IEA predicts surge in AI energy needs, but downplays climate impact. By 2030, US data processing for AI may surpass energy use in heavy industries.
Amazon Bedrock Agents streamline generative AI app development by breaking down tasks and using FMs. Human-in-the-loop interaction ensures safe and effective agent operations, with HITL patterns for validation.
Organizations are adopting a multi-LLM approach for generative AI applications, allowing for more versatile and efficient models tailored to specific tasks and requirements. Implementing effective multi-LLM routing is key to directing user prompts to the right LLM for diverse use cases, from text generation to complex analysis, across different domains of expertise.
Amazon, Google, and Microsoft are constructing datacentres in water-scarce regions worldwide, potentially exacerbating existing water scarcity issues. With support from Donald Trump, the tech giants plan to expand their operations, raising concerns about the impact on local populations.
Catboost introduces a new method of calculating the Target Statistic for categorical variables, avoiding issues like sparsity and memory problems. By replacing one-hot encoding with a smoothed average, Catboost provides a practical solution for real-world tasks.
Donald Trump signs executive orders to boost coal industry, sparking environmentalist backlash over impact on climate change. Environmentalists criticize move as regressive, claiming it will increase costs for consumers and hinder progress towards cleaner energy sources.
Organizations turn to synthetic data to navigate privacy regulations and data scarcity in AI development. Amazon Bedrock offers secure, compliant, and high-quality synthetic data generation for various industries, addressing challenges and unlocking the potential of data-driven processes.
ML models need to run in a production environment, which may differ from the local machine. Docker containers help ensure models can run anywhere, improving reproducibility and collaboration for Data Scientists.
Transformer-based LLMs have advanced in tasks, but remain black boxes. Anthropic's new paper on circuit tracing aims to reveal LLMs' internal logic for interpretability.
Australian team revives US composer Alvin Lucier, sparking AI authorship debate. Eerie symphony plays without musicians, only a fragment of a performer remains.
Automated Valuation Models (AVMs) use AI to predict home values, but uncertainty can lead to costly mistakes. AVMU quantifies prediction reliability, aiding smarter decisions in real estate purchases.
Evolutionary optimization training for Kernel Ridge Regression shows promise but caps at 90-93% accuracy due to scalability issues. Traditional matrix inverse technique outperforms in accuracy and speed.
John Hinkley compares AI to a piano in creative industries. Reform UK leaflet grumbles about bin collection, prompting nerdish suggestions.
Amazon Bedrock offers high-performing foundation models and end-to-end RAG workflows for creating accurate generative AI applications. Utilize S3 folder structures and metadata filtering for efficient data segmentation within a single knowledge base, ensuring proper access controls across different business units.
NVIDIA highlights physical AI advancements during National Robotics Week, showcasing technologies shaping intelligent machines across industries. IEEE honors NVIDIA researchers for groundbreaking work in scalable robot learning, real-world reinforcement learning, and embodied AI.