Enterprise AI hinges on high-accuracy outputs, requiring better data context, unified architectures, and rigorous measurement frameworks, says Bavesh Patel, senior vice president at Databricks, and Rajan Padmanabhan, unit technology officer at Infosys. Artificial intelligence may be dominating boardroom agendas, but many enterprises are discovering that the biggest obstacle to meaningful adoption is the state of their data. While consumer-facing AI tools have dazzled users with speed and ease, enterprise leaders are discovering that deploying AI at scale requires something far less glamorous but far more consequential: data infrastructure that is unified, governed, and fit for purpose. That gap between AI ambition and enterprise readiness is becoming one of the defining challenges of this next phase of digital transformation. As Bavesh Patel, senior vice president of Databricks, puts it, "the quality of that AI and how effective that AI is, is really dependent on information in your ...

A modern data fabric makes it possible to turn existing enterprise knowledge into a trusted foundation for AI. Artificial intelligence is moving quickly in the enterprise, from experimentation to everyday use. Organizations are deploying copilots, agents, and predictive systems across finance, supply chains, human resources, and customer operations. By the end of 2025, half of companies used AI in at least three business functions, according to a recent survey. But as AI becomes embedded in core workflows, business leaders are discovering that the biggest obstacle is not model performance or computing power but the quality and the context of the data on which those systems rely. AI essentially introduces a new requirement: Systems must not only access data -- they must understand the business context behind it.

Hierarchical clustering is a data analysis method that has been used for decades. Despite its widespread use, the method has an underdeveloped analytical foundation. Having a well understood foundation would both support the currently used methods and help guide future improvements. The goal of this paper is to give an analytic framework to better understand observations seen in practice. This paper considers the dual of a problem framework for hierarchical clustering introduced by Dasgupta.

Epstein's shadow: Why Bill Gates pulled out of Modi's AI summit Microsoft founder Bill Gates has cancelled his keynote speech at India's flagship AI summit just hours before he was due to take the stage on Thursday. Gates, who has faced renewed scrutiny over his past ties to the late sex offender Jeffrey Epstein, withdrew to "ensure the focus remains on the AI Summit's key priorities", the Gates Foundation said in a statement. India's Prime Minister Narendra Modi had billed the summit as an opportunity for India to shape the future of AI, drawing high-profile attendees, including French President Emmanuel Macron and Brazilian President Luiz Inacio Lula da Silva. Instead, it has been dogged by controversy, from Gates's abrupt exit to an incident in which an Indian university tried to pass off a Chinese-made robotic dog as its own innovation. So, what exactly went wrong at India's flagship AI gathering and why has it drawn such intense scrutiny?

From threat modeling to encrypted collaboration apps, we've collected experts' tips and tools for safely and effectively building a group--even while being targeted and tracked by the powerful. Rarely in modern US history have so many Americans opposed the actions of the federal government with so little hope for a top-down political solution. That's left millions of people seeking a bottom-up approach to resistance: grassroots organizing. Yet as Americans assemble their own movements to protect and support immigrants, push back against the Department of Homeland Security's dangerous incursions into cities, and protest for civil rights and policy changes, they face a federal government that possesses vast surveillance powers and sweeping cooperation from the Silicon Valley companies that hold Americans' data. That means political, social, and economic organizing presents a risky dilemma. How do you bring people of all ages, backgrounds, and technical abilities into a mass movement without exposing them to monitoring and targeting by a government--and in particular Immigration and Customs Enforcement and Customs and Border Protection, agencies with paramilitary ambitions, a tendency to break the law, and more funding than some countries' militaries. Organizing safely in an age of surveillance increasingly requires not only technical security know-how, but also a tricky balance between secrecy and openness, says Eva Galperin, the director of cybersecurity at the Electronic Frontier Foundation, a nonprofit focused on digital civil liberties.

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