Urban world: Mapping the economic power of cities

Until now, a lack of global data at the city level has prevented companies and policymakers from tracking the evolving role of cities in the global economy and positioning their business and policy activities accordingly. To help close this "white space" in our understanding of the global economy, the McKinsey Global Institute (MGI), McKinsey & Company's business and economics research arm, has built on its extensive body of research on the urbanization of China, India, and Latin America to develop the MGI Cityscope, a database of more than 2,000 metropolitan areas around the world that we believe is the largest of its kind. By analyzing demographic, income, and household trends in these cities, the database offers actionable insights on the choices facing companies looking for new markets and policy makers seeking to improve their urban management and the alignment of their diplomatic efforts with their countries' trade interests

AI and the Opportunity for Shared Prosperity: Lessons from the History of Technology and the Economy

Recent progress in artificial intelligence (AI) marks a pivotal moment in human history. It presents the opportunity for machines to learn, adapt, and perform tasks that have the potential to assist people, from everyday activities to their most creative and ambitious projects. It also has the potential to help businesses and organizations harness knowledge, increase productivity, innovate, transform, and power shared prosperity. This tremendous potential raises two fundamental questions: (1) Will AI actually advance national and global economic transformation to benefit society at large? and (2) What issues must we get right to fully realize AI's economic value, expand prosperity and improve lives everywhere? We explore these questions by considering the recent history of technology and innovation as a guide for the likely impact of AI and what we must do to realize its economic potential to benefit society. While we do not presume the future will be entirely like that past, for reasons we will discuss, we do believe prior experience with technological change offers many useful lessons. We conclude that while progress in AI presents a historic opportunity to advance our economic prosperity and future wellbeing, its economic benefits will not come automatically and that AI risks exacerbating existing economic challenges unless we collectively and purposefully act to enable its potential and address its challenges. We suggest a collective policy agenda - involving developers, deployers and users of AI, infrastructure providers, policymakers, and those involved in workforce training - that may help both realize and harness AI's economic potential and address its risks to our shared prosperity.Comment: 37 page

Data Commons

Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC)) are vital resources for policy makers, students and researchers across different disciplines. Combining data from different sources requires the user to reconcile the differences in schemas, formats, assumptions, and more. This data wrangling is time consuming, tedious and needs to be repeated by every user of the data. Our goal with Data Commons (DC) is to help make public data accessible and useful to those who want to understand this data and use it to solve societal challenges and opportunities. We do the data processing and make the processed data widely available via standard schemas and Cloud APIs. Data Commons is a distributed network of sites that publish data in a common schema and interoperate using the Data Commons APIs. Data from different Data Commons can be joined easily. The aggregate of these Data Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can then be searched over using Natural Language questions utilizing advances in Large Language Models. This paper describes the architecture of Data Commons, some of the major deployments and highlights directions for future work

Network approach to internet bandwidth distributions

This study examines the communications networks formed by direct international Internet links, weighted by bandwidth capacity, each year over the 2002–2011 period. Specifically, we analyze changes in bandwidth distributions at country, regional, and continental levels during the period and identify network communities at these different levels. We apply an urn-based model developed with country-level data to bandwidth distributions at regional and continental levels. While the 2011 global Internet network closely resembles that of 2002, the network has become more tightly interconnected over time, and the high international bandwidth regions of Northern Europe, Northern America, and Western Europe have seen a modest decline in their share of total global bandwidth. As a consequence, international bandwidth concentration is showing a slow decline. Relative connectedness as measured by percentage of bandwidth staying within UN geographic regions is decreasing, whereas the percentage remaining within the continent has been fairly constant during the analysis period. All of this must be understood in the context of enormous total international bandwidth growth between 2002 and 2011 at all levels of analysis

An information-theoretic approach to data fusion and sensor management

The use of multi-sensor systems entails a Data Fusion and Sensor Management requirement in order to optimize the use of resources and allow the synergistic operation of sensors. To date, data fusion and sensor management have largely been dealt with separately and primarily for centralized and hierarchical systems. Although work has recently been done in distributed and decentralized data fusion, very little of it has addressed sensor management. In decentralized systems, a consistent and coherent approach is essential and the ad hoc methods used in other systems become unsatisfactory. This thesis concerns the development of a unified approach to data fusion and sensor management in multi-sensor systems in general and decentralized systems in particular, within a single consistent information-theoretic framework. Our approach is based on considering information and its gain as the main goal of multi-sensor systems. We develop a probabilistic information update paradigm from which we derive directly architectures and algorithms for decentralized data fusion and, most importantly, address sensor management. Presented with several alternatives, the question of how to make decisions leading to the best sensing configuration or actions, defines the management problem. We discuss the issues in decentralized decision making and present a normative method for decentralized sensor management based on information as expected utility. We discuss several ways of realizing the solution culminating in an iterative method akin to bargaining for a general decentralized system. Underlying this is the need for a good sensor model detailing a sensor's physical operation and the phenomenological nature of measurements vis-a-vis the probabilistic information the sensor provides. Also, implicit in a sensor management problem is the existence of several sensing alternatives such as those provided by agile or multi-mode sensors. With our application in mind, we detail such a sensor model for a novel Tracking Sonar with precisely these capabilities making it ideal for managed data fusion. As an application, we consider vehicle navigation, specifically localization and map-building. Implementation is on the OxNav vehicle (JTR) which we are currently developing. The results show, firstly, how with managed data fusion, localization is greatly speeded up compared to previous published work and secondly, how synergistic operation such as sensor-feature assignments, hand-off and cueing can be realised decentrally. This implementation provides new ways of addressing vehicle navigation, while the theoretical results are applicable to a variety of multi-sensing problems.</p

The Obesity Crisis

If trends persist, nearly half of the world’s adult population will be overweight or obese by 2030. A comprehensive intervention strategy is required to fight a scourge as damaging to the global economy as war

Race in the workplace: The Black experience in the US private sector

Advancing racial equity in companies is action-oriented work. Black workers, in particular, face challenges – from the structural inequities of geography to underrepresentation in industries that might create additional opportunity to the cultures and behaviors within their workplaces. This report, which is part of a new comprehensive series by McKinsey & Company, was produced In collaboration with the W.K. Kellogg Foundation, PolicyLink and Walmart, lifts Black American voices and shares their experiences in the U.S. private sector. The research is organized in three parts: first, a summary of Black Americans' participation in the U.S. private sector economy; second, their representation, advancement and experiences in companies; and third, recommendations and actions companies can take in response, along with additional actions a wider set of stakeholders can take to accelerate progress on diversity, equity and inclusion