Chapter 1. Neural search

Suppose you want to learn something about the latest research breakthroughs in artificial intelligence. What will you do to find information? How much time and work does it take to get the facts you’re looking for? If you’re in a (huge) library, you can ask the librarian what books are available on the topic, and they will probably point you to a few they know about. Ideally, the librarian will suggest particular chapters to browse in each book.

That sounds easy enough. But the librarian generally comes from a different context than you do, meaning you and the librarian may have different opinions about what’s significant. The library could have books in various languages, or the librarian might speak a different language. Their information about the topic could be outdated, given that latest is a fairly relative point in time, and you don’t know when the librarian last read anything about artificial intelligence, or if the library regularly receives publications in the field. Additionally, the librarian may not understand your inquiry properly. The librarian may think you’re talking about intelligence from the psychology perspective,^[1] requiring a few iterations back and forth before you understand one another and get to the pieces of information you need.

¹This happened to me in real life.

1.1. Neural networks and deep learning

1.2. What is machine learning?

1.3. What deep learning can do for search

1.4. A roadmap for learning deep learning

1.5. Retrieving useful information

1.6. Unsolved problems

1.7. Opening the search engine black box

1.8. Deep learning to the rescue

1.9. Index, please meet neuron

1.10. Neural network training

1.11. The promises of neural search

Summary