A blog post comparing when to use regular Google search versus LLMs for research, outlining the strengths and weaknesses of each. It details scenarios where search engines excel (facts, current events, specific sources) and where LLMs shine (analysis, synthesis, creative thinking). It also lists tasks LLMs struggle with, such as complex reasoning, real-time information, and fact verification.
An analysis of the quality of AI-generated summaries of a technical paper, comparing outputs from Gemini, ChatGPT, Claude, Grok, Perplexity, and NotebookLM. The author finds Gemini to be the best, highlighting the importance of context in prompting and the potential usefulness of AI summaries as 'extended abstracts'.
This blog post details an experiment testing the ability of LLMs (Gemini, ChatGPT, Perplexity) to accurately retrieve and summarize recent blog posts from a specific URL (searchresearch1.blogspot.com). The author found significant issues with hallucinations and inaccuracies, even in models claiming live web access, highlighting the unreliability of LLMs for even simple research tasks.
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.
- 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.
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.
- 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.
