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.

Family Interest in Artificial Intelligence

Parents Look For Ways to “Tech-Proof” Their Family for Impact of Artificial Intelligence

A recent study commissioned by Iridescent reveals 86% of parents want new ways to learn critical computer skills outside traditional classrooms such as taking a class, joining a club, or participating in events for more guidance on at-home education. The online study, conducted by VeraQuest, surveyed parents of 3rd – 8th graders to better understand their views of Artificial Intelligence (AI) and their children’s learning experiences. In addition to new approaches to learning, the study found parents do not understand the extent to which AI is already integrated into their everyday lives, but an overwhelming majority (92%) understand that technology, such as AI, is rapidly advancing and their children need to learn about these new technologies to be prepared for the future.

Today, only 36% of children receive technology education outside of their school, and parents expressed concern in the current gap between their child’s interest level in learning about future technology and their preparedness for it. These trends are consistent with studies conducted by Google and Gallup, which found interest in computer science learning continues to be strong, but all students do not yet have access to these learning opportunities in class. The education gap is especially prominent in low-income communities. “We often talk to concerned parents who are wondering how to provide their children the tools and skills they need to have a bright future as technology and the skill sets needed to succeed rapidly evolve,” said Tara Chklovski, Founder and CEO, Iridescent. “We want to help these parents feel confident and optimistic about their family’s future in a world filled with new technologies. That’s why we created the Curiosity Machine AI Family Challenge.” Through the Curiosity Machine AI Family Challenge Iridescent is filling the education gap with immersive AI curriculum for children and their families. The program introduces AI to underserved families in a way that fosters a deeper understanding of AI and its real world applications and makes technology education accessible to all communities.Parents learn alongside their children as they create AI-based products that solve problems in their community. “My daughter very much likes science,” said a mother surveyed in the study. “I think [the Curiosity Machine AI Family Challenge] will give her an upper hand in the [AI] field as well as allow her to be as creative as she wants to be in building skills for her future.” Iridescent Curiosity Machine AI Family Challenge Family

Additional study findings for AI education:

Fears and Misperceptions Around AI

  • Our research found that 85% of parents understand that new AI technology develops rapidly, but less than 20% of parents know that Facebook, targeted ads, or other recommendation engines use AI technology. There is real danger the lack of AI knowledge and its rapid development will widen the “digital divide,” or information gap, between parents and technology.

Interest in Exploring New Technology

  • Regardless of their concerns, we found that parents still had a positive outlook on the future of technology. 63% of parents believe AI will be used to make the world a better place and 78% were especially interested in learning more about AI.

Join the conversation

Iridescent is hosting a series of panel conversations with leading AI and technology companies and researchers. Join us for a deeper dive into this new study and a thoughtful conversation about how to support families, parents, and communities in the face of a rapidly changing world. # # #

About the Survey

Methodology The survey was conducted online from January 11th to January 17th, 2018. The sample was comprised of 1,566 respondents in the United States ages 25+ who have a child in grades three through eight. The sample was constructed from U.S. Census proportions to be representative of the population based on age, income, education, race/ethnicity and geography. Targets were also used for residential status and grade level of child. The low-income group (585 respondents) also had targets for each of the above variables. These targets were created to be specifically representative of families earning under $50K annually with a child in third to eighth grade.

Rationale Iridescent, in partnership with the Association for the Advancement of Artificial Intelligence (AAAI), the League of United Latin American Citizens (LULAC), and NVIDIA Corporation, is encouraging families to learn about Artificial Intelligence technology through the Curiosity Machine AI Family Challenge. Over the next two years, the Curiosity Machine AI Family Challenge will invite 3rd – 8th grade students and their families to explore core concepts of AI research, apply AI tools to solve problems in their communities and have an opportunity to enter their ideas into a global competition.

Elizabeth Clark: Creative Writing with AI

As part of our AI in Your Community series, I spoke to Elizabeth Clark, who won the Amazon Alexa Prize for her work with Sounding Board, a social bot. Elizabeth is studying natural language processing and working on tools for collaborative storytelling.

Elizabeth Clark

Tara Chklovski: Tell me a little bit about what you’re working on.

Elizabeth Clark: Very broadly I’m working on natural language processing, so looking at how language and computers interact, and helping computers process language – either written text or speech. More specifically I’ve been looking at collaborative writing systems, which give people support and offer suggestions to them as they write. I’m exploring how we can build models that will generate suggestions that are helpful to people as they try to write, say, a short story. There are different levels to offer help to people as they write. You could point out grammatical errors or spelling mistakes, or you could offer suggestions about structure. The type of suggestions we’re interested in are focused on the actual content for your story.

Our goal is to look at what type of suggestions people want, and determine how we can give them suggestions that are coherent with the story that has come so far, but are still creative and surprising – all to try and spark their creativity as they write. As for what are useful suggestions, we’ve found that it really depends on who is using the system. Different people want different things out of these suggestions. Some people really like silly suggestions, that have these unexpected elements, and they’ll work really hard to try to find a way to work it into their story, embracing it as a challenge…where other people know exactly what they want to write and if the suggestion isn’t in line with that, then they will just delete it and write their own story. There does seem to be a tradeoff between the level of unexpectedness of the suggestion and how coherent it is with what has come before.

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Virtual humans and decision-making: A conversation with Stacy Marsella

As part of our ongoing AI In your Community series, I talked to Stacy Marsella, professor in the College of Computer and Information Science at Northeastern University and the Psychology Department. Professor Marsella’s research is grounded in computational modeling of human cognition, emotion, and social behavior as well as evaluation of those models. Tara Chklovski: […]

Machine Learning and combining common sense with data: An Interview with Fabio Cozman

As part of our AI in Your Community project, I spoke to Fabio Gagliardi Cozman, who is a Full Professor at the Engineering School  at the University of Sao Paulo, Brazil. He  works in the Department of Mechatronics and Mechanical Systems, in the Decision Making Lab, which focuses on Artificial intelligence. Tara Chklovski: Tell us […]

Game Theory and Machine Learning: An Interview with Fei Fang

As part of our AI in Your Community series, I sat down with Fei Fang, an Assistant Professor in the Institute for Software Research in the School of Computer Science at Carnegie Mellon University. She works on game theory and machine learning, researching the strategic behavior of multiple agents, which has applications to many societal challenges […]