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 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.

Pierre Bonnet, Alexis Joly and Jean-François Molino, winners of 2016 La Recherche prize | © Pl@ntNet / 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 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.

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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 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.

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!

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.

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Iridescent Year End Review: What we learned in 2018

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.

Map of problems Iridescent students choose to solve by country

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.

Iridescent 2018 Mentor Skills Development

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!

How you can explore new technology together as a family: Mother-Daughter AI Innovators in Detroit

Christina is 9 years old. She loves dancing, art, and science, and every morning she recites her own affirmations to remind herself of her inner strength and values.

Dina is Christina’s mom. She encourages and supports Christina to try new things, reminds her of her beauty and her intelligence, and helps find new opportunities to continually help Christina explore her curiosity. So when the community resources center where Christina takes dance lessons announced they would be offering the Curiosity Machine AI Family Challenge, Dina signed them up, excited by the opportunity to learn more about science and technology. With that, Christina and Dina became part of the community of nearly 7,000 students and parents  participating in the AI Family Challenge across the world.

Since then, Dina and Christina have attended every session of the program, learning more each week as they work through progressively harder projects. For Dina, it’s led to her feeling more positively about AI –  “I feel like I’m open to AI, I think it benefits our society, but [there’s] a small percentage that feels like will it take over… but after the AI class, it gives you more of the insight on how safe it can be vs. a human as an example. It gives you behind the scenes insight.”

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Hands-On AI Activities Welcome Parents to the World of Technology

“[Today’s activities] showed us that math and science can be very fun; when you work as a team you can accomplish anything.”  – Flodinita Santillan, Parent


Shifting From Fear to Fun

“We had fun racking our brains to figure out how to complete each activity and they liked it! This really gave me an idea of the engineering field.” – Bianca Loaiza, parent

While AI isn’t new, the media’s sudden focus on it – good and bad – has brought it to everyone’s attention. The perception of AI-driven machines is both, embraced and shunned, for its potential impact on society. Arizona has been especially ridden with fear after an incident in March where a self-driving car killed a woman.

When Iridescent’s Founder and CEO, Tara Chklovski, asked the girls and their families who found AI mysterious and scary, a room full of hands shot into the air. Tara explained that we are not familiar with positive instances of AI in action because these examples, such as video games or search engines, are so integrated into our everyday lives we don’t recognize them as AI.

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An Interview with Gabriel Torres: AI, Agriculture and Drones

Gabriel Torres

Gabriel Torres

As part of our AI in Your Community series, I sat down with Gabriel Torres, an unmanned systems expert and CEO and co-founder of MicaSense, which brings unmanned systems and sensors to the agricultural industry. We discussed his work with MicaSense and how they’re working to help the agricultural industry take better advantage of tools and technologies.

Tara Chklovski: What problem are you working on?

Gabriel Torres: In general, optimization. Ever since I was in school I’ve been interested in efficiency. 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 how I can impact somebody’s life for the better. I’ve done that throughout my career.It’s been really rewarding, to really make an impact and to touch lives in ways that I would normally not be able to.

TC: Tell me more about MicaSense.

GT: With MicaSense we started to make it easier for the agricultural industry to catch up with and take advantage of technologies that haven’t really been available or accessible, and provide a benefit, in terms of quality, quantity, and yield.

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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.

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

Kasia Muldner: 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

As part of our AI in Your Community series, I sat down with Manuela Veloso, a renowned expert in artificial intelligence and robotics. Manuela Veloso is the Herbert A. Simon University Professor in the School of Computer Science at Carnegie Mellon University and a former President of the Association for the Advancement of Artificial Intelligence (AAAI). She is also the co-founder and a Past President of the RoboCup Federation. We discussed her work, how she started working with robots, and what advice she has for students about identifying good problems to solve.

Manuela Veloso

Manuela Veloso

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 is the ability to interpret data, or assess the world through sensors – and then I combine it with reasoning, which is cognition, or the part of thinking to plan actions that allows an AI system to achieve some kind of goal. And then the third component of this kind of AI story is the action, where these robots, these intelligent agents, do take action in the world by changing the state of the world.

So, I have been working on this problem of integrating perception, cognition, and action. I can also usually think about these as autonomy – so, agents or AI systems exhibit autonomy in their integrations of assessing the world through their perceptions, eventually making decisions through their cognition, and finally actuating in the world. So, within that goal of autonomous agents, I’ve been working a lot with autonomous robots. But I also work with software agents.

TC: And have you created robots to try to accomplish a particular task?

MV: Yeah – basically, these autonomous robots can be thought of as designed to achieve tasks. I’ve also worked on robots that have been part of the robot soccer team. These soccer robots address multiple problems of working on a team – problems of coordination and trying to work as teammates, and in very uncertain environments like in the presence of an opponent. I’ve done robot soccer research since the mid-90s, always trying to make these teams of robots capable of addressing the complications of playing an adversary. Soccer captures all sorts of problems of teamwork at the physical-space level.

So, I’ve done that, and then I’ve also worked on autonomous robots called “service robots” which are capable of performing tasks for humans like driving people in a particular kind of building, taking them to particular locations, picking up and delivering objects. And so that involves navigation, but instead of being on roads it’s inside of buildings. I’ve been working on these mobile indoor service robots – not just service robots that will wash your dishes, but service robots that actually navigate our environments.And then I’ve done many autonomous robots for lots of education purposes, like NAO robots, humanized robots, all sorts and shapes of robots, but basically they are all machines or artificial creatures with a way to perceive the world, like cameras or sensors and then potentially some computing that enables them to think about what they should be doing, which they then actuate with their wheels or their legs or their arms or their gestures.

TC: That’s totally fascinating. And I love the robot soccer! I’m curious, how did you get interested in thinking about making robots play soccer?

MV: I actually was not interested in the soccer problem per se. I was working on planning and execution for robots when one of my students, Peter Stone, was exposed to this idea through a little demonstration of small robots playing soccer. And then we started doing this research on autonomous robots that would be able to play on a team. I don’t have an interest in soccer really, but it gave us a tremendously challenging and exciting infrastructure to think about the problems of autonomy.It’s hard to do autonomy – for instance, autonomous cars are expensive. Plus it’s complicated. And the soccer problem in the lab became a very good framework to actually study the problem of autonomy. And it’s still very important. It’s still very challenging as we add to the research challenges and scale up (by making the teams larger, for example). The soccer was always kind of an excuse to study these very difficult autonomy problems.

TC: It constrains it, right? What advice would you give children as they try to find a problem in their communities that they can solve using technology?

MV: I think that’s a very interesting question. I can’t really advise children to follow what they like to do, since that didn’t apply to me. I never thought about doing robotics. I never loved robots, although I liked math. I liked the rigor of mathematics very much. And then as time went on I became an electrical engineer, and then I started to be very interested in computers.But for children I think the important thing is to think currently. Many of the jobs that are exciting now, be it a medical doctor, an economist, a banker, a teacher, a computer scientist – there is a lot of data involved. So, these computers that we have now, the Fitbits that we wear, the little Alexas we talk to, everything is about accumulating data, accumulating information. Computers now have access to everywhere you go because you are carrying a cell phone that has GPS. So, children need to understand the beautiful concept of trying to make use of all this data. There is a kind of data skill-set that maybe kids could be exposed to. For example, count how many steps it takes to go from here all the way to the kitchen. How many times did you see a specific person this month? Let’s do a little counting here and then they can start understanding probability, they can start understanding distributions, they can start understanding that a lot of the information to be processed is of this data nature. It’s beautiful to try to understand that these children are going to more and more be in a digital world in which a lot of the information is data. This concept of space, this concept of data, this concept of being aware that many things are numbers that become parts of a computer, I think it’s an interesting way of thinking.In addition to reading and writing and math there are these new skills that in some sense are data skills. They talk with Alexa, they can count things, they have this understanding of how these things get in a computer.

TC: One last question before we go: in your research I’m sure you encounter a lot of obstacles. So, what are some strategies that have worked for you that help you to stay motivated and keep pursuing the end goal?

MV: Very good question. I think that the way to think about this given that there are so many difficulties in research – there’s a lot of bottlenecks and very few breakthroughs – it’s the ability to actually collaborate with other people. When you are stuck, there is a component of collaboration, to engage in thinking together about difficult problems rather than facing the difficult problem by one’s self. That always helped me.At the research level I’ve worked very closely with my students; it’s a joint pursuit of difficult problems. And in children, we need to also grow that skill of collaborating with other people. It helps to interact with others, and of course having depth and understanding and persistence and not giving up.Problems that are hard are the most interesting ones, but are the ones that require more dedication, more persistence, more thoroughness. You become aware that when you are stuck in a difficult problem it’s both an opportunity to make a great discovery at the same time as it is frustration of being stuck. So, if life would not have any difficult problems then you would solve all these simple things that then would not have an impact. So, it’s good to have challenges, but you also know that you might have the potential to make a big difference if you persist on solving challenging problems.

TC: Yeah. And I think as a child, if you have not had that much experience being successful then it’s even harder because then it’s scary, right? Like do I have what I need to succeed? And so, you need to have the mentors and the parents all part of it.

MV: Yeah. So, Tara, one final thought then: it’s true that it’s difficult, but on the other hand somehow when you collaborate with other people, older people or just other people, by magic someone helps you break this difficult problem into steps and it becomes, “let’s see if we can do this thing, which is much simpler, not the actual big problem.” But it becomes an ability to let the child make incremental progress towards solving problems of a staggering nature. So, there’s no problem that can’t be solved without this kind of breaking them into pieces.And so, I think that’s what people should think and tell children. Even if it seems very hard maybe there is a much simpler problem that’s on the way to solve the hard problem that they can address now. That’s basically what we do in the research world, and that’s basically what we do in our education. We build an upon the difficult concepts. We don’t just present them. They are broken down. It’s a confidence‑building process, but it’s also about showing you are on the path so solve the bigger problem.That’s a skill that we develop as we become teachers, and as we become researchers and parents. The problems are difficult but it’s good that they are difficult. It’s just that you can make incremental progress as you go.

TC: Totally. All right, Manuela, thank you so, so much.