Researchers at Georgia Tech are creating accessible museum exhibits that explain artificial intelligence (AI) to middle school students, including the LuminAI interactive AI-based dance partner developed by Regents' Professor Brian Magerko.

Ph.D. students Yasmine Belghith and Atefeh Mahdavi co-led a study in a museum setting that observed how middle schoolers interact with the popular AI chatbot ChatGPT. 

“It’s important for museums, especially science museums, to start incorporating these kinds of exhibits about AI and about using AI so the general population can have that avenue to interact with it and transfer that knowledge to everyday tools,” Belghith said.

Belghith and Mahdavi conducted their study with nine focus groups of 24 students at Chicago’s Museum of Science and Industry. The team used the findings to inform their design of AI exhibits that the museum could display as early as 2025. 

Belghith is a Ph.D. student in human-centered computing. Her advisor is Assistant Professor Jessica Roberts in the School of Interactive Computing. Magerko advises Mahdavi, a Ph.D. student in digital media in the School of Literature, Media, and Communication.

Belghith and Mahdavi presented a paper about their study in May at the Association for Computing Machinery (ACM) 2024 Conference on Human Factors in Computing Systems (CHI) in Honolulu, Hawaii.

Their work is part of a National Science Foundation (NSF) grant dedicated to fostering AI literacy among middle schoolers in informal environments.

Expanding Accessibility

While there are existing efforts to reach students in the classroom, the researchers believe AI education is most accessible in informal learning environments like museums.

“There’s a need today for everybody to have some sort of AI literacy,” Belghith said. “Many middle schoolers will not be taking computer science courses or pursuing computer science careers, so there needs to be interventions to teach them what they should know about AI.”

The researchers found that most of the middle schoolers interacted with ChatGPT to either test its knowledge by prompting it to answer questions or socialize with it by having human-like conversations. 

Others fit the mold of “content explorers.” They did not engage with the AI aspect of ChatGPT and focused more on the content it produced.

Mahdavi said regardless of their approach, students would get “tunnel vision” in their interactions instead of exploring more of the AI’s capabilities.

“If they go in a certain direction, they will continue to explore that,” Mahdavi said. “One thing we can learn from this is to nudge kids and show them there are other things you can do with AI tools or get them to think about it another way.”

The researchers also paid attention to what was missing in the students’ responses, which Mahdavi said was just as important as what they did talk about.

“None of them mentioned anything about ethics or what could be problematic about AI,” she said. “That told us there’s something they aren’t thinking about but should be. We take that into account as we think about future exhibits.”

Making an Impact

The researchers visited the Museum of Science and Industry June 1-2 to conduct the first trial run of three AI-based exhibits they’ve created. One of them is LuminAI, which was developed in Magerko’s Expressive Machinery Lab.

LuminAI is an interactive art installation that allows people to engage in collaborative movement with an AI dance partner. Georgia Tech and Kennesaw State recently held the first performance of AI avatars dancing with human partners in front of a live audience.

Duri Long, a former Georgia Tech Ph.D. student who is now an assistant professor at Northwestern University, designed the second exhibit. KnowledgeNet is an interactive tabletop exhibit in which visitors build semantic networks by adding different characteristics to characters that interact together.

The third exhibit, Data Bites, prompts users to build datasets of pizzas and sandwiches. Their selections train a machine-learning classifier in real time.

Belghith said the exhibits fostered conversations about AI between parents and children.

“The exhibit prototypes successfully engaged children in creative activities,” she said. “Many parents had to pull their kids away to continue their museum tour because the kids wanted more time to try different creations or dance moves.”

Adoptive T-cell therapy has revolutionized medicine. A patient’s T-cells — a type of white blood cell that is part of the body’s immune system — are extracted and modified in a lab and then infused back into the body, to seek and destroy infection, or cancer cells. 

Now Georgia Tech bioengineer Ankur Singh and his research team have developed a method to improve this pioneering immunotherapy. 

Their solution involves using nanowires to deliver therapeutic miRNA to T-cells. This new modification process retains the cells’ naïve state, which means they’ll be even better disease fighters when they’re infused back into a patient.

“By delivering miRNA in naïve T cells, we have basically prepared an infantry, ready to deploy,” Singh said. “And when these naïve cells are stimulated and activated in the presence of disease, it’s like they’ve been converted into samurais.”

Lean and Mean

Currently in adoptive T-cell therapy, the cells become stimulated and preactivated in the lab when they are modified, losing their naïve state. Singh’s new technique overcomes this limitation. The approach is described in a new study published in the journal Nature Nanotechnology.

“Naïve T-cells are more useful for immunotherapy because they have not yet been preactivated, which means they can be more easily manipulated to adopt desired therapeutic functions,” said Singh, the Carl Ring Family Professor in the Woodruff School of Mechanical Engineering and the Wallace H. Coulter Department of Biomedical Engineering. 

The raw recruits of the immune system, naïve T-cells are white blood cells that haven’t been tested in battle yet. But these cellular recruits are robust, impressionable, and adaptable — ready and eager for programming.

“This process creates a well-programmed naïve T-cell ideal for enhancing immune responses against specific targets, such as tumors or pathogens,” said Singh.

The precise programming naïve T-cells receive sets the foundational stage for a more successful disease fighting future, as compared to preactivated cells.

Nakia Melecio, the VentureLab Principal under the Office of Commercialization, has made remarkable progress in enhancing the grant programs of the National Institutes of Health (NIH). Over an intensive eight-month period, Melecio was part of an evaluation committee that focused on refining initiatives of the NIH’s National Cancer Institute, particularly the Small Business Transition Grant and other related grant programs.

“The experience was both enlightening and challenging,” Melecio said. “We engaged with faculty from over 20 universities to understand the diverse needs — from early-stage research to the commercialization of technology.” His pivotal role in streamlining the application processes for the Small Business Innovation Research and Small Business Technology Transfer programs has significantly enhanced their efficiency and accessibility.

A key aspect of Melecio’s efforts was to increase participation from historically underrepresented groups, with a significant focus on supporting historically Black colleges and universities and minority-serving institutions. His strategic insights have helped reshape these programs to better facilitate the commercialization of technologies developed within academic institutions.

Keith McGreggor, director of VentureLab, praised Melecio’s contributions. “Nakia’s work embodies our mission to transform research into viable market opportunities, ensuring broad access and benefit. The approval of the committee’s recommendations by the NCI leadership marks a significant advancement in our ongoing effort to make NIH funding more accessible and effective,” he said.

As these enhancements are implemented, they promise to significantly improve the landscape for commercializing technologies originating from university research, fostering broader innovation and practical application of groundbreaking discoveries.

The Office of Commercialization at Georgia Tech continues to support faculty and researchers as they navigate these improved opportunities, ensuring that the leadership and expertise within our community are directed toward national and global advancements in science and technology.

Stay engaged with the Office of Commercialization for ongoing updates on this and other initiatives.

Ask a person to find a frying pan, and they will most likely go to the kitchen. Ask a robot to do the same, and you may get numerous responses, depending on how the robot is trained.

Since humans often associate objects in a home with the room they are in, Naoki Yokoyama thinks robots that navigate human environments to perform assistive tasks should mimic that reasoning.

Roboticists have employed natural language models to help robots mimic human reasoning over the past few years. However, Yokoyama, a Ph.D. student in robotics, said these models create a “bottleneck” that prevents agents from picking up on visual cues such as room type, size, décor, and lighting. 

Yokoyama presented a new framework for semantic reasoning at the Institute of Electrical and Electronic Engineers (IEEE) International Conference on Robotics and Automation (ICRA) last month in Yokohama, Japan. ICRA is the world’s largest robotics conference.

Yokoyama earned a best paper award in the Cognitive Robotics category with his Vision-Language Frontier Maps (VLFM) proposal.

Assistant Professor Sehoon Ha and Associate Professor Dhruv Batra from the School of Interactive Computing advised Yokoyama on the paper. Yokoyama authored the paper while interning at the Boston Dynamics’ AI Institute.

“I think the cognitive robotic category represents a significant portion of submissions to ICRA nowadays,” said Yokoyama, whose family is from Japan. “I’m grateful that our work is being recognized among the best in this field.”

Instead of natural language models, Yokoyama used a renowned vision-language model called BLIP-2 and tested it on a Boston Dynamics “Spot” robot in home and office environments.

“We rely on models that have been trained on vast amounts of data collected from the web,” Yokoyama said. “That allows us to use models with common sense reasoning and world knowledge. It’s not limited to a typical robot learning environment.”

What is Blip-2?

BLIP-2 matches images to text by assigning a score that evaluates how well the user input text describes the content of an image. The model removes the need for the robot to use object detectors and language models. 

Instead, the robot uses BLIP-2 to extract semantic values from RGB images with a text prompt that includes the target object. 

BLIP-2 then teaches the robot to recognize the room type, distinguishing the living room from the bathroom and the kitchen. The robot learns to associate certain objects with specific rooms where it will likely find them.

From here, the robot creates a value map to determine the most likely locations for a target object, Yokoyama said.

Yokoyama said this is a step forward for intelligent home assistive robots, enabling users to find objects — like missing keys — in their homes without knowing an item’s location. 

“If you’re looking for a pair of scissors, the robot can automatically figure out it should head to the kitchen or the office,” he said. “Even if the scissors are in an unusual place, it uses semantic reasoning to work through each room from most probable location to least likely.”

He added that the benefit of using a VLM instead of an object detector is that the robot will include visual cues in its reasoning.

“You can look at a room in an apartment, and there are so many things an object detector wouldn’t tell you about that room that would be informative,” he said. “You don’t want to limit yourself to a textual description or a list of object classes because you’re missing many semantic visual cues.”

While other VLMs exist, Yokoyama chose BLIP-2 because the model:

• Accepts any text length and isn’t limited to a small set of objects or categories.
• Allows the robot to be pre-trained on vast amounts of data collected from the internet.
• Has proven results that enable accurate image-to-text matching.

Home, Office, and Beyond

Yokoyama also tested the Spot robot to navigate a more challenging office environment. Office spaces tend to be more homogenous and harder to distinguish from one another than rooms in a home. 

“We showed a few cases in which the robot will still work,” Yokoyama said. “We tell it to find a microwave, and it searches for the kitchen. We tell it to find a potted plant, and it moves toward an area with windows because, based on what it knows from BLIP-2, that’s the most likely place to find the plant.”

Yokoyama said as VLM models continue to improve, so will robot navigation. The increase in the number of VLM models has caused robot navigation to steer away from traditional physical simulations.

“It shows how important it is to keep an eye on the work being done in computer vision and natural language processing for getting robots to perform tasks more efficiently,” he said. “The current research direction in robot learning is moving toward more intelligent and higher-level reasoning. These foundation models are going to play a key role in that.”

Top photo by Kevin Beasley/College of Computing.

Knitting, the age-old craft of looping and stitching natural fibers into fabrics, has received renewed attention for its potential applications in advanced manufacturing. Far beyond their use for garments, knitted textiles are ideal for designing and fabricating emerging technologies like wearable electronics or soft robotics — structures that need to move and bend. 

Knitting transforms one-dimensional yarn into two-dimensional fabrics that are flexible, durable, and highly customizable in shape and elasticity. But to create smart textile design techniques that engineers can use, understanding the mechanics behind knitted materials is crucial. 

Physicists from the Georgia Institute of Technology have taken the technical know-how of knitting and added mathematical backing to it. In a study led by Elisabetta Matsumoto, associate professor in the School of Physics, and Krishma Singal, a graduate researcher in Matsumoto’s lab, the team used experiments and simulations to quantify and predict how knit fabric response can be programmed. By establishing a mathematical theory of knitted materials, the researchers hope that knitting — and textiles in general — can be incorporated into more engineering applications.

Their research paper, “Programming Mechanics in Knitted Materials, Stitch by Stitch,” was published in the journal Nature Communications. 

“For centuries, hand knitters have used different types of stitches and stitch combinations to specify the geometry and ‘stretchiness’ of garments, and much of the technical knowledge surrounding knitting has been handed down by word of mouth,” said Matsumoto.

But while knitting has often been dismissed as unskilled, poorly paid “women’s work,” the properties of knits can be more complex than traditional engineering materials like rubbers or metals. 

For this project, the team wanted to decode the underlying principles that direct the elastic behavior of knitted fabrics. These principles are governed by the nuanced interplay of stitch patterns, geometry, and yarn topology — the undercrossings or overcrossings in a knot or stitch. "A lot of yarn isn’t very stretchy, yet once knit into a fabric, the fabric exhibits emergent elastic behavior," Singal said. 

“Experienced knitters can identify which fabrics are stretchier than others and have an intuition for its best application,” she added. “But by understanding how these fabrics can be programmed and how they behave, we can expand knitting’s application into a variety of fields beyond clothing.”

Through a combination of experiments and simulations, Matsumoto and Singal explored the relationships among yarn manipulation, stitch patterns, and fabric elasticity, and how these factors work together to affect bulk fabric behavior. They began with physical yarn and fabric stretching experiments to identify main parameters, such as how bendable or fluffy the yarn is, and the length and radius of yarn in a given stitch. 

They then used the experiment results to design simulations to examine the yarn inside a stitch, similar to an X-ray. It is difficult to see inside stitches during the physical measurements, so the simulations are used to see what parts of the yarn have interacted with other parts. The simulations are used to recreate the physical measurements as accurately as possible.

Through these experiments and simulations, Singal and Matsumoto showed the profound impact that design variations can have on fabric response and uncovered the remarkable programmability of knitting. "We discovered that by using simple adjustments in how you design a fabric pattern, you can change how stretchy or stiff the bulk fabric is," Singal said. "How the yarn is manipulated, what stitches are formed, and how the stitches are patterned completely alter the response of the final fabric."

Matsumoto envisions that the insights gleaned from their research will enable knitted textile design to become more commonly used in manufacturing and product design. Their discovery that simple stitch patterning can alter a fabric’s elasticity points to knitting’s potential for cutting-edge interactive technologies like soft robotics, wearables, and haptics.

“We think of knitting as an additive manufacturing technique — like 3D printing, and you can change the material properties just by picking the right stitch pattern,” Singal said.

Matsumoto and Singal plan to push the boundaries of knitted fabric science even further, as there are still numerous questions about knitted fabrics to be answered. 

"Textiles are ubiquitous and we use them everywhere in our lives," Matsumoto said. "Right now, the hard part is that designing them for specific properties relies on having a lot of experience and technical intuition. We hope our research helps make textiles a versatile tool for engineers and scientists too."

Note: Sarah Gonzalez (Georgia Tech) and Michael Dimitriyev (Texas A&M) are also co-first authors of the study. 

Citation: Singal, K., Dimitriyev, M.S., Gonzalez, S.E. et al. Programming mechanics in knitted materials, stitch by stitch. Nat Commun 15, 2622 (2024). 

DOI: https://doi.org/10.1038/s41467-024-46498-z

Funding: Research Corporation for Science Advancement, National Science Foundation, and the Alfred P. Sloan Foundation 

Georgia Tech has been selected as one of six universities globally to receive funding for the newly established Global Industrial Technology Cooperation Center. The announcement was made by the Ministry of Trade, Industry, and Energy in South Korea during the Global Open Innovation Strategy Meeting in April.

The KIAT-Georgia Tech Semiconductor Electronics Center will receive $1.8 million to establish a sustainable semiconductor electronics research partnership between Korean companies, researchers, and Georgia Tech. 

“I am thrilled to announce that we have secured funding to launch a groundbreaking collaboration between Georgia Tech’s world-class researchers and Korean companies,” said Hong Yeo, associate professor and Woodruff Faculty Fellow in the George W. Woodruff School of Mechanical Engineering and the Wallace H. Coulter Department of Biomedical Engineering. “This initiative will drive the development of cutting-edge technologies to advance semiconductor, sensors, and electronics research.”

Yeo will lead the center, and Michael Filler, interim executive director for the Institute of Electronics and Nanotechnology, and Muhannad Bakir, director of the 3D Advanced Packaging Research Center, will serve as co-PIs.

The center will focus on advancing semiconductor research, a critical area of technology that forms the backbone of modern electronics.

The Cooperation Center is a global technology collaboration platform designed to facilitate international joint research and development planning, partner matching, and local support for domestic researchers. The selection of Georgia Tech underscores the Institute’s leadership and expertise in the field of semiconductors.

The Biden-Harris Administration announced that the U.S. Department of Commerce and Absolics, an affiliate of the Korea-based SKC, have signed a non-binding preliminary memorandum of terms to provide up to $75 million in direct funding under the CHIPS and Science Act to help advance U.S. technology leadership. The proposed investment would support the construction of a 120,000 square-foot facility in Covington, Georgia and the development of substrates technology for use in semiconductor advanced packaging. Started through a collaboration with the 3D Packaging Research Center at Georgia Tech, Absolics’ project serves as an example of American lab-to-fab development and production.

"Glass-core packaging holds the promise to revolutionize the field of advanced packaging and impact major paradigms such as artificial intelligence, mm-wave/THz communication, and photonic connectivity," said Muhannad Bakir, Dan Fielder Professor in the School of Electrical and Computer Engineering and Director of the 3D Systems Packaging Research Center at Georgia Tech.  "We look forward to supporting Absolics in establishing a glass-core packaging facility in the State of Georgia through workforce development initiatives." 

Because of President Biden’s CHIPS and Science Act, this proposed investment would support over an estimated 1,000 construction jobs and approximately 200 manufacturing and R&D jobs in Covington and enhance innovation capacity at Georgia Institute of Technology, supporting the local semiconductor talent pipeline. 

The proposed investment with Absolics is the first proposed CHIPS investment in a commercial facility supporting the semiconductor supply chain by manufacturing a new advanced material.

Read the full story

An electrochemical process developed at Georgia Tech could offer new protection against bacterial infections without contributing to growing antibiotic resistance.

The approach capitalizes on the natural antibacterial properties of copper and creates incredibly small needle-like structures on the surface of stainless steel to kill harmful bacteria like E. coli and Staphylococcus. It’s convenient and inexpensive, and it could reduce the need for chemicals and antibiotics in hospitals, kitchens, and other settings where surface contamination can lead to serious illness.

It also could save lives: A global study of drug-resistant infections found they directly killed 1.27 million people in 2019 and contributed to nearly 5 million other deaths — making these infections one of the leading causes of death for every age group.

Researchers described the copper-stainless steel and its effectiveness May 20 in the journal Small.

Read the full story on the College of Engineering website.

Georgia Tech researchers say non-English speakers shouldn’t rely on chatbots like ChatGPT to provide valuable healthcare advice. 

A team of researchers from the College of Computing at Georgia Tech has developed a framework for assessing the capabilities of large language models (LLMs).

Ph.D. students Mohit Chandra and Yiqiao (Ahren) Jin are the co-lead authors of the paper Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries. 

Their paper’s findings reveal a gap between LLMs and their ability to answer health-related questions. Chandra and Jin point out the limitations of LLMs for users and developers but also highlight their potential. 

Their XLingEval framework cautions non-English speakers from using chatbots as alternatives to doctors for advice. However, models can improve by deepening the data pool with multilingual source material such as their proposed XLingHealth benchmark.     

“For users, our research supports what ChatGPT’s website already states: chatbots make a lot of mistakes, so we should not rely on them for critical decision-making or for information that requires high accuracy,” Jin said.   

“Since we observed this language disparity in their performance, LLM developers should focus on improving accuracy, correctness, consistency, and reliability in other languages,” Jin said. 

Using XLingEval, the researchers found chatbots are less accurate in Spanish, Chinese, and Hindi compared to English. By focusing on correctness, consistency, and verifiability, they discovered: 

• Correctness decreased by 18% when the same questions were asked in Spanish, Chinese, and Hindi. 
• Answers in non-English were 29% less consistent than their English counterparts. 
• Non-English responses were 13% overall less verifiable. 

XLingHealth contains question-answer pairs that chatbots can reference, which the group hopes will spark improvement within LLMs.  

The HealthQA dataset uses specialized healthcare articles from the popular healthcare website Patient. It includes 1,134 health-related question-answer pairs as excerpts from original articles.  

LiveQA is a second dataset containing 246 question-answer pairs constructed from frequently asked questions (FAQs) platforms associated with the U.S. National Institutes of Health (NIH).  

For drug-related questions, the group built a MedicationQA component. This dataset contains 690 questions extracted from anonymous consumer queries submitted to MedlinePlus. The answers are sourced from medical references, such as MedlinePlus and DailyMed.   

In their tests, the researchers asked over 2,000 medical-related questions to ChatGPT-3.5 and MedAlpaca. MedAlpaca is a healthcare question-answer chatbot trained in medical literature. Yet, more than 67% of its responses to non-English questions were irrelevant or contradictory.  

“We see far worse performance in the case of MedAlpaca than ChatGPT,” Chandra said. 

“The majority of the data for MedAlpaca is in English, so it struggled to answer queries in non-English languages. GPT also struggled, but it performed much better than MedAlpaca because it had some sort of training data in other languages.” 

Ph.D. student Gaurav Verma and postdoctoral researcher Yibo Hu co-authored the paper. 

Jin and Verma study under Srijan Kumar, an assistant professor in the School of Computational Science and Engineering, and Hu is a postdoc in Kumar’s lab. Chandra is advised by Munmun De Choudhury, an associate professor in the School of Interactive Computing. 
 
The team will present their paper at The Web Conference, occurring May 13-17 in Singapore. The annual conference focuses on the future direction of the internet. The group’s presentation is a complimentary match, considering the conference's location.  

English and Chinese are the most common languages in Singapore. The group tested Spanish, Chinese, and Hindi because they are the world’s most spoken languages after English. Personal curiosity and background played a part in inspiring the study. 

“ChatGPT was very popular when it launched in 2022, especially for us computer science students who are always exploring new technology,” said Jin. “Non-native English speakers, like Mohit and I, noticed early on that chatbots underperformed in our native languages.” 

School of Interactive Computing communications officer Nathan Deen and School of Computational Science and Engineering communications officer Bryant Wine contributed to this report.