In Conversation with Tribal Qonf Speaker – Davar Ardalan
To connect our Tribal Qonf Speakers and Audience better, we interviewed our speakers over a few important questions. The conversations we had were just amazing.
In this edition, we are publishing the Interview we did with Davar Ardalan. Davar answered many interesting questions and we are sure you will enjoy the read.
Tell us a little about what inspired you to develop an interest in AI?
Davar: I’m Founder and CEO of IVOW bringing Cultural Intelligence to AI and focusing on addressing a much-needed market: the convergence of artificial intelligence to preserve culture with the need for marketers to better understand the culture. I was also a journalist at NPR News for many years. My last position was Senior Producer of the Identity and Culture Unit and as I looked ahead at the future of automation and AI, I knew that some of the same issue public media grapple with around the need to reach more diverse communities will be amplified in the age of AI.
I was also Managing Editor at Hanson Robotics working with Sophia the Robot, for the field of AI and personalization to be effective and culturally relevant, we must create comprehensive datasets to nurture cultural intelligence in machines and even in social robots like Sophia of Hanson Robotics, who has traveled to over 30 countries. Each time Sophia travels, she meets people of all different backgrounds who talk to her. Collecting culturally prominent datasets in an efficient and scalable manner today is paramount to the future commercial success of any AI solutions and products and inclusive AI.
How are you practicing your skills during COVID-19?
Davar: On June 9th we presented our Indigenous Knowledge Graph Demo and interactive report that features Sina, our AI on Google Assistant. I’m co-chair of Cultural AI for the 2020 AI For Good Summit. My team and I worked with six cultural experts including three Native American technologists on an Indigenous Knowledge Graph Demo focusing on the evolution of food.
How will your talk motivate the attendees and one lesson they will carry from the conference?
Davar: How can testers prepare for the future of AI, testing, and personalization? Testing any AI platform like the one we are building is a complex task. We are collaborating with our technology partner, Kiwi-Tech as we build our platform and I’ll share some of our findings and strategies around data source and conditioning testing, algorithm testing, and API testing.
From an enterprise perspective, diversity in the tech workforce is critical to the future of innovation across the globe. Creating new AI-ready data featuring the stories of women in history, including the civic tech sector, can be a powerful way to bring visibility to the contributions of women and inspire future generations to join the tech workforce.
From a technology development standpoint, making AI and data culturally relevant is imperative as we develop technology that is usable, engaging, and beneficial to human thriving. Think of a Fitbit geared towards women, or a human resources AI that shares inspiring stories about women and technology.
AI-ready datasets on our global stories can only help us ensure that cultures around the world are preserved, enriched, used, shared, and loved as technology becomes an integral part of our lives. At IVOW, our north star is making data culturally relevant.
Please tell us about the bots you have created to share information about Women in History?
Davar: As children, we learn our history through the stories our family and friends tell us about our community and where we come from. We can take the same approach to teach machines about our heritage, our communities, our myths, and legends. We understand the complex nature of the problem and therefore believe that deep collaboration, diversity, and transparency will lead to the best outcomes.
Take our digital storyteller Sina. She is a young conversational AI and at the moment a demo on Google Assistant. Sina is designed as an AI storyteller and built by our journalists and developers at IVOW, our startup developing cultural intelligence for AI. Sina has been around for a while, maybe longer than you. She loves learning about human history and then sharing those stories with others. That’s what gives Sina purpose.
So to make Sina and other AI’s smarter, together with TopCoder, we are launching our Women in History Data Ideation Challenge. That’s because we believe as storytelling technologists we have a role in designing this new future. Artificial intelligence tools must understand the cultural context and be able to respond to it effectively.
We know that AI algorithms and datasets are limited in understanding different cultural contexts, inhibiting the effectiveness of businesses and government from expanding into new markets and serving citizens. We can bring governments and businesses closer to the audiences they’re targeting. What’s missing is cultural intelligence and AI-ready datasets that are inclusive and diverse.
The goal of our first dataset ideation challenge is to explore how data on the stories of women throughout history can be sourced and used to gain new insights for AI products and services with a focus on women. The challenge will be conducted in collaboration with TopCoder, the world’s largest on-demand digital talent platform. The dataset and methodology ideation is only a first step, but an exciting and critical step towards creating culturally relevant AI that is truly useful to society. The final winning methodology will appear on AI-Commons.