Paper by John Michael Maxel Okoche et al: “Despite significant technology advances especially in artificial intelligence (AI), crowdsourcing platforms still struggle with issues such as data overload and data quality problems, which hinder their full potential. This study addresses a critical gap in the literature how the integration of AI technologies in crowdsourcing could help overcome some these challenges. Using a systematic literature review of 77 journal papers, we identify the key limitations of current crowdsourcing platforms that included issues of quality control, scalability, bias, and privacy. Our research highlights how different forms of AI including from machine learning (ML), deep learning (DL), natural language processing (NLP), automatic speech recognition (ASR), and natural language generation techniques (NLG) can address the challenges most crowdsourcing platforms face. This paper offers knowledge to support the integration of AI first by identifying types of crowdsourcing applications, their challenges and the solutions AI offers for improvement of crowdsourcing…(More)”.
AI Is Evolving — And Changing Our Understanding Of Intelligence
Essay by Blaise Agüera y Arcas and James Manyika: “Dramatic advances in artificial intelligence today are compelling us to rethink our understanding of what intelligence truly is. Our new insights will enable us to build better AI and understand ourselves better.
In short, we are in paradigm-shifting territory.
Paradigm shifts are often fraught because it’s easier to adopt new ideas when they are compatible with one’s existing worldview but harder when they’re not. A classic example is the collapse of the geocentric paradigm, which dominated cosmological thought for roughly two millennia. In the geocentric model, the Earth stood still while the Sun, Moon, planets and stars revolved around us. The belief that we were at the center of the universe — bolstered by Ptolemy’s theory of epicycles, a major scientific achievement in its day — was both intuitive and compatible with religious traditions. Hence, Copernicus’s heliocentric paradigm wasn’t just a scientific advance but a hotly contested heresy and perhaps even, for some, as Benjamin Bratton notes, an existential trauma. So, today, artificial intelligence.
In this essay, we will describe five interrelated paradigm shifts informing our development of AI:
- Natural Computing — Computing existed in nature long before we built the first “artificial computers.” Understanding computing as a natural phenomenon will enable fundamental advances not only in computer science and AI but also in physics and biology.
- Neural Computing — Our brains are an exquisite instance of natural computing. Redesigning the computers that power AI so they work more like a brain will greatly increase AI’s energy efficiency — and its capabilities too.
- Predictive Intelligence — The success of large language models (LLMs) shows us something fundamental about the nature of intelligence: it involves statistical modeling of the future (including one’s own future actions) given evolving knowledge, observations and feedback from the past. This insight suggests that current distinctions between designing, training and running AI models are transitory; more sophisticated AI will evolve, grow and learn continuously and interactively, as we do.
- General Intelligence — Intelligence does not necessarily require biologically based computation. Although AI models will continue to improve, they are already broadly capable, tackling an increasing range of cognitive tasks with a skill level approaching and, in some cases, exceeding individual human capability. In this sense, “Artificial General Intelligence” (AGI) may already be here — we just keep shifting the goalposts.
- Collective Intelligence — Brains, AI agents and societies can all become more capable through increased scale. However, size alone is not enough. Intelligence is fundamentally social, powered by cooperation and the division of labor among many agents. In addition to causing us to rethink the nature of human (or “more than human”) intelligence, this insight suggests social aggregations of intelligences and multi-agent approaches to AI development that could reduce computational costs, increase AI heterogeneity and reframe AI safety debates.
But to understand our own “intelligence geocentrism,” we must begin by reassessing our assumptions about the nature of computing, since it is the foundation of both AI and, we will argue, intelligence in any form…(More)”.
Behavioral AI: Unleash Decision Making with Data
Book by Rogayeh Tabrizi: “…delivers an intuitive roadmap to help organizations disentangle the complexity of their data to create tangible and lasting value. The book explains how to balance the multiple disciplines that power AI and behavioral economics using a combination of the right questions and insightful problem solving.
You’ll learn why intellectual diversity and combining subject matter experts in psychology, behavior, economics, physics, computer science, and engineering is essential to creating advanced AI solutions. You’ll also discover:
- How behavioral economics principles influence data models and governance architectures and make digital transformation processes more efficient and effective
- Discussions of the most important barriers to value in typical big data and AI projects and how to bring them down
- The most effective methodology to help shorten the long, wasteful process of “boiling the ocean of data”
An exciting and essential resource for managers, executives, board members, and other business leaders engaged or interested in harnessing the power of artificial intelligence and big data, Behavioral AI will also benefit data and machine learning professionals…(More)”
A Century of Tomorrows
Book by Glenn Adamson: “For millennia, predicting the future was the province of priests and prophets, the realm of astrologers and seers. Then, in the twentieth century, futurologists emerged, claiming that data and design could make planning into a rational certainty. Over time, many of these technologists and trend forecasters amassed power as public intellectuals, even as their predictions proved less than reliable. Now, amid political and ecological crises of our own making, we drown in a cacophony of potential futures-including, possibly, no future at all.
A Century of Tomorrows offers an illuminating account of how the world was transformed by the science (or is it?) of futurecasting. Beneath the chaos of competing tomorrows, Adamson reveals a hidden order: six key themes that have structured visions of what’s next. Helping him to tell this story are remarkable characters, including self-proclaimed futurologists such as Buckminster Fuller and Stewart Brand, as well as an eclectic array of other visionaries who have influenced our thinking about the world ahead: Octavia Butler and Ursula LeGuin, Shulamith Firestone and Sun Ra, Marcus Garvey and Timothy Leary, and more.
Arriving at a moment of collective anxiety and fragile hope, Adamson’s extraordinary bookshows how our projections for the future are, always and ultimately, debates about the present. For tomorrow is contained within the only thing we can ever truly know: today…(More)”.
Working With Cracks
An excerpt from Everyday Habits for Transforming Systems by Adam Kahane: “Systems are structured to keep producing the behaviors and results they are producing, and therefore often seem solid and unchangeable—but they are not. They are built, and they collapse. They crack and are cracked, which opens up new possibilities that some people find frightening and others find hopeful. Radical engagement involves looking for, moving toward, and working with these cracks—not ignoring or shying away from them. We do this by seeking out and working with openings, alongside others who are doing the same…
Al Etmanski has pioneered the transformation of the living conditions of Canadians with disabilities, away from segregation, dependency, and second-class status toward connection, agency, and justice. I have spoken with him and studied what he and others have written about his decades of experience, and especially about how his strategy and approach have evolved and enabled him to make the contributions he has. He has advanced through repeatedly searching out and working with openings or cracks (breakdowns and bright spots) in the social-economic-political-institutional-cultural “disability system.”..(More)”.
Energy and AI
Report by the International Energy Agency (IEA): “The development and uptake of artificial intelligence (AI) has accelerated in recent years – elevating the question of what widespread deployment of the technology will mean for the energy sector. There is no AI without energy – specifically electricity for data centres. At the same time, AI could transform how the energy industry operates if it is adopted at scale. However, until now, policy makers and other stakeholders have often lacked the tools to analyse both sides of this issue due to a lack of comprehensive data.
This report from the International Energy Agency (IEA) aims to fill this gap based on new global and regional modelling and datasets, as well as extensive consultation with governments and regulators, the tech sector, the energy industry and international experts. It includes projections for how much electricity AI could consume over the next decade, as well as which energy sources are set to help meet it. It also analyses what the uptake of AI could mean for energy security, emissions, innovation and affordability…(More)”.
Data Sharing: A Case-Study of Luxury Surveillance by Tesla
Paper by Marc Schuilenburg and Yarin Eski: “Why do people voluntarily give away their personal data to private companies? In this paper, we show how data sharing is experienced at the level of Tesla car owners. We regard Tesla cars as luxury surveillance goods for which the drivers voluntarily choose to share their personal data with the US company. Based on an analysis of semi-structured interviews and observations of Tesla owners’ posts on Facebook groups, we discern three elements of luxury surveillance: socializing, enjoying and enduring. We conclude that luxury surveillance can be traced back to the social bonds created by a gift economy…(More)”.
Unlocking Public Value with Non-Traditional Data: Recent Use Cases and Emerging Trends
Article by Adam Zable and Stefaan Verhulst: “Non-Traditional Data (NTD)—digitally captured, mediated, or observed data such as mobile phone records, online transactions, or satellite imagery—is reshaping how we identify, understand, and respond to public interest challenges. As part of the Third Wave of Open Data, these often privately held datasets are being responsibly re-used through new governance models and cross-sector collaboration to generate public value at scale.
In our previous post, we shared emerging case studies across health, urban planning, the environment, and more. Several months later, the momentum has not only continued but diversified. New projects reaffirm NTD’s potential—especially when linked with traditional data, embedded in interdisciplinary research, and deployed in ways that are privacy-aware and impact-focused.
This update profiles recent initiatives that push the boundaries of what NTD can do. Together, they highlight the evolving domains where this type of data is helping to surface hidden inequities, improve decision-making, and build more responsive systems:
- Financial Inclusion
- Public Health and Well-Being
- Socioeconomic Analysis
- Transportation and Urban Mobility
- Data Systems and Governance
- Economic and Labor Dynamics
- Digital Behavior and Communication…(More)”.
2025 Technology and innovation report
UNCTAD Report: Frontier technologies, particularly artificial intelligence (AI), are profoundly transforming our economies and societies, reshaping production processes, labour markets and the ways in which we live and interact. Will AI accelerate progress towards the Sustainable Development Goals, or will it exacerbate existing inequalities, leaving the underprivileged further behind? How can developing countries harness AI for sustainable development? AI is the first technology in history that can make decisions and generate ideas on its own. This sets it apart from traditional technologies and challenges the notion of technological neutrality.
The rapid development of AI has also outpaced the ability of Governments to respond effectively. The Technology and Innovation Report 2025 aims to guide policymakers through the complex AI
andscape and support them in designing science, technology and innovation (STI) policies that foster inclusive and equitable technological progress.
The world already has significant digital divides, and with the rise of AI, these could widen even further. In response, the Report argues for AI development based on inclusion and equity, shifting the focus from
technology to people. AI technologies should complement rather than displace human workers and production should be restructured so that the benefits are shared fairly among countries, firms and
workers. It is also important to strengthen international collaboration, to enable countries to co-create inclusive AI governance.
The Report examines five core themes:
A. AI at the technological frontier
B. Leveraging AI for productivity and workers’ empowerment
C. Preparing to seize AI opportunities
D. Designing national policies for AI
E. Global collaboration for inclusive and equitable AI…(More)”
Community Data: Creative Approaches to Empowering People with Information
Book by Rahul Bhargava: “…new toolkit for data storytelling in community settings, one purpose-built for goals like inclusion, empowerment, and impact. Data science and visualization has spread into new domains it was designed for – community organizing, education, journalism, civic governance, and more. The dominant computational methods and processes, which have not changed in response, are causing significant discriminatory and harmful impacts, documented by leading scholars across a variety of populations. Informed by 15 years of collaborations in academic and professional settings with nonprofits and marginalized populations, the book articulates a new approach for aligning the processes and media of data work with social good outcomes, learning from the practices of newspapers, museums, community groups, artists, and libraries.
This book introduces a community-driven framework as a response to the urgent need to realign data theories and methods around justice and empowerment to avoid further replicating harmful power dynamics and ensure everyone has a seat at the table in data-centered community processes. It offers a broader toolbox for working with data and presenting it, pushing beyond the limited vocabulary of surveys, spreadsheets, charts and graphs…(More)”.