As the everyday use of AI has exploded in recent years, so have the energy demands of the computing infrastructure that supports it. But the environmental toll of these large data centers, which suck ...

The amount of capital pouring into AI data center projects is staggering. Last week, Microsoft, Alphabet, Meta, and Amazon reported their 2025 capital expenditures would total roughly $370 billion, ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

Google is turning its vast public data trove into a goldmine for AI with the debut of the Data Commons Model Context Protocol (MCP) Server — enabling developers, data scientists, and AI agents to ...

What if the programming language you rely on most is on the brink of a transformation? For millions of developers worldwide, Python is not just a tool, it’s a cornerstone of their craft, powering ...

Companies across sectors have invested in AI at some of their new manufacturing sites. AI can help to increase production, predict machine breakdowns, and forecast demand. This article is part of "How ...

In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...

Welcome to the Data Structures and Algorithms Repository! My aim for this project is to serve as a comprehensive collection of problems and solutions implemented in Python, aimed at mastering ...

Explores foundational computer science concepts essential for graduate level coursework. Examines discrete mathematics principles, including probabilities and combinations, along with proof techniques ...

Two data centers in San Antonio have used 463 million gallons of water in the past two years. The Alamo City facilities, owned by Microsoft and the Army Corps, have drained water from the surrounding ...

Computer science involves much more than writing code. It blends technical knowledge —like programming, algorithms and data systems — with soft skills, such as communication and problem-solving.