Women Leading The AI Industry: “Visibility is key, so I think the more awareness of female leaders already in the industry, the better.” with Amy Hodler and Tyler Gallagher

Visibility is key, so I think the more awareness of female leaders already in the industry, the better. As leading women, we can make inroads for younger women who are just breaking into the industry. To me, simple, open-ended introductions between those in more junior roles with senior mentors can go a long way. When […]

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Visibility is key, so I think the more awareness of female leaders already in the industry, the better. As leading women, we can make inroads for younger women who are just breaking into the industry. To me, simple, open-ended introductions between those in more junior roles with senior mentors can go a long way. When conversations don’t have an agenda, I find they are more beneficial long term.

As part of my series about the women leading the Artificial Intelligence industry, I had the pleasure of interviewing Amy Hodler, a network science devotee, AI and Graph Analytics Program Manager at Neo4j, and a co-author of the O’Reilly book, “Graph Algorithms: Practical Examples in Apache Spark and Neo4j.” She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior. Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray Inc. Amy has a love for science and art with a fascination for complexity studies and graph theory.

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?

My decision to become involved in analytics and AI developed organically. I have always been interested in how things work, and in particular, how we use inference to come to conclusions. Though I didn’t start my career in analytics, by following my interests and curiosity I found work in a few projects in that field.

About eight years ago I read a book that changed the way I think about analyzing problems, called “The Information: A History, a Theory, a Flood” by James Glieck. It presented a lot about information theory and complexity studies and changed my view on moving away from reductionist type approaches to looking at systems holistically.

I took several classes related to complexity studies and network science and spent time reading and listening to anything related to these concepts. It became apparent that some things were just too complicated or large to analyze with common approaches. Machine learning and AI offered alternative ways to gather and develop insights that could better deal with these large hairy problems.

At the same time as the above, I developed an interest in graph theory — a specific area of mathematics developed to deal with the study of the complex networks/systems. When I started to read about how graphs could add context to AI an enhanced ML, it was a perfect fit for all my interests.

What lessons can others learn from your story?

We should all pull on the threads the interest us. It may not always be apparent at first, but there are often commonalities between the things we enjoy doing, the things we enjoy learning about, and the things we end up being the more proficient at. Following the path that causes us to ask new and more questions is the way to develop a larger surface of our own understanding. It will make us more confident and willing to put in the extra effort and in return, we’ll have a greater energy for our pursuits.

Don’t wait for your bliss to land in your lap. Your curiosity is your path to satisfaction and joy in your work.

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

I just finished co-authoring a book with my colleague Mark Needham on graph algorithms that will be published this spring. It explains the basics of how classic graphic algorithms work and provides practical examples with popular algorithms like PageRank and Louvain Modularity (the algorithm named after the University of Louvain in Louvain, Belgium). These algorithms calculate results differently than standard analysis and now, people without this particular expertise can more easily pick them up and apply them.

This is one of the first books on graph algorithms that has extensive examples in the two of the most popular platforms for enterprise developers interested in graph: Neo4j, where I work, and Apache Spark™. I’m proud to be part of making these powerful tools more accessible to the business world because historically, it has been the business world bringing funding and focus to using these tools in new ways for new discoveries. I expect as more enterprise businesses pick up graph algorithms, we’ll push forward our understanding of real-world networks and how to use them to greater benefit.

The other project that I’m working on right now is related to using graph to enhance machine learning predictions. For example, we are working with companies to extract connected features to develop better machine learning models and improve the precision, accuracy and recall of models and systems that AI solutions rely on. I find it really exciting that graph-based attributes, combined with other statistical measures, are more predictive than any single type of feature or attribute alone. This means that many people already working on AI/ML models may be able to improve their results fairly easily by just adding in connected features.

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?

There are many people who contributed to my success in my career and throughout my life. One person in particular stands out, however, because I met him at a time in my life when I was ready to accelerate my career but not yet confident in my ability to do so.

I met JD Marymee while I was a product manager at HP and he worked in the Microsoft System Center group. We developed a relationship around promoting novel ideas around product strategies. He invited me to participate in an innovation, brainstorming, and idea vetting group (called “a charrette”) that involved many smart people from the tech industry as well as venture capitals.

Initially, I was the only woman in the group, but I never felt out of place. The focus of those groups centered on intellectual integrity and new ways of thinking, something that was really eye opening for me. Through participating in these groups I came to understand that I had every bit as much offer as anyone else. The group also provided me valuable skills in learning how to evaluate ideas.

This simple introduction and time with more influential people in my field led to other opportunities as well as added to my network of people that I could bounce ideas off of.

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

1. A treasure trove of ways to solve our most complicated, pressing issues today.

2. Productivity gains across different industries, indicating the potential for a broad economic uplift.

3. A reduction of mundane tasks, thanks to automation.

4. Findings that cause us to question current de facto assumptions, beliefs, or chase new questions in new directions. For example, researchers make their careers with new findings, not by testing old findings. The ability to go back and validate old research is sorely needed, and with AI, we can get there.

5. New ways of learning precisely because AI is not human intelligence. This will truly open the door to possibilities we can’t even imagine, challenging the way we think for the better.

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

1. The level of assumed correctness that comes with AI — the loss of oversight and a loss of challenging the presented results.

2. A loss of human responsibility and attitudes that fall under the, “not my fault” or “well, it’s out of my control” categories.

3. The potential reliance on AI that may bring forth less creativity and intuition.

4. The potential for unintended consequences, for example algorithmic bias and its amplifications.

5. Unscrupulous people and criminal elements. AI is just a tool but it’s a very powerful one. Unfortunately, we need to be thinking about how people may intentionally misuse it.

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?

I generally have a much more practical, Bayesian take on the potential dangers. It’s important to consider potential risk and have these conversations, but the probability of human risk from AI seems much lower than other threats. I don’t tend to worry about this danger, but as any good Bayesian would say, we should update our positions as we get more information.

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?

First and foremost, we need to fund research in a variety of areas. There is so much to be considered — from decision design to control mechanisms, and misuse to evaluating AI’s eventual connectedness. We need to involve people from all disciplines and backgrounds in the investment and research processes so we can consider the potential implications of our AI from legal, societal, and economic standpoints.

I also believe we need to work on being able to understand AI decision flows and provide visibility into it. It is an extremely difficult task today, however, there is now ongoing research with promising results that will lead us down the path of AI explainability. With this, we will be able to better and more fully grasp the pathways that lead to particular AI outcomes.

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

I’ve been truly lucky to have great mentors in my career and I’ve done by best to relay that gratitude by being present and available to others. I have a deep respect for my colleagues, friends, acquaintances, and communities and challenge myself to be present for them. This means taking the time and effort to really listen and consider when challenges or questions arise, either in the form of a simple sounding board or mentorship.

Entering the field of STEM is not easy, and can be challenging as a woman. I’ve spent time with young women (middle and high school aged) discussing what it’s like to work in tech, listening to their concerns and answering questions. I do my best to reassure them that it’s okay to feel uncomfortable and it’s natural to feel perhaps a bit insecure, but that they also don’t need to have all the answers today. Challenging yourself daily to follow your curiosity and to put yourself out there will have great returns in the long run.

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. Follow your curiosity. It was such an important lesson for me to learn, and I can’t emphasize how big an impact it has had on my life. I challenge other young women to follow their curiosity as it will guide them to places unknown in the best way possible.

2. Realize that you’re more competent and smarter than you think. Women tend to suffer from a confidence gap and that can be a big opportunity killer.

3. Ask for what you want. Then ask again. Then insist. It’s important to be an advocate for yourself and your career. This can take hold in a variety of ways, but it is ultimately about being active in developing your own path.

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

Visibility is key, so I think the more awareness of female leaders already in the industry, the better. As leading women, we can make inroads for younger women who are just breaking into the industry. To me, simple, open-ended introductions between those in more junior roles with senior mentors can go a long way. When conversations don’t have an agenda, I find they are more beneficial long term.

I also think women sometimes fall into a trap of assuming they will be asked to participate if they are qualified. This means it’s often not enough to advertise a certain opportunity and I think as women in this industry, we can do better by conducting specific outreach and focusing on good mentor pairing.

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

My favorite quote comes from my yoga instructor: “Find the ease within the effort.” I misunderstood this for so many years. I think many of us live with binary concepts: something is either easy or hard, you either like something or you don’t, etc.

For me, I didn’t know how to do something without overdoing it. And if I didn’t overdo it, then I wasn’t excited about it. I’ve only started to learn what this quote truly means and I’m astounded by the power of it. I can be truly passionate about something, love it, extend great effort on it, but if there’s no sense of calm within it, then there is usually a costly downside.

This lesson has been harder to learn with my intellectual and work endeavors. Writing the book for example, was a true labor of love but unfortunately I developed tendinosis and it’s been personally difficult to step away in order to heal. I love to work but will admit that I overdid it.

This lesson has been easier for me to learn when applied to physical efforts, for example long distance cycling. I love to cycle and extending a great effort over long distances requires you to settle in, relax, and find the ease within the effort. Sometimes you’re uncomfortable and sometimes things are hard, but you get the sense for what’s reasonable and there is a great joy to be found in the act itself.

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 believe we need to be more mindful of consumption and of doing more with less. We need to be more thoughtful about how we live and I think we can start by looking at our own consumption practices. That includes our overall energy consumption practices as well as trying to find more efficient energy storage and transporting. We need to be more holistic in our thinking and not just focus on one or two popular options. Even some taboo ideas may be worth revisiting with a data-driven approach as opposed to halting some efforts based on emotional/political reasons.

How can our readers follow you on social media?

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

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