Pods of Science | Episode 3 | How to Do Science Faster with Artificial Intelligence

Intro:

Welcome. I’m your host, Jess Wisse. On today’s episode we’ll talking about a new research center created by the U.S. Department of Energy. Stay tuned to learn more.

Music

JW: Pacific Northwest National Laboratory, Sandia National Laboratories. Georgia Institute of Technology. What do these have in common? They are three powerhouses in the realm of artificial intelligence, and now they are working together.

Want to know who the man is at the helm of this new collaboration? Meet, Roberto.

RG: My name is Roberto Gioiosa. I'm a senior computer scientist at PNNL in the high-performance computing group. My background is in hardware and software core design and mostly focus on the design of operating systems, runtimes, and programming model in particularly looking at emerging a future architecture both for processing, memory, and networking.

I came to PNNL in 2012 after years of other experiences in both academia and industry. Ever since I joined PNNL, I've been trying to lead efforts on software and novel hardware for computational scientists to speed up the solution to their problem and therefore solve scientific challenges.

JW: Artificial intelligence and machine learning seems to be cropping up everywhere these days. From self driving cars to your new smart phone, its everywhere. Even Alexa, Amazon’s voice assistant, is getting smarter with each passing day. Soon, she will be able to guess what you might be thinking with a new feature called Alexa Hunches.

Originally called thinking machines in the 1950s, artificial intelligence is a sub-field of computer science where machines develop the ability to think and learn on their own. Artificial intelligence, also known as AI, allows computers to perform tasks that historically could only be done by humans; think of things like visual perception, speech recognition, language translation. And that’s just the beginning.

RG: Artificial intelligence and machine learning is something that is helping us revolutionize the way we do research. Rather than starting from the top and using first principle to solve a problem, we are trying to see what the data tells us about the problem. This is something that you see every day— look at natural phenomenon and you're trying to find a correlation between what you observe and what are the reasons for that the causal relationships that are in there. In some cases, you know this naturally is complicated and it's not easy to go and have a complete understanding of what is happening without knowing anything about the entire process. What AI is doing for us is helping us do reverse engineering of natural phenomenon.

You have probably seen tons of movies about AI and how that can help, but the fundamental thing is we are looking at the data and we are trying to infer the structure of the phenomenon from the data.

JW: Roberto is the director of a new co-design center, known as the Center for Artificial Intelligence-focused Architectures and Algorithms, or (ARIAA). ARIAA is taking AI & machine learning to the next level.

RG: ARIAA is essentially a tool, a means, in which we are trying to understand what are the

requirements from our application domains. In this case, are power grid, cybersecurity, graph analytics, and chemistry, and how artificial intelligence and machine learning can support these domains to allow novel discoveries.

JW: AARIA will explore how AI and machine learning can support four areas that touch virtually every American’s life. Whether we’re aware of it or not we encounter power grid, cybersecurity, graph analytics, and computational chemistry almost every day. These are the disciplines where new medicines are created, where the fate of our online identity lies, it’s how masses of information is analyzed, and where our lights magically turn on with a flip of the switch.

RG: AI is revolutionizing our world. You see that from your mobil