Opinion Columnist
Thomas Hale writes about institutions, innovation and the tradeoffs of technological change. His columns consider durability, accountability and the practical limits of ambitious policy. He favors arguments that take implementation as seriously as intention.
388 published articles · Showing 81–100
A new prompting paradigm, RTLC (Research, Teach-to-Learn, Critique), has significantly boosted the accuracy of Large Language Models (LLMs) when evaluating other LLMs, marking a critical step towards more reliable AI assessment arXiv CS. AI....
Read article →The digital frontier just got a bit more complicated, or perhaps, a bit more structured. Three distinct research papers, all published on arXiv CS....
Read article →The arXiv pre-print server today revealed a concerted push by AI researchers to dramatically improve the efficiency of large language models (LLMs) across multiple layers of the computational stack. This surge in optimization research, spanning hardware compilation, model compres...
Read article →While the public conversation about artificial intelligence often fixates on splashy new applications or existential threats, the real work — the kind that truly enables future innovation — quietly progresses in the mathematical depths of academic research. Today, two new arXiv p...
Read article →It appears that measuring AI 'intelligence' is about as straightforward as teaching a cat to fetch, and perhaps just as prone to misinterpretation. A series of new research papers published on arXiv CS....
Read article →While much of the digital ether fizzes with breathless pronouncements about the latest AI breakthroughs, a quieter, yet profoundly more important, set of developments is unfolding in the academic trenches. A concentrated wave of theoretical machine learning papers, all published...
Read article →While the spotlight often fixates on the ever-expanding parameter counts of large language models, a quieter, more pragmatic revolution is brewing in machine learning research. A fresh wave of papers published on arXiv points to a profound pivot: less emphasis on sheer computatio...
Read article →A flurry of research published today on arXiv CS. AI indicates a significant pivot in the pursuit of Artificial General Intelligence (AGI), challenging the prevailing dogma that endless scaling of monolithic models is the sole viable path....
Read article →In a week underscoring the delicate balance between information access, corporate accountability, and market efficiency, two distinct but related developments have put data integrity and regulatory oversight squarely in the spotlight. U....
Read article →Even as whispers of a "tougher startup market" echo through the venture capital community, defense tech firm Anduril has defied the gravity of conventional wisdom, securing a staggering $5 billion in fresh capital and doubling its valuation to $61 billion TechCrunch. This isn't m...
Read article →The opaque curtain that has long shrouded advanced scientific simulations is beginning to lift, thanks to a new agentic AI workflow. Researchers have developed GWAgent, a large language model (LLM)-based system capable of constructing interpretable analytic surrogates directly fr...
Read article →While headlines often scream about the dangers of a singular, omniscient AI, recent research suggests the true power of artificial intelligence, much like human innovation, might lie in the messy, decentralized interactions of many. Two groundbreaking papers, published on arXiv o...
Read article →While the public obsesses over gargantuan foundation models, the real architectural shifts in AI often begin quietly, in academic papers addressing fundamental flaws. Three new pre-print papers released today on arXiv CS....
Read article →The realm of artificial intelligence reasoning is witnessing a critical shift, with two new research papers from arXiv CS. AI revealing novel approaches to making AI cognition more stable and computationally efficient....
Read article →The prevailing wisdom dictates that cutting-edge artificial intelligence, particularly large language models (LLMs) and sophisticated recurrent neural networks (RNNs), must consume power and computational resources with the voracity of a small data center. This assumption, while ...
Read article →A trio of research preprints, all released on May 13, 2026, on arXiv, signals a quiet but profound shift in how artificial intelligence tackles the sheer volume and intricate nature of modern data. These papers demonstrate advancements in data compression, classification, and com...
Read article →A flurry of academic preprints released today on arXiv CS. AI signals a significant, if understated, shift in how artificial intelligence systems interact with and understand our increasingly complex digital and physical worlds....
Read article →For those who envision decentralized AI as a pristine landscape, recent academic papers released today on arXiv offer a refreshing dose of reality: the path to robust federated learning is paved with complex technical challenges, and the market for solutions is booming. These stu...
Read article →The persistent fear that artificial intelligence will render software engineers obsolete appears, once again, to be a triumph of imagination over evidence. New research emerging from arXiv on May 13, 2026, suggests the opposite: AI is not merely automating coding, but fundamental...
Read article →While the digital chatter often fixates on AI's boundless potential, a pair of recent arXiv papers offers a bracing reminder that even our most sophisticated models occasionally forget the laws of physics. It appears generative AI, for all its textual dexterity and visual flair, ...
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