ORNL Outreach Event Introduces Girls to AI and Machine Learning

Fri, 07/27/2018

The Women in Computing (WiC) networking group at Oak Ridge National Laboratory (ORNL) is inspiring the next-generation of women scientists with an educational series that teaches young girls coding and computer science.

WiC’s “Introduce Your Daughter” series, hosted annually at ORNL, attracts staff volunteers from across the laboratory who have contributed to three successful events so far. Following two consecutive installments of “Introduce Your Daughter to Code,” this year’s workshop sought to spotlight the growing fields of machine learning and artificial intelligence (AI) with an “Introduce Your Daughter to AI” event for daughters of ORNL staff members ranging in ages from 10 to 18.

“Last year, participants expressed they wanted something new, so we kicked off our outreach events this year with a day of AI activities,” said Swaroop Pophale, WiC chair and computer scientist in the Oak Ridge Leadership Computing Facility’s Computer Science Research group.

ORNL’s Women in Computing group hosted “Introduce Your Daughter to AI,” a workshop on artificial intelligence and machine learning for daughters of ORNL staff. Volunteers from organizations across the laboratory, including the Urban Dynamics Institute, guided 24 girls, aged 10 to 18, in programming activities and games designed to teach kids about neural networks. Photo credit: Genevieve Martin/Oak Ridge National Laboratory; US Dept. of Energy.

WiC is an ORNL networking group established to promote and support women in computing careers through professional development and outreach opportunities.

The AI-themed workshop, held June 22, was led by Katie Schuman and Dasha Herrmannova from ORNL’s Computational Data Analytics group. Assisted by volunteers from multiple divisions and directorates at the lab, Schuman and Herrmannova guided 24 daughters of ORNL staff and their parents through machine learning activities to demonstrate how computers assimilate information by analyzing input data and producing output based on learned patterns.

The girls first explored an interactive 3D visualization of a fully connected convolutional neural network, which let them draw numbers that the network would then guess based on a grid system. Convolutional neural networks are artificial neural networks that employ a sliding window to look at information; therefore, they are exceptional at analyzing visual input such as images.

Using sketch-rnn, an interactive experiment created by Google research scientist David Ha, the girls learned about recurrent neural networks, which are designed to process sequential information. Sketch-rnn suggests ways to finish a drawing based on the initial input it is given. Having trained on millions of doodles from the Quick, Draw! game, sketch-rnn offers more than 100 models to draw from.

Participants explored the sketch-rnn program by Google research scientist David Ha. After selecting an item, such as “garden,” from a list of models, the girls began drawing. Having been trained on millions of doodles from the Quick, Draw! game, the recurrent neural network suggests dozens of ways to finish each drawing.

The girls also navigated the TensorFlow playground, an educational visualization of an artificial neural network.

The highlight of the day was the human neural network game, said Anne Berres, a computer scientist from ORNL’s Geographic Information Science and Technology group and the Urban Dynamics Institute.

Schuman and team colleague Steven Young collaborated with the Neutron Scattering Division’s Thomas Proffen to develop the game, which demonstrates through play how a machine receives input, interprets it, and produces output.

“It is similar to the concept of the telephone game with multiple starting points and multiple parallel midpoints,” said Berres. “The activity was a creative and interactive way to explore machine and deep learning concepts, and kids and adults alike had a lot of fun with it.”

During the game, the girls each took on roles as different kinds of neurons, cells that transmit information in the brain. Neural networks build on this biological concept.

“The idea is that a deep neural network has several different layers,” said Herrmannova. “The input neurons talk to neurons in the middle layers, and the neurons in the middle layers talk to the output neurons. Activities like this are useful in explaining neural networks because they allow people to actually walk through the concepts rather than trying to comprehend them through a screen.”

The outreach event broadly aimed to engage young girls with computer science and spur their interest in educational and career paths. Another goal was to raise awareness of the social impact of AI and machine learning.

“Machine learning has grown so much and become so powerful in recent years, it’s almost unprecedented,” Herrmannova said. “It’s likely going to grow and transform all industries—not just computer science. It’s good for everyone to have an understanding of what it is and how it works, so we can be prepared for that change.”

ORNL staff volunteers who contributed to the 2018 “Introduce Your Daughter to AI” event include Dasha Herrmannova, Katie Schuman, Swaroop Pophale, Anne Berres, Kate Carter, Seema Chouhan, Amy Coen, Katherine Engstrom, Megan Fielden, Rachel Harken, Emily Herron, Matthew Legate, Julie Mitchell, Katie Nuchols, and Aiden Rutter.

The OLCF is a US Department of Energy (DOE) Office of Science User Facility located at ORNL.

The Urban Dynamics Institute, located at ORNL, is pursuing novel science and technological solutions for global to local urban challenges.

ORNL is managed by UT-Battelle for the DOE Office of Science, the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit https://science.energy.gov.–By Rachel Harken.