:
Can Artificial Intelligence remove unintended bias from health care? Clinicians optimistic, but wary - Medical University of South Carolina

DUBLIN--(BUSINESS WIRE)--The "From Sustainability to Purpose: Climate Action" report has been added to ResearchAndMarkets.com's offering.

This report explores what consumers and industry experts think about artificial intelligence, including concerns such as data exploitation, and advantages such as increasing efficiency and innovation. Case studies underline the developments. Topics explored include emerging technologies like robots, virtual reality in the car and education robots.

Strategy Briefings offer unique insight into emerging trends world-wide. Aimed squarely at strategists and planners, they draw on the analyst's vast information resources to give top line insight across markets and within consumer segments.

Why buy this report?

Key Topics Covered:

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For more information about this report visit https://www.researchandmarkets.com/r/oyat2i

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Climate Action Study 2022: From Sustainability to Purpose - Explore what Consumers and Industry Experts Think About Artificial Intelligence -...

Op-ed: Aaron Yeardley, Carbon Reduction Engineer, Tunley Engineering

The fourth industrial revolution (Industry 4.0) is already happening, and its transforming the way manufacturing operations are carried out. Industry 4.0 is a product of the digital era as automation and data exchange in manufacturing technologies shift the central industrial control system to a smart setup that bridges the physical and digital world, addressed via the Internet of Things (IoT).

Industry 4.0 is creating cyber-physical systems that can network a production process enabling value creation and real-time optimisation. The main factor driving the revolution is the advances in artificial intelligence (AI) and machine learning. The complex algorithms involved in AI use the data collected from cyber-physical systems, resulting in smart manufacturing.

The impact that Industry 4.0 will have on manufacturing will be astronomical as operations can be automatically optimised to produce increased profit margins. However, the use of AI and smart manufacturing can also benefit the environment. The technologies used to optimise profits can also be used to produce insights into a companys carbon footprint and accelerate its sustainability. Some of these methods are available to help companies reduce their GHG emissions now. Other methods have the potential to reduce global GHG emissions in the future.

Scope 3 emissions are the emissions from a companys supply chain, both upstream and downstream activities. This means scope 3 covers all of a companys GHG emission sources except those that are directly created by the company and those created from using electricity. It comes as no surprise that on average Scope 3 emissions are 5.5 times greater than the combined amount from Scope 1 and Scope 2. Therefore, companies should ensure all three scopes are quantitated in their GHG emissions baseline.

However, in comparison to Scope 1 and Scope 2 emissions, Scope 3 emissions are difficult to measure and calculate. This is because of a lack of transparency in supply chains, lack of connections with suppliers, and complex industrial standards that provide misleading information. The major issues concerning Scope 3 emissions are as follows:

AI-based tools can help establish baseline Scope 3 emissions for companies as they are used to model an entire supply chain. The tools can quickly and efficiently sort through large volumes of data collected from sensors. If a company deploys enough sensors across the whole area of operations, it can identify sources of emissions and even detect methane plumes.

A digital twin is an AI model that works as a digital representation of a physical piece of equipment or an entire system. A digital twin can help the industry optimise energy management by using the AI surrogate models to better monitor and distribute energy resources and provide forecasts to allow for better preparation. A digital twin will optimise many sources of data and bring them onto a dashboard so that users can visualise it in real-time. For example, a case study in the Nanyang Technological University used digital twins across 200 campus buildings over five years and managed to save 31% in energy and 9,600 tCO2e. The research used IESs ICL technology to plan, operate, and manage campus facilities to minimise energy consumption.

Digital twins can be used as virtual replicas of building systems, industrial processes, vehicles, and many other opportunities. The virtual environment enables more testing and iterations so that everything can be optimised to its best performance. This means digital twins can be used to optimise building management making smart strategies that are based on carbon reduction.

Predictive maintenance of machines and equipment used in industry is now becoming common practice because it saves companies costs in performing scheduled maintenance, or costs in fixing broken equipment. The AI-based tool uses machine learning to learn how historical sensor data maps to historical maintenance records. Once a machine learning algorithm is trained using the historical data, it can successfully predict when maintenance is required based on live sensor readings in a plant. Predictive maintenance accurately models the wear and tear of machinery that is currently in use.

The best part of predictive maintenance is that it does not require additional costs for extra monitoring. Algorithms have been created that provide accurate predictions based on operational telemetry data that is already available. Predictive maintenance combined with other AI-based methods such as maintenance time estimation and maintenance task scheduling can be used to create an optimal maintenance workflow for industrial processes. Conversely, improving current maintenance regimes which often contribute to unplanned downtime, quality defects and accidents is appealing for everybody.

An optimal maintenance schedule produced from predictive maintenance prevents work that often is not required. Carbon savings will be made via the controlled deployment of spare parts, less travel for people to come to the site, and less hot shooting of spare parts. Intervening with maintenance only when required and not a moment too late will save on the use of electricity, efficiency (by preventing declining performance) and human labour. Additionally, systems can employ predictive maintenance on pipes that are liable to spring leaks, to minimise the direct release of GHGs such as HFCs and natural gas. Thus, it has huge potential for carbon savings.

Research has shown that underpinning the scheduling of maintenance activities on predictive maintenance and maintenance time estimation can produce an optimal maintenance scheduling (Yeardley, Ejeh, Allen, Brown, & Cordiner, 2021). The work optimised the scheduling by minimising costs based on plant layout, downtime, and labour constraints. However, scheduling can also be planned by optimising the schedule concerning carbon emissions. In this situation, maintenance activities can be performed so that fewer journeys are made and GHG emissions are saved.

The internet of things (IoT) is the digital industrial control system, a network of physical objects that are connected over the internet by sensors, software and other technologies that exchange data with each thing. In time, the implementation of the IoT will be worldwide and every single production process and supply chain will be available as a virtual image.

Open access to a worldwide implementation of the IoT has the potential to provide a truly circular economy. Product designers can use the information available from the IoT and create value from other peoples waste. Theoretically, we could establish a work where manufacturing processes are all linked so that there is zero extracted raw materials, zero waste disposed and net-zero emissions.

Currently, the world has developed manufacturing processes one at a time, not interconnected value chains across industries. It may be a long time until the IoT creates the worldwide virtual image required, but once it has the technology is powerful enough to address losses from each process and exchange material between connected companies. Both materials and energy consumption can be shared to lower CO2 emissions drastically. It may take decades, but the IoT provides the technology to create a circular economy.

ConclusionAI has enormous potential to benefit the environment and drive the world to net-zero. The current portfolio of research being conducted at the Alan Turning Institute (UKs national centre for data science) includes projects that explore how machine learning can be part of the solution to climate change. For example, an electricity control room algorithm is being developed to provide decision support and ensure energy security for a decarbonised system. The national grids electricity planning is improved by forecasting the electricity demand and optimising the schedule. Further, Industry 4.0 can plan for the impact that global warming and decarbonisation strategies have on our lives.

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Artificial intelligence drives the way to net-zero emissions - Sustainability Magazine