“Prompt Engineering, AI Agents, and LLMs: Kick-Start a New Year of Learning” sets the tone for the new year, introducing thought-provoking articles. Sheila Teo’s GPT-4 Competition win and Oren Matar’s ChatGPT review offer insights. Mariya Mansurova discusses LLM-Powered Analysts, while Heston Vaughan and others delve into AI agents and music AI breakthroughs. The newsletter also…

The researchers propose DL3DV-10K as a solution to the limitations in Neural View Synthesis (NVS) techniques. The benchmark, DL3DV-140, evaluates SOTA methods across diverse real-world scenarios. The potential of DL3DV-10K in training generalizable Neural Radiance Fields (NeRFs) is explored, highlighting its significance in advancing 3D representation learning. The work influences the future trajectory of NVS…

The development of Large Language Models (LLMs) like GPT and BERT presents challenges in training due to computational intensity and potential failures. Addressing the need for efficient management and recovery, Alibaba and Nanjing University researchers introduce Unicron, which enhances LLM training resilience through innovative features, including error detection, cost-efficient planning, and efficient transition strategies, achieving…

A new study proposes a three-step system to evaluate artificial intelligence’s ability to reason like a human, acknowledging the limitations of the Turing test due to AI’s capacity to imitate human responses.

A group of researchers led by Prof. Qu Kun has developed SPACEL, a deep-learning toolkit consisting of Spoint, Splane, and Scube modules, to overcome limitations in spatial transcriptomics analysis. By accurately predicting cell types, identifying spatial domains, and constructing 3D tissue architecture, SPACEL outperforms existing techniques, offering a powerful solution for comprehensive spatial transcriptomic analysis.

Large Language Models (LLMs) have revolutionized processing multimodal information, leading to breakthroughs in multiple fields. Prompt engineering, introduced by researchers at MBZUAI, focuses on optimizing prompts for LLMs. Their study outlines 26 principles for crafting effective prompts, emphasizing conciseness, context relevance, task alignment, and advanced programming-like logic to improve LLMs’ responses.

The article explores the intersection of philosophy and data science, focusing on causality. It delves into different philosophical theories of causality, such as deterministic vs probabilistic causality, regularity theory, process theory, and counterfactual causation. The author emphasizes the importance of understanding causality in data science to provide valuable recommendations.