Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
This paper argues that the current AI boom is not just a software story but a massive physical infrastructure buildout, requiring huge investments in data centers, power systems, cooling, and specialized chips. It estimates that U.S. data center capacity could expand by 200 GW from 2026 to 2032, implying about $8.2 trillion in investment and a major shift toward externally financed, increasingly complex capital structures.
his review paper examines AI-based predictive cooling in data centers and edge devices, covering air, liquid, immersion, spray, and hybrid cooling systems alongside control strategies such as PID, model predictive control, and reinforcement learning. It also broadens the discussion to thermochemical process control, arguing that AI, machine learning, and deep learning could improve energy efficiency, adaptive control, and sustainability across thermal-management systems.
This SSRN preprint examined how first data center entry affects U.S. local economies and found that activation was associated with higher employment, wages, business establishments, median household income, and building permit activity. The gains were concentrated in metropolitan counties, tended to be larger for Big Tech and clustered entries, and were accompanied by evidence of higher electricity prices where utility-territory overlap allowed cleaner identification.
A Vanderbilt policy paper argues that today’s AI investment boom could trigger not just a tech bubble burst but a broader economic crash due to circular financing, opaque debt, and systemic interdependence across the AI sector. It urges Congress to prepare now with structural reforms, including worker protections, a public cloud built from stranded data centers, tighter financial rules, and bans on surveillance-based business
Researchers at the University of Cambridge developed a hafnium oxide-based memristor that mimics brain-like learning while dramatically lowering the power needed for AI computation. The device offers ultra-low-power switching, stable analog-like states, and neuromorphic capabilities that could help make future AI hardware more efficient and sustainable.
Geologists at KIT developed a machine-learning method that predicts rock porosity and permeability from thin-section microscopy, offering a faster and more scalable way to assess reservoir quality.
Trained on data from 51 wells across central Europe, the models showed strong accuracy and could help cut costs in geoenergy, gas storage, and carbon storage projects.
Researchers developed an AI-driven discovery pipeline called AAPSI that designed and experimentally validated a new photosensitizer, HB4Ph, with strong singlet oxygen generation and red-shifted light absorption for photodynamic therapy. The study also introduced a large public database of photosensitizer-solvent pairs and showed that AI can speed multi-objective molecular design from years to days.
A new research chapter argues that artificial intelligence could help social entrepreneurs overcome persistent funding barriers by improving how investors and lenders assess financial risk, social impact, and business potential. The authors suggest that AI-driven fintech tools, impact data, and alternative funding models may create more inclusive and transparent pathways to finance for mission-led enterprises.
Assistant Professor Gaurav Malhotra studies how human cognition, perception, and decision-making can both inform and be explained by artificial intelligence, blending psychology, neuroscience, and computational modeling. At UAlbany, his research explores how humans adapt to complex environments, manage cognitive effort, and represent the world efficiently, with implications for safer and more energy-efficient AI systems.
Researchers at Rice University brought together international experts to explore how artificial intelligence and machine learning could help the Deep Underground Neutrino Experiment analyze vast datasets, detect rare signals, and improve detector operations. The workshop highlighted how AI could accelerate neutrino research by strengthening simulation, monitoring, and data management across one of the world’s most ambitious physics collaborations.
A study shows that vision-enabled AI medical scribes combining audio and visual data significantly improve clinical documentation accuracy, capturing key medication details often missed by audio-only systems. The findings suggest multimodal AI could reduce clinician workload and errors, while still requiring human oversight for safe implementation.
Researchers used AI simulations to reconstruct the likely rules of an ancient Roman board game from a limestone artefact, identifying it as a strategic “blocking” game. The study demonstrates how AI can decode archaeological mysteries by linking wear patterns to plausible gameplay mechanics.
A new study finds that failures in human–AI collaboration often stem from misalignment in how tasks, roles, and expectations are shared, rather than limitations in AI capability alone. The research highlights “hybrid cognitive alignment” as a dynamic process essential for effective teamwork, requiring ongoing adaptation, training, and calibrated trust.
A new benchmarking study shows that even advanced AI models achieve only moderate accuracy when generating structured code outputs, with performance dropping further in complex multimodal tasks.
The findings highlight persistent limitations in reliability and consistency, reinforcing the need for human oversight in AI-assisted software development.
More than half of U.S. adolescents reported using AI “nudification” tools, with substantial proportions also experiencing non-consensual creation and sharing of sexualized images. The findings highlight widespread normalization of AI-driven sexual image manipulation among teens, raising significant concerns around consent, exploitation, and digital harm.
A novel preprocessing framework inspired by bird flocking organizes sentences into structured clusters, enabling large language models to generate more accurate and less redundant summaries. By reducing noise and preserving key information, the approach significantly improves factual fidelity across thousands of documents.
Artificial intelligence is reshaping the graduate job market by reducing demand for routine entry-level roles while increasing competition and expectations for AI literacy. Rather than eliminating jobs entirely, AI is transforming career pathways, emphasizing technical fluency, domain expertise, and uniquely human skills.
Artificial intelligence is reshaping environmental science by integrating large, complex datasets to enable predictive, system-level insights across water, soil, air, and waste systems. It is evolving from a supporting analytical tool into an active partner in scientific discovery, enabling real-time monitoring and more precise environmental management.
Researchers found that AI-powered conversational toys may support language development but can misinterpret emotions, disrupt social play, and encourage children to form potentially misleading emotional bonds.
Researchers evaluated leading AI weather models and found they can accurately predict tropical cyclone tracks but often struggle to reproduce the physical wind structures that determine storm impacts.
The findings highlight both the promise of rapid AI forecasting and the need for continued scientific oversight to ensure physically realistic storm simulations.
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