9 June 2023 10:45 pm
What role can blockchain play in developing smart cities

What role can blockchain play in developing smart cities

May 13, 2023

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What role can blockchain play in developing smart cities and the IoT when growing cities are a critical fact of the 21st Century representing the greatest challenge . . . 

The author states that, for instance, by ”using blockchain, citizens could receive tokens for waste disposal.”

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Smart cities are urban areas that use advanced technologies such as sensors, data analytics, and the Internet of Things (IoT) to improve the quality of life for their citizens. As these kinds of cities grow and become smarter, managing the vast amounts of data generated by IoT devices raises concerns about privacy and security. Blockchain technology can provide a secure and transparent way to manage data and administrative processes and improve safety. It can play a significant role in developing smart cities and the IoT.

How can Blockchain help develop Smart Cities and IoT?

1. Secure Data Management

Imagine a smart city with sensors that collect data on traffic, energy consumption, and air quality. All this data is like puzzle pieces that can help city planners make better decisions to improve the city. However, they need to ensure that the data is secure and only accessible by authorized people. Blockchain can help with that by creating a transparent and secure data management system. It’s like having a locked box where only authorized people have the key. This way, they can track who owns the data and how it’s being shared between parties like the city government, businesses, and citizens.

2. Decentralized Energy Grid

Let’s say you are a city government official responsible for waste management. You want to incentivize citizens to dispose of their trash and recycle properly, but you’re unsure how to track and reward individual efforts. Using blockchain technology, citizens could receive tokens for proper waste disposal, which they could then exchange for rewards like discounts at local businesses or even tax credits. This creates a more efficient and transparent way to incentivize good behavior and promote sustainability in the city.

3. Digital Identity Management

Blockchain can be used to create a safe and reliable way for citizens to prove their identity, reducing the chance of someone stealing or committing fraud. For example, blockchain technology can create digital IDs that allow citizens to vote or access government services, making these processes faster and more efficient.

4. Smart Contract Integration

Blockchain smart contracts can automate many aspects of city management, including traffic management, waste management, and emergency response. This could reduce costs, improve efficiency, and enhance citizen safety.

5. Public Records Management

Blockchain technology can make public records like property titles and business registrations more secure and transparent. This can reduce bureaucratic processes and enhance the accuracy and accessibility of public records. For instance, when buying a property, the buyer and seller can use blockchain to automate the transfer of ownership, making the process more secure and transparent.

Conclusion

Blockchain technology has the potential to play a vital role in the development of smart cities and the Internet of Things. By providing secure and transparent data management, decentralized energy grids, digital identity management, smart contract integration, and public records management, blockchain could help to create more efficient, sustainable, and livable cities for all.

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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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High Tech Innovations Are Key To A Greener Economy

Apr 25, 2023

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In a Forbes Business Development Council article, it is held that High Tech Innovations Are Key To A Greener Economy.  Syed Alam, Forbes Councils Member offers us 5 Ways To Ensure A More Sustainable Future.  

Environmentally Responsible and Resource-efficient in the MENA region, was and still is concerned for anything green that were second to that fundamentally frantic development of buildings and all related infrastructure to nevertheless greater and greater awareness of their various environmental impact. 

The image above is Getty

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High Tech Innovations Are Key To A Greener Economy: 5 Ways To Ensure A More Sustainable Future

Syed Alam, Forbes Councils Member

Forbes Business Development Council

 

Syed is Accenture’s High Tech global lead, helping clients reinvent their business, optimize supply chain and create new revenue models.

The high-tech industry is central to moving the sustainability agenda forward and enabling a greener planet through the design of more sustainable products using the rise of smart sensors as a way to better manage energy consumption.

At my company Accenture, we have already seen great progress in a wide variety of products, from smart thermostats and solar-powered smart watches to electric vehicles and more power-efficient CPUs in data centers. These products are not only more sustainable and good for the environment, but they are also good for business and future growth.

A recent study from United Nations Global Compact and Accenture shows strategies and business models with sustainability at their core are not only a climate imperative but also the foundation for better security, growth and resilience. This is supported by another recent study’s indication that the supply chain is key to fighting climate change, as supply chains generate up to 60% of global emissions.

While many companies have mastered Scope 1 emissions, most companies lack visibility into the upstream supplier base, called “Scope 3” emissions. For high-tech companies, 86% of upstream Scope 3 emissions sit outside their Tier 1 suppliers.

High-tech companies are deploying strategies to help the industry meet environmental sustainability goals. The Semiconductor Climate Consortium is one excellent example of semiconductor companies coming together to collaborate and align on common approaches and technology innovations to continuously reduce greenhouse gas emissions.

In this article, I will outline five strategies high-tech leaders can adopt to ensure a more sustainable future both within their own organizations and across the supply chain.

1. Recycling Products

E-waste, driven in part by consumers upgrading to the latest smartphones and data centers swapping out servers to keep up with the demands of AI, is both damaging to the planet and costing high-tech companies money. According to the United Nations, global e-waste volumes grew 17% between 2014 and 2019, with over 53 million tons of e-waste in 2019.

High-tech companies are in a unique position to help reduce e-waste by designing products for reuse, resale, repair, refurbishment and remanufacturing, which Accenture and the United Nations study shows can boost operating profit by 16%.

Many technology giants already have successful recycling programs in place that encourage partner participation. In 2022, Accenture partner Cisco launched the Environmental Sustainability Specialization (ESS), a program to educate customers, promote product takeback and assist in the move to circular business models.

As many companies have proven, this can constitute a great opportunity to save money and create new revenue streams while reducing carbon footprints by avoiding single-use inputs and designing for refurbishment and longevity.

2. Selecting Cleaner Raw Materials

As the demand for more sustainable materials rises, more companies are starting to use cleaner minerals such as copper, lithium, nickel and cobalt. Fortunately, materials suppliers have stepped up efforts to deliver eco-friendly solutions to enable companies to make this transition.

Accenture partner Solvay, a supplier of alternative materials, has been developing new solutions to reduce waste materials generated by semiconductor manufacturing. Its products are helping customers recycle polyvinylidene fluoride, a byproduct of chipmaking.

3. Adopting Greener Manufacturing Processes

Many manufacturing companies are making strides in reducing electricity consumption, recycling water and adopting greener manufacturing practices.

Accenture partner Lam Research invested in LED lighting processes and improvements to HVAC equipment such as air compressors. Likewise, companies such as Winbond are using a new low-temperature soldering (LTS) process to reduce the temperatures needed for the assembly of components. These lower temperatures can lead to faster manufacturing throughput while also lowering temperatures to reduce carbon emissions.

Leaders continue to adopt solutions capable of streamlining production processes, using digital tools and deploying more efficient supply chains to save energy and optimize logistics to reduce truck rolls, which can help lower carbon footprints.

Accenture partner Hitachi’s Lumada Manufacturing Insights is a perfect example, as it is helping manufacturers develop data-driven operations, increase supply chain visibility and enable smart factory solutions to improve productivity and lower asset downtime.

4. Designing More Power-Efficient Products

At this year’s CES, we saw many energy-efficient products come to life as companies introduced products focused on managing home energy usage, including battery packs, solar panels and EV chargers. Accenture partner Schneider Electric released the “Home” energy platform to monitor energy usage, manage backup power during an outage and connect to utility programs for savings on electricity bills.

The industry migration to the cloud has also helped significantly reduce global power consumption. Because the cloud supports many products at a time, it can more efficiently distribute resources among users. Companies like Accenture partner Google have made inroads in making their cloud services power efficient, with claims new data centers are twice as energy efficient as a typical enterprise data center—delivering five times as much computing power for the same amount of electrical power as five years ago.

5. Embedding Sustainability Into Supplier Selection And Management

As companies source new suppliers and optimize existing ones, they should embed sustainability in every step of the supply chain management process. This includes analyzing the supplier base to determine the biggest source of emissions and having data-driven conversations with suppliers to reduce emissions.

Digital tools such as digital twins can be used to map physical material flows to uncover sub-tier suppliers and risks. By proactively working with suppliers on an ongoing basis, high-tech companies can identify bottlenecks within the supply chain and help mitigate disruptive events while improving their own decarbonization performance.

Social Innovations Without Waste

While the industry has made great strides toward global sustainability, there is still much work to be done. With the value of global sustainability assets rising above $220 billion, it is increasingly evident that investing in sustainability is not just morally responsible but financially savvy.

Organizations must reduce massive surges in energy consumption, water usage and CO2 emissions and develop sustainable products and services to help customers in their own sustainability transformations. The transition to sustainability presents a tremendous revenue-generating opportunity for companies that act quickly to develop—and adopt—greener technologies.

 

Follow me on Twitter or LinkedIn. Check out my website.

Syed Alam is Accenture’s high tech global lead, helping clients reinvent their business, optimize supply chain and

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Waiting for quantum computers to arrive

Apr 17, 2023

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A man takes a photo of a model of the IBM Q System One quantum computer during the 2020 CES in Las Vegas, Nevada, U.S. January 7, 2020. REUTERS/Steve Marcus/File Photo

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Waiting for quantum computers to arrive, software engineers get creative

By 

Robert Parrish, Head of Quantum Chemistry, and Matt Johnson, CEO of Quantum Chemistry, review computations on large metalloproteins, as Quantum software startup QC Ware launches a quantum-inspired software platform called Promethium to speed up the simulation of large molecules on conventional computers, in Palo Alto, California, U.S., March 29, 2023. QC Ware/Handout via REUTERS

 

OAKLAND, Calif., April 17 (Reuters) – Quantum computers promise to be millions of times faster than today’s fastest supercomputers, potentially revolutionizing everything from medical research to the way people solve problems of climate change. The wait for these machines, though, has been long, despite the billions poured into them.

But the uncertainty and the dismal stock performance of publicly-listed quantum computer companies including Rigetti Computing Inc (RGTI.O) have not scared investors away. Some are turning to startups who are pivoting to using powerful chips to run quantum-inspired software on regular computers as they bide their time.

Lacking quantum computers that customers can use today to get an advantage over classical computers, these startups are developing a new breed of software inspired by algorithms used in quantum physics, a branch of science that studies the fundamental building blocks of nature.

Once too big for conventional computers, these algorithms are finally being put to work thanks to today’s powerful artificial intelligence chips, industry executives told Reuters.

QC Ware, a software startup that has raised more than $33 million and initially focused only on software that could run on quantum computers, said it needed to change tack and find a solution for clients today until the future quantum machines arrive.

So QC Ware CEO Matt Johnson said it turned to Nvidia Corp’s (NVDA.O) graphic processing units (GPU) to “figure out how can we get them something that is a big step change in performance … and build a bridge to quantum processing in the future.”

GPUs are microchips that were made to process video for gaming and became so powerful that they do the bulk of AI computing today. They are now being used in quantum development, as well.

This week, QC Ware is unveiling a quantum-inspired software platform called Promethium that will simulate chemical molecules – to see how they interact with things like protein – on a traditional computer using GPUs.

The software can cut simulation time from hours to minutes for molecules of 100 atoms, and months to hours for molecules of up to 2000 atoms, compared with existing software solutions, said QC Ware’s head of quantum chemistry Robert Parrish.

$1 BILLION RAISED

Big-name investors and funds are backing the future, such as Alphabet Inc’s (GOOGL.O) former chairman Eric Schmidt, asset manager T. Rowe Price (TROW.O), Samsung Ventures, and the venture arm of U.S. intelligence agencies In-Q-Tel.

The startups receiving the largesse say they are able to generate revenue as customers are lining up to be ready for when quantum computing’s “iPhone” moment arrives. That, in turn, is luring investors.

In the past 18 months, quantum software startups including SandBoxAQ – an Alphabet spinoff – raised about $1 billion, according to data firm PitchBook. To be sure, development of this technology is nascent and these startups must work hard to convince some prospective clients.

SandBoxAQ CEO Jack Hidary said it was only 24 months ago that AI chips became powerful enough to simulate hundreds of thousands of chemical interactions simultaneously.

It developed a quantum-inspired algorithm for biopharma simulation on Google’s AI chip called a Tensor Processing Unit (TPU) that generates revenue today. SandBoxAQ told Reuters in February it raised $500 million.

Jason Turner, who founded Entanglement Inc in 2017 to be a “quantum only lab,” became impatient with the slow pace of quantum hardware development.

“It’s been ten years away for what, 40 years now, right?” he said. He finally relented, turning to Silicon Valley AI chip startup Groq to help him run a cybersecurity quantum-inspired algorithm.

Ultimately, the software inspired by quantum physics won’t perform well on quantum computers without some changes, said William Hurley, boss of Austin-based quantum software startup Strangeworks.

Still, he said companies that start using them will have engineers “learning about quantum and the phenomenon and the process, which will better prepare them to use quantum computers at the point that they do so.” That moment could arrive suddenly, he said.

Strangeworks, which also operates a cloud with over 60 quantum computers on it, raised $24 million last month from investors including IBM (IBM.N).

Reporting by Jane Lanahee Lee in Oakland, California; Editing by Peter Henderson, Sayantani Ghosh and Nick Zieminski
– Reuters

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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