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.
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.
So You Want to Think Like a Data Scientist? The Importance of Visualizations in the Data Science Workflow
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.
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.”
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.
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.
Clinical diagnostic support. Loan approval. Predictive policing and parole. These are all examples of consequential systems, meaning that they are systems with immediate, long-term, impactful consequences on people within them.
Science is “the systematic study of the structure and behavior of the physical and natural world through observation and experimentation.” Since its emergence during the late renaissance, scientific progress has been made primarily through the aptly named scientific method.
Artificial intelligence (AI) is getting better all the time. You can see it all around you, from Alexa and Siri keeping your appointments and shopping lists, to news articles about self-driving cars, to a program called AlphaZero (Silver et al., 2018) that will probably never lose to any human in chess or GO.
How do we prepare for the inevitable change in the world today? How do we take into account not just the way the world is now – but the way it looks in the future?
Somewhere on a whiteboard in a classroom at the Universities of Shady Grove, swims a fish. Drawn in black marker, complete with a fedora, sunglasses, and a goatee, the sketchy-looking ichthyoid intones into a word bubble…
Is artificial intelligence (AI) the way of the future… or already the way of the present?
Applications of AI surround us in our daily lives – ever use an app to get around traffic? How about checking your social media feeds? As our society integrates AI into our daily lives, it’s important to note that the upcoming generation has always lived with AI.