All Notes Project Overview
Updated March 22, 2025

Talk To Books

Talk to Books was an experimental semantic search engine developed by Google Research that used natural language processing to allow users to have “conversations” with a database of books. Released publicly in 2018, it represented an early application of semantic search technology that would later influence conversational AI and digital twin development.

Technology Overview

Talk to Books employed advanced semantic matching rather than traditional keyword search:

  • Used a “medium-sized” language model to encode sentences into vector representations
  • Mapped user queries and book passages into the same semantic vector space
  • Retrieved passages based on semantic similarity rather than lexical matching
  • Allowed natural language questions to find conceptually relevant content
  • Demonstrated an early version of retrieval-based conversational AI
  • Provided a novel interface for exploring knowledge contained in books

Development and Release

The project emerged from Google’s AI research efforts:

  • Developed by a team that included Ray Kurzweil, Director of Engineering at Google
  • Released publicly in April 2018 as part of Google’s “Semantic Experiences”
  • Launched alongside “Semantris,” a word association game using the same technology
  • Demonstrated at Google I/O and other tech conferences as an experimental AI interface
  • Later discontinued as a public service as Google shifted focus to newer AI systems
  • Principles behind it evolved into more sophisticated retrieval and search technologies

Fredbot Connection

Before its public release, a version of the Talk to Books algorithm was used internally at Google to create Fredbot:

  • In 2016, Ray Kurzweil used a proprietary version of the technology to create a chatbot of his father
  • The system was adapted to search through Fred Kurzweil’s digitized writings instead of books
  • This implementation demonstrated the technology’s ability to navigate a specific corpus of text
  • Fredbot represented a personalized application of the semantic search capabilities
  • The project showed how retrieval-based AI could preserve and interact with personal knowledge
  • This private application predated the public Talk to Books release by approximately 2 years

Technical Implementation

The core functionality of Talk to Books operated through:

  • Sentence-level encoding using neural networks trained on conversational data
  • Vector similarity calculations to find relevant passages
  • A large preprocessed database of sentences from books with their vector representations
  • Ranking algorithms to determine which passages were most semantically relevant
  • A conversational interface that framed search as a dialogue
  • No fine-tuning or modification of the original text, preserving authenticity

Legacy and Impact

Talk to Books influenced subsequent developments in AI and search:

  • Demonstrated the potential of semantic search as an alternative to keyword matching
  • Provided an early public example of AI systems that could “understand” natural language queries
  • Influenced retrieval-augmented generation systems that combine search and language models
  • Contributed to the evolution of conversational AI interfaces
  • Showed how AI could help users navigate large text collections in intuitive ways
  • Inspired projects in digital preservation and memorial technology like Fredbot

Connections

References

  • “Introducing Semantic Experiences with Talk to Books and Semantris” (Google Research Blog, 2018)
  • “How Ray Kurzweil and His Daughter Brought A Relative Back From The Dead” (PC Magazine, 2023)
  • “How Amy and Ray Kurzweil used AI to reconnect with a lost loved one” (NPR TED Radio Hour, 2025)
  • “Talk to Books: Semantic Search for Books” (Google AI experiments documentation)
  • DeepResearch - Ray Kurzweil AI Twins