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Energy#Energy Efficiency📰 NewsAnalyzed: Dec 26, 2025 13:05

Unplugging these 7 common household devices easily reduced my electricity bill

Published:Dec 26, 2025 13:00
1 min read
ZDNet

Analysis

This article highlights a practical and easily implementable method for reducing energy consumption and lowering electricity bills. The focus on "vampire devices" is effective in drawing attention to the often-overlooked energy drain caused by devices in standby mode. The article's value lies in its actionable advice, empowering readers to take immediate steps to save money and reduce their environmental impact. However, the article could be strengthened by providing specific data on the average energy consumption of these devices and the potential cost savings. It would also benefit from including information on how to identify vampire devices and alternative solutions, such as using smart power strips.
Reference

“You might be shocked at how many 'vampire devices' could be in your home, silently draining power.”

PermalinkZDNet
Research#Graph Neural Networks📝 BlogAnalyzed: Jan 3, 2026 07:14

Dr. Petar Veličković (Deepmind) - Categories, Graphs, Reasoning [NEURIPS22 UNPLUGGED]

Published:Dec 8, 2022 23:45
1 min read
ML Street Talk Pod

Analysis

This article summarizes an interview with Dr. Petar Veličković, a prominent researcher at DeepMind, discussing his work on category theory, graph neural networks, and reasoning, presented at NeurIPS 2022. It highlights his contributions to Graph Attention Networks and Geometric Deep Learning. The article provides a table of contents for the interview, links to relevant resources, and mentions the host, Dr. Tim Scarfe.
Reference

“The article doesn't contain direct quotes, but summarizes the discussion on category theory and graph neural networks.”

PermalinkML Street Talk Pod
Research#NLU📝 BlogAnalyzed: Jan 3, 2026 07:15

Dr. Walid Saba on Natural Language Understanding [UNPLUGGED]

Published:Mar 7, 2022 13:25
1 min read
ML Street Talk Pod

Analysis

The article discusses Dr. Walid Saba's critique of using large statistical language models (BERTOLOGY) for natural language understanding. He argues this approach is fundamentally flawed, likening it to memorizing an infinite amount of data. The discussion covers symbolic logic, the limitations of statistical learning, and alternative approaches.
Reference

“Walid thinks this approach is cursed to failure because it’s analogous to memorising infinity with a large hashtable.”

PermalinkML Street Talk Pod