Resilient road infrastructure is a cornerstone of the UN Sustainable Development Goals. Yet a primary indicator of network functionality and resilience is critically lacking: a comprehensive global baseline of road surface information. Here, we overcome this gap by applying a deep learning framework to a global mosaic of Planetscope satellite imagery from 2020 and 2024. The result is the first global multi-temporal dataset of road pavedness and width for 9.2 million km of critical arterial roads, achieving 95.5% coverage where nearly half the network was previously unclassified. This dataset reveals a powerful multi-scale geography of human development. At the planetary scale, we show that the rate of change in pavedness is a robust proxy for a country's development trajectory (correlation with HDI = 0.65). At the national scale, we quantify how unpaved roads constitute a fragile backbone for economic connectivity. We further synthesize our data into a global Humanitarian Passability Matrix with direct implications for humanitarian logistics. At the local scale, case studies demonstrate the framework's versatility: in Ghana, road quality disparities expose the spatial outcomes of governance; in Pakistan, the data identifies infrastructure vulnerabilities to inform climate resilience planning. Together, this work delivers both a foundational dataset and a multi-scale analytical framework for monitoring global infrastructure, from the dynamics of national development to the realities of local governance, climate adaptation, and equity. Unlike traditional proxies such as nighttime lights, which reflect economic activity, road surface data directly measures the physical infrastructure that underpins prosperity and resilience - at higher spatial resolution.

The global rise in homelessness calls for urgent and alternative policy solutions. Non-profits and governmental organizations alert about the many challenges faced by people experiencing homelessness (PEH), which include not only the lack of shelter but also the lack of opportunities for personal development. In this context, the capability approach (CA), which underpins the United Nations Sustainable Development Goals (SDGs), provides a comprehensive framework to assess inequity in terms of real opportunities. This paper explores how the CA can be combined with agent-based modelling and reinforcement learning. The goals are: (1) implementing the CA as a Markov Decision Process (MDP), (2) building on such MDP to develop a rich decision-making model that accounts for more complex motivators of behaviour, such as values and needs, and (3) developing an agent-based simulation framework that allows to assess alternative policies aiming to expand or restore people's capabilities. The framework is developed in a real case study of health inequity and homelessness, working in collaboration with stakeholders, non-profits and domain experts. The ultimate goal of the project is to develop a novel agent-based simulation framework, rooted in the CA, which can be replicated in a diversity of social contexts to assess policies in a non-invasive way.

In moving from excluding the majority of the world's languages to blindly adopting what we make for English, we first risk importing the same harms we have at best mitigated and at least measured for English. For instance, Yong et al. [15] showed how prompting GPT-4 in low-resource languages circumvents guardrails that are effective in English. However, in evaluating and mitigating harms arising from adopting new technologies into such contexts, we often disregard (1) the actual community needs of Language Technologies, and (2) biases and fairness issues within the context of the communities. Here, we consider fairness, bias, and inclusion in Language Technologies through the lens of the Capabilities Approach [12]. The Capabilities Approach centers what people are capable of achieving, given their intersectional social, political, and economic contexts instead of what resources are (theoretically) available to them. In the following sections, we detail the Capabilities Approach, its relationship to multilingual and multicultural evaluation, and how the framework affords meaningful collaboration with community members in defining and measuring harms of Language Technologies. 2 THE CAPABILITIES APPROACH The Capabilities Approach is a framework in developmental economic studies proposed by Amartya Sen in a series of articles published as far back as 1974 [1]. It has been applied to varied fields including environmental justice [e.g.

Profound changes to the substance and structure of our lives -- wrought by disruptive technologies ranging from smartphones and social media to newly ascendent AI -- often go unnoticed amidst the rush of daily life. Over 30 percent of U.S. adults report "almost constant" online activity, something that would have been impossible only two decades ago. From an early age, children are exposed to digital technologies, and one recent study found that two- and three-year-olds average two hours of screen time daily. Nor is this phenomenon simply a matter of media consumption. Ordinary market transactions, whether online shopping or home mortgage applications, are now facilitated through sophisticated algorithmic systems.

The idea of artificial intelligence overthrowing humankind has been talked about for decades, and in 2021, scientists delivered their verdict on whether we'd be able to control a high-level computer super-intelligence. The catch is that controlling a super-intelligence far beyond human comprehension would require a simulation of that super-intelligence which we can analyze (and control). But if we're unable to comprehend it, it's impossible to create such a simulation. Rules such as'cause no harm to humans' can't be set if we don't understand the kind of scenarios that an AI is going to come up with, suggest the authors of the new paper. Once a computer system is working on a level above the scope of our programmers, we can no longer set limits.

The idea of artificial intelligence overthrowing humankind has been talked about for many decades, and in January 2021, scientists delivered their verdict on whether we'd be able to control a high-level computer super-intelligence. The catch is that controlling a super-intelligence far beyond human comprehension would require a simulation of that super-intelligence which we can analyze. But if we're unable to comprehend it, it's impossible to create such a simulation. Rules such as'cause no harm to humans' can't be set if we don't understand the kind of scenarios that an AI is going to come up with, suggest the authors of the 2021 paper. Once a computer system is working on a level above the scope of our programmers, we can no longer set limits.

Join our XPotential Community, future proof yourself with courses from XPotential University, connect, watch a keynote, or browse my blog. Have you ever heard people ask whether or not AI will destroy the world, or ask if we'll ever be able to control future Artificial Intelligence's? If not then firstly what rock have you been hiding under, and is there space for one more, and if you have then you'll know that no one ever comes up with a decent answer. That said though, and for what it's worth, every once in a while Elon Musk tells everyone that one day AI could become an immortal dictator, which would suggest he thinks we couldn't control it, and every once in a while Google announces it's still not succeeded in creating a kill switch that will let it terminate rogue AI's, which, again, just suggests more of the same. And let's not even go anywhere near the "Doomsday Games" event where hundreds of the world's top experts and scientists couldn't figure out how to solve the majority of the world's doomsday scenarios, or the time Google demonstrated that more powerful AI's get "aggressive" and "kill" weaker ones … And as for my answer it'd also be no, categorically, especially as we get closer to realising the dawn of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI) – two events which will change our world beyond all recognition.

The global expansion of schooling in the past three decades is unprecedented: Primary school enrollment is near-universal, expected years of schooling have risen rapidly, and the number of children out of school has fallen sharply. Yet the greatest challenge for the global education system, a “learning crisis” per the World Bank, is that these gains in schooling are not translating into commensurate gains in learning outcomes. This crisis is well exemplified by India, which has the largest education system in the world. Over 95% of children aged 6 to 14 years are in school, but nearly half of students in grade 5 in rural areas cannot read at a grade 2 level, and less than one-third can do basic division ([ 1 ][1]). India's new National Education Policy (NEP) of 2020 (the first major revision since 1986) recognizes the centrality of achieving universal foundational literacy and numeracy. Whether India succeeds in this goal matters intrinsically through its impact on over 200 million children and will also have lessons for other low- and middle-income countries. We review the NEP's discussion of school education in light of accumulated research evidence that may be relevant to successfully implementing this ambitious goal. India has made tremendous progress on access to schooling since the 1990s. Yet multiple nationally representative datasets suggest that learning levels have remained largely flat over the past 15 years. A large body of evidence has shown that increasing “business as usual” expenditure on education is only weakly correlated with improvement in learning ([ 2 ][2]). Two key constraints that limit the translation of spending (of time and money) into outcomes are weaknesses in governance and pedagogy. Governance challenges are exemplified by high rates of teacher absence in public schools, with nearly one in four teachers absent at the time of surprise visits ([ 3 ][3]). Even when teachers are present, instructional time is low for a variety of reasons, including large amounts of administrative paperwork. Further, teacher recognition for performance and sanctions for nonperformance are low. Studies in India and elsewhere have shown that even modest amounts of performance-linked bonus pay for teachers can improve student learning in a cost-effective way ([ 4 ][4]). By contrast, unconditional increases in teacher pay (the largest component of education budgets) have no impact on student learning ([ 4 ][4], [ 5 ][5]). Overall, improving governance and management in public schools may be a much more cost-effective way of improving student learning than simply expanding education spending along default patterns. An even greater challenge in translating school attendance into learning outcomes may be weaknesses in pedagogy. Even motivated teachers primarily focus on completing the textbook, without recognizing the mismatch between the academic standards of the textbook and student learning levels. The rapid expansion of school enrollment has brought tens of millions of first-generation learners into the formal education system who lack instructional support at home and often fall behind grade-appropriate curricular standards. The mismatch is clearly illustrated in the figure, which presents the levels and dispersion of student achievement in mathematics in a sample of students from public middle schools in Delhi ([ 6 ][6]). There are three points to note about this figure: (i) The vast majority of students are below curricular standards (represented by the blue line of equality), with the average grade 6 student 2.5 years behind; (ii) the average rate of learning progress is much flatter than that envisaged by the curricular standards, resulting in widening learning gaps at higher grades; (iii) there is enormous variation in learning levels of students in the same grade, spanning five to six grade levels in all grades. The figure captures many features that we think are central to understanding the Indian education system. It suggests a curriculum that targets the top of the achievement distribution and moves much faster than the actual achievement level of students. Coupled with social promotion—grade retention is forbidden by law until grade 8—this leads to student achievement being widely dispersed within the same grade and most students receiving instruction that they are not academically prepared for. Similar patterns likely exist in many other developing countries ([ 6 ][6]). The figure may also help explain why increased expenditures on items such as teacher salaries and school infrastructure may have little impact on learning. Students, having fallen so far behind the curriculum, may not gain much from the default of textbook-linked instruction. By contrast, pedagogical interventions that target instruction at the level of students' academic preparation can be highly effective ([ 6 ][6]–[ 8 ][7]). The figure also highlights the stark inequality in Indian education. The true inequality is likely even greater because the figure does not reflect the large number of students in private schools. A comparison of data from two Indian states to countries included in an international learning assessment found that learning inequality in India is second only to South Africa ([ 9 ][8]). Thus, although the academically strongest Indian students are internationally competitive, with many ultimately achieving world-renowned success, most Indian children fail to acquire even basic skills at the end of their schooling. To better understand the Indian education system, it is useful to recognize that education systems have historically served two very different purposes: (i) to impart knowledge and skills (a “human development” role) and (ii) to assess, classify, and select students for higher education and skill-intensive occupations (a “sorting and selection” role). The Indian education system primarily serves as a “sorting and selection” or a “filtration” system rather than a “human development” system. The system focuses primarily on setting high standards for competitive exams to identify those who are talented enough to meet those standards, but it ends up neglecting the vast majority of students who do not. Thus, a fundamental challenge for Indian education policy is to reorient the education system from one focused on sorting and identifying talented students to one that is focused on human development that can improve learning for all. The NEP, released in 2020, does an excellent job of reflecting key insights from research. Three points are especially noteworthy. First, and most important, is the centrality accorded to universal foundational literacy and numeracy, which the NEP calls an “urgent and necessary prerequisite for learning.” This represents a substantial shift in the definition of education “quality” from inputs and expenditure to actual learning outcomes. Relatedly, the NEP recognizes the importance of early childhood care and education and brings preschool education into the scope of national education policy alongside school education. The NEP's focus on stronger and universal preschool education is consistent with global recognition of the importance of “the early years” in developing cognitive and socioemotional skills. Second, consistent with the evidence, the NEP aims to strengthen teacher effectiveness through a combination of improving their skills, reducing extraneous demands on their time, and rewarding performance. Notably, the NEP highlights the need for “a robust merit-based structure of tenure, promotion, and salary structure.” This is a meaningful departure from the status quo that does not reward good performance. If implemented well, improving teacher motivation and effort can be a force multiplier for the effectiveness of other input-based spending. School inputs on their own do not seem to translate into learning gains ([ 2 ][2]), but inputs can be highly effective when teachers and principals are motivated to improve learning outcomes ([ 10 ][9]). Third, the NEP recognizes that improving school effectiveness may require changes to how schools are organized and managed. Large-scale school construction in the 1990s played an important role in promoting universal school access by providing a school in every habitation. However, as of 2016, over 417,000 government primary schools (∼40% of schools) had fewer than 50 students across grades 1 to 5 ([ 11 ][10]). Small and spread-out schools present challenges for governance (by making supervision difficult), pedagogy (by requiring teachers to simultaneously teach students in multiple grades), and infrastructure quality (by being too small for libraries and computer laboratories), as well as cost-effectiveness. The NEP, therefore, recommends investing in larger school complexes and also recognizes the importance of school management, emphasizing the need for customized school development plans to anchor a process of continuous school improvement. Given large improvements in rural road construction, it will be viable to provide buses or other transport to ensure universal school access for all children while also obtaining the benefits of larger-scale schools. ![Figure][11] Achievement versus curricular standards The estimated level of student achievement (determined by a computer-aided instruction program) in mathematics in public middle schools in Delhi is plotted against the grade in which students are actually enrolled. See ([ 6 ][6]) for details and data. Most students are below curricular standards (line of equality), average progress in learning is flatter than curricular standards, and there is substantial variation in achievement. GRAPHIC: ADAPTED FROM ([ 6 ][6]) BY H. BISHOP/ SCIENCE ; © AMERICAN ECONOMIC ASSOCIATION; REPRODUCED WITH PERMISSION OF THE AMERICAN ECONOMIC REVIEW Although the NEP is an excellent document that reflects research and evidence, delivering on its promise will require sustained attention to implementation. The glaring gaps between the high quality of policy and program design on one hand, and the low quality of implementation on the other, are widely recognized in India across many dimensions of public policy. Preliminary findings from two of our recent projects illustrate this challenge in relation to policy recommendations in the NEP. First, in a large-scale randomized controlled trial covering over 5000 schools in the state of Madhya Pradesh, we found no notable effects on school functioning or student achievement of an ambitious reform that aimed to improve school management, largely through the type of school development plans that are recommended in the NEP ([ 12 ][12]). Yet, this model is perceived to be successful and has been scaled up to over 600,000 schools nationally (and aims to reach 1.6 million schools). Our work suggests that this perception is based primarily on completion of paperwork (such as school assessments and improvement plans), even though there was no change in management, pedagogy, or learning outcomes. The second example illustrates how even measuring learning outcomes accurately is challenging. The state of Madhya Pradesh administers an annual state-level standardized assessment to all children in public schools from grades 1 to 8. This has been declared a national “best practice” and the NEP recommends a similar assessment for students in all schools in grades 3, 5, and 8. Yet, an independent audit that administered the same test questions to the same students a few weeks after the official tests showed that levels of student achievement are severely overstated in official data ([ 13 ][13]). The audit found that a large fraction of students did not possess even basic skills even though most of these students were shown as having passed the test. In light of such challenges, we highlight three key principles that may increase the likelihood of success. The first is measurement. India's success in achieving universal enrollment shows that the system is capable of delivering on well-defined goals that are easily measured. A similar approach needs to be implemented for delivering universal foundational literacy and numeracy. Although the challenge of data integrity is real, one reason for optimism is that there is evidence that using technology-based independent testing sharply reduced the extent to which data on learning was inflated ([ 13 ][13]). Thus, investing in independent ongoing measurement of learning outcomes in representative samples to set goals and monitor progress will be a foundational investment. The second key principle is ongoing evaluations of policy and program effectiveness. An important lesson from the past two decades of research on education is that many commonly advocated interventions for improving education (such as increasing teacher salaries, providing school grants, or giving out free textbooks) may have very little impact on learning outcomes, whereas other interventions (such as teaching at the right level) may be highly effective. Even in the same class of policies, different interventions may have widely varying effectiveness; for instance, in the case of education technology, the impact of providing hardware alone is zero or even negative, but personalized adaptive learning programs have been found to be highly effective ([ 6 ][6], [ 7 ][14]). Yet, use of rigorous, experimental evidence in education policy-making remains more an exception than the rule. Disciplining interventions under the NEP with high-quality evaluations can accelerate the scaling up of effective programs as well as course corrections of ineffective ones. The third key principle is cost-effectiveness. Evidence has shown pronounced variation in the cost-effectiveness of education interventions, with many expensive policies having no impact and inexpensive ones being very effective. Given limited resources and competing demands on them, cost-effectiveness is not only an economic consideration but also a moral one. The World Bank and the UK Foreign and Commonwealth Development Office recently synthesized a large body of evidence on the most cost-effective education interventions ([ 14 ][15]). India would do well to heed these recommendations (suitably modified to its context) when allocating scarce public resources. Education has been sharply disrupted around India and the world by the COVID-19 shock. Public schools in India have been mostly closed and are likely to remain so for the entire academic year. This presents one major threat and two opportunities. The threat is that the learning crisis will worsen. Children who have missed a year of school—especially those without educated parents—are likely to have regressed in their learning and suffer long-term learning losses. Thus, the challenges (see the figure) are likely to have worsened, making it imperative to provide high-quality supplementary instruction when schools reopen, including perhaps through reducing holidays and vacation days. Yet, there may also be two important longer-term opportunities. The first is the rapid acceleration in the use of education technology by both households and the government. Given evidence of strong positive effects of personalized instruction, the widespread adoption of education technology may help accelerate the NEP's stated goal of reducing the digital divide and leveraging potential benefits of technology for education, such as opportunities to increase student engagement and personalize instruction to individual student needs. The second is increasing engagement with parents and families. Households play a critical role in education. Yet, education policy has mostly focused on school-based interventions, reflecting a belief that it is more feasible to improve schools than to intervene in households at scale. The COVID-19 crisis and the resulting growth in the use of mobile phones for engaging children have sharply increased educators' engagement with parents, with approaches ranging from text-message reminders to check their child's homework to parent groups for peer coaching and motivation. Work is under way to evaluate the impacts of these promising approaches. The benefits of increased parental engagement may persist even after schools reopen. Effective reform will require a confluence of ideas, interests, institutions, and implementation. Our focus has been on the ideas of the NEP and the extent to which they are supported, or may be refined by, research evidence. The NEP also pays attention to institutional infrastructure needed to deliver on this vision and acknowledges the centrality of implementation. However, both the NEP and our discussion are silent on the interests, specifically on political and bureaucratic constraints. We remain optimistic that substantial improvements are possible. In particular, backing the intent of the NEP with a commitment to regular independent measurement and reporting of learning outcomes in a representative sample of all children—as envisaged by the NEP in setting up a quasi-independent national testing agency—may help to provide an institutionalized focus on learning to both political and bureaucratic leadership. The NEP's proposal to provide such information to parents directly, if implemented in easily accessible formats, may catalyze improvements in both public and private schools. Such reforms are particularly urgent given India's demographic transition. In many states, especially in South India, total fertility rates are already below replacement levels, and cohort sizes in primary schooling are shrinking. Thus, much of the country has already passed the peak of potential demographic dividend without having solved the learning crisis. Some large populous states in Northern India, such as Uttar Pradesh and Bihar, still have a window for intervention, but this window is shrinking. The one silver lining is that declining cohort sizes may increase resources per student in coming years, thus freeing up fiscal space for cost-effective investments. There is nothing inevitable about low learning levels in Indian schools. Other developing countries, such as Vietnam, have been able to achieve substantially superior learning outcomes at very similar levels of per capita incomes. Research suggests that a key explanation is the greater productivity of Vietnam's schooling system, which focuses attention on ensuring that even the weakest students reach minimum standards of learning ([ 15 ][16]). The NEP provides an important opportunity to move Indian education from “sorting and selection” to “human development,” enabling every student to develop to their maximum potential. 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Intelligent algorithms are used to create paintings, write poems, and compose music. According to a study by an international team of researchers from the Massachusetts Institute of Technology (MIT), and the Center of Humans and Machines at the Max Planck Institute for Human Development, whether people perceive artificial intelligence (AI) as the ingenious creator of art or simply another tool used by artists depends on how information about AI art is presented. The results were published in the journal iScience. In October 2018, a work of art by Edmond de Belamie, which was created with the help of an intelligent algorithm, was auctioned for 432,500 USD at Christie's Auction House. According to Christie's auction advertisement, the portrait was created by artificial intelligence (AI).

We can already see the potential for artificial intelligence (AI) in international development: the seemingly endless possibilities to enhance productivity and innovation across healthcare, agriculture, education, transportation, and governance. Yet it is also becoming abundantly clear that AI could have negative repercussions as well, particularly in countries with weaker institutional capacity and legal protections. AI has the potential to threaten democratic processes, employment, human rights and -- because of the weaponization of AI tools -- privacy, policing, and defense. Apart from these potential benefits and threats, the transformative potential of AI for both good and harm will be magnified in the Global South, where existing gender and socio-economic inequalities could either be tempered or exacerbated. Given the opportunities and potential consequences of new automation and mechanization techniques and advanced analysis through machine learning and neural networks, IDRC is investing in applied research across a number of domains to advance the public good with the use of artificial intelligence for development (AI4D).