NEWS IN BRIEF: AI/ML FRESH UPDATES

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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.

GloVe Embeddings: The Key to Codenames Hacking

Using a GloVe embedding-based algorithm, achieve 100% accuracy in the game "Codenames" by automating the roles of spymaster and operative. Representing word meaning with pre-trained GloVe embeddings to maximize accuracy in decoding clues and choosing words efficiently.

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.

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.