AI Signals From Tomorrow
Signals from Tomorrow is a podcast channel designed for curious minds eager to explore the frontiers of artificial intelligence. The format is a conversation between Voyager and Zaura discussing a specific scientific paper or a set of them, sometime in a short format and sometime as a deep dive.
Each episode delivers clear, thought-provoking insights into how AI is shaping our world—without the jargon. From everyday impacts to philosophical dilemmas and future possibilities, AI Signals from Tomorrow bridges the gap between cutting-edge research and real-world understanding.
Whether you're a tech enthusiast, a concerned citizen, or simply fascinated by the future, this podcast offers accessible deep dives into topics like machine learning, ethics, automation, creativity, and the evolving role of humans in an AI-driven age.
Join Voyager and Zaura as they decode the AI signals pointing toward tomorrow—and what they mean for us today.
AI Signals From Tomorrow
Architectures, Abilities, and Evolution of Large Language Models
Large Language Models: A Survey academic paper (https://arxiv.org/pdf/2402.06196v1) offers a comprehensive overview of Large Language Models (LLMs), tracing their development from earlier language models and highlighting the impact of models like GPT, LLaMA, and PaLM. It details the methods of building LLMs, including data preparation, tokenization, and various pre-training and fine-tuning techniques, such as instruction tuning and alignment methods like RLHF and KTO. The text also explores how LLMs are utilized and enhanced, covering prompting strategies like Chain-of-Thought and Tree-of-Thought, and augmentation techniques like Retrieval Augmented Generation (RAG) and tool usage, which contribute to the development of LLM-based agents. Furthermore, it surveys popular datasets and benchmarks for evaluating LLM performance across different tasks like reasoning, coding, and world knowledge. Finally, the paper concludes by addressing current challenges and future directions in LLM research, including the pursuit of smaller, more efficient models, new architectural paradigms, and the rise of multi-modal LLMs.