Archive for December, 2012

Building Intelligent Agents: Google Now versus Apple SIRI?

Friday, December 14th, 2012

It has been a long time since our last blog post. Why? We’ve been busy learning how to build better intelligent agents.

Today, Kurt and I were discussing ways to improve feature detection algorithms for use in a prototype application called ai-BrainDocs. This is a system that detects concepts within legal documents. This is a hard problem because legal concepts (or ideas) use the same words. That is, there are no distinguishing features in the text.

ai-one’s technology is able to solve this problem by understanding how the same word (keyword) can mean different things by its context (as defined by association words). Together, keywords and associations create an array that we call an ai-Fingerprint. This can be thought of as a graph that can be represented as G[V,E]. ai-Fingerprints are easy to build using our Topic-Mapper API.

We pondered how the intelligent agents for Android developed by Google (called Google Now) and Apple iOS (called SIRI) might perform on a simple test. We picked a use case where the words were sparse but unique — looking for the status for a departing flight on American Airlines. Both Google Now and Apple SIRI have a tremendous advantages over ai-one because they: 1) have a lot more money to spend on R&D, 2) use expensive voice recognition technologies, and 3) they store all queries made by every user so they can apply statistical  machine learning to refine results from natural language processing (NLP).

Unlike Apple and Google, ai-one’s approach is not statistical. We use a new form of artificial neural network (ANN) that detects features and relationships without any training or human intervention.  This enables us to do something that Google and Apple can’t: Autonomic learning. This is a huge advantage for situations where you need to develop machine learning applications to find information where you can’t define what you are seeking. This is common in so-called “Big Data” problems. It is also much cheaper, faster and accurate than using the statistical machine learning tools that Apple and Google are pushing.


Posted by: Olin Hyde