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Want to see Hilary Mason, the CEO and founder at Fast Forward Labs, get fired up? Tell her about your new connected product and its machine learning algorithm that will help it anticipate your needs over time and behave accordingly. “That’s just a bunch of marketing bullshit,” said Mason when I asked her about these claims.

Mason actually builds algorithms and is well-versed in what they can and cannot do. She’s quick to dismantle the cult that has been built up around algorithms and machine learning as companies try to make sense of all the data they have coming in, and as they try to market products built on learning algorithms in the wake of Nest’s $3.2 billion sale to Google (I call those efforts faithware). She’ll do more of this during our opening session with Data collective co-managing partner Matt Ocko at Structure Data on March 18 in New York. You won’t want to miss it.

Lately, algorithms have been touted as the new saviors, capable of helping humans parse terabytes of data to find the hypothetical needle in the haystack. Or they are portrayed as mirrors of our biases coolly replicating our own racist or classist institutions in code.

Mason thinks of them differently. An algorithm is a method, or recipe, or set of instructions for a computer to follow, she said. “It’s just a recipe you type in to get a consistent result. In some ways chocolate chip cookie recipes are my favorite algorithms. You put a bunch of bad-for-you stuff in a bowl and get a delicious result.”

As for the phrase “machine learning,” which has begun replacing “algorithm” in many of the marketing and Kickstarter pitches I see for connected devices that learn your habits, Mason said that’s no more magical. “It’s a false distinction,” she said. Machine learning algorithms may tend to use statistical methods and techniques, but they are still just algorithms.

Essentially, you’re combining what you know about the properties of a given data set with the recipe you built. For an email spam filter, you might build an algorithm that detects spam by looking for words that commonly appear in spam and then combining that with a statistical distribution of the countries that spam often comes from. Voila, the magic has become mundane — or at least mathematical.

At the end of the day, it’s still just math. Really awesome math.

Updated: This story was updated, to clarify some of the points Mason was making.