Top 10 Emerging Technologies to Address Global Challenges

Curated on Posted on by Stefaan Verhulst

World Economic Forum: “The Top 10 Emerging Technologies of 2024 are:

  • 1. AI for scientific discovery: While artificial intelligence (AI) has been used in research for many years, advances in deep learning, generative AI and foundation models are revolutionizing the scientific discovery process. AI will enable researchers to make unprecedented connections and advancements in understanding diseases, proposing new materials, and enhancing knowledge of the human body and mind​​.
  • 2. Privacy-enhancing technologies: Protecting personal privacy while providing new opportunities for global data sharing and collaboration, “synthetic data” is set to transform how information is handled with powerful applications in health-related research.
  • 3. Reconfigurable intelligent surfaces: These innovative surfaces turn ordinary walls and surfaces into intelligent components for wireless communication while enhancing energy efficiency in wireless networks. They hold promise for numerous applications, from smart factories to vehicular networks​​.
  • 4. High-altitude platform stations: Using aircraft, blimps and balloons, these systems can extend mobile network access to remote regions, helping bridge the digital divide for over 2.6 billion people worldwide​​.
  • 5. Integrated sensing and communication: The advent of 6G networks facilitates simultaneous data collection (sensing) and transmission (communication). This enables environmental monitoring systems that help in smart agriculture, environmental conservation and urban planning. Integrated sensing and communication devices also promise to reduce energy and silicon consumption.
  • 6. Immersive technology for the built world: Combining computing power with virtual and augmented reality, these technologies promise rapid improvements in infrastructure and daily systems​. This technology allows designers and construction professionals to check for correspondence between physical and digital models, ensuring accuracy and safety and advancing sustainability.
  • 7. Elastocalorics: As global temperatures rise, the need for cooling solutions is set to soar. Offering higher efficiency and lower energy use, elastocalorics release and absorb heat under mechanical stress, presenting a sustainable alternative to current technologies.
  • 8. Carbon-capturing microbes: Engineered organisms convert emissions into valuable products like biofuels, providing a promising approach to mitigating climate change.
  • 9. Alternative livestock feeds: protein feeds for livestock sourced from single-cell proteins, algae and food waste could offer a sustainable solution for the agricultural industry.
  • 10. Genomics for transplants: The successful implantation of genetically engineered organs into a human marks a significant advancement in healthcare, offering hope to millions awaiting transplants​​…(More)”.

Using AI to Inform Policymaking

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Paper for the AI4Democracy series at The Center for the Governance of Change at IE University: “Good policymaking requires a multifaceted approach, incorporating diverse tools and processes to address the varied needs and expectations of constituents. The paper by Turan and McKenzie focuses on an LLM-based tool, “Talk to the City” (TttC), developed to facilitate collective decision-making by soliciting, analyzing, and organizing public opinion. This tool has been tested in three distinct applications:

1. Finding Shared Principles within Constituencies: Through large-scale citizen consultations, TttC helps identify common values and priorities.

2. Compiling Shared Experiences in Community Organizing: The tool aggregates and synthesizes the experiences of community members, providing a cohesive overview.

3. Action-Oriented Decision Making in Decentralized Governance: TttC supports decision-making processes in decentralized governance structures by providing actionable insights from diverse inputs.

CAPABILITIES AND BENEFITS OF LLM TOOLS

LLMs, when applied to democratic decision-making, offer significant advantages:

  • Processing Large Volumes of Qualitative Inputs: LLMs can handle extensive qualitative data, summarizing discussions and identifying overarching themes with high accuracy.
  • Producing Aggregate Descriptions in Natural Language: The ability to generate clear, comprehensible summaries from complex data makes these tools invaluable for communicating nuanced topics.
  • Facilitating Understanding of Constituents’ Needs: By organizing public input, LLM tools help leaders gain a better understanding of their constituents’ needs and priorities.

CASE STUDIES AND TOOL EFFICACY

The paper presents case studies using TttC, demonstrating its effectiveness in improving collective deliberation and decision-making. Key functionalities include:

  • Aggregating Responses and Clustering Ideas: TttC identifies common themes and divergences within a population’s opinions.
  • Interactive Interface for Exploration: The tool provides an interactive platform for exploring the diversity of opinions at both individual and group scales, revealing complexity, common ground, and polarization…(More)”

An Anatomy of Algorithm Aversion

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Paper by Cass R. Sunstein and Jared Gaffe: “People are said to show “algorithm aversion” when (1) they prefer human forecasters or decision-makers to algorithms even though (2) algorithms generally outperform people (in forecasting accuracy and/or optimal decision-making in furtherance of a specified goal). Algorithm aversion also has “softer” forms, as when people prefer human forecasters or decision-makers to algorithms in the abstract, without having clear evidence about comparative performance. Algorithm aversion is a product of diverse mechanisms, including (1) a desire for agency; (2) a negative moral or emotional reaction to judgment by algorithms; (3) a belief that certain human experts have unique knowledge, unlikely to be held or used by algorithms; (4) ignorance about why algorithms perform well; and (5) asymmetrical forgiveness, or a larger negative reaction to algorithmic error than to human error. An understanding of the various mechanisms provides some clues about how to overcome algorithm aversion, and also of its boundary conditions…(More)”.

The use of AI for improving energy security

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Rand Report: “Electricity systems around the world are under pressure due to aging infrastructure, rising demand for electricity and the need to decarbonise energy supplies at pace. Artificial intelligence (AI) applications have potential to help address these pressures and increase overall energy security. For example, AI applications can reduce peak demand through demand response, improve the efficiency of wind farms and facilitate the integration of large numbers of electric vehicles into the power grid. However, the widespread deployment of AI applications could also come with heightened cybersecurity risks, the risk of unexplained or unexpected actions, or supplier dependency and vendor lock-in. The speed at which AI is developing means many of these opportunities and risks are not yet well understood.

The aim of this study was to provide insight into the state of AI applications for the power grid and the associated risks and opportunities. Researchers conducted a focused scan of the scientific literature to find examples of relevant AI applications in the United States, the European Union, China and the United Kingdom…(More)”.

Framework for Governance of Indigenous Data (GID)

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Framework by The National Indigenous Australians Agency (NIAA): “Australian Public Service agencies now have a single Framework for working with Indigenous data.

The National Indigenous Australians Agency will collaborate across the Australian Public Service to implement the Framework for Governance of Indigenous Data in 2024.

Commonwealth agencies are expected to develop a seven-year implementation plan, guided by four principles:

  1. Partner with Aboriginal and Torres Strait Islander people
  2. Build data-related capabilities
  3. Provide knowledge of data assets
  4. Build an inclusive data system

The Framework represents the culmination of over 18 months of co-design effort between the Australian Government and Aboriginal and Torres Strait Islander partners. While we know we have some way to go, the Framework serves as a significant step forward to improve the collection, use and disclosure of data, to better serve Aboriginal and Torres Strait Islander priorities.

The Framework places Aboriginal and Torres Strait Islander peoples at its core. Recognising the importance of authentic engagement, it emphasises the need for First Nations communities to have a say in decisions affecting them, including the use of data in government policy-making.

Acknowledging data’s significance in self-determination, the Framework provides a stepping stone towards greater awareness and acceptance by Australian Government agencies of the principles of Indigenous Data Sovereignty.

It offers practical guidance on implementing key aspects of data governance aligned with both Indigenous Data Sovereignty principles and the objectives of the Australian Government…(More)”.

Artificial Intelligence Opportunities for State and Local Departments Of Transportation

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Report by the National Academies of Sciences, Engineering, and Medicine: “Artificial intelligence (AI) has revolutionized various areas in departments of transportation (DOTs), such as traffic management and optimization. Through predictive analytics and real-time data processing, AI systems show promise in alleviating congestion, reducing travel times, and enhancing overall safety by alerting drivers to potential hazards. AI-driven simulations are also used for testing and improving transportation systems, saving time and resources that would otherwise be needed for physical tests…(More)”.

Multi-disciplinary Perspectives on Citizen Science—Synthesizing Five Paradigms of Citizen Involvement

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Paper by Susanne Beck, Dilek Fraisl, Marion Poetz and Henry Sauermann: “Research on Open Innovation in Science (OIS) investigates how open and collaborative practices influence the scientific and societal impact of research. Since 2019, the OIS Research Conference has brought together scholars and practitioners from diverse backgrounds to discuss OIS research and case examples. In this meeting report, we describe four session formats that have allowed our multi-disciplinary community to have productive discussions around opportunities and challenges related to citizen involvement in research. However, these sessions also highlight the need for a better understanding of the underlying rationales of citizen involvement in an increasingly diverse project landscape. Building on the discussions at the 2023 and prior editions of the conference, we outline a conceptual framework of five crowd paradigms and present an associated tool that can aid in understanding how citizen involvement in particular projects can help advance science. We illustrate this tool using cases presented at the 2023 conference, and discuss how it can facilitate discussions at future conferences as well as guide future research and practice in citizen science…(More)”.

Governing with Artificial Intelligence

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OECD Report: “OECD countries are increasingly investing in better understanding the potential value of using Artificial Intelligence (AI) to improve public governance. The use of AI by the public sector can increase productivity, responsiveness of public services, and strengthen the accountability of governments. However, governments must also mitigate potential risks, building an enabling environment for trustworthy AI. This policy paper outlines the key trends and policy challenges in the development, use, and deployment of AI in and by the public sector. First, it discusses the potential benefits and specific risks associated with AI use in the public sector. Second, it looks at how AI in the public sector can be used to improve productivity, responsiveness, and accountability. Third, it provides an overview of the key policy issues and presents examples of how countries are addressing them across the OECD…(More)”.

Green Light

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Google Research: “Road transportation is responsible for a significant amount of global and urban greenhouse gas emissions. It is especially problematic at city intersections where pollution can be 29 times higher than on open roads.  At intersections, half of these emissions come from traffic accelerating after stopping. While some amount of stop-and-go traffic is unavoidable, part of it is preventable through the optimization of traffic light timing configurations. To improve traffic light timing, cities need to either install costly hardware or run manual vehicle counts; both of these solutions are expensive and don’t provide all the necessary information. 

Green Light uses AI and Google Maps driving trends, with one of the strongest understandings of global road networks, to model traffic patterns and build intelligent recommendations for city traffic engineers to optimize traffic flow. Early numbers indicate a potential for up to 30% reduction in stops and 10% reduction in greenhouse gas emissions (1). By optimizing each intersection, and coordinating between adjacent intersections, we can create waves of green lights and help cities further reduce stop-and-go traffic. Green Light is now live in 70 intersections in 12 cities, 4 continents, from Haifa, Israel to Bangalore, India to Hamburg, Germany – and in these intersections we are able to save fuel and lower emissions for up to 30M car rides monthly. Green Light reflects Google Research’s commitment to use AI to address climate change and improve millions of lives in cities around the world…(More)”

Using Artificial Intelligence to Accelerate Collective Intelligence

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Paper by Róbert Bjarnason, Dane Gambrell and Joshua Lanthier-Welch: “In an era characterized by rapid societal changes and complex challenges, institutions’ traditional methods of problem-solving in the public sector are increasingly proving inadequate. In this study, we present an innovative and effective model for how institutions can use artificial intelligence to enable groups of people to generate effective solutions to urgent problems more efficiently. We describe a proven collective intelligence method, called Smarter Crowdsourcing, which is designed to channel the collective intelligence of those with expertise about a problem into actionable solutions through crowdsourcing. Then we introduce Policy Synth, an innovative toolkit which leverages AI to make the Smarter Crowdsourcing problem-solving approach both more scalable, more effective and more efficient. Policy Synth is crafted using a human-centric approach, recognizing that AI is a tool to enhance human intelligence and creativity, not replace it. Based on a real-world case study comparing the results of expert crowdsourcing alone with expert sourcing supported by Policy Synth AI agents, we conclude that Smarter Crowdsourcing with Policy Synth presents an effective model for integrating the collective wisdom of human experts and the computational power of AI to enhance and scale up public problem-solving processes.

The potential for artificial intelligence to enhance the performance of groups of people has been a topic of great interest among scholars of collective intelligence. Though many AI toolkits exist, they too often are not fitted to the needs of institutions and policymakers. While many existing approaches view AI as a tool to make crowdsourcing and deliberative processes better and more efficient, Policy Synth goes a step further, recognizing that AI can also be used to synthesize the findings from engagements together with research to develop evidence-based solutions and policies. This study contributes significantly to the fields of collective intelligence, public problem-solving, and AI. The study offers practical tools and insights for institutions looking to engage communities effectively in addressing urgent societal challenges…(More)”