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The Deep Learning Revolution in Space Science

Justin Fletcher joins the show to talk about how the US Space Force is using deep learning with telescope data to monitor satellites, potentially lethal space debris, and identify and prevent catastrophic collisions. 

Oct 2022
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Key Takeaways

1

Peer review is essential in order to ensure you don’t make critical decisions with a fatal misunderstanding of data, faulty research, or improper application.

2

Working with the Department of Defense offers new and compelling problem domains to explore that standard tech industry roles do not, such as working with the US Space Force. 

3

Communication about artificial intelligence problems is really about specificity and relevance. You need to be specific enough that people know what you're talking about and put it in terms that are physically comprehensible.

Key Quotes

As long as you're brief but specific, key stakeholders will understand what you mean and they will gain a very specific understanding of what it is and isn’t possible. I've seen abstract communication about artificial intelligence fail multiple times in organizations across multiple industries because senior leaders make the assumption that technology is much more mature than it is and make investment decisions based on that misunderstanding. That’s why I'm almost fanatical about talking about things in concrete terms.

We do what's called correlation of the orbits to predict where objects in space will go in the future. There's a lot of uncertainty, especially for small objects that have solar radiation pressure effects. While it's true that there's not a lot of stuff and there's plenty of space, the problem is that because you don't know where an object is going, there's a cone of uncertainty around where it might be in the future. That must be taken into consideration to calculate risk reduction and potentially maneuver a satellite in the off-chance that they will collide. If you think about these objects existing in probability space, they spread out across time, so they're really small, and there's a lot of space except that because we don't know where they're going, they are, in effect, larger. That's why we do computer vision for these problems: with better information, we can effectively reduce the size of those objects.

About Justin Fletcher


Photo of Justin Fletcher
Guest
Justin Fletcher

Justin Fletcher is responsible for artificial intelligence and autonomy technology development within the Space Domain Awareness Delta of the United States Space Force Space Systems Command. With over a decade of experience spanning space domain awareness, high-performance computing, and air combat effectiveness, Justin is a recognized leader in defense applications of artificial intelligence and autonomy.


Photo of Richie Cotton
Host
Richie Cotton

Richie helps organizations get from a vague sense of "hey we ought to get better at using data" to having realistic plans to become successful data-driven organizations. He's been a data scientist since before it was called data science, and has written several books and created many DataCamp courses on the subject.

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