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.
Classrooms today are no strangers to coding or robotics―but few classrooms in the US currently teach artificial intelligence, despite AI being applied across almost every industry. Seeing this need developing, in 2017 Iridescent teamed up with NVIDIA to develop a curriculum that would demystify AI for youth, teaching them real-world ways AI can be used for good and introducing them directly to AI tools they can use themselves.
Iridescent CEO Tara Chklovski and Joe Bungo, Deep Learning Institute Program Manager at NVIDIA, recently had the opportunity to discuss this collaboration and share what we’ve learned in the first year of running the AI Family Challenge at SXSWEDU.
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.
She recently sat down with Iridescent CEO Tara Chklovski to discuss her work with education technology, the elements of a good problem, and her advice to staying motivated in the face of setbacks.
Tara Chklovski:Thank you so much for talking to me today. Tell me about the problems you work on and why you chose them.
Rose Luckin: My work is really about trying to help individual learners understand more about themselves and develop a more sophisticated understanding of where knowledge comes from, what evidence is and why they should believe something or not. And then, beyond understanding themselves in terms of their knowledge, also understanding themselves in terms of their emotions, social intelligence and awareness of their physicality in the world.
Most of what I do is trying to understand human intelligence and see how we can use artificial intelligence to help support our own intelligence. I find this increasingly involves talking to broad audiences to help people understand what AI is and what it’s good for.
That’s about half of my time. The other half of my time I spend working with startups and small and medium enterprises, some of whom are using AI to develop tools, techniques or methods that can support teaching and learning. I have a program called EDUCATE, which links startups and SMEs to researchers who are working in an area that’s relevant to them and to educators and learners for whom they are trying to develop — trying to raise the quality of the conversation around evidence and how we know if something works.
It’s National Engineers Week! National Engineers Week is an opportunity to celebrate engineers and their work. In particular, we like to focus on celebrating the amazing engineers who mentor students and families and share their passion and expertise with learners around the world.
Meet Vigneshwer, a data scientist in Coimbatore, India. Vigneshwer volunteered as a mentor for the AI Family Challenge this year, where he worked with local students and their families and taught them about artificial intelligence. Inspired to help people move beyond seeing AI as a “black box they’re not really familiar with” to understanding the ways AI is a tool they can use to make their lives better, Vigneshwar guided families through hands-on projects and lessons about AI concepts and tools, and hoped that they would also tap into their curiosity to keep learning beyond the program.
“You need to learn continuously…that’s the most important thing as a human that you need to do.”
This week the Trump administration released an executive order on American leadership in artificial intelligence (AI). The order outlined education and funding priorities necessary for the U.S. to remain competitive in AI, one of the most rapidly advancing technologies in history. Recently Iridescent CEO and founder, Tara Chklovski, shared her initial thoughts about the plan with Education Week. While it’s great to see the administration prioritizing AI research and workforce retraining, she noted it’s missing two crucial pieces: K-12 AI education and ethical development of AI.
“The key to U.S. competitiveness in AI may be locked inside the minds of the children and teenagers who will grow up in a world increasingly defined by automation technologies,” Chklovski explained. “Without a concerted effort to teach AI principles to children, the U.S. risks putting students at a disadvantage once they enter the global workforce.”
K-12 AI education must go beyond technical information in textbooks
Highlighting countries making substantial investments in AI education like China, she pointed out hands-on, project-based curriculum as an opportunity for the U.S. to create richer learning environments. Teaching soft skills alongside technical ones helps prepare learners for a career path where the impact of emerging technologies on the future of work is less known.
“Countries like China, with students frequently outperforming American students in science and math, are investing a lot of money in AI education. The U.S. has an opportunity to excel by building skills that go beyond textbooks. One way is connecting technical skill building with opportunities to solve real-world problems. Through our AI Family Challenge program we’ve found that challenging children and adults to learn about technologies like AI and then having them apply those skills to solve real-world problems helps them build job skills like curiosity, creativity, and collaboration.”
The ethical development of AI needs the same level of care and attention as privacy concerns
In addition to K-12 AI education, the plan doesn’t address the issue of ethics. Trust and safety considerations like data privacy are important. But issues of bias, fairness and algorithmic transparency are also crucial to ensure AI technologies are representative of the populations they serve.
“There is an alarming lack of diversity among the people who are currently building the algorithms transforming industries,” said Chklovski. “If the issue is not addressed on a national scale, the gap between the people who can access and provide input on building the future of AI and those who cannot could lead to long-term bias against the latter and greater economic disparity in the country.”
Adults and children must feel empowered to learn about new technologies and have the opportunity to use them in meaningful ways. The new executive order puts attention on an important conversation. But, it is only the beginning of what needs to be a much larger, ongoing partnership between government, industry and academia.
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 with 30 million + users), a MacArthur Fellowship recipient, and Computer Science Professor at Stanford University.
Recently, she discussed machine learning in the biomedical industry as well as what she thinks are vital characteristics to achieve individual success with Iridescent CEO & Founder Tara Chklovski.
Tara Chklovski (TC): Can you share two of the most interesting problems that you’ve tried to tackle in your career and what you’re working on now?
Daphne Koller (DK): My work cuts across a variety of fields including biomedical, education, and problems like computer vision. There are two specific biomedical projects I’m very proud of. The first one involved working with a neonatologist, a doctor for premature babies, to predict survival rates for neonates. Neonates are premature, teeny babies – they’re about the size of your hand and weigh less than 200 grams. They often struggle to survive. The doctor and I worked together to predict the risk of death for these babies. Our hope was to help doctors identify and help the babies at highest risk survive. In our study we didn’t rely on the traditional tests for babies’ health (which we found don’t work well for babies born that early), and we didn’t want to rely on invasive measures like sticking these tiny babies with needles! So we used the data from the bedside monitor. The bedside monitor measures things like the baby’s heart rate, respiratory rate, and their oxygen saturation. It turns out that there are markers in that non-invasive data that can predict survival rates, and that you don’t need that much data to make useful recommendations. We were able to make predictions based on data collected from the first few days of the baby’s life.
Another project I did with a PhD student of mine who was an MD/PhD pathologist. We were looking at images of tumors from breast cancer patients and trying to predict five-year survival rates to help organize patients by risk. We took a data-driven approach. Pathologists have been looking at these sort of images for a century. For our project, we included more features in our dataset than the standard features pathologists consider. We put in everything that we could think of, including hundreds of features that no one has ever looked at before. As a result, our predictions were better than pathologists’, and the features that were being used by our predictor were quite different from the ones that pathologists had been looking at. Specifically, they had been looking at features of the tumor cells, and it turned out that the features of the cells that surround the tumor were actually more predictive! Today, this is well recognized as a critical factor in cancer survival, and is called the tumor microenvironment. This microenvironment where cancer lives, and the immune system are factors that are absolutely critical in cancer patient survival. Our paper was one of the earliest pieces of evidence supporting that.
AI is changing the world – everything from the way we shop for groceries to the way we hire people for jobs, but it doesn’t really reflect the world it’s changing. Wired estimates that only 12% of leading machine learning researchers are women, and we know that a lack of diverse AI researchers means that the technology they build skews towards a white and male default representation of the world, effectively reproducing and worsening existing human bias.
The technology that’s shaping our future needs to be built by people who represent the diversity of humanity, in order to ensure that the future is designed for all of us. Today we’ll start highlighting the many amazing women already doing inspiring work with Artificial Intelligence. In the first of a series highlighting women using technology to solve big problems, here are 5 women in AI you should know, sharing their work, their inspiration, and how they find problems they want to solve.
As part of our AI in Your Community series, I spoke with Pierre Bonnet, a tropical botanist, and Alexis Joly, a computer scientists who have been working on a project called Pl@ntNet for the past ten years. Pierre and Alexis work together to develop tools that teach people about biodiversity and plant identification while also building a collaborative data set that spans continents.
Tara Chklovski: Let’s start by having you introduce yourselves and tell me a little bit about your work and the problems that you’re trying to solve.
Pierre Bonnet: I’m Pierre Bonnet, I’m a scientist, mainly working in tropical botany. I work at the CIRAD Institute – we conduct research in tropical regions, which are hotspots for biodiversity. I’ve been working in the field of biodiversity informatics for 12 years now. From my point of view, my purpose is to collaborate with computer scientists to design a new approach to solve problems, like the problem presented by identifying hard-to-identify plants at a large scale.
I have worked with developers on tools for plant identification in tropical Africa and southeast Asia, and for the last ten years or so, I’ve been working with Alexis on the Pl@ntNet project. With Pl@ntNet we’re dedicated to trying to solve the problem of identifying plants at a large scale using images. My field is mainly botany so I collaborate with engineers and computer scientists like Alexis – Alexis has been my main collaborator for ten years now. Alexis?
Alexis Joly: My name is Alexis Joly. I’m a computer scientist and part of a research organization in France, called Inria. I’m a specialist of machine learning and computer vision technologies, and I’ve been applying this research to biodiversity and informatics for more than ten years. As for the Pl@ntNet project, at the beginning it was really a research project, with the idea of building and evaluating the technology, and so we have spent many years improving all these technologies and evaluating them at a large scale with researchers.
For three years we have been funded by an educational initiative called Floris’tic, and we have collaborated with similar associations all over the world to do a lot of activities related to education.