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.
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 [email protected] 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 [email protected] project. With [email protected] 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 [email protected] 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.
As part of our AI in Your Community series, I sat down to interview Erin Bradner, the Director of Robotics at Autodesk, which aims to solve complex design problems from ecological challenges to smart design practices. Erin has researched topics ranging from from the future of computer-aided design to how to use robots in novel ways to automate processes in manufacturing and construction.
Tara Chklovski: Tell me about what you’re working on.
Erin Bradner: I’m now the Director of Robotics at Autodesk, where we make professional software for architects, engineers, and animators. And what they’re looking to do is create more flexible manufacturing lines. And in construction, they’re looking to automate aspects of construction that have not been automated before.
In that sense, construction is aiming to be more like manufacturing, with assembly happening off-site to allow you to bring pre-assembled parts onto the construction site and have the construction site become an open-air assembly line. The traditional sort of stick-built architecture where you’re cutting timber on site is inefficient. The construction industry has not received the productivity gains that other fields have received through technology over the last 20 years. It’s been flat, and we’re helping to address that. There are a lot of interesting startups in construction at work too!
Tara Chklovski: Like which ones?
Erin Bradner: Well startups are doing what startups do – they’re laser focused on innovative, focused technology. For example, Built Robotics is looking at autonomous Bobcats to grade a building site. Usually a Bobcat is operated by an engineer and comes in to clear the site, but taking the technology used for autonomous vehicles, like LIDAR and vision sensing, they’ve developed an autonomous Bobcat that can clear the site on its own.
There’s another company called Canvas that’s just getting off the ground and is using soft pneumatic robots that are human-safe and applying them to the construction site to do dirty and repetitive jobs. Their robots are still in development, but they likely requires quite a bit of AI to integrate.
What Autodesk is looking to do, being a software provider, is not to make robots, but rather to connect our CAD software to robots and other machines to make it easier to build what has been designed. Because CAD – computer-aided design software – is what’s used to specify nearly everything that is manufactured or engineered today. There is CAD to map terrain for those Bobcats, there is CAD for the walls and the floor and other elements of the buildings. We want to bring CAD into these platforms, along with simulations, so you can simulate the robot in its environment before ever running an operation, and also use machine learning to train the robot to complete its tasks.
2018 was an important year for us – we launched the AI Family Challenge in 13 countries, reached more girls through Technovation than ever before, and helped Technovation students tell their story on Good Morning America. We helped demystify AI for students, families, and the general public through a series of public panels and debates, interviews, and created free curriculum in partnership with researchers and industry experts.
We’re proud of the work we’ve done this year, and more impressed than ever by the young people, families, educators, and community and corporate partners we work with who all tap into their courage to learn something new and create solutions to community problems.
As 2019 gets underway, we’ve been reflecting on our progress this past year (we ask our students to reflect on their work, so it’s only fair we do it too!) and the lessons we learned about our programs and their impact.
Our participants are ready to change the world. Every year Technovation students address the same Sustainable Development Goals that the UN asks world leaders to tackle – like health, the environment, education, and inequality. We want to make sure that they have the skills they need to keep working on them long after our programs end.
Preparing for the Future: Computational Thinking and 21st Century Skills
We equip students to solve the problems they care about most by teaching them basic technological literacy skills – and then having them apply those skills directly.
Technovation students develop a basic understanding of programming and improve their computational thinking skills. In partnership with MIT, we evaluated projects submitted in 2018 and found that students demonstrate development of key computational thinking skills.
Evaluations from MIT, WestEd and Oregon State University found that after participating in our programs, students are more self-confident, better problem solvers, better entrepreneurs, moreresilient, and more self-reliant. We even found that after continuous exposure (16 or more hours) to our programs, students perform better on standardized tests.
Getting Ready for the Future of Work: Professional Development for Mentors
Mentors are vital to our programs’ success, and we are committed to ensuring that their experience supporting girls and families is positive and enriching. In 2018, we engaged over 4,500 mentors in our programs.
Mentoring helps professionals develop the soft skills they can use to advance and adapt in their field. Before beginning the season, only 25% of Technovation mentors were confident in their ability to mentor a team. By the end of the season, 82% of mentors expressed confidence in their mentorship abilities.
- 66% felt they had improved at mentoring youth
- 50% improved at ideation (developing innovative ideas)
- 47% improved their team building skills
We anticipate that these skills will continue to increase in demand as professional development efforts focus on “soft skills” and lifelong learning initiatives in 2019 and beyond.
Take a look at our 2018 Year’s End infographic to learn more about what students and mentors learn and how they grow more prepared to solve big problems. You can also get a peek at what we think the top trends in Artificial Intelligence fields will be!