Hilary Mason: Use data science and machine intelligence to build a better future
Image: Erin Carson/TechRepublic
An algorithm can creatively reimagine the Mona Lisa.
In the opening keynote of the Grace Hopper Women in Computing Conference 2015 in Houston, Texas, Fast Forward Labs CEO Hilary Mason talked about the burgeoning world of data science and machine intelligence, and several of the considerations for how they will affect the future.
But first, in a subtle nod to the #ILookLikeAnEngineer movement, Mason introduced herself like this: “I’m a computer scientist, a data scientist, a software engineer, I’m also a CEO and I look like all of those things.”
And then she dove into machine intelligence.
“Machines are starting to do things that we might have thought were more in the creative domain of humans,” she said, showing several computer-generated takes on the classic Da Vinci painting. Or, she also pointed out some of her favorite data-based apps that have already changed the ways that users function, like Google Maps, Foursquare, or Dark Sky.
Mason outlined reasons why data science and machine learning are having a moment: we have the computing power, we know what to do with data when we have it, and, we’re getting access to more and more of it.
Looking at her own history with data, Mason described a moment she and a co-worker had while she was working at Bitly as chief scientist. They were making changes to a Hadoop cluster they had. In order to test a job, they decided to find out what the cutest animal on the internet was.
“We had just used hours of compute time and a petabyte of data to answer the most frivolous question,” she said. That ability, though, to “play” with data is important. Mason also referenced a Kickstarter for a LED light up “disco dog” suite — it’s a smart phone-controlled vest for your dog.
“When you start to see the ridiculous things occurring, you know something interesting is happening because that means the technology is something we all can use,” Mason said.
But, in building new things, even silly things it’s important to remember unintended and unforeseen consequences. For example, in 1999, Sony was building and selling a toy robotic dog called Aibo. Recently, though, they stopped supporting them, so if someone happened to still be using their robodog and it malfunctioned, there was no reviving it. And that was actually more common than one would think, leading to funerals for those longtime robotic pets by bereaved owners.
Part of this evolution involves the role of a data scientist and what it means to be a professional in changing landscape.
“It’s a pretty wild and undefined field, and that’s what’s wonderful about it,” she said.
Also, Mason provided this handy venn diagram on what it means to be a data scientist.
Image: Erin Carson/TechRepublic
The changing field prompts the question of not only where innovation comes from, but what the system looks like that’s producing it, whether in large corporations or startups. For any of those types of companies, the pace of technology adoption means that they have to innovate in order to maintain success and in order to make new products. People look to tech to help them do that, but it’s not always an easy process.
For Mason, that’s where Fast Forward Labs—a machine intelligence research company—comes in. Mason said they sit in the middle of established companies, startups, and academic research.
In defining what makes for machine intelligence technology, they look for:
1. A theoretical breakthrough, like a piece of research that’s been done in an area
2. A change in the economics that constrain what you’re trying to build
3. A capability becomes a commodity, something that lets you build without having to reinvent
4. New data that might become available to make it possible to execute an idea
The 8-person team makes products that delve into areas like deep learning and natural language generation. For example, they came up with a prototype for writing effective real estate ads. They nixed an idea for an app to generate restaurant reviews, on the other hand, playing off that idea of considering the consequences of what you build.
Her last slide stated: “You’re building the future. Please build the one you want to live in.”
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