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
Short Review of LLM
These two sources (https://arxiv.org/pdf/2402.06196v1 and https://arxiv.org/pdf/2303.18223) provide comprehensive surveys of the field of Large Language Models (LLMs). They cover the foundational aspects, starting with the background and evolution of language models, highlighting the significance of scaling and emergent abilities in LLMs, particularly Transformer-based models.
The surveys detail how LLMs are built, including the crucial steps of pre-training on massive datasets, discussing data preparation methods like filtering and tokenization. They also delve into adaptation techniques such as Instruction Tuning and Reinforcement Learning with Human Feedback (RLHF) to align models with specific tasks or human preferences.
Furthermore, the papers describe how LLMs are utilized through strategies like prompting and In-Context Learning (ICL), including methods like Chain-of-Thought prompting. A significant portion is dedicated to capacity evaluation, reviewing various benchmarks and metrics used to assess abilities like language generation, knowledge utilization, and reasoning, while also addressing challenges like hallucination. Topics like Retrieval-Augmented Generation (RAG) and available resources are also covered.