Science and Technology

How is liquid cooling evolving to handle AI data center heat loads?

Next-Gen Liquid Cooling for AI Data Center Heat

Artificial intelligence workloads are transforming data centers into extremely dense computing environments. Training large language models, running real-time inference, and supporting accelerated analytics rely heavily on GPUs, TPUs, and custom AI accelerators that consume far more power per rack than traditional servers. While a conventional enterprise rack once averaged 5 to 10 kilowatts, modern AI racks can exceed 40 kilowatts, with some hyperscale deployments targeting 80 to 120 kilowatts per rack.This surge in power density directly translates into heat. Traditional air cooling systems, which depend on large volumes of chilled air, struggle to remove heat efficiently at these levels. As…
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What techniques are improving AI reliability and reducing hallucinations?

Secure AI: Techniques to Prevent Hallucinations and Increase Reliability

Artificial intelligence systems, especially large language models, can generate outputs that sound confident but are factually incorrect or unsupported. These errors are commonly called hallucinations. They arise from probabilistic text generation, incomplete training data, ambiguous prompts, and the absence of real-world grounding. Improving AI reliability focuses on reducing these hallucinations while preserving creativity, fluency, and usefulness.Higher-Quality and Better-Curated Training DataImproving the training data for AI systems stands as one of the most influential methods, since models absorb patterns from extensive datasets, and any errors, inconsistencies, or obsolete details can immediately undermine the quality of their output.Data filtering and deduplication: Removing…
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How do investors evaluate liquidity risk in private markets?

Reducing AI Hallucinations: Key Reliability Techniques

Artificial intelligence systems, particularly large language models, may produce responses that sound assured yet are inaccurate or lack evidence. These mistakes, widely known as hallucinations, stem from probabilistic text generation, limited training data, unclear prompts, and the lack of genuine real‑world context. Efforts to enhance AI depend on minimizing these hallucinations while maintaining creativity, clarity, and practical value.Higher-Quality and Better-Curated Training DataOne of the most impactful techniques is improving the data used to train AI systems. Models learn patterns from massive datasets, so inaccuracies, contradictions, or outdated information directly affect output quality.Data filtering and deduplication: By eliminating inconsistent, repetitive, or…
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How are enterprises adopting retrieval-augmented generation for knowledge work?

RAG’s Impact on Enterprise Knowledge Work Adoption

Retrieval-augmented generation, often shortened to RAG, combines large language models with enterprise knowledge sources to produce responses grounded in authoritative data. Instead of relying solely on a model’s internal training, RAG retrieves relevant documents, passages, or records at query time and uses them as context for generation. Enterprises are adopting this approach to make knowledge work more accurate, auditable, and aligned with internal policies.Why enterprises are moving toward RAGEnterprises face a recurring tension: employees need fast, natural-language answers, but leadership demands reliability and traceability. RAG addresses this tension by linking answers directly to company-owned content.Key adoption drivers include:Accuracy and trust:…
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How are enterprises adopting retrieval-augmented generation for knowledge work?

The Rise of RAG in Enterprise Knowledge Work

Retrieval-augmented generation, often shortened to RAG, combines large language models with enterprise knowledge sources to produce responses grounded in authoritative data. Instead of relying solely on a model’s internal training, RAG retrieves relevant documents, passages, or records at query time and uses them as context for generation. Enterprises are adopting this approach to make knowledge work more accurate, auditable, and aligned with internal policies.Why enterprises are moving toward RAGEnterprises face a recurring tension: employees need fast, natural-language answers, but leadership demands reliability and traceability. RAG addresses this tension by linking answers directly to company-owned content.Key adoption drivers include:Accuracy and trust:…
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Sleep curiosities: why we dream and what it’s for

Curious About Sleep? Discover Why We Dream

Dreaming is a nearly universal human experience, with most individuals drifting into several dreams each night, although what they see, how vivid it feels, and what they later remember can differ greatly. Researchers investigate dreams to explore how the brain handles memory, emotion, creativity, and overall activity. Although no single, definitive explanation clarifies why dreaming occurs, a growing body of evidence from neurobiology, psychology, evolutionary perspectives, and clinical research suggests a multifaceted set of purposes and underlying processes.How the brain operates while dreamingDreams are typically most intense during rapid eye movement (REM) sleep, yet they can also emerge throughout non-REM…
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How are enterprises adopting retrieval-augmented generation for knowledge work?

RAG’s Impact on Enterprise Knowledge Work Adoption

Retrieval-augmented generation, commonly known as RAG, merges large language models with enterprise information sources to deliver answers anchored in reliable data. Rather than depending only on a model’s internal training, a RAG system pulls in pertinent documents, excerpts, or records at the moment of the query and incorporates them as contextual input for the response. Organizations are increasingly using this method to ensure that knowledge-related tasks become more precise, verifiable, and consistent with internal guidelines.Why enterprises are increasingly embracing RAGEnterprises face a recurring tension: employees need fast, natural-language answers, but leadership demands reliability and traceability. RAG addresses this tension by…
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New experiments show Earth’s core may hold vast ‘oceans’ of an essential element for life

Scientists’ New Experiments Point to Vast ‘Oceans’ of Essential Life Element in Earth’s Core

Earth’s core may contain vast hidden reserves of hydrogen, reshaping theories about planet’s water origins. Beneath our feet lies a hidden reservoir that could dwarf all of Earth’s oceans. The discovery could transform our understanding of how Earth formed and where its water came from.Far below the crust and mantle, at depths unreachable by drilling technology, Earth’s core remains one of the least accessible regions of our planet. Yet new scientific findings suggest that this remote and extreme environment may hold an extraordinary secret: a vast store of hydrogen potentially equivalent to several times the volume contained in all of…
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New experiments show Earth’s core may hold vast ‘oceans’ of an essential element for life

Scientists’ New Experiments Point to Vast ‘Oceans’ of Essential Life Element in Earth’s Core

Earth’s core might harbor immense concealed stores of hydrogen, a possibility that could overturn long‑standing ideas about the planet’s water origins, with a hidden cache beneath the surface potentially surpassing the volume of all existing oceans.This finding may radically shift current views of Earth’s formation and the true source of its water.Far below the crust and mantle, at depths unreachable by drilling technology, Earth’s core remains one of the least accessible regions of our planet. Yet new scientific findings suggest that this remote and extreme environment may hold an extraordinary secret: a vast store of hydrogen potentially equivalent to several…
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