# 1 Deumbra: Jonathan Mugan on developing robots who understand our world the way human children do
# 1 Deumbra: Jonathan Mugan on developing robots who understand our world the way human children do
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Introduction To Guest
On our first episode of Brains Behind AI, Ari had the opportunity to speak to Jonathan Mugan, a principal scientist at Deumbra, Co-founder of the AI company Deep Grammar, advisory to Kung FU AI, and author of Curiosity Cycle, a book for educators and parents on how to prepare our children to be lifelong learners. Jonathan graduated from Texas A & M with a degree in Psychology, and later received his Masters and PhD in Computer Science at UT Austin. Since he was young, Jonathan always had a keen interest in combining the human mind with science and Artificial Intelligence.
The Call To Action
With his numerous degrees and outside of the box ways of thinking, Jonathan began to ponder: “how can an agent or robot understand the world at a basic level, and in a way that humans and children do?” All in all, how can you teach Artificial Intelligence fundamental concepts that children know, at a basic level? Jonathan sought out to create a way to have robots make theories to what is happening in the world around them. Working for the Department of Defense at DeUmbra, Jonathan had the opportunity to work with different AI technologies, which included neuro linguistic programming (NLP). However, he realized that when it comes to Artificial Intelligence, a fundamental understanding of the world is needed in order to advance.
Founding A Team
When it came to founding his team, Jonathan credits blog posts, talks, and podcasts — and a lot of outreach. He has been able to find people who are interested in the same problems that he is interested in. Jonathan was also able to form deeper relationships with his connections over time. His team is not only based on synergetic ideas, but trust as well.
Testing The Product & Validating The Market
As a Principal Data Scientist at DeUmbra for over nine years, Jonathan specializes in machine learning, and is currently focused on applying deep learning (artificial neural networks) to problems in spatial and relational reasoning. The company DeUmbra started with a simple thought: to create cutting-edge technology solutions to secure the world by leveraging smart algorithms to find patterns in suspicious behavior amongst massive volumes of data. At DeUmbra is testing his hypotheses and theories through gaming — building interesting non-player characters that play well with different functions, every single game. DeUmbra is working on using Artificial Intelligence to build up simulations, so when the simulations improve, robots trained in the specific simulation will improve as well. Jonathan is hoping in ten years the simulations that underline video games will start to approximate the fidelity of the real world.
Challenges Encountered
The specific challenges Jonathan is facing is creating gaming characters that are able to play well, while using fundamental concepts that children use to learn. Most Artificial Intelligence is a time and variant function. The difficulty with robotics is achieving what you want to predict — which only happens rarely. The second trick is how to write generalizable code while finding someone who will invest in this idea. All concepts and ideas are on the horizon to be discovered.
Future Endeavors
Moving forward, Jonathan’s goal is to create partnerships with video game companies who are interested in using DeUmbra’s new algorithm discovery, which will allow the non-player characters to perform differently every time. Jonathan would also like to see physical robots not only performing basic tasks, but possess the ability to function in their respective environments. Perhaps overtime, we will see the gap between common sense AI and non-common sense AI begin to close.
www.jonathanmugan.com
Twitter: @jmugan
About the Host
Ari Yacobi is a data scientist, a teacher and a storyteller who has spent his career at…Read the Bio
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