An Interview with Cristina Conati: Artificial Intelligence and Educational Games

Iridescent founder and CEO Tara Chklovski recently sat down with Cristina Conati, a professor in the Department of Computer Science at the University of British Columbia. Professor Conati is interested in creating intelligent interactive systems that can adapt to individual users’ needs. One application of this is educational games that need to both entertain and teach their players.

Cristina ConatiTara Chklovski: Thank you for sitting down with me! Tell me about the problem or area of research you are working on.

Cristina Conati: In general I’m interested in the idea of user-adaptive interaction, which is using AI techniques to create tools that can personalize their interactions with users by capturing the user’s needs and preferences as they interact. Within that, I’ve done work on intelligent educational games, which are computer games that have a pedagogical purpose. There are activities that are designed to engage and amuse the players, but also to teach specific concepts – for instance, mathematical concepts.

AI technology in educational games enables intelligent, ongoing personalization, and the ability to gauge whether the player is engaged and whether they’re actually learning. That’s important because there are a number of educational games that are engaging but are not always effective at teaching. Creating engaging activities that are also effective at teaching is difficult through design alone, because players are different – what’s engaging for one person is different for another. The same is true of teaching strategies. But if we can create educational games that can adjust in real time to suit the person playing because it can understand their abilities, personality, and preferences, then students will have better learning interactions with the games.

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An Interview with Yoshua Bengio: thoughts on neural networks, deep learning and being a dreamer

Yoshua Bengio is best known for his groundbreaking work in artificial intelligence, particularly his work on artificial neural networks and deep learning. He is a Professor at the University of Montreal, scientific director of Mila (the Montreal Institute for Learning Algorithms), and co-founder of Element AI, a company that delivers software products for practical business applications of AI. He recently won the 2019 Turing Award  alongside collaborators Yann LeCun and Geoffrey Hinton.

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Event Recap: AI Experts Answer NYC Teens’ Questions and Concerns About the Technology and Its Impact on Their Lives

Recently Iridescent and The Cooper Union hosted a panel featuring influential women working in artificial intelligence (AI) across a variety of industries. Applying their diverse experience and perspectives from AI in research, neuroscience, and art, they addressed NYC teens’ questions and concerns about the impact of AI on their lives in Cooper Union’s Great Hall.

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5 Women working in AI to Know

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.

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An Interview with Rose Luckin: Using Artificial Intelligence to Support Human Intelligence and Learning

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.


Rose Luckin. Source: UCL

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.

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Why K-12 Education is Key to American Leadership in AI

This week the Trump administration released an executive order on American leadership in artificial intelligence (AI). The order outlined education and funding priorities necessary for the U.S. to remain competitive in AI, one of the most rapidly advancing technologies in history. Recently Iridescent CEO and founder, Tara Chklovski, shared her initial thoughts about the plan with Education Week. While it’s great to see the administration prioritizing AI research and workforce retraining, she noted it’s missing two crucial pieces: K-12 AI education and ethical development of AI.

“The key to U.S. competitiveness in AI may be locked inside the minds of the children and teenagers who will grow up in a world increasingly defined by automation technologies,” Chklovski explained. “Without a concerted effort to teach AI principles to children, the U.S. risks putting students at a disadvantage once they enter the global workforce.”

A family works together to create their own “self-driving car game” as part of the AI Family Challenge.

K-12 AI education must go beyond technical information in textbooks

Highlighting countries making substantial investments in AI education like China, she pointed out hands-on, project-based curriculum as an opportunity for the U.S. to create richer learning environments. Teaching soft skills alongside technical ones helps prepare learners for a career path where the impact of emerging technologies on the future of work is less known.

“Countries like China, with students frequently outperforming American students in science and math, are investing a lot of money in AI education. The U.S. has an opportunity to excel by building skills that go beyond textbooks. One way is connecting technical skill building with opportunities to solve real-world problems. Through our AI Family Challenge program we’ve found that challenging children and adults to learn about technologies like AI and then having them apply those skills to solve  real-world problems helps them build job skills like curiosity, creativity, and collaboration.”

A family works together to complete a project as part of the AI Family Challenge.

The ethical development of AI needs the same level of care and attention as privacy concerns

In addition to K-12 AI education, the plan doesn’t address the issue of ethics. Trust and safety considerations like data privacy are important. But issues of bias, fairness and algorithmic transparency are also crucial to ensure AI technologies are representative of the populations they serve.

“There is an alarming lack of diversity among the people who are currently building the algorithms transforming industries,” said Chklovski. “If the issue is not addressed on a national scale, the gap between the people who can access and provide input on building the future of AI and those who cannot could lead to long-term bias against the latter and greater economic disparity in the country.”

Adults and children must feel empowered to learn about new technologies and have the opportunity to use them in meaningful ways. The new executive order puts attention on an important conversation. But, it is only the beginning of what needs to be a much larger, ongoing partnership between government, industry and academia.

Machine Learning and Medicine: An Interview with Daphne Koller

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.

Daphne Koller

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.

Source: Science/AAAS;
Example of tumor images that have been stained and then labeled to identify more aggressive tumors.

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.

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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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