Vivienne Ming: Women in Tech, Careers & Industry Barriers

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To mark International Women’s Day 2016, we interviewed leading women in the fields of science and technology, to celebrate their achievements and to explore the challenges faced by women in the sector. 

Vivienne Ming, named one of 10 Women to Watch in Tech in 2013 by Inc Magazine, is a theoretical neuroscientist, technologist and entrepreneur. Vivienne holds high-level roles in many companies, including Co-founder and Executive Chair at Socos, Chief Science Adviser at Shiftgig, and Vice-Chair of the Board of Directors for StartOut.org.

What inspired you to begin your work in technology?
I've always been a scientist at heart, and all my life everyone assumed it was a destiny. I managed to trick them all by ruining my life and becoming nothing. Years later I realised I didn't want to do science, I wanted to good. It turns out that science is an amazing tool for good. Becoming a theoretical neuroscientist gave me the set of tools I needed to achieve my purpose... and it happens to be fantastically cool :)

What do you find exciting about your current roles?
I travel the world working on dozens of different projects: neuroscience, cybernetics, AI, education, labor markets, diversity & equity, and more. The privilege of touching so many lives is more than I ever thought I'd achieve.

What challenges are you tackling in your work?
Someone recently described me, in a bout of flattering exaggeration, as a "one woman Alphabet", with multiple companies and nonprofits solving problems in many different fields. At the highest level I work to maximize human potential, assuring the everyone's life story is filled with health, happiness, and impact. This ranges from writing books to studying the labor economics and AI to developing machine learning tools to support bipolar suffers. A principal focus of mine right now is maximising the life outcomes of young children by combining machine learning, learning sciences research, and simple text messages.

What can we do to ensure equality in science and technology?
One of the core tenets of science is that human intuition in not a guide to truth. We've developed sophisticated methodologies to actively challenge our own biases in the search for understanding, and yet we routinely let these same biases and limitations define our assessment of others. Discrimination is a tax that compounds over a career. It's root is in our inability to accurately value people who differ from societal norms and from ourselves. We need to stop "blaming" young women for not "leaning in". Stop instructing them to be more like men.

I put the burden on leaders. The best science and the most impactful technology will come from institutions and companies whose leaders accept that change needs to come from them.

How can we help and inspire more people from under-represented groups to become involved in science and tech?
The number one predictor that an exceptional young person goes into science, technology, or any other high-value field is the belief that they can do it too. Not simply that they are capable, but that they will be accepted. Specifically that means role models and active support.

Which emerging or future technologies are you excited about?
Cognitive neuroprosthetics -- I want to literally make smarter people. And yes, it is also the technology of which I'm most terrified. 

What areas of emerging technology do you think will have the biggest impact your field?
Near term, the biggest impact will come from ubiquitous computing. Low cost, distributed sensor systems, from wearables to smart building, can transform the human experience. I am worried, however, that fights over platform ownership, data hoarding, and proprietary formats will hobble the emergence and value of these systems. We need a more open and pluralistic approach to data.

What do you think the biggest barriers are for women entering the industry? How can we overcome these?
The human brain. We treat our intuitions as truth when they might better be thought of as the answers to questions we didn’t realise we’d asked. If we never probe those questions, how can we understand the answers. For example, the answer, “I want to hire him,” means something very different if the real question is “Who do I want as a drinking buddy?”

Do you feel you have faced challenges in your field because of your gender?
I spent one career in science and entrepreneurship as a man and another as a woman. The differences have been profound and stark. My life as been an experiment with a very clear result: bias is an inescapable part of human experience, but it can be overcome.

What do you feel are the biggest challenges that the world faces today? How can technology and science solve them?
There are many real, existential challenges we must solve. My own focus is equity -- globally and locally, race and gender, economic and political. But I’m not interested in settling for passive, undemanding equity. I want a world were we make an active effort fully realise everyone’s creative potential.

What advice would you give to someone starting a career in science or tech?
Read the webcomic Saturday Morning Breakfast Cereal (generally) and the strip Many Lives (specifically). Take real risks with your lifetimes and please make those lives count. 

My own additional advice is to find your fanaticism -- that thing you do even when people tell you not to. That is your path to life and career success. It may not be somethings you “love” to do; it’s something you have to do. Pursue that Muse and good things will come to you.

Check out our Women in Tech & Science series for more Q&As. If you'd like to contribute to the blog, please get in contact with Sophie via our form here.

We are holding an evening of discussions & networking around the progress and application of machine intelligence in the healthcare sector, and celebrating the women advancing the field. If you would like to join us at the Women in Machine Intelligence & Healthcare Dinner in London on 12 October, please visit the event site here to book your tickets.

Machine Learning Neuroscience International Women's Day Women in Tech Machine Intelligence


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