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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!

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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Natural Language Processing and Bias: An Interview with Maarten Sap

As part of the AI in your Community series, I recently spoke with Maarten Sap, a PhD student at the University of Washington. Maarten is interested in natural language processing, and social science applications of AI. Maarten is also the 2017 winner of the Alexa Prize, an Amazon competition to further conversational artificial intelligence. Maarten […]

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 […]

Intelligent Music: Emilia Gómez on Big Data and Making Music More Accessible

In our ongoing AI in Your Community series, I spoke with Emilia Gómez, a researcher on music information retrieval—an interdisciplinary area dealing with music and artificial intelligence. She works at the Music Technology Group, Universitat Pompeu Fabra in Barcelona, Spain. Tara Chklovski: What problem are you currently working on? Emilia Gómez: My research deals with […]

Matching Real Solutions to Real Needs — Dr. Carolyn Rosé on Conversation-Improving AI and Collaborative Learning

Tara Chklovski: What is your current work? Carolyn Rosé: I work in the area of education, trying to apply artificial intelligence techniques to support students’ learning. In particular, I’m trying to identify characteristics of people that would make them work well together and then connecting them. So we very much are not trying to replace […]

The essence of happiness — Dr. Qiang Yang on teaching computers and humans

Tara Chklovski: What research areas are you working on? Qiang Yang: I’m working on developing algorithms for machine learning. Machine learning is where you train a computer to do things that you do and are an expert at. The way we teach computers is by first generating lots of examples — this is known as […]