This fall, we’re sharing a series of blog posts exploring AI’s impact on highly regulated industries and the major compliance barrier that stands in the way: the “black box” problem. Setting up the rest of the series, this week’s blog post provides clear explanations of core AI technology and terminology. Artificial Intelligence, Explained There’s a […]
Artificial Intelligence (AI) is what we use to build algorithms that solve complex problems. What’s a complex problem? Good question. It’s a problem where a simple relationship between inputs and outputs is difficult to create. Voice to text is a perfect example of such a problem.
Imagine you’re designing a voice to text solution. The first challenge is calibrating your system to analyze the input. The input isn’t text: It’s a signal, a wave—a time series. And each instance will be unique. Even the same person repeating the same word doesn’t produce a perfectly identical, repeating pattern. As a consequence, you cannot create clean definitions for inputs or write “if/then” rules: if I see X and Y wave pattern, then I know that it’s the word “Z”. Even if you’re only considering inputs, you’re looking at a complex problem.
To solve your dilemma, you have to have a more sophisticated means of mapping inputs to outputs. What you need is a more flexible methodology of learning the key attributes that would allow your system to match a signal pattern to a given word. Here’s where AI comes in. Using machine learning, you can teach your system to recognize given signal-to-word matches by letting them build a model of how they relate.
First, you create (or buy) a dataset composed of input/output matches. We typically call it a “gold standard,” as it provides the true, human-curated mapping of inputs to outputs. In this dataset, there would be a variety of pronunciations of a particular spoken word coupled with the appropriate written word. For example, numerous audio files of the word “helicopter” would be labelled as inputs and matched to “helicopter” as a labelled text output. These input/output matches are then fed to a machine learning (ML) algorithm, which, through this training process, develops an understanding of the core features of the signal patterns that map to particular words. This understanding is known as a model, and it allows you to build voice to text applications that can deal with variety in input.
What I’ve just described to you is classic ML, and it is through ML that we build applications for hard problems. While there are different types of ML (which we will get to later), this is the key approach that defines modern AI.
CB Insights’ Future of Fintech conference was a star-studded event. Built around “fireside chats” with the industry’s leading minds, the conference had a uniquely intimate atmosphere. For those who couldn’t make the trip (or are just curious), Basis Technology COO Steve Cohen shares five key takeaways: 1. The unbundling of financial services In his keynote, […]
A sneak peek at #HLTCon 2017 “I see…practical anticipatory intelligence for your agency in your future…” Artificial intelligence, machine learning, and their ilk are ready for national security applications (of which anticipatory intelligence is something of a holy grail), posits Charlie Greenbacker. As VP, Analytics at In-Q-Tel, a strategic investor of the U.S. intelligence and […]
Basis Technology R&D team pioneers new technique and open sources WikiSem500, a dataset for multilingual word embedding evaluation The most time consuming and expensive aspect of machine learning research is data preparation—aggregation and cleaning—and every data scientist has been frustrated by it. However the importance of good, testing data makes it hard to cut corners. […]