5 Women working in AI to Know

AI is reshaping our world, from the way we hire people for jobs to the way we drive, but currently it’s a poor reflection of the world it’s changing. Most leading machine learning researchers are white men – and we know that a lack of diversity in the people who build technology is reflected in the impact of that technology. If we want a future that’s designed for all of us, we need to make sure that the people helping build the technology that shapes that future are a fair representation of the world at large. This is the second installment of our series highlighting women using technology to solve big problems. Meet 5 women in AI you should know.

Rose Luckin, Professor of Learner Centered Design at the UCL Knowledge Lab

Field of focus: Educational technology

Rose Luckin is a Professor of Learner Centered Design at the UCL Knowledge Lab in London. She researches how educational technology is designed and how it is evaluated. Professor Luckin is particularly interested in using AI to show teachers and students how people learn and how learning is cognitively, socially and emotionally shaped. She is also the Director of EDUCATE, a hub for Ed-Tech startups in London. In 2017, Rose was named on the Seldon List as one of the 20 most influential people in Education.

What she likes about working in AI:

We asked Rose about what she likes about working in AI, and what advice she would have for people as they learn about AI:

“For me, AI is all about problem solving, and it’s all about understanding the problem well enough that you can specify a solution and then decide how you use AI to help you with that solution. Be really curious, and demand of anybody who’s offering you AI that they explain to you how it works. Don’t be shy. Don’t be brushed off. Demand an explanation.”

Daphne Koller, CEO and Founder, Insitro

Field of focus: Biomedical applications of Artificial Intelligence, Online education

Daphne Koller leads Insitro, a company applying machine learning to pharmaceutical drug development. She is also a co-founder of Coursera (one of the world’s largest online education platforms), a MacArthur Fellowship recipient, and Computer Science Professor at Stanford University. Two of her recent projects have included using data from bedside monitors to help doctors identify and help the most at-risk premature babies survive and using images of tumors from breast cancer patients to predict five-year survival rates.

What she likes about working in AI:

We asked Daphne about what she finds most exciting about AI:

“I think AI is a fundamental technology that enables us to use computers to solve problems that are so hard that we don’t know how to solve ourselves. That includes everything – from recognizing images, to designing cars that drive themselves, to designing better drugs. People have been bashing their heads against those problems for a very long time and not succeeding.

There are so many problems around us that are really important. People should go and seek them out.”

Emilia Gonzalez

Emilia Gomez, Lead Scientist of the HUMAINT project

Field of focus: Music Information Retrieval

Emilia Gómez is Lead Scientist of the HUMAINT project at the Centre for Advanced Studies, Joint Research Centre, European Commission. She researches music information retrieval—an interdisciplinary area dealing with music and artificial intelligence – at the Music Technology Group, Universitat Pompeu Fabra in Barcelona, Spain. She also recently announced the divinAI initiative to monitor the presence of women in AI events.

What she likes about working in AI:

We asked Emilia  about what she finds most enjoys most about working in AI, and what excites her about the future of AI:

“I enjoy working with data and discovering certain patterns that cannot be seen on a first sight. Artificial Intelligence can be used in many different ways, so it is important to create AI tools that can assist in different tasks, complement our skills, and increase our opportunities. It is also important to understand how artificial intelligence works, and the secrets and good practices that should be followed.”

Anne Carpenter

Anne Carpenter, Senior Director of the Imaging Platform at Broad Institute

Field of focus: Biomedical Research, Computational Biology

Dr. Anne Carpenter is Senior Director of the Imaging Platform at Broad Institute of MIT and Harvard. She leads the Carpenter Lab where she directs a team of biologists and computer scientists in developing image analysis and data exploration methods. She and her team are developing software to accelerate drug discovery using image analysis methods.

What she likes about working in AI:

We asked Anne about how she finds problems she wants to solve and what makes a good solution to those problems:

“Don’t assume that everyone is exactly like you – think how might this be used by somebody who’s older than you, younger than you, a different gender, or in some kind of different life circumstance. Ideally, try to talk to those people and have them test your software as well. So many apps these days are written by young professional Caucasian men, leaving a lot of opportunity out there if you see a need in the world that they don’t.”

Julita Vassileva

Julita Vassileva, Professor in Computer Science at the University of Saskatchewan

Field of focus: Social Computing

Julita Vassileva is a professor in Computer Science at the University of Saskatchewan who is currently focused on building successful online communities and social computing applications. She is particularly interested in user participation, user motivation, and designing systems that incentivize people to continue participating in online communities.

What she likes about working in AI:

We asked Julita to give us advice about finding a problem to solve:

“Start simple, to solve a specific problem. Don’t wait to become interested because if you wait, there are so many interesting areas which you never get exposed to in school. So how would you know about this? How would you know if you’re interested? So start somewhere, work hard to become good in it, and then you’ll get interested. You’ll get excited, and it becomes a passion. Once you get passionate you will be good at it.”

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