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google-germinates-gemini-3.1-pro-in-ongoing-ai-model-race

Google germinates Gemini 3.1 Pro in ongoing AI model race

If you want an even better AI model, there could be reason to celebrate. Google, on Thursday, announced the release of Gemini 3.1 Pro, characterizing the model's arrival as "a step forward in core reasoning." Measured by the release cadence of machine learning models, Gemini 3.1 Pro is hard on the heels of recent model

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MULTI-evolve accelerates protein engineering with machine learning

The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20^100 possible variants-more combinations than atoms in the observable universe. Traditional engineering methods might test hundreds of variants but limit exploration to narrow regions of the sequence space. Recent machine learning approaches enable broader searches through computational

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why-cloud,-data-centers,-and-utilities-should-be-cybersecurity-partners-|-google-cloud-blog

Why cloud, data centers, and utilities should be cybersecurity partners | Google Cloud Blog

From electric vehicles to new manufacturing facilities to data centers, it’s no secret that consumer demands on energy infrastructure are spiking. At the same time, we’re seeing AI-augmented adversarial threats targeting power and energy generation on the rise, too. These dual pressures, to make energy more resilient against cyberattacks just as experts are forecasting accelerated

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Ghost in the Machine: Exposing the Hidden Personalities of AI – Neuroscience News

Summary: Large language models (LLMs) like ChatGPT and Claude are more than just text generators; they contain complex, abstract “personas” and biases buried within their code. A team of researchers has developed a revolutionary method to expose and manipulate these hidden concepts. Using an algorithm called a Recursive Feature Machine (RFM), researchers can now identify

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how-nvidia-extreme-hardware-software-co-design-delivered-a-large-inference-boost-for-sarvam-ai’s-sovereign-models-|-nvidia-technical-blog

How NVIDIA Extreme Hardware-Software Co-Design Delivered a Large Inference Boost for Sarvam AI’s Sovereign Models | NVIDIA Technical Blog

As global AI adoption accelerates, developers face a growing challenge: delivering large language model (LLM) performance that meets real-world latency and cost requirements. Running models with tens of billions of parameters in production, especially for conversational or voice-based AI agents, demands high throughput, low latency, and predictable service-level performance. For startups building sovereign AI models

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Top 10: IT Service Management Tools (ITSM)

IT Service Management (ITSM) has evolved from its reactive break-fix ticketing  origins to become the central nervous system of digital enterprise.  Because managing complex, hybrid infrastructures and distributed workforces requires more than just organised logs, autonomous, agentic AI allows companies to predict outages and self-healing endpoints.  This comes as unplanned downtime now costs large enterprises

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AI Optimization’s Impact on Use of Time, Space and Resources in Schools

Get stories like this delivered straight to your inbox. Sign up for The 74 Newsletter Class Disrupted is an education podcast featuring author Michael Horn and Futre’s Diane Tavenner in conversation with educators, school leaders, students and other members of school communities as they investigate the challenges facing the education system in the aftermath of

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opening-the-black-box:-explainable-ai-for-automated-bioturbation-analysis-in-cores-and-outcrops-–-scientific-reports

Opening the black box: explainable AI for automated bioturbation analysis in cores and outcrops – Scientific Reports

References Dalrymple, R. W. & James, N. P. Facies Models 4 (Geological Association of Canada, 2010). Pemberton, S. G. et al. Ichnology and Sedimentology of Shallow to Marginal Marine Systems: Ben Nevis and Avalon Reservoirs, Jeanne d’Arc Basin Vol. 15 (Geological Association of Canada, 2001). de Pires, R., Bonar, A., Coronado, D. D., Marfurt, K.

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The North America data center GPU market is projected to reach USD 79.81 billion by 2030 from USD 43.19 billion in 2025, at a CAGR of 13.1% during the forecast period. The North American data center GPU market is experiencing robust growth due to the rising adoption of artificial intelligence (AI) and machine learning (ML)

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