5 Women You Should Know Working in AI

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.

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An interview with Pierre Bonnet and Alexis Joly: AI, plant recognition, and biodiversity

As part of our AI in Your Community series, I spoke with Pierre Bonnet, a tropical botanist, and Alexis Joly, a computer scientist, 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.

Pierre Bonnet, Alexis Joly and Jean-François Molino, winners of 2016 La Recherche prize | © [email protected] / Rémi Knaff

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.

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An Interview with Erin Bradner: Using AI to make construction easier

As part of our AI in Your Community series, I sat down with 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.

Erin Bradner, Director of Robotics at Autodesk

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!

TC: Like which ones?

EB: 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.

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An Interview with Gabriel Torres: AI, Agriculture and Drones

Tara Chklovski: What problem are you working on? Gabriel Torres:  In general, optimization. Ever since I was in school, optimization has been something that’s been a big part of me. I see inefficiencies in the world, and I see opportunities for things to be easier, better, more transparent, more universal. I always like to see […]

Kasia Muldner: Artificial Intelligence and Student Learning

As part of our AI in your Community Series, I recently had the opportunity to sit down with Kasia Muldner, an assistant professor in the Institute of Cognitive Science at Carleton University. She works with intelligent tutoring systems to better understand student learning, problem solving, and creativity and the factors that affect them.

Kasia MuldnerTara Chklovski: Tell me a little bit about what kinds of problems you’re working on.

Kasia Muldner: I work in the field of learning and cognition, with applications to intelligent tutoring systems. My research focuses on student learning, including both cognitive and affective components. I’m particularly interested in factors that influence student learning and ways to improve it, which is where technology comes in.

TC: Can you explain what you mean by cognitive and affective components? What are the differences and how do you define them?

KM: Sure. Cognitive factors have traditionally been linked to domain knowledge – like the knowledge needed to isolate a variable in an algebra equation by subtracting some value from both sides of the equation.

Affect on the other hand is commonly used to refer to feelings, moods, or emotions – although these terms have distinctive definitions, they are often used interchangeably. When it comes to computer tutors, the field used to focus on designing support for cognitive factors, like having the tutor give the student domain hints. However, there is a lot of evidence that how students are feeling when they’re learning really influences what they learn and even whether they learn at all. So there is now a lot more work developing tutors that can both detect how students are feeling and respond to that emotion.

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An Interview with Manuela Veloso: AI and Autonomous Agents

Tara Chklovski: Maybe you can start by telling us a little bit about what problem you are excited about and what you’re working on. Manuela Veloso: I’ve been working in the field of AI for many, many years. In particular I look at AI research as the challenge to integrate what I call perception, which […]

An Interview with Chelsea Finn: AI for Robotics

As part of our AI in Your Community series, I had the chance to speak to Chelsea Finn, a PhD student at UC Berkeley currently doing work with machine learning and robotics. Through her work she teaches robots how to perform tasks in multiple environments, with the goal of having these robots perform tasks for humans that they can’t perform, or that it would be dangerous for humans to perform.

Chelsea Finn

Tara Chklovski: Tell me a bit about your work. What research are you working on?

Chelsea Finn: I am a PhD student at UC Berkeley, and I work on machine learning and AI for robotics. A lot of my work entails having physical robots learn how to do things in the world, like screw a cap onto a bottle, or use a spatula, or pick up objects and rearrange them. Our goal is to have systems that can learn to do these different tasks so that they can go into a variety of environments and perform those tasks for humans – or perform dangerous jobs that we don’t want humans to do.
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An Interview with Julita Vassileva: Artificial Intelligence and Online Communities

As part of our AI in Your Community series, I recently spoke with Julita Vassileva, a professor in Computer Science at the University of Saskatchewan who is currently focused on building successful online communities and social computing applications. Julita Vassileva is particularly interested in user participation and user modeling, as well as user motivation and […]