In this installment of the MITRE Knowledge Driven Podcast, Data Analytics Engineer Steffani Silva, gives us an inside look at the inner workings of Generation AI Nexus, a collaboration among MITRE, universities, and government that educates students, as well as professors, on the power of artificial intelligence and accessible data.
Have you ever wondered how Google Maps works? How does the application calculate the fastest route for you, and how does it do this so quickly? The answer might surprise you – at the backbone of these navigational applications and software is graph theory.
According to the independent website Government Technology, “Many government agencies have an unstructured data nightmare, with bits and bytes scattered across servers, clouds and hypervisors.” Vernor Vinge calls this so-called nightmare “data glut.” In an era of Internet of Things (IoT), the data glut may not get better, and, in fact, is likely to get worse.
Once upon a time, when it was uncommon to wear a medical mask in public, I was a mechanical engineering student at the University of Oklahoma. As anyone who’s been to the Southeast will know, Oklahoma is oil country. Therefore, when I went to the career fair in the Spring semester of my junior year, I had filtered out all of the companies that had anything to do with the petroleum industry. I was left with only a few companies that piqued my interest. One of these companies was MITRE.
As data science has exploded in popularity and use, so have the tools used to solve problems in the domain. Some of these open source programs and programming suites have become extremely popular, and thus developers have designed a whole host of code that might be referred to as add-ons, extensions, and packages to improve functionality and save time for users, both experienced and new.
Although the moniker data scientist implies that the role centers around manipulating data and modeling it, visualizing data and creating visualizations are an integral part of the daily workflow for practicing data scientists like me. Not only do visualizations allow us to communicate results quickly and efficiently, but visualizing is a key tool during exploratory data analysis, data cleaning, modeling, and other steps of the process of telling stories with numbers.
The classic conception of an intern is a minion who brings people coffee, fixes printer jams, and does grunt work. The interns hired by MITRE’s Emerging Technologies Department, however, are not minions. They are student investigators charged with helping to solve overwhelming societal problems. Read profiles of four of these interns and their summer work in the public interest.
A computer scientist looks at a refugee crisis to model a pandemic.
How does that connection work exactly and why? If it appears to resemble a scenario in which a farmer learns how to plant an apple orchard by examining the supply chain for manufacturing orange juice, well, you may actually be onto something. The novel coronavirus has presented unprecedented medical, social, and economic challenges, which means that the thoughtful people responding have no choice but to innovate. However, in 2020, innovation doesn’t mean going where no one has gone before. It means harvesting everything that has come before in a disciplined way to see what might bear fruit.
In November 2019, a customer of Apple Card complained when he and his wife separately applied for Apple Card credit, and his wife was given a credit limit twenty times lower than his despite the fact that they jointly owned all assets in a community property state. According to Neil Vigdor in an article in the New York Times, an Apple Card representative checked into the matter and came back with the explanation that “It was the algorithm.”
As graduating seniors at Florida International University (FIU), Charlie Ramirez and Sephora Jean-Mary headed into their final semester aiming to find a real-life business problem they could solve. Their senior design capstone project was supposed to enable these two front-end developers to display what they’d already learned as computer science students.
MITRE has taken on a challenge: to shape America’s future workforce and economy by alerting college students to the power of artificial intelligence (AI). That vision is now taking shape at schools across the country through an initiative known as Generation AI Nexus (Gen AI).
Instead of hitting the beach over the third weekend in September, more than 1,000 students from several Florida and southeast universities loaded up on caffeine, went without sleep, and were driven by a “will to do good.”
A woman who has always identified as Ashkenazi Jewish received a DNA testing kit from one of the ancestry services and participates on a whim. Surprise! Turns out her father was actually a non-Jewish sperm donor. It’s one of many fascinating and recent cases of renegotiating identity, along with stories about an adopted child finding their true birth family, or even individuals tracing their ancestry back to someone practicing witchcraft.
Dr. Philip Barry is the Technical Director for Modeling, Simulation, Experiments, & Analysis here at MITRE. When he’s not leading simulations work, he is teaching Risk Management at George Mason. Ever focused on bringing new tools and methodologies into the classroom, Dr. Barry partnered with George Mason and Joe Garner and Ali Zaidi from MITRE’s Generation AI Nexus (Gen AI) team, to create a first-of-its-kind lesson blending risk management with artificial intelligence (AI).
Ali Zaidi is a MITRE data scientist tackling an interesting challenge for MITRE as part of his work for Generation AI Nexus. As the fields of machine learning and data science have grown, the need for machine learning education has become a necessity of many fields few would associate with computer science.
Jesse Buonanno, a Cyber Security Engineer at MITRE, focuses on cyber operations. Jesse spends his spare time learning about blockchain and cryptocurrencies.
What child can resist the challenge of building a tiny robot (or “bot”) using the head of a toothbrush, a button battery, and a pager motor?
Congratulations! You’ve built your self-driving car! Now what? Take it out for a spin on that cross-country trip, watching movies and the landscape as you go from sea to shining sea?
So you’ve heard about Symphony™ – MITRE’s automated provisioning framework that rapidly builds secure analytic cells for geospatial, AI, and machine learning applications. Have you tried explaining it to a college student?
Welcome to the second installment of the Knowledge-Driven Podcast. In this series, Software Systems Engineer Cameron Boozarjomehri interviews technical leaders at MITRE who have made knowledge sharing and collaboration an integral part of their practice.