NEWS IN BRIEF: AI/ML FRESH UPDATES

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America First: Trump Allies Push for AI Dominance

Allies of Trump draft AI executive order to boost military tech and reduce regulations, signaling potential policy shift in 2025. Proposed order includes "Manhattan Projects" for military AI and industry-led agencies to safeguard systems, benefiting companies like Palantir and Anduril.

Revolutionizing Material Predictions with AI

Researchers from MIT developed a new machine-learning framework to predict phonon dispersion relations 1,000 times faster than other AI-based techniques, aiding in designing more efficient power generation systems and microelectronics. This breakthrough could potentially be 1 million times faster than traditional non-AI approaches, addressing the challenge of managing heat for increased efficie...

Boost AI Training with NeMo on Amazon EKS

The NVIDIA NeMo Framework simplifies distributed training of large language models, optimizing for efficiency and scalability. Amazon EKS is recommended for managing NVIDIA NeMo, offering robust integrations and performance features for running training workloads.

The Climate vs. AI: Energy Showdown

Artificial intelligence companies aim to achieve great feats, but the energy needed threatens environmental goals. Can AI's energy problem be solved in time? Hear from the Guardian's Jillian Ambrose and Alex Hern.

Microsoft CTO Stands Firm on LLM Scaling Laws

Microsoft CTO Kevin Scott emphasizes the potential of large language model scaling laws in driving AI progress. Scott played a crucial role in the $13 billion technology-sharing deal between Microsoft and OpenAI, highlighting the impact of scaling up model size and training data on AI capabilities.

Ensuring AI Trustworthiness: Pre-Deployment Assessment

Researchers from MIT and the MIT-IBM Watson AI Lab developed a technique to estimate the reliability of foundation models, like ChatGPT and DALL-E, before deployment. By training a set of slightly different models and assessing consistency, they can rank models based on reliability scores for various tasks.