2 October 2023 6:32 pm
How Smart Cities are Transforming Urban Living

How Smart Cities are Transforming Urban Living

May 2, 2023

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Throughout the world, cities are increasingly looking to digitize services or become more technology-forward. In so doing, the Intersection of AI and IoT is an obligatory passage resulting in the author wondering How Smart Cities are Transforming Urban Living? 

A Smart City is an urban area that utilizes advanced technologies, data analytics, and interconnected systems to optimize urban processes, infrastructure, and services. By integrating data collection and communication technologies with the Internet of Things (IoT), a Smart City can improve its citizens’ efficiency, sustainability, and overall quality of life while reducing environmental impact and promoting economic growth.

The image above is of IStock.

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The Intersection of AI and IoT: How Smart Cities are Transforming Urban Living

Assad Abbas

 

Last updated: 
KEY TAKEAWAYS

The combination of AI and IoT technologies is revolutionizing the way we live and work in smart cities, making them more efficient, sustainable, and livable. Real-time data analysis from multiple devices is simplifying decision-making and administrative tasks, optimizing resource utilization, and improving public safety. The smart city concept uses technology to improve the quality of life, including transportation, solid waste management, pollution reduction, sustainable communities, irrigation, public safety, traffic management, and healthcare, among others. Cities like Singapore, Amsterdam, Barcelona, and Dubai are leveraging the benefits of AI and IoT technologies to transform urban living.

Artificial Intelligence (AI) and the Internet of Things (IoT) technologies are being used together to leverage each other’s advantages. The interconnection of various data-generating devices, such as sensors, computers, vehicles, smartphones, buildings, and software through the Internet, has revolutionized how we live today.

The interplay of AI and IoT technologies has completely transformed the way we interpret and analyze the massive amount of data that is continuously generated by IoT devices with the help of AI techniques.

As a result, decision-making, optimizing industrial processes, making predictions, and identifying anomalies in industrial settings becomes easier than ever. Similarly, AI and IoT technologies are being used together in smart city applications to improve urban infrastructure and the quality of life.

Understanding smart cities

The idea of smart cities is described below, and various constituent components and examples of smart cities are also provided.

Defining smart cities

Before delving into how AI and IoT are transforming smart cities, it is important to understand what a smart city is and how it functions. The concept of smart cities emerged after the term “pervasive computing” started gaining popularity in the first decade of this century. Pervasive computing simply refers to “computing everywhere”. Therefore, pervasive computing and smart cities are closely related in several ways.

We can define smart cities as urban areas that utilize technology strategically and efficiently to perform day-to-day operations and improve their inhabitants’ quality of life. This includes incorporating technology in every aspect of life to offer better civic services, such as transportation, solid waste management and collection, pollution-free and sustainable communities, irrigation, healthcare, public safety and policing, traffic management, and many others. In a nutshell, “a smart city is an interconnected and technology-enabled sustainable environment designed to improve the standard of living of its residents.”

Components of smart cities

Smart cities comprise a variety of components, each of which is crucial for their functioning. The components include:

  • IoT devices: these include various data-collecting devices, such as sensors, traffic, air quality, energy usage monitoring devices, and so on.
  • Data analytics component: the component is responsible for processing and analyzing the data collected through the IoT devices
  • Communication Networks: are used for data transmission among IoT devices, data analytics systems, and other infrastructure components.
  • Metropolitan infrastructure and public services: are essential for the functioning of smart cities. Infrastructure includes buildings, roads, and other public areas, which can be transformed through data analytics and IoT. On the other hand, public services can be transportation, healthcare, education, and public safety, which may be improved through AI and data analytics.

Examples of smart cities

Recently, many cities worldwide have started implementing smart technologies to uplift the living standard of their citizens. Some of the cities include Singapore, Amsterdam, Barcelona, and Dubai. Singapore’s smart city initiative utilizes IoT data and performs analytics to improve mobility and healthcare services, support businesses, and optimize traffic flows and energy usage. Likewise, Amsterdam, in addition to the ones discussed above, emphasizes sustainable solutions to mobility by providing smart traffic systems and electric charging stations.

Barcelona is not behind the others and also relies on IoT devices and data analytics methods. In particular, smart lighting systems based on motion sensors, green spaces, energy-efficient buildings, smart bike sharing, and waste reduction are among the few initiatives that make Barcelona a smart city. Similarly, automated buses and the urban metro system, smart grids, smart and energy-efficient buildings, smart healthcare, and policing have made Dubai emerge as one of the rapidly developing smart cities. The initiatives, such as Dubai Blockchain Strategy, the Dubai Future Accelerators program, and the Smart Dubai Platform, are pivotal in making Dubai one of the top living choices.

How AI and IoT are transforming urban living?

The intersection of AI and IoT technologies is transforming living and work in smart cities, and their impacts are becoming significant daily. By combining these two technologies, a new era of innovation, efficiency, and sustainability is emerging, which once could have only been dreamt of by humans. Real-time analysis of continuously generated data by multiple devices simultaneously has made decision-making and administrative tasks easier without much human involvement. For example, traffic signals equipped with IoT sensors can monitor traffic flow which can further be analyzed using AI algorithms and consequently can help traffic lights adapt to the traffic situation at a particular intersection in the city.

Likewise, another exciting usage scenario is in the solid waste collection and management domain, where the smart waste bins equipped with IoT ultrasonic sensors can notify about the levels of waste in the bins. AI techniques can schedule pickups, reducing unnecessary trips of waste collection vehicles and the environmental impact. Similarly, in smart buildings equipped with IoT devices, such as sensors, HVAC, lighting, etc., the data analytics techniques, with the help of the current sensor readings and historical data, may direct the control modules to optimize energy usage or predict any failures of the equipment. Moreover, the HVAC systems in smart buildings can be automatically adjusted based on occupancy and outside environmental conditions.

There are numerous advantages to using the two diverse spheres of technology together. Primarily, they result in increased efficiency, optimal resource utilization, reduced human involvement, savings of time and finances, etc. Moreover, sustainability is also vital in smart cities and can be improved through several environment-friendly initiatives. With the help of the sensors installed city-wide, the data about air quality and water usage is collected and analyzed by AI techniques. The data is subsequently used to issue alerts to the authorities of the areas where attention is required, for example, where high pollution levels are in the air or where water is being wasted.

AI and IoT technologies also help improve public safety through real-time monitoring. AI-powered security cameras are used to detect suspicious behavior through continuous surveillance. Similarly, monitoring the infrastructure for possible safety hazards through sensing devices enables timely alerts and quicker responses from the concerned authorities. In addition, greater civic engagement is promoted by providing citizens access to real-time data through various platforms and enabling them to provide decision-making feedback, leading to more impartial outcomes.

Challenges and Limitations of AI and IoT in Smart Cities

Though there are several benefits of integrating AI and IoT technologies in smart cities provides. However, numerous challenges and limitations must be addressed.

  • Device heterogeneity 

A lack of standardization across heterogeneous IoT devices and their communication protocols often results in compatibility issues, thus demanding the standardization of IoT protocols and interfaces for effective device integration and efficient data communication.

  • Data deluge 

The large volumes of data generated by IoT devices demand powerful computing resources and storage capabilities, hence elevating the need for data centers and cloud computing infrastructure.

  • Data security and privacy

Data security is crucial in smart cities due to the risk of cyber-attacks and data breaches, necessitating robust security measures. Moreover, continuous surveillance could also lead to privacy issues.

  • Ethical concerns

Addressing ethical concerns, such as bias introduced by the computational algorithms, may lead to discriminatory outcomes (for example, unfair treatment of certain groups), which is undesirable for equity and diversity in societies.

  • Job displacement and economic inequality 

Integrating AI and IoT in smart cities could lead to job displacement, especially for those who have little technical skills in sectors such as transport manufacturing, or logistics. This may further increase inequality of income and lead to a large number of workers not being adequately supported. Strategies to mitigate negative impacts should be developed in view of the possible impact on workers.

  • Massive investments 

Finally, significant investments are needed to realize smart city initiatives which can be challenging to manage initially.

Conclusion

In conclusion, the intersection of AI and IoT has paved the way for developing smarter and more sustainable cities. From optimizing energy consumption and transportation to enhancing public safety and citizen engagement, these technologies are revolutionizing how we live and interact in urban environments. While some challenges and limitations need to be addressed, the potential benefits of AI and IoT in smart cities are immense and should be exploited for better communities.

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AI in Smart Cities

Apr 11, 2023

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AI in Smart Cities is turning out to be of great help as demonstrated here in an AITHORITY article.

The image above is of Microsoft.

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AI in Smart Cities: How Innovative Technology is Enabling Smart Cities to Meet Their Sustainability Goals

By Jayashree Jayashankar

The evolution of Smart Cities has been inspiring and remarkable to watch. In the recent past, a typical resident might not have found the technical description of a smart city all that enticing, but today, citizens are more aware and more conscious. They are far more concerned about the environment and climatic changes.

Government and civic agencies across various countries, with the help of state-of-the-art artificial intelligence technology, are focusing on reducing carbon footprints, improving infrastructure, and meeting the sustainability goals of smart cities.

Did you know that according to a report by McKinsey Global Institute, ‘Smart Cities’ have the potential to refine the basic quality of life by 10-30%? It can reduce crimes, lower carbon emissions, better health management and improve traffic management and deliver an enhanced quality of life.

McKinsey Global Institute’s report stated that cities house more than half of the world’s population, and another 2.5 billion people are predicted to move there by 2050.

Today, artificial intelligence and the Internet of Things (IoT), the two concepts that have a major role to play in the development of Smart Cities, are better understood.

What are Smart Cities and Where Does Technology Come into Play?

Let’s begin by understanding the definition of a Smart City. Smart Cities are an intelligent culmination of data and digital technology. They are synonymous with intelligent economic and civic infrastructure with minimal carbon footprints.

It ensures that its citizens enjoy cutting-edge technology, utility, and mobility while eliminating bureaucratic red tape. At the end of the day, a Smart City’s ultimate goal is to improve people’s quality of life, simplify living, boost economic growth, and contribute to its long-term development.

 

But, is it enough for cities to just fall under the Smart Cities bracket and do little to meet their sustainability goals? That’d be a very unlikely situation. Smart Cities can only be successful if they are built keeping the people as well as the environment in mind.

According to Unesco,

“A smart sustainable city is an innovative city that uses ICTs (information and communication technologies) and other means to improve quality of life, the efficiency of urban operation and services, and competitiveness while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects.”

AI in Smart Cities

From more accessible, efficient services to lowering people’s overall carbon footprint, the many smart city technologies now available and on the horizon might cut expenses, increase safety, better protect the environment, and improve our quality of life.

Traffic Flow Management

Intelligent Traffic Management systems can help to alleviate traffic congestion by warning vehicles of bottlenecks and delays. Using Deep Learning algorithms, it can predict and reduce traffic, hence lowering carbon emissions. Traffic infraction detection systems and AI-enabled cameras can drastically minimize road accidents.

AI is used to evaluate real-time traffic data from cameras and IoT devices, such as vehicles like cars, buses, and trains. It recognizes patterns in data and decreases safety hazards and reoccurring accidents, as well as controlling traffic light systems.

Artificial intelligence is rapidly transforming the world around us, and smart city technology, such as parking management and traffic control systems, is one of the most effective answers it offers. With the use of artificial intelligence, one may properly forecast the flow of people, cars, and objects at various locations of interconnected transportation networks.

Smart Parking Spots

Parking has always been one of the major concerns for urban residents, and spending even five minutes looking for a parking spot can be overwhelming. Smart parking spots will allow commuters to reserve parking reservations through a mobile app, reducing the amount of time spent looking for parking spots, cutting urban traffic, lessening our carbon footprint, and conserving gasoline.

AI video analytics can detect the number of vehicles and identify parking lines, thus helping in predicting vacant parking spots. This system comes especially handy when a big public event, concert, or game is about to take place and there are high chances of congestion and struggle to park. AI can assist in identifying likely busy regions and recommending the best parking spots. It can assist drivers in avoiding traffic and saving time.

 

By now, several countries are already leveraging intelligent parking systems to help their citizen save time as well as money. The parking system first spots vacant parking and notify through an app or an indicator.  It can also assist in locating available parking spaces in congested places where traffic flow is frequently excessive.

This innovative parking solution collates data from different devices including sensors and cameras. Most of the time, these devices are embedded into the parking lots or are somewhere in proximity to instantly locate vacant spots.

Alternative Transportation

Infrastructure data is truly a blessing. It empowers smart cities as well as different modes of transportation. Today, people have the luxury to opt for alternative transportation like e-bikes, and electric vehicles. Benefit from the usage of 4G, 5G, and IoT sensors to better analyze traffic patterns, trends, and effects through AI, cutting travel time, reducing unproductive idling, and lowering total climate impact.

In electric cars, AI assists in the control of energy consumption, safety, security, and the construction of a pollution-free eco-friendly environment, which is a wish of today’s and tomorrow’s civilizations.

Recently, computer giant Acer launched e-bikes powered by advanced artificial intelligence. The bike, aimed at urban commuters, weighs only 16kg and has been calibrated for “stable and nimble riding,” according to Acer. The intelligent ebiiAssist learns from the rider’s pedaling force, riding circumstances, and chosen level of help to provide a more personalized experience.

Energy Management

Is it even possible to fathom a smart city without thinking of a smart Energy Management System (EMS)? Now the next question is, what is energy management based on? Mostly, it is based on cutting-edge climate and geospatial technology powered by AI and data analytics. They have the ability to improve our reaction to climate change as well as the overall environmental quality of smart cities.

Energy Management System is a software-based solution that assists companies and businesses in monitoring, controlling, and optimizing their energy usage. Some of the top players in the global energy management systems market are IBM Corporation, General Electric Co., Cisco Systems Inc., and Siemens AG.

Consumers and businesses are becoming more conscious of the environmental impact of their actions and are seeking for solutions to lower their carbon footprint. This is driving the use of EMS solutions as a means of reducing energy consumption and meeting sustainability goals. The growing popularity of smart homes and buildings is driving the use of EMS solutions in the building automation market.

According to Vantage Market Research, the global energy management systems market was valued at $36.4 billion in 2021 and is predicted to rise at a compound annual growth rate (CAGR) of 15.8% from 2022 to 2028.

  • Because of their flexibility, scalability, and cost-effectiveness, cloud-based EMS solutions are gaining popularity. These solutions allow for remote monitoring and control of energy consumption, making it easier for businesses to optimize their energy consumption.
  • MS solutions include the Internet of Things (IoT) and artificial intelligence (AI) technology to enhance energy efficiency and save expenses. IoT sensors can give real-time data on energy consumption, which AI algorithms can analyze and discover areas for improvement.
  • With the growing use of renewable energy sources such as solar and wind power, EMS solutions are being developed to control and optimize their utilization. Integration with smart grids and battery storage systems is required to ensure an efficient and dependable energy supply.
  • EaaS models are gaining popularity, especially in the commercial and industrial sectors. These models enable enterprises to outsource their energy management to third-party providers, who deploy EMS technologies to optimize energy consumption and save expenses.

Water Pressure and Leak Detection 

According to the American Water Works Association, the 237,600 water line breaks that occur in the United States each year cost public water utilities around $2.8 billion.

According to the American Society of Civil Engineers, aging, leaking pipes drain 7 billion gallons every day from our water systems. The World Bank estimated that non-revenue water (NRW) – the cost of water lost due to leaks, as well as standard theft and billing problems – is approaching $14 billion globally.

The World Bank estimated that non-revenue water (NRW) – the cost of water lost due to leaks, as well as standard theft and billing problems – is approaching $14 billion globally.

 

These numbers are worrisome. But, we have smart technologies to fix it. In the past decade, smart water meters have been the highlight of this evolution. Water losses in municipal water systems could be drastically reduced with the help of sensors and modern artificial intelligence (AI) technology.

  • The technique, developed by researchers at the University of Waterloo in partnership with industrial partners, can detect even minor leaks in pipelines. It uses sophisticated signal processing techniques and artificial intelligence software to detect leaks in water pipelines via sound.
  • The audio signals are captured by hydrophone sensors, which may be readily and inexpensively put in existing fire hydrants without the need for excavation or shutting them down.
  • Aside from the economic implications of losing treated water, chronic leaks can pose health risks, cause structural damage, and degrade with time.

Air Quality Prediction and Automated Actions

Air pollution has a negative impact on millions of individuals around the world and global solutions are the only way to address these global issues. Artificial intelligence is a practical technique to dealing with and reducing air pollution. AI can collect sensor and satellite data and assist academics in the blending of climate models.

Let’s take a look at how artificial intelligence-based solutions for cleaner air.

  • Artificial intelligence has the potential to improve data collecting and qualitative measurement. AI can detect patterns in data sets to aid with analysis.
  • AI can forecast future air quality and direct relevant agencies to take the necessary actions ahead of time.
  • Artificial intelligence can provide maintenance insights for sensors and other equipment.
  • AI and IoT provide recommended tools for real-time monitoring of air pollution. AI technology can swiftly and correctly identify sources of air pollution. Smart sensors, for example, can identify the source of a gas leak in a company and effectively apply corrective measures.
  • AI can aid in the reduction of air pollution in the automotive zone. AI technology allows autonomous vehicles to be fuel-efficient. AI-powered traffic signals can potentially help to reduce air pollution. We can utilize machine vision to adjust to traffic flow, reducing driving time.

AI technologies can greatly help government organizations and commercial firms by monitoring air purity levels and alerting personnel if air quality falls below a specific threshold.

  • IBM researchers are collaborating with the Beijing government to use artificial intelligence to combat air pollution and monitor environmental health. Machine learning and cognitive abilities are being used by researchers to increase forecast accuracy. AI can help predict air pollution levels 10 days in advance. Scientists are combining artificial intelligence (AI) technologies to do scenario analysis and take necessary measures such as traffic control, plant shutdowns, and more.
  • Scientists at Loughborough University in the United Kingdom have created an AI-based algorithm that predicts air quality in advance. The model examines sensor data and assists policymakers in understanding the reasons and methods for reducing air pollution.
  • CleanAir. AI is a Canadian IoT firm that provides air filtration for homes and buildings using AI-based technology. The startup employs AI and IoT to provide actionable information on indoor and outdoor air quality, deliver cleaner air, and save energy.

Final Thoughts

A smart city has a wide range of components, and each one has its effects on the quality of urban dwellers. How we live, work, and play will change as smart cities grow and become more connected. From weather monitoring and pollution management to saving energy and water and waste management, Smart Cities may be a work in progress but they are gradually becoming the epitome of urban living.

[To share your insights with us, please write to sghosh@martechseries.com]

General-purpose artificial intelligence

Mar 31, 2023

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General-purpose artificial intelligence written by Tambiama Madiega for the European Parliamentary Research Service is an eye-opener in the area of problem-solving human activities by machines.

The above image is © Angelov / Adobe Stock

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General-purpose artificial intelligence

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While there is no globally agreed definition of artificial intelligence, scientists largely share the view that technically speaking there are two broad categories of AI technologies: ‘artificial narrow intelligence’ (ANI) and ‘artificial general intelligence’ (AGI).

General-purpose artificial intelligence (AI) technologies, such as ChatGPT, are quickly transforming the way AI systems are built and deployed. While these technologies are expected to bring huge benefits in the coming years, spurring innovation in many sectors, their disruptive nature raises policy questions around privacy and intellectual property rights, liability and accountability, and concerns about their potential to spread disinformation and misinformation. EU lawmakers need to strike a delicate balance between fostering the deployment of these technologies while making sure adequate safeguards are in place.

Notion of general-purpose AI (foundation models)

While there is no globally agreed definition of artificial intelligence, scientists largely share the view that technically speaking there are two broad categories of AI technologies: ‘artificial narrow intelligence’ (ANI) and ‘artificial general intelligence’ (AGI). ANI technologies, such as image and speech recognition systems, also called weak AI, are trained on well-labelled datasets to perform specific tasks and operate within a predefined environment. By contrast, AGI technologies, also referred to as strong AI, are machines designed to perform a wide range of intelligent tasks, think abstractly and adapt to new situations. While only a few years ago AGI development seemed moderate, quick-paced technological breakthroughs, including the use of large language model (LLM) techniques have since radically changed the potential of these technologies. A new wave of AGI technologies with generative capabilities – referred to as ‘general purpose AI’ or ‘foundation models‘ – are being trained on a broad set of unlabelled data that can be used for different tasks with minimal fine-tuning. These underlying models are made accessible to downstream developers through application programming interface (API) and open-source access, and are used today as infrastructure by many companies to provide end users with downstream services.

Applications: Chat GPT and other general-purpose AI tools

In 2020, research laboratory OpenAI – which has since entered into a commercial partnership with Microsoft – released GPT-3, a language model trained on large internet datasets that is able to perform a wide range of natural language processing tasks (including language translation, summarisation and question answering). In 2021, OpenAI released DALL-E, a deep-learning model that can generate digital images from natural language descriptions. In December 2022, it launched its chatbot ChatGPT, based on GPT-3 and trained on machine learning models using internet data to generate any type of text. Launched in March 2023, GPT-4, the newest general-purpose AI tool, is expected to have even more applications in areas such as creative writing, art generation and computer coding.

General-purpose AI tools are now reaching the general public. In March 2023, Microsoft launched a new AI‑powered Bing search engine and Edge browser incorporating a chat function that brings more context to search results. It also released a GPT-4 platform allowing businesses to build their own applications (for instance for summarising long-form content and helping write software). Google and its subsidiary DeepMind are also developing general-purpose AI tools; examples include the conversational AI service, Bard. Google unveiled a range of generative AI tools in March 2023, giving businesses and governments the ability to generate text, images, code, videos, audio, and to build their own applications. Developers are using these ‘foundation models‘ to roll out and offer a flurry of new AI services to end users.

General-purpose AI tools have the potential to transform many areas, for example by creating new search engine architectures or personalised therapy bots, or assisting developers in their programming tasks. According to a Gartner study, investments in generative AI solutions are now worth over US$1.7 billion. The study predicts that in the coming years generative AI will have a strong impact on the health, manufacturing, automotive, aerospace and defence sectors, among others. Generative AI can be used in medical education and potentially in clinical decision-making or in the design of new drugs and materials. It could even become a key source of information in developing countries to address shortages of expertise.

Concerns and calls for regulation

The key characteristics identified in general-purpose AI models – their large size, opacity and potential to develop unexpected capabilities beyond those intended by their producers – raise a host of questions. Studies have documented that large language models (LLMs), such as ChatGPT, present ethical and social risks. They can discriminate unfairly and perpetuate stereotypes and social biases, use toxic language (for instance inciting hate or violence), present a risk for personal and sensitive information, provide false or misleading information, increase the efficacy of disinformation campaigns, and cause a range of human-computer interaction harms (such as leading users to overestimate the capabilities of AI and use it in unsafe ways). Despite engineers’ attempts to mitigate those risks, LLMs, such as GPT-4, still pose challenges to users’ safety and fundamental rights (for instance by producing convincing text that is subtly false, or showing increased adeptness at providing illicit advice), and can generate harmful and criminal content.

Since general-purpose AI models are trained by scraping, analysing and processing publicly available data from the internet, privacy experts stress that privacy issues arise around plagiarism, transparency, consent and lawful grounds for data processing. These models represent a challenge for education systems and for common-pool resources such as public repositories. Furthermore, the emergence of LLMs raises many questions, including as regards intellectual property rights infringement and distribution of copyrighted materials without permission. Some experts warn that AI-generated creativity could significantly disrupt the creative industries (in areas such as graphic design or music composition for instance). They are calling for incentives to bolster innovation and the commercialisation of AI-generated creativity on the one hand, and for measures to protect the value of human creativity on the other. The question of what liability regime should be used when general-purpose AI systems cause damage has also been raised. These models are also expected to have a significant impact on the labour market, including in terms of work tasks.

Against this backdrop, experts argue that there is a strong need to govern the diffusion of general-purpose AI tools, given their impact on society and the economy. They are also calling for oversight and monitoring of LLMs through evaluation and testing mechanisms, stressing the danger of allowing these tools to stay in the hands of just a few companies and governments, and highlighting the need to assess the complex dependencies between companies developing and companies deploying general-purpose AI tools. AI experts are also calling for a 6-month pause, at least, in the training of AI systems more powerful than GPT‑4.

General-purpose AI (foundation models) in the proposed EU AI act

EU lawmakers are currently engaged in protracted negotiations to define an EU regulatory framework for AI that would subject ‘high-risk’ AI systems to a set of requirements and obligations in the EU. The exact scope of a proposed artificial intelligence act (AI act) is a bone of contention. While the European Commission’s original proposal did not contain any specific provisions on general-purpose AI technologies, the Council has proposed that they should be considered. Scientists have meanwhile warned that any approach classifying AI systems as high-risk or not depending on their intended purpose would create a loophole for general purpose systems, since the future AI act would regulate the specific uses of an AI application but not its underlying foundation models.

In this context, a number of stakeholders, such as the Future of Life Institute, have called for general-purpose AI to be included in the scope of the AI act. Some academics favouring this approach have suggested modifying the proposal accordingly. Helberger and Diakopoulos propose to consider creating a separate risk category for general-purpose AI systems. These would be subject to legal obligations and requirements that fit their characteristics, and to a systemic risk monitoring system similar to the one under the Digital Services Act (DSA). Hacker, Engel and Mauer argue that the AI act should focus on specific high-risk applications of general-purpose AI and include obligations regarding transparency, risk management and non-discrimination; the DSA’s content moderation rules (for instance notice and action mechanisms, and trusted flaggers) should be expanded to cover such general-purpose AI. Küspert, Moës and Dunlop call for the general-purpose AI regulation to be made future-proof, inter alia, by addressing the complexity of the value chain, taking into account open-source strategies and adapting compliance and policy enforcement to different business models. For Engler and Renda, the act should discourage API access for general-purpose AI use in high-risk AI systems, introduce soft commitments for general-purpose AI system providers (such as a voluntary code of conduct) and clarify players’ responsibilities along value chains.

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Achieving AI and Machine Learning to accelerate the energy transition

Jun 3, 2022

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A successful and timely energy transition needs Artificial Intelligence and Machine Learning (AI\ML ) to accelerate change. 
The transition to sustainable construction could well be at the forefront of such a transition.  

Achieving AI and Machine Learning to accelerate the energy transition

By  Microsoft Corporation

AI energy – Image credit: 123rf.com

Reducing costs, enabling more performant (new) energy businesses and the complex coordination of multiple energy players are crucial in this transformation. However we’re still in the early stages of AI\ML, how can we achieve AI\ML rapid adoption at scale?

Why there is no energy transition without Intelligence Intensity

For the green deal to succeed, we need to start moving towards a whole system approach, interconnecting sectors from diverse energy carriers to industries, transport, and buildings, driving Power-to-X, industrial clusters, industrial smart steering, 24 by 7 green energy matching, hybrid energy parks, and new low-carbon energy value chains leading to billions of networked “things”. Flexible yet complex coordination is required that is close to real-time and optimised for multiple, varying stakeholder interests – impossible to be done by humans.

The key role AI/ML plays in reducing the gigantic investments required for the energy transition can lower the levelised costs of energy, accelerate the issuing of permits and grid connections, and optimise yield, thus speeding up the deployment of the massive renewable generation required. Grid capacity can be expanded digitally, avoiding traditional grid reinforcements that are expensive and time-consuming to build. AI\ML also enables flexibility services coordination for maximum DERs value and infrastructure usage.

Microsoft is fully committed to a rapid AI\ML adoption at scale which is already evolving into a technical reality with higher use than anticipated. Partnerships and co-innovation with clients and partners and the wider ecosystem accelerate the creation of missing digital solutions and the development of digital accelerators for wider, faster, and simpler adoption of digital.

Accelerating AI\ML innovation through open data platforms, open ecosystems, open-source

AI\ML needs a lot of data! Strengthened open energy data platforms give innovators in the ecosystem access in a safe, scalable and performant way to vast volumes of quality data essential to train AI models. Microsoft joined OSDU (Open Subsurface Data Universe) to create an open-source, cloud-agnostic platform to collect subsurface data from O&G operations valuable to O&G but also to renewable offshore players.

Energy Datahubs in Europe also play a vital role in driving innovation. This is why Microsoft and Energinet partnered to co-create the open-source Green Energy Hub blueprints on GitHub for experts to contribute and for others to develop their own data hubs, creating an accelerator for the future smart green solutions.

With AI still in its early stages, it is key to inspire energy players of its successful, tangible impact and to facilitate access to solutions. Microsoft launched the Open AI Energy Initiative (OAI), an open ecosystem for operators, independent software vendors, and equipment providers to offer additional solutions, and the global AI Centre of Excellence for Energy called Microsoft Energy Core features over 40 partner solutions.

The driving co-innovation force of strategic partnerships with energy leaders

Strategic partnerships with market makers enables the acceleration of transformation but also to co-invest deeper and wider in the creation of leading-edge digital solutions for current operations and for the complex chain orchestration needed for a successful energy transition. Foundational research for AI in energy and energy-specific platform-based capabilities are not only developed faster.

These intelligence-intense, leading-edge lighthouse use cases inform the industry for fast followers and create digital optimism for speed. Together we become a driving force for the formation of new value chains, ecosystems, and business models that accelerate meeting the goals of the green agenda.

Utilities specific digital accelerators for wider, faster, and simpler adoption

Energy players want more pre-built capabilities specific to utilities for faster time to market AI\ML models. The 15 years of enhanced utilities-specific industry data models acquired from ADRM exemplify the current enrichment with automation of data ingestion from multiple sources, addressing a major hurdle on data.

Another example is the common domain-specific ontologies that are fundamental to accelerating the development of digital twin solutions. Microsoft, together with Agder Energi, launched the open-source Energy Grid Ontology to be added by others for smart cities and smart buildings.

More broadly, the road ahead is for industry clouds. Energy players can focus much higher in the technology stack at the business applications layer, thus shortening innovation cycles, getting faster into the predictive era, and simplifying adoption.

Through co-investment, Microsoft is accelerating the development of energy-specific platform-based capabilities allowing energy players to focus their AI efforts at the business applications level such as for portfolio optimisation, risk management, and also trading.

WATCH: Why AI is key in solving complex energy transition challenges

Learn more about Microsoft

The above-featured image is of Shell on the very subject of Energy Transition through AI, etc.

Developing countries are being left behind in the AI race

Apr 14, 2022

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Developing countries are being left behind in the AI race in spite of what is constantly vented out by the local media in the MENA region.

Developing countries are being left behind in the AI race – and that’s a problem for all of us

By Joyjit Chatterjee, University of Hull and Nina Dethlefs, University of Hull

Artificial Intelligence (AI) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make people’s daily lives easier, especially in the developed world.

As of October 2021, 44 countries were reported to have their own national AI strategic plans, showing their willingness to forge ahead in the global AI race. These include emerging economies like China and India, which are leading the way in building national AI plans within the developing world.

Oxford Insights, a consultancy firm that advises organisations and governments on matters relating to digital transformation, has ranked the preparedness of 160 countries across the world when it comes to using AI in public services. The US ranks first in their 2021 Government AI Readiness Index, followed by Singapore and the UK.

Notably, the lowest-scoring regions in this index include much of the developing world, such as sub-Saharan Africa, the Carribean and Latin America, as well as some central and south Asian countries.

The developed world has an inevitable edge in making rapid progress in the AI revolution. With greater economic capacity, these wealthier countries are naturally best positioned to make large investments in the research and development needed for creating modern AI models.

In contrast, developing countries often have more urgent priorities, such as education, sanitation, healthcare and feeding the population, which override any significant investment in digital transformation. In this climate, AI could widen the digital divide that already exists between developed and developing countries.

The hidden costs of modern AI

AI is traditionally defined as “the science and engineering of making intelligent machines”. To solve problems and perform tasks, AI models generally look at past information and learn rules for making predictions based on unique patterns in the data.

AI is a broad term, comprising two main areas – machine learning and deep learning. While machine learning tends to be suitable when learning from smaller, well-organised datasets, deep learning algorithms are more suited to complex, real-world problems – for example, predicting respiratory diseases using chest X-ray images.

Many modern AI-driven applications, from the Google translate feature to robot-assisted surgical procedures, leverage deep neural networks. These are a special type of deep learning model loosely based on the architecture of the human brain.

Crucially, neural networks are data hungry, often requiring millions of examples to learn how to perform a new task well. This means they require a complex infrastructure of data storage and modern computing hardware, compared to simpler machine learning models. Such large-scale computing infrastructure is generally unaffordable for developing nations.

The developed world has an inevitable edge in the AI revolution. MikeDotta/Shutterstock

Beyond the hefty price tag, another issue that disproportionately affects developing countries is the growing toll this kind of AI takes on the environment. For example, a contemporary neural network costs upwards of US$150,000 to train, and will create around 650kg of carbon emissions during training (comparable to a trans-American flight). Training a more advanced model can lead to roughly five times the total carbon emissions generated by an average car during its entire lifetime.

Developed countries have historically been the leading contributors to rising carbon emissions, but the burden of such emissions unfortunately lands most heavily on developing nations. The global south generally suffers disproportionate environmental crises, such as extreme weather, droughts, floods and pollution, in part because of its limited capacity to invest in climate action.

Developing countries also benefit the least from the advances in AI and all the good it can bring – including building resilience against natural disasters.

Using AI for good

While the developed world is making rapid technological progress, the developing world seems to be underrepresented in the AI revolution. And beyond inequitable growth, the developing world is likely bearing the brunt of the environmental consequences that modern AI models, mostly deployed in the developed world, create.

But it’s not all bad news. According to a 2020 study, AI can help achieve 79% of the targets within the sustainable development goals. For example, AI could be used to measure and predict the presence of contamination in water supplies, thereby improving water quality monitoring processes. This in turn could increase access to clean water in developing countries.

The benefits of AI in the global south could be vast – from improving sanitation to helping with education, to providing better medical care. These incremental changes could have significant flow-on effects. For example, improved sanitation and health services in developing countries could help avert outbreaks of disease.

But if we want to achieve the true value of “good AI”, equitable participation in the development and use of the technology is essential. This means the developed world needs to provide greater financial and technological support to the developing world in the AI revolution. This support will need to be more than short term, but it will create significant and lasting benefits for all.

Joyjit Chatterjee, Data Scientist (KTP Associate), University of Hull and Nina Dethlefs, Senior Lecturer in Computer Science, University of Hull

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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