Apple researchers have developed DeepPCR, an innovative algorithm to speed up neural network training and inference. It reduces computational complexity from O(L) to O(log2 L), achieving significant speed gains, particularly for high values of L. DeepPCR has been successfully applied to multi-layer perceptrons and ResNets, demonstrating substantial speedups without sacrificing result quality.

Summary: The article discusses the tension between data scientists’ desire for large volumes of data and the need for data privacy and security. It emphasizes the importance of finding a middle ground in data retention and usage, while also highlighting the complexities of managing data in organizations and the impact of data security regulations.

DeepMind researchers unveiled “FunSearch,” using Large Language Models to generate new mathematical and computer science solutions. FunSearch combines a pre-trained LLM to create code-based solutions, verified by an automated evaluator, refining them iteratively. It has successfully provided novel insights into key mathematical problems and demonstrated potential in broad scientific applications, marking a transformative development in…

AI-generated disinformation is threatening the upcoming Bangladesh national elections. Pro-government groups are using AI tools to create fake news clips and deep fake videos to sway public opinion and discredit the opposition. The lack of robust AI detection tools for non-English content exacerbates the problem, highlighting the need for effective regulatory measures.

Researchers at the Karlsruhe Institute of Technology (KIT) have utilized artificial intelligence (AI) to enhance the accuracy of global climate models in predicting precipitation. Their model, employing a Generative Adversarial Network (GAN), improves temporal and spatial resolution, offering better forecasts for extreme weather events linked to climate change. This advancement holds promise for more precise…

Researchers from the University of Wisconsin–Madison and Amazon Web Services studied improving Large Language Models of code (Code-LLMs) to detect potential bugs. They introduced the task of buggy-code completion (bCC), evaluated on datasets buggy-HumanEval and buggy-FixEval. Code-LLMs’ performance degraded significantly, for which post-mitigation methods were proposed, although performance gaps persisted. The work enhances understanding of…