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While she was in business school at the University of Pennsylvania, Allie Miller told her professor that all she wanted to do was find work in artificial intelligence (AI). His response: Then be a beacon for the space and go build the world’s largest conference on AI.
Her internal reaction: “What are you talking about? That would be ridiculous.” But she tried anyway, inviting anyone in her class at Wharton who was interested to sign up for a roundtable focused on AI’s role in business.
Six people responded. She gathered them at a pizza place, announced herself president, and recruited a co-president for a group that would eventually grow to hundreds of members and come to be known as the Penn AI Initiative, an effort that influenced a $5 million newly funded curriculum at Penn’s Wharton School. (The professor who encouraged her, author Adam Grant, is known for inspiring people to rethink basic assumptions.)
To some people, a class-wide invitation that drew six people would be discouraging. But for Miller, who is now global head of machine learning business development for startups and venture capital for Amazon Web Services (AWS), success is about building in increments.
“You have to think of everything as an avalanche that starts with one snowball,” Miller said. “Being willing to be a beginner and not be a success from the beginning is probably the best thing I did for my career.”
“There’s no better job”
Since that pizza dinner years ago, AI technologies have seen widespread adoption across industry sectors. For many startups, the question isn’t whether to use AI — it’s where to start. Miller helps guide this process for AWS customers, consulting with them on everything from how to handle distributed model training on AWS to raising $100 million from venture capital to strategies on hiring a more diverse engineering team.
Nearly every company can benefit from integrating machine learning into their processes, Miller said. That’s because the vast majority of data companies capture is unstructured. Leaving out this data means missing potential insights from emails, social media, images, video, and other assets that natural language processing and computer vision can capture and analyze.
I’m leading the fastest growing technology, machine learning, for the fastest moving market segment, startups, at the largest cloud provider in the world, AWS. There’s no better job.
“So much of AI is about being able to take into account more data sources and being able to manage that data with smarter research and smarter algorithms,” Miller added.
Before coming to Amazon in 2019, Miller spent three years as a lead product manager at IBM Watson, where she helped the company advance its product development in conversational AI, computer vision, and multi-modal AI. She enjoyed her time at IBM but missed the startup world, where she had worked as a head of product and consultant for several years after graduating from Dartmouth College. A contact she met through Grant emailed her the AWS job posting. It was the kind of role she couldn’t have dreamed of a few years before, she said. It was the first job of its kind in the world.
“When people ask me if I like my job, I say, ‘I’m leading the fastest growing technology, machine learning, for the fastest moving market segment, startups, at the largest cloud provider in the world, AWS,'” she said. “There’s no better job.”
Expanding access to AI
As an undergrad at Dartmouth, Miller majored in cognitive science and studied natural language processing. “I loved the idea that you could code an email,” she recalled. The focus within AI on understanding how humans think and make decisions appealed to her.
“I’m an extreme extrovert. So I just liked that AI was a field where I could combine my extreme math nerdiness and my obsession with statistics and computers with my love of hanging around people and learning how they think,” she explained.
Still, her journey to Amazon was an iterative process, much like machine learning itself. She explored many career options, from music production to speech pathology to corporate social responsibility.
Courtesy of Allie Miller
“I don’t think it was ever this light bulb moment of, ‘Oh my God, I have to pivot into AI right now,'” she recalled. “Taking the inspiration of machine learning and applying it to my real life and thinking of it as this constant optimization and retraining has been wonderful for my career.”
Now she’s on a mission to expand access to AI not just for businesses but as a career choice. Named to LinkedIn’s Top Voices in Data and AI in 2019 and 2020, she has more than one million followers on the platform and writes extensively about topics such as getting started in machine learning, interesting use cases, and best practices in data management.
Miller also speaks at a variety of events and has championed the importance of “mentoring in public”, all with the goal of pulling in underrepresented groups into the field. Earlier this year, she was one of several women in AI leadership positions interviewed for Women in AI, a report by AWS partner Deloitte and their AI Institute. Her goal, she said, is to educate a billion people about AI.
Actively increasing diversity
As part of the effort to expand representation, she co-founded Girls of the Future, an organization that spotlights girls aged 13 to 18 who are innovating in STEM. The Geena Davis Institute on Gender in Media has found that, “Nearly 80% of young women say that in order to pick STEM as a career, it was critical to see other women in STEM.”
One of the misconceptions business leaders have about expanding diversity, Miller said, is that you recruit, hire, and check the box that you’ve made the effort. But creating real change in the industry is about so much more than that.
Encourage more women in your life to consider joining AI teams and companies.
I just looked at an AI company’s team page, and it had 1 dog, 2 women, and 46 men.
— Allie K. Miller (@alliekmiller) June 8, 2021
“It’s actually creating inclusive environments. It’s creating transparency in how they get promoted. It’s providing them mentorship opportunities. It’s providing clear guidance on how to get to the next level. It’s the ability to have learning budgets to be able to upskill, or free training. It’s a guarantee of equal management opportunities,” she said. “Recruiting is just the beginning.”
Many people who write to Miller wonder how AI will affect their particular field and how they can get started in learning about it. She counsels people not to be scared away by jargon and to start small: read one article, listen to one talk. Subject matter experts can start by learning about AI in their own fields.
“AI is for all people, even if you don’t see yourself. It’s for all fields, even if you don’t hear about yours. And it’s at all levels — startups, enterprise, public sector, academics. It doesn’t matter what sector you’re in,” she said, “everyone can get involved.”
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