Women of the AI: “ Unless women take the leap to join the industry in spite of these stereotypes, this culture becomes a self-fulfilling cycle.” with Cynthia Freeman and Tyler Gallagher

We (both women AND men) need to rewrite the prevailing culture in the technology industry. At this point, many women are choosing to stay away from the industry based on its reputation as a male-dominated, insular environment. Unless women take the leap to join the industry in spite of these stereotypes, this culture becomes a […]

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We (both women AND men) need to rewrite the prevailing culture in the technology industry. At this point, many women are choosing to stay away from the industry based on its reputation as a male-dominated, insular environment. Unless women take the leap to join the industry in spite of these stereotypes, this culture becomes a self-fulfilling cycle.

I had the pleasure of interviewing Cynthia Freeman, a research engineer at Verint Intelligent Self-Service, a developer of conversational AI systems. She holds an MS in applied mathematics from the University of Washington and a BS in mathematics from Gonzaga University. Cynthia is currently pursuing her PhD in computer science at the University of New Mexico, where she works on time series analysis and developing new anomaly detection methods.

Can you share with us the ‘backstory” of how you decided to pursue this career path?

Although I am sure there are many people out there who knew very early on what they wanted to be, I originally had no idea! I majored in pure mathematics in my undergrad studies but was not entirely sure on where I wanted to go from there. My internship with NextIT (which is now Verint Intelligent Self-Service) was very enjoyable, and that helped steer me to focusing more in computer science, machine learning, and NLP.

What lessons can others learn from your story?

I think that it is always important to acknowledge that one does not get to where they are without the help and support of others. My family, friends, teachers, and mentors play such a big role in my life, and I hope to pay it forward in the future.

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

We have a patent pending for a system we invented for detecting bias in training data for IVA understanding. The technology we’ve developed highlights any bias found and provides suggestions to reduce or eliminate them. It’s been an incredibly exciting project to work on as it gets at one of the most pervasive problems with AI today.

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?

I have to give two thank yous here: one professional and one personal.

Professionally, I have to thank my boss Ian Beaver who guided me a lot during my internship and my career. One thing that he has told me that has greatly inspired me is that every morning, he is excited to go to work because he loves what he does. Life is too short, and it is my hope that I will be forunate enough to always work on what I am passionate about.

Personally, I have to thank my husband’s family. They took care of me when I was in high school and my biological family fell apart. Having a safe place to finish my high school studies and prepare for college definitely helped me in being where I am today.

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

1) Machine Learning Interpretability: This is vital as we want to not only know the classifier’s decisions but also WHY it makes its decisions. If the data used to train the classifier is biased, the classifier will be biased also. This is dangerous especially when the classifier is being used to guide self-driving cars, automate college application decisions, diagnose diseases, etc. I am glad there is a rising interest in the field of interpretability and hope that it can help us build trust in classifiers.

2) Reproducibility: It is becoming increasingly important to create reproducible research results. Reproducible research is vital for not just confirming the accuracy of results but also for extending such works. Reproducible research is also necessary for businesses expecting returns from investing in innovation and data analysis.

3) Easier access: It is becoming easier and easier for people without a computer science background to learn more about ML and AI. You do not even need to go to school for this knowledge; for example, Coursera offers many classes on these topics online for free.

4) BERT: Developed by Google, this is a new language representation modeling technique that has achieved state-of-the-art results in many NLP tasks such as Question Answering. See more details on the BERT paper here:

5) AutoML (Automated Machine Learning): This is sort of along the same lines as (3), but imagine if you could easily create machine learning classifiers without coding! Check out Auto-Keras, for example:

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

1) Terminology: Machine learning, deep learning, and artificial intelligence are super hip words to use these days, but unfortunately, lots of people still don’t know how to define it. This can create a lot of problems between what customers/clients may expect and what can actually be done.

2) Academia vs Industry: I am a big, big fan of industry, but I am also afraid for the fate of academia. Academic talent is being rapidly recruited into industry (there is more money and resources there) which can create a lot of problems in raising a new generation of AI and ML researchers. In the long run, this would hurt industry also. Academia and industry need to work hand in hand for the future.

3) ML for good: Google recently announced they would not be renewing their contract with the military involving the integration of machine learning with battlefield technology (Project Maven). With the rise of new technologies, we must always think about the potential consequences.

4) Human Oversight: Software developers are not building in robust enough human oversight of AI systems. This risks putting us in the position, five years from now, of it being too costly and disruptive to retool solutions for proper oversight, tempting enterprises to run largely self-governed AI within their firewall.

5) Security: Human error continues to cause the majority of security breaches. As long as we are building systems that blend humans and artificial intelligence to accomplish a task, that risk could extend to some of the most sensitive data. My concern is that the development of AI is prioritized far above the security systems and processes that should protect the data that fuels 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?

We SHOULD be concerned. However, I think there is also progress and healthy discussion; for example, due to massive employee protest, Google cancelled its Project Maven contract with the military.

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?

We need input from developers (like in the Google example) and also citizens, especially with regards to the usage of private data. It would also help if companies are more transparent on how they are using and protecting customer data. Relevant to machine learning interpretability, we also need to watch for classifiers that can exhibit biases and yet make decisions that greatly impact human lives (e.g. hiring decisions).

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

I think passion is contagious. I bring passion to every presentation I participate in, big or small. I find that this keeps the audience engaged regardless of subject matter. If my words inspire others to dig deeper, then my drive is renewed.

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) Find a mentor. A great mentor can make such a huge difference in your career.

2) Always be learning. Obtaining a degree does not mean you are done with seeking new knowledge and keeping up to date with new technologies. Many companies will also pay for you to join a professional organization or further your education part-time, but even if they don’t, you should identify what’s a priority for you and invest resources there. It’s the best way to ensure you stay competitive and engaged with the work.

3) Remember to take time for yourself. This is a hard one for me to follow admittedly! Exercise, eat healthy, and get enough sleep. I find that I work more efficiently and come up with better ideas when I am healthy.

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

We (both women AND men) need to rewrite the prevailing culture in the technology industry. At this point, many women are choosing to stay away from the industry based on its reputation as a male-dominated, insular environment. Unless women take the leap to join the industry in spite of these stereotypes, this culture becomes a self-fulfilling cycle.

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

”A satisfied life is better than a successful life. Because our success is measured by others, but our satisfaction is measured by our own soul, mind, and heart.”

Academia is an insanely competitive environment, and you have to learn to not define yourself by how many papers you have published or grants accepted. That is a path to stress and anxiety.

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. 🙂

The two areas I think would provide the most amount of good for people is in education and healthcare.

I think it is important to provide early computer science educational opportunities for youth. For example, CSforALL is making strides on this front. I’m also excited to see the ways in which technology can function as a tool to increase access to institutions like healthcare, as well as generally improve the patient experience. Already, we’re seeing the way technologies like IVAs are bringing the hospital home with patients and offering more continuous care. Additionally, we’re seeing artificial intelligence aid in catching diseases faster. I’d love to see even more momentum in these efforts, so we can offer improved care to even more people.

How can our readers follow you on social media?


Thank you for joining us!

About the Author:

Tyler Gallagher is the CEO and Founder of Regal Assets, a “Bitcoin IRA” company. Regal Assets is an international alternative assets firm with offices in the United States, Canada, London and United Arab Emirates focused on helping private and institutional wealth procure alternative assets for their investment portfolios. Regal Assets is an Inc. 500 company and has been featured in many publications such as Forbes, Bloomberg, Market Watch and Reuters. With offices in multiple countries, Regal Assets is uniquely positioned as an international leader in the alternative assets industry and was awarded the first ever crypto-commodities license by the DMCC in late 2017. Regal Assets is currently the only firm in the world that holds a license to legally buy and sell cryptos within the Middle East and works closely with the DMCC to help evolve and grow the understanding and application of blockchain technology. Prior to founding Regal Assets, Tyler worked for a Microsoft startup led by legendary tech giant Karl Jacob who was an executive at Microsoft, and an original Facebook board member.

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