Google AI’s ‘Talk to Books’: Conversing with Literature via Semantic Search

Google Research’s latest demo, dubbed “Talk to Books,” illustrates the current peak of natural‑language processing by allowing users to type a query or statement and receive entire sentences from a corpus of books that are semantically related, not merely keyword‑matched.

The system was trained on roughly one billion conversational sentence pairs, enabling it to recognize appropriate responses and to infer meaning such as linking the word “detective” with “investigator.” For example, a search for “He is the greatest detective ever” returned a sentence that did not contain the exact query terms but was semantically relevant.

In addition to “Talk to Books,” Google released a companion site called Semantris, a word‑association game that highlights the AI’s ability to identify opposite and adjacent concepts, similar to a Tetris‑style interactive experience.

The underlying technology relies on word‑vector models that capture relationships between words based on real‑world usage, a development that has driven recent advances in NLP. According to Google researchers Ray Kurzweil and Rachel Bernstein, these demos point to future applications such as classification, semantic similarity, clustering, whitelist selection, and semantic search.

Google has open‑sourced a TensorFlow module implementing these models, allowing other researchers and developers to build AI‑driven applications that understand language at a deeper level.