Thought Vectors, Deep Learning & the Future of AI - Deeplearning4j: Open-source, distributed deep learning for the JVM

"Thought vector" is a term popularized by Geoffrey Hinton, the prominent deep-learning researcher now at Google, which is using vectors based on natural language to improve its search results. A thought vector is like a word vector, which is typically a vector of 300-500 numbers that represent a word. A word vector represents a word's meaning as it relates to other words (its context) with a single column of numbers. That is, the word is embedded in a vector space using a shallow neural network like word2vec, which learns to generate the word's context through repeated guesses. A thought vector, therefore, is a vectorized thought, and the vector represents one thought's relations to others.