Women Leading The AI Industry: “There is just no excuse for the current gender disparities in tech company leadership”, with Martha Amram and Tyler Gallagher

The underlying issue to solve for is creating a welcome place for women in tech. Women make up more than 50% of the college graduates. Women dominate the top half of high school students. So there is just no excuse for the current gender disparities in tech company leadership. We are getting a strong pipeline, […]

The underlying issue to solve for is creating a welcome place for women in tech. Women make up more than 50% of the college graduates. Women dominate the top half of high school students. So there is just no excuse for the current gender disparities in tech company leadership. We are getting a strong pipeline, let’s get them on-board!

As part of my series about the women leading the Artificial Intelligence industry, I had the pleasure of interviewing Martha Amram, CEO of Glynt.AI. Martha has led the company during the development of their leading-edge machine learning product that liberates data trapped in documents. Martha has had a varied career in software, consulting and academia. She holds a Ph.D. from MIT in Applied Economics and is the author of several books and dozens of published papers.

Thank you so much for doing this with us! Can you share with us the ‘backstory” of how you decided to pursue this career path?

I’m having a bit of a laugh at the word “decided” in your question. My career has followed my curiosity about how markets work, in a very incremental fashion. For example, GLYNT.AI grew out of a software platform we built for the energy markets. The platform transfers utility bills from utility websites to third-parties, such as solar installers, who need the data to size solar systems. We built a machine learning system to extract the data from the bills and then started getting calls from other markets such as healthcare. It was clear there was much more potential and so we formed GLYNT. For the past few years I have developing the market strategy for GLYNT. Entirely opportunistic and incremental. Yet, with my academic training I could see how the new markets work and where GLYNT can provide value add. Fun and rewarding!

What lessons can others learn from your story?

The days of signing up for one job for 20 or 30 years are over. I’ve had such a varied career — academia, consulting, startups, software — and I’ve been able to take years off when my kids were young. So it turns out I’ve done the modern career. It has worked out well for me, but it could easily have been frustrating and mediocre.

I think the key to success is the phrase: “Get ready to be lucky.” It captures the humility of the big picture; we can’t control everything about our lives. And it captures the hard work, perseverance and striving for continual improvement that is needed for success.

Can you tell our readers about the most interesting projects you are working on now?

Today, GLYNT is on the frontlines of AI. Our machine learning system surpasses human performance levels. So as companies adopt GLYNT, they see a huge boost in productivity from staff who previously entered data from documents by hand. GLYNT delivers the same data faster, better and cheaper.

We can directly observe how AI is changing jobs. And we can see how AI systems bypass current office workflows. It’s a window into the future. Our vantage point allows us to see it first. The economist in me is fascinated. I have a front-row seat to a once in a generation change in how we work. It doesn’t get better than this.

None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?

Two great mentors stand out. When I was in high school I was devoted to music and playing the clarinet. Through the hours of practice, and a teacher that kept pulling me forward, I learned how it feels in your bones to know something. I could play difficult pieces from memory with artistry in front of others. I learned how it feels to be prepared and unprepared. And how to prepare.

The second mentor is here in Silicon Valley. He was an investor in a company I worked in 20 years ago and took me on as a mentee. It seems crazy today, but as a Greek immigrant he was one of the first non-WASPs to be funded by venture capitalists. A great mentor for a woman in technology; he knows how to navigate and seize opportunities from outside the traditional networks. It has been a real delight to have someone tell it like it is (when I’m off my game), think through new opportunities, and who believes I can do it.

What are the 5 things that most excite you about the AI industry? Why?

AI is a once in a generation change. It is hard to see what comes after because AI delivers so much improvement. For example, if GLYNT cuts data extraction errors down to 0.5%, how can the next technology get a foothold? There is not much remaining room for improvement. So, AI is exciting not only because it is a powerful advantage today, but because it is here to stay.

AI also is part of the elevation of the “math nerd” in the technology landscape. Men and women with an affinity for math have an advantage. What a change from the past in which advantage was obtained through relationships or who you know. I love the math-centric meritocracy, and the spillovers from that core into increased demand for data analysts, and how mathy product features drive to competitive advantage.

While tech is still dominated by men, there is such a talent shortage in AI that it feels much more open to women. Talent wins over past practices. This is an exciting change and I’m glad to be part of it.

What are the 5 things that concern you about the AI industry? Why?

Income inequality is a key concern for our modern economy. At the least, huge income disparities lead to political unrest. But the most important to me is how it feels to be on the wrong side of the inequality. In particular, how do these kids get access to our mathy AI jobs? The U.S. does a poor job in math education. Without new on-ramps for aspiring teenagers, AI increases the economic divide.

The second way AI increases the economic divide is through its ability to quickly disrupt current business practices. AI products become an on-going profit stream that is hard to dislodge. The riches of AI go to a very few: People who have the right training, companies who have foresight, cash and technology to pursue AI endeavors, and investors who see the AI future. Most of these winners are concentrated in a relatively few zip codes across the U.S., leading to huge increase in the concentration of wealth.

Others who share these concerns have proposed a basic income for everyone. I don’t see how the numbers add up, there is just not enough wealth to put everyone into the middle class. So I have a constant anxiety: How does this all work out?

A lot of folks have a concern about hidden biases in AI systems. I am really proud of how we set up GLYNT with complete transparency. I am proud to check that box.

As you know, there is an ongoing debate between prominent scientists, (personified as a debate between Elon Musk and Mark Zuckerberg,) about whether advanced AI has the future potential to pose a danger to humanity. What is your position about this?

The danger referenced in the question comes from control, so the question might be: Given that AI is so powerful, are you worried that we will give AI-powered services too much control? “Too much control” means that personal decisions, liberties and creativity are hampered or constrained by AI. But like everything else, AI is a bundle of good and bad.

As many have noticed, there is a generational divide about willingness to share data. Younger generations see the benefits of a software service and are quite willing to share personal data to get the benefit. So as we attempt to think through AI’s bundle of good and bad, it is not possible to declare there will be “too much control.” It is entirely subjective and our norms and consensus will shift over time.

What can be done to prevent such concerns from materializing? And what can be done to assure the public that there is nothing to be concerned about?

I don’t have the global answer to this question, the insight that will cut through and provide enormous clarity. What I do have are insights from a front-row seat on how AI works, and how it is turned into products and services. Because we’re on the cutting-edge, I’m often a human bridge, translating between the technical details and our immediate learnings and this big question.

From this experience I find that the two sides are often speaking past each other. The public needs help in framing the question. Too many pundits lack the math and computer science skills to really understand AI. To them it is just one more black box. At the same time AI practioners, deep in the weeds, don’t bring forward the technical dimensions and constraints in a nuanced manner for the non-tech audience.

So my answer is “Let’s have the conversation. Again and again.” From there we’ll figure it out.

How have you used your success to bring goodness to the world? Can you share a story?

Perhaps I don’t have one big story, but a constellation of smaller ones. I’m on the board of Growth Sector, a non-profit that helps community college students prepare for a STEM-related career. For the kids that participate, the program is life-changing. I’m on the board of Every Voice Engaged, a non-profit that attempts to increase citizen input into public policy through interactive technology and live events, such as running “Budget Games” (citizen input into city budget priorities) for the city of San Jose’, CA. And my company WattzOn has employed hundreds of high school students to be energy ambassadors in their community. Student have changed their college majors and post-high school plans because of their experience. The big story is to never stop, to try to find the angle that makes an impact.

As you know, there are not that many women in your industry. Can you share 3 things that you would you advise to other women in the AI space to thrive?

1. Persevere. Rome was not built in a day, same for your career. Keep at it!

2. Work hard. Effort matters. One needs to dig in, get those 10,000 hours over and over.

3. Keep investing in yourself. If you coast, you’re out. The AI world changes too fast.

Can you advise what is needed to engage more women into the AI industry?

Just as my advice to women in the previous question is not particularly about AI, so is my response to this question. The underlying issue to solve for is creating a welcome place for women in tech. Women make up more than 50% of the college graduates. Women dominate the top half of high school students. So there is just no excuse for the current gender disparities in tech company leadership. We are getting a strong pipeline, let’s get them on-board!

I think we need two changes to engage more women in tech: Women in leadership roles AND broader interview processes. It doesn’t work when four twenty-nine year old guys are doing all the interviews. Folks just hire what they understand, people like themselves. So with a change at the top and a broader talent pool doing the first-round interviews, I think we can increase the number of women getting hired in tech at all levels.

But don’t forget, tech is a fairly well-functioning meritocracy. So women coming through this improved system have to put in the hours, persevere and prove themselves.

What is your favorite “Life Lesson Quote”? Can you share a story of how that had relevance to your own life?

As you heard, my life lesson quote is “Get ready to be lucky”. GLYNT is a great example, arising out of a niche market in the energy industry and now solving a big problem for corporate America.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂

I suspect at this point in the interview, readers will be able to predict my response: I’d like to start a movement that creates the many types of on-ramps we need to help low-income and disadvantaged students gain access to STEM careers, including employment in AI. Everyone should have a chance to get a seat at the table.

How can our readers follow you on social media?

Watch the blog posts at GLYNT.AI or follow me on Medium.

This was very inspiring. Thank you so much for joining us!

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