Paper by xiaohui jiang and Masaru Yarime: “The Chinese government has been playing an important role in stimulating innovation among Chinese enterprises. Small and medium-sized enterprises (SMEs), with their limited internal resources, particularly face a severe challenge in implementing innovation activities that depend upon data, funding sources, and talents. However, the rapidly developing smart city projects in China, where significant amounts of data are available from various sophisticated devices and generous funding opportunities, are providing rich opportunities for SMEs to explore data-driven innovation. Chinese Governments are trying to actively engage SMEs in the process of smart city construction. When cooperating with the government, the availability of and access to data involved in the government contracts and the ability required in the project help SMEs to train and improve their innovation ability.In this article, we intend to address how obtaining different types of government contracts (equipment supply, platform building, data analysis) can influence firms’ performance on innovation. Obtaining different types of government contracts are regarded as receiving different types of treatments. The hypothesis is that the data analysis type of contracts has a larger positive influence on improving the innovation ability compared to the platform building type, while the platform building type of contracts can have a larger influence compared to equipment supply. Focusing on the case of SMEs in China, this research aims to shed light on how the government and enterprises collaborate in smart city projects to facilitate innovation. Data on companies’ registered capital, industry, and software products from 1990– 2020 is compiled from the Tianyancha website. A panel dataset is established with the key characteristics of the SMEs, software productions, and their record on government contracts. Based on the company’s basic characteristics, we divided six pairs of treatment and control groups using propensity score matching (PSM) and then ran a validity test to confirm that the result of the division was reliable. Then based on the established control and treatment pairs, we run a difference-in-difference (DID) model, and the result supports our original hypothesis. The statistics shows mixed result, Hypothesis 1 which indicates that companies obtaining data analysis contracts will experience greater innovation improvements compared to those with platform-building contracts, is partially confirmed when using software copyright as an outcome variable. However, when using patent data as an indicator, the statistics is insignificant. Hypothesis 2, which posits that companies with platform-building contracts will show greater innovation improvements than those with equipment supply contracts, is not supported. Hypothesis 3 which suggests that companies receiving government contracts will have higher innovation outputs than those without, is confirmed. The case studies later have revealed the complex mechanisms behind the scenario…(More)”.
Why Big Tech is threatened by a global push for data sovereignty
Article by Damilare Dosunmu: “A battle for data sovereignty is brewing from Africa to Asia.
Developing nations are challenging Big Tech’s decades-long hold on global data by demanding that their citizens’ information be stored locally. The move is driven by the realization that countries have been giving away their most valuable resource for tech giants to build a trillion-dollar market capitalization.
In April, Nigeria asked Google, Microsoft, and Amazon to set concrete deadlines for opening data centers in the country. Nigeria has been making this demand for about four years, but the companies have so far failed to fulfill their promises. Now, Nigeria has set up a working group with the companies to ensure that data is stored within its shores.
“We told them no more waivers — that we need a road map for when they are coming to Nigeria,” Kashifu Inuwa Abdullahi, director-general of Nigeria’s technology regulator, the National Information Technology Development Agency, told Rest of World.
Other developing countries, including India, South Africa, and Vietnam, have also implemented similar rules demanding that companies store data locally. India’s central bank requires payment companies to host financial data within the country, while Vietnam mandates that foreign telecommunications, e-commerce, and online payments providers establish local offices and keep user data within its shores for at least 24 months…(More)”.
Mapping the Unmapped
Article by Maddy Crowell: “…Most of St. Lucia, which sits at the southern end of an archipelago stretching from Trinidad and Tobago to the Bahamas, is poorly mapped. Aside from strips of sandy white beaches that hug the coastline, the island is draped with dense rainforest. A few green signs hang limp and faded from utility poles like an afterthought, identifying streets named during more than a century of dueling British and French colonial rule. One major road, Micoud Highway, runs like a vein from north to south, carting tourists from the airport to beachfront resorts. Little of this is accurately represented on Google Maps. Almost nobody uses, or has, a conventional address. Locals orient one another with landmarks: the red house on the hill, the cottage next to the church, the park across from Care Growell School.
Our van wound off Micoud Highway into an empty lot beneath the shade of a banana tree. A dog panted, belly up, under the hot November sun. The group had been recruited by the Humanitarian OpenStreetMap Team, or HOT, a nonprofit that uses an open-source data platform called OpenStreetMap to create a map of the world that resembles Google’s with one key exception: Anyone can edit it, making it a sort of Wikipedia for cartographers.
The organization has an ambitious goal: Map the world’s unmapped places to help relief workers reach people when the next hurricane, fire, or other crisis strikes. Since its founding in 2010, some 340,000 volunteers around the world have been remotely editing OpenStreetMap to better represent the Caribbean, Southeast Asia, parts of Africa and other regions prone to natural disasters or humanitarian emergencies. In that time, they have mapped more than 2.1 million miles of roads and 156 million buildings. They use aerial imagery captured by drones, aircraft, or satellites to help trace unmarked roads, waterways, buildings, and critical infrastructure. Once this digital chart is more clearly defined, field-mapping expeditions like the one we were taking add the names of every road, house, church, or business represented by gray silhouettes on their paper maps. The effort fine-tunes the places that bigger players like Google Maps get wrong — or don’t get at all…(More)”
Why are “missions” proving so difficult?
Article by James Plunkett: “…Unlike many political soundbites, however, missions have a strong academic heritage, drawing on years of work from Mariana Mazzucato and others. They gained support as a way for governments to be less agnostic about the direction of economic growth and its social implications, most obviously on issues like climate change, while still avoiding old-school statism. The idea is to pursue big goals not with top-down planning but with what Mazzucato calls ‘orchestration’, using the power of the state to drive innovation and shape markets to an outcome.
For these reasons, missions have proven increasingly popular with governments. They have been used by administrations from the EU to South Korea and Finland, and even in Britain under Theresa May, although she didn’t have time to make them stick.
Despite these good intentions and heritage, however, missions are proving difficult. Some say the UK government is “mission-washing” – using the word, but not really adopting the ways of working. And although missions were mentioned in the spending review, their role was notably muted when compared with the central position they had in Labour’s manifesto.
Still, it would seem a shame to let missions falter without interrogating the reasons. So why are missions so difficult? And what, if anything, could be done to strengthen them as Labour moves into year two? I’ll touch on four characteristics of missions that jar with Whitehall’s natural instincts, and in each case I’ll ask how it’s going, and how Labour could be bolder…(More)”.
China is building an entire empire on data
The Economist: “CHINA’S 1.1BN internet users churn out more data than anyone else on Earth. So does the country’s vast network of facial-recognition cameras. As autonomous cars speed down roads and flying ones criss-cross the skies, the quality and value of the information flowing from emerging technologies will soar. Yet the volume of data is not the only thing setting China apart. The government is also embedding data management into the economy and national security. That has implications for China, and holds lessons for democracies.
China’s planners see data as a factor of production, alongside labour, capital and land. Xi Jinping, the president, has called data a foundational resource “with a revolutionary impact” on international competition. The scope of this vision is unparalleled, affecting everything from civil liberties to the profits of internet firms and China’s pursuit of the lead in artificial intelligence.
Mr Xi’s vision is being enacted fast. In 2021 China released rules modelled on Europe’s General Data Protection Regulation (GDPR). Now it is diverging quickly from Western norms. All levels of government are to marshal the data resources they have. A sweeping project to assess the data piles at state-owned firms is under way. The idea is to value them as assets, and add them to balance-sheets or trade them on state-run exchanges. On June 3rd the State Council released new rules to compel all levels of government to share data.
Another big step is a digital ID, due to be launched on July 15th. Under this, the central authorities could control a ledger of every person’s websites and apps. Connecting someone’s name with their online activity will become harder for the big tech firms which used to run the system. They will see only an anonymised stream of digits and letters. Chillingly, however, the ledger may one day act as a panopticon for the state.
China’s ultimate goal appears to be to create an integrated national data ocean, covering not just consumers but industrial and state activity, too. The advantages are obvious, and include economies of scale for training AI models and lower barriers to entry for small new firms…(More)”.
Trends in AI Supercomputers
Paper by Konstantin F. Pilz, James Sanders, Robi Rahman, and Lennart Heim: “Frontier AI development relies on powerful AI supercomputers, yet analysis of these systems is limited. We create a dataset of 500 AI supercomputers from 2019 to 2025 and analyze key trends in performance, power needs, hardware cost, ownership, and global distribution. We find that the computational performance of AI supercomputers has doubled every nine months, while hardware acquisition cost and power needs both doubled every year. The leading system in March 2025, xAI’s Colossus, used 200,000 AI chips, had a hardware cost of $7B, and required 300 MW of power, as much as 250,000 households. As AI supercomputers evolved from tools for science to industrial machines, companies rapidly expanded their share of total AI supercomputer performance, while the share of governments and academia diminished. Globally, the United States accounts for about 75% of total performance in our dataset, with China in second place at 15%. If the observed trends continue, the leading AI supercomputer in 2030 will achieve 2×1022 16-bit FLOP/s, use two million AI chips, have a hardware cost of $200 billion, and require 9 GW of power. Our analysis provides visibility into the AI supercomputer landscape, allowing policymakers to assess key AI trends like resource needs, ownership, and national competitiveness…(More)”.
What Counts as Discovery?
Essay by Nisheeth Vishnoi: “Long before there were “scientists,” there was science. Across every continent, humans developed knowledge systems grounded in experience, abstraction, and prediction—driven not merely by curiosity, but by a desire to transform patterns into principles, and observation into discovery. Farmers tracked solstices, sailors read stars, artisans perfected metallurgy, and physicians documented plant remedies. They built calendars, mapped cycles, and tested interventions—turning empirical insight into reliable knowledge.
From the oral sciences of Africa, which encoded botanical, medical, and ecological knowledge across generations, to the astronomical observatories of Mesoamerica, where priests tracked solstices, eclipses, and planetary motion with remarkable accuracy, early human civilizations sought more than survival. In Babylon, scribes logged celestial movements and built predictive models; in India, the architects of Vedic altars designed ritual structures whose proportions mirrored cosmic rhythms, embedding arithmetic and geometry into sacred form. Across these diverse cultures, discovery was not a separate enterprise—it was entwined with ritual, survival, and meaning. Yet the tools were recognizably scientific: systematic observation, abstraction, and the search for hidden order.
This was science before the name. And it reminds us that discovery has never belonged to any one civilization or era. Discovery is not intelligence itself, but one of its sharpest expressions—an act that turns perception into principle through a conceptual leap. While intelligence is broader and encompasses adaptation, inference, and learning in various forms (biological, cultural, and even mechanical), discovery marks those moments when something new is framed, not just found.
Life forms learn, adapt, and even innovate. But it is humans who turned observation into explanation, explanation into abstraction, and abstraction into method. The rise of formal science brought mathematical structure and experiment, but it did not invent the impulse to understand—it gave it form, language, and reach.
And today, we stand at the edge of something unfamiliar: the possibility of lifeless discoveries. Artificial Intelligence machines, built without awareness or curiosity, are beginning to surface patterns and propose explanations, sometimes without our full understanding. If science has long been a dialogue between the world and living minds, we are now entering a strange new phase: abstraction without awareness, discovery without a discoverer.
AI systems now assist in everything from understanding black holes to predicting protein folds and even symbolic equation discovery. They parse vast datasets, detect regularities, and generate increasingly sophisticated outputs. Some claim they’re not just accelerating research, but beginning to reshape science itself—perhaps even to discover.
But what truly counts as a scientific discovery? This essay examines that question…(More)”
The Global A.I. Divide
Article by Adam Satariano and Paul Mozur: “Last month, Sam Altman, the chief executive of the artificial intelligence company OpenAI, donned a helmet, work boots and a luminescent high-visibility vest to visit the construction site of the company’s new data center project in Texas.
Bigger than New York’s Central Park, the estimated $60 billion project, which has its own natural gas plant, will be one of the most powerful computing hubs ever created when completed as soon as next year.
Around the same time as Mr. Altman’s visit to Texas, Nicolás Wolovick, a computer science professor at the National University of Córdoba in Argentina, was running what counts as one of his country’s most advanced A.I. computing hubs. It was in a converted room at the university, where wires snaked between aging A.I. chips and server computers.
“Everything is becoming more split,” Dr. Wolovick said. “We are losing.”
Artificial intelligence has created a new digital divide, fracturing the world between nations with the computing power for building cutting-edge A.I. systems and those without. The split is influencing geopolitics and global economics, creating new dependencies and prompting a desperate rush to not be excluded from a technology race that could reorder economies, drive scientific discovery and change the way that people live and work.
The biggest beneficiaries by far are the United States, China and the European Union. Those regions host more than half of the world’s most powerful data centers, which are used for developing the most complex A.I. systems, according to data compiled by Oxford University researchers. Only 32 countries, or about 16 percent of nations, have these large facilities filled with microchips and computers, giving them what is known in industry parlance as “compute power.”..(More)”.
AI is supercharging war. Could it also help broker peace?
Article by Tina Amirtha: “Can we measure what is in our hearts and minds, and could it help us end wars any sooner? These are the questions that consume entrepreneur Shawn Guttman, a Canadian émigré who recently gave up his yearslong teaching position in Israel to accelerate a path to peace—using an algorithm.
Living some 75 miles north of Tel Aviv, Guttman is no stranger to the uncertainties of conflict. Over the past few months, miscalculated drone strikes and imprecise missile targets—some intended for larger cities—have occasionally landed dangerously close to his town, sending him to bomb shelters more than once.
“When something big happens, we can point to it and say, ‘Right, that happened because five years ago we did A, B, and C, and look at its effect,’” he says over Google Meet from his office, following a recent trip to the shelter. Behind him, souvenirs from the 1979 Egypt-Israel and 1994 Israel-Jordan peace treaties are visible. “I’m tired of that perspective.”
The startup he cofounded, Didi, is taking a different approach. Its aim is to analyze data across news outlets, political discourse, and social media to identify opportune moments to broker peace. Inspired by political scientist I. William Zartman’s “ripeness” theory, the algorithm—called the Ripeness Index—is designed to tell negotiators, organizers, diplomats, and nongovernmental organizations (NGOs) exactly when conditions are “ripe” to initiate peace negotiations, build coalitions, or launch grassroots campaigns.
During ongoing U.S.-led negotiations over the war in Gaza, both Israel and Hamas have entrenched themselves in opposing bargaining positions. Meanwhile, Israel’s traditional allies, including the U.S., have expressed growing frustration over the war and the dire humanitarian conditions in the enclave, where the threat of famine looms.
In Israel, Didi’s data is already informing grassroots organizations as they strategize which media outlets to target and how to time public actions, such as protests, in coordination with coalition partners. Guttman and his collaborators hope that eventually negotiators will use the model’s insights to help broker lasting peace.
Guttman’s project is part of a rising wave of so-called PeaceTech—a movement using technology to make negotiations more inclusive and data-driven. This includes AI from Hala Systems, which uses satellite imagery and data fusion to monitor ceasefires in Yemen and Ukraine. Another AI startup, Remesh, has been active across the Middle East, helping organizations of all sizes canvas key stakeholders. Its algorithm clusters similar opinions, giving policymakers and mediators a clearer view of public sentiment and division.
A range of NGOs and academic researchers have also developed digital tools for peacebuilding. The nonprofit Computational Democracy Project created Pol.is, an open-source platform that enables citizens to crowdsource outcomes to public debates. Meanwhile, the Futures Lab at the Center for Strategic and International Studies built a peace agreement simulator, complete with a chart to track how well each stakeholder’s needs are met.
Guttman knows it’s an uphill battle. In addition to the ethical and privacy concerns of using AI to interpret public sentiment, PeaceTech also faces financial hurdles. These companies must find ways to sustain themselves amid shrinking public funding and a transatlantic surge in defense spending, which has pulled resources away from peacebuilding initiatives.
Still, Guttman and his investors remain undeterred. One way to view the opportunity for PeaceTech is by looking at the economic toll of war. In its Global Peace Index 2024, the Institute for Economics and Peace’s Vision of Humanity platform estimated that economic disruption due to violence and the fear of violence cost the world $19.1 trillion in 2023, or about 13 percent of global GDP. Guttman sees plenty of commercial potential in times of peace as well.
“Can we make billions of dollars,” Guttman asks, “and save the world—and create peace?” ..(More)”….See also Kluz Prize for PeaceTech (Applications Open)
Sentinel Cities for Public Health
Article by Jesse Rothman, Paromita Hore & Andrew McCartor: “In 2017, a New York City health inspector visited the home of a 5-year-old child with an elevated blood lead level. With no sign of lead paint—the usual suspect in such cases—the inspector discovered dangerous levels of lead in a bright yellow container of “Georgian Saffron,” a spice obtained in the family’s home country. It was not the first case associated with the use of lead-containing Georgian spices—the NYC Health Department shared their findings with authorities in Georgia, which catalyzed a survey of children’s blood lead levels in Georgia, and led to increased regulatory enforcement and education. Significant declines in spice lead levels in the country have had ripple effects in NYC also: not only a drop in spice samples from Georgia containing detectable lead but also a significant reduction in blood lead levels among NYC children of Georgian ancestry.
This wasn’t a lucky break—it was the result of a systematic approach to transform local detection into global impact. Findings from local NYC surveillance are, of course, not limited to Georgian spices. Surveillance activities have identified a variety of lead-containing consumer products from around the world, from cosmetics and medicines to ceramics and other goods. Routinely surveying local stores for lead-containing products has resulted in the removal of over 30,000 hazardous consumer products from NYC store shelves since 2010.
How can we replicate and scale up NYC’s model to address the global crisis of lead poisoning?…(More)”.