Machine learning

Neural networks have been responsible for most of the top-performing AI systems of the past decade, but they tend to be big, which means they tend to be slow. That’s a problem for systems like Alexa, which depend on neural networks to process spoken requests in real time.

In May 2018, Amazon launched Alexa’s Remember This feature, which enables customers to store “memories” (“Alexa, remember that I took Ben’s watch to the repair store”) and recall them later by asking open-ended questions (“Alexa, where is Ben’s watch?”).

At a recent press event on Alexa's latest features, Alexa’s head scientist, Rohit Prasad, mentioned multistep requests in one shot, a capability that allows you to ask Alexa to do multiple things at once. For example, you might say, “Alexa, add bananas, peanut butter, and paper towels to my shopping list.” Alexa should intelligently figure out that “peanut butter” and “paper towels” name two items, not four, and that bananas are a separate item.

In recent years, data representation has emerged as an important research topic within machine learning.

Language models are a key component of automatic speech recognition systems, which convert speech into text. A language model captures the statistical likelihood of any particular string of words, so it can help decide between different interpretations of the same sequence of sounds.

Suppose that you say to Alexa, “Alexa, play Mary Poppins.” Alexa must decide whether you mean the book, the video, or the soundtrack. How should she do it?