#Innovidual Ron Keesing
We’re a company full of innovative individuals, or as we call them, #innoviduals. This recurring series highlights some of our best and brightest employees by sharing their unique wisdom and technical know-how.
Our latest #innovidual is Ron Keesing, director of Artificial Intelligence and Machine Learning (AI/ML). Ron has been with Leidos for around 18 years and is responsible for leading our AI/ML Accelerator and practices across the Leidos enterprise. Working with his team of almost 40 senior scientists, researchers, and engineers who are really the deep experts in this field, to develop common tools, methods, and approaches to solve many difficult AI problems across a multitude of markets and a range of customers.
Where did it all begin?
I’ve spent much of my entire career looking at connections between natural and artificial intelligence, dating back as an undergraduate studying biology and neuroscience as well as AI. In graduate school, while studying how ant colonies represent information, I had a great opportunity to venture off and work on autonomous systems as part of a NASA mission called Deep Space 1. The system I worked on — called Remote Agent — was the first AI system to control a spacecraft in deep space. It was a great opportunity to apply what I learned and conceptualized to the real world.
I was also lucky to have the opportunity to move overseas and teach computer science at the University of the South Pacific in Fiji for four years after the launch of Deep Space 1. This was a great life experience that provided a chance to do something very different, experience new cultures, and meet extremely talented people. I then moved into Science Applications International Corporation (SAIC), later becoming Leidos, where I have been researching and building solutions using artificial intelligence (AI) and machine learning (ML).
AI and ML are currently very popular fields, but it’s an area I’ve been working in long before it was cool. I love making the connections between natural and artificial intelligence and finding ways to help solve hard problems using data — it’s a really great and fun way to address a lot of hard problems.
Tell us a little about your job and the responsibilities that come with it.
Here at Leidos, I’m responsible for our AI/ML Accelerator, and that means I lead a team of almost 40 senior scientists, researchers, and engineers who are really the deep experts in this field. I am also responsible for leading our AI/ML practice across the Leidos enterprise. My team helps the company by developing common tools, methods, and approaches to solve many difficult AI problems across a multitude of markets and a range of customers. I’m lucky to have such a great team to work with.
Why is innovation important in your role?
It’s important for a couple of reasons, we, ‘my team, and I,’ are called in to solve the toughest and most challenging AI problems across the company. Being innovative is particularly critical to solving these previously unsolved problems through the application of AI.
Innovation is also important because AI is such a rapidly moving field, and we want to remain leaders in specific areas like Trusted AI that are really important to our customers. To continue being thought leaders, we focus on specific areas we can innovate, both by developing our own novel research ideas and by extending new and emerging work from academia and the commercial world to help solve the most challenging problems faced by our customers.
In fact, you could say that innovation is baked into our mission within the AI/ML Accelerator. One of our core responsibilities is to develop and deliver technology discriminators for all of Leidos. To do that requires by definition, for us to be innovators, so innovation is truly baked into our mission.
What does innovation mean to you?
For me, innovation often means finding connections between previously unconnected problems or fields, taking ideas from different technical areas combining them in new ways to solve previously unsolved problems or develop new capabilities. For example, as part of our Trusted AI, we’ve been researching how to help AI deal with adversarial attacks. These types of attacks occur when people put misleading information into data — like an image — to cause AI to make a wrong decision or mistake. One type of example attack is called an adversarial patch, and these can be downloaded off the internet and used to attack systems in the real world. People are concerned that adversarial patches could be used to fool self-driving cars into ignoring a stop sign or making other important and dangerous mistakes.
To defend AI against these threats, my colleagues and I are exploring an innovative technique that takes its inspiration from a completely different area of mathematics. We hope this approach will allow AI to detect when an adversarial patch is being shown, allowing the system to take corrective action.
Often I find innovation is about finding those connections between different fields or technical areas that haven’t previously been linked to before. These are great sources of innovation to my team, and me.
Are there any innovations or individuals that inspire you in your work?
The inspiration for my career path took place in my early teenage years by a book called Gödel, Escher, Bach by Douglas Hofstadter about the connections between natural and artificial intelligence. Later, as an undergraduate, I was incredibly lucky to work with one of the pioneers in the field of machine learning: David Rumelhart. The way I learned to think about machine learning from David is now the foundation of how almost all modern AI is built. The algorithms I was taught in school were not widely used back then. Today they underlie the algorithms that power Google’s™ searches that help process natural language and understand speech on your Apple™ devices through Siri™. These algorithms, everyone uses today, can trace their ancestry back to the work David and his colleagues did back in the 80s!
I also take a lot of inspiration that I apply back to my work from the connections I make on so many of my adventures by exploring and experiencing new cultures. Having had an incredible opportunity to experience new people and cultures in great depth living overseas in Fiji for over 4 years. It has provided me with a different perspective on different ways the Fijians solve problems.
We all tend to get “stuck” in ways of thinking and believe that the way we currently do things is the best way to do everything. However, I have learned through encountering many different cultures and journeys; people solve problems in different ways; this excites me tremendously, as we stay open to new experiences we can always look for new and better ways to do things.
One of the strongest assets we have in our AI practice at Leidos is the diversity of our team. That diversity means we all are able to bring different perspectives and background to solving hard problems. In a team like that — where we draw on and value those different perspectives — innovation happens quite naturally.