This article explores the use of Google's NotebookLM (NLM) as a tool for research, particularly in analyzing the impact of the Aswan High Dam on schistosomiasis in Egypt. The author details how NLM can be used to create a research assistant-like experience, allowing users to 'have a conversation' with uploaded content to gain insights and answers from the material.
- "Deep Research" is a new trend in AI-driven research using large language models for multi-step investigations.
- The article compares Deep Research systems, highlighting capabilities and limitations like generating tangential content and handling nonsensical queries.
- Includes systems such as Gemini Advanced 1.5 Pro, OpenAI’s Deep Research, Perplexity’s Deep Research Mode, and You.com’s Research Feature.
Key concept: Setting mental models can help users understand how to interact with products that adapt over time. This chapter covers: Identifying existing mental models Onboarding in stages Planning for co-learning Accounting for user expectations of human-like interaction Key concept: To build effective mental models of AI-powered products, consider what you want people to know about your product before their first use, how to explain its features, and when it will need feedback from them to improve.
- This blog discusses using Large Language Models (LLMs) such as Bard and ChatGPT4 to summarize lengthy texts.
- The author compares the performance of these LLMs on summarizing texts, particularly focusing on the classic gothic tale, Frankenstein by Mary Shelley, and Chapter 10 of their book, The Joy of Search.
- While both Bard and ChatGPT4 show promise in creating decent summaries, there are notable differences between the two, with ChatGPT4 being more adept at handling larger amounts of information.
