Whether it’s typing an email or guiding travel from one destination to the next, artificial intelligence (AI) already plays a role in simplifying daily tasks.

But what if it could also help people live more efficiently — that is, more sustainably, with less waste?

It’s a concept that often runs through the mind of Iesha Baldwin, the inaugural Georgia AIM Fellow with the Partnership for Inclusive Innovation (PIN) at the Georgia Institute of Technology’s Enterprise Innovation Institute. Born out of the Georgia Tech Manufacturing Institute, the Georgia AIM (Artificial Intelligence in Manufacturing) project works with PIN fellows to advance the project's mission of equitably developing and deploying talent and innovation in AI for manufacturing throughout the state of Georgia.

When she accepted the PIN Fellowship for 2023, she saw an opportunity to learn more about the nexus of artificial intelligence, manufacturing, waste, and education. With a background in environmental studies and science, Baldwin studied methods for waste reduction, environmental protection, and science education.

“I took an interest in AI technology because I wanted to learn how it can be harnessed to solve the waste problem and create better science education opportunities for K-12 and higher education students,” said Baldwin.

This type of unique problem-solving is what defines the PIN Fellowship programs. Every year, a cohort of recent college graduates is selected, and each is paired with an industry that aligns with their expertise and career goals — specifically, cleantech, AI manufacturing, supply chain and logistics, and cybersecurity/information technology. Fellowships are one year, with fellows spending six months with a private company and then six months with a public organization.

Through the experience, fellows expand their professional network and drive connections between the public and private sectors. They also use the opportunity to work on special projects that involve using new technologies in their area of interest.

With a focus on artificial intelligence in manufacturing, Baldwin led an inventory management project at the Georgia manufacturer Freudenberg-NOK, where the objective was to create an inventory management system that reduced manufacturing downtime and, as a result, increased efficiency, and reduced waste.

She also worked in several capacities at Georgia Tech: supporting K-12 outreach programs at the Advanced Manufacturing Pilot Facility, assisting with energy research at the Marcus Nanotechnology Research Center, and auditing the infamous mechanical engineering course ME2110 to improve her design thinking and engineering skills.

“Learning about artificial intelligence is a process, and the knowledge gained was worth the academic adventure,” she said. “Because of the wonderful support at Georgia Tech, Freudenberg NOK, PIN, and Georgia AIM, I feel confident about connecting environmental sustainability and technology in a way that makes communities more resilient and sustainable.”

Since leaving the PIN Fellowship, Baldwin connected her love for education, science, and environmental sustainability through her new role as the inaugural sustainability coordinator for Spelman College, her alma mater.  In this role, she is responsible for supporting campus sustainability initiatives.

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A new in-depth analysis shows that users who reply to misinformation about the Covid-19 vaccine on X, formerly known as Twitter, with a positive attitude, politeness, and strong evidence are more likely to encourage others to disbelieve the incorrect information.

Researchers from three Georgia Tech schools found the most effective way to confront vaccine misinformation on the X platform. 

They also created a predictive tool to show users whether their reply will succeed in changing minds or backfire and reinforce the misinformation. It can also pinpoint well-meaning replies meant to contradict misinformation but that interfere with social correction. 

A research paper with the full findings will be presented this week at the ACM Web Science Conference in Stuttgart, Germany.

Like white blood cells attacking a virus, social media users have been known to band together and debunk online misinformation being spread online in a phenomenon researchers call social correction. 

The success rate of social correction on most social media sites has not been determined. However, researchers now have a clearer picture of how successful user input can be on X. 

Their method uses a blend of artificial intelligence with a dataset of 1.5 million tweets containing misinformation about the Covid-19 vaccine. The researchers then studied user replies to misinformation as well as the consequences of those replies. 

In the paper, the researchers write that their data set pre-dates the rollout of X’s community notes feature, which allows users to submit corrections to posts on the platform. They point out that this system restricts users from responding to fact-checking text and labels and does not reflect the large flow of information on the site. 

As one of the first taxonomies of user social correction on the X platform, the researchers hope will aid future fact-checking efforts. While the paper only focused on text posts in the English language, it is a framework that can be expanded to address the growing threat of misinformation online. 

Corrective or Backfire: Characterizing and Predicting User Response to Social Correction was co-authored by Ph.D. students Bing He and Yingchen (Eric) Ma and their advisors Regents’ Entrepreneur Mustaque Ahamad, a professor with joint appointments in the School of Cybersecurity and Privacy and the School of Computer Science, and School of Computational Science and Engineering Assistant Professor Srijan Kumar. 

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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.

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Cardiologists and surgeons could soon have a new mobile augmented reality (AR) tool to improve collaboration in surgical planning.

ARCollab is an iOS AR application designed for doctors to interact with patient-specific 3D heart models in a shared environment. It is the first surgical planning tool that uses multi-user mobile AR in iOS.

The application’s collaborative feature overcomes limitations in traditional surgical modeling and planning methods. This offers patients better, personalized care from doctors who plan and collaborate with the tool.

Georgia Tech researchers partnered with Children’s Healthcare of Atlanta (CHOA) in ARCollab’s development. Pratham Mehta, a computer science major, led the group’s research.

“We have conducted two trips to CHOA for usability evaluations with cardiologists and surgeons. The overall feedback from ARCollab users has been positive,” Mehta said. 

“They all enjoyed experimenting with it and collaborating with other users. They also felt like it had the potential to be useful in surgical planning.”

ARCollab’s collaborative environment is the tool’s most novel feature. It allows surgical teams to study and plan together in a virtual workspace, regardless of location.

ARCollab supports a toolbox of features for doctors to inspect and interact with their patients' AR heart models. With a few finger gestures, users can scale and rotate, “slice” into the model, and modify a slicing plane to view omnidirectional cross-sections of the heart.

Developing ARCollab on iOS works twofold. This streamlines deployment and accessibility by making it available on the iOS App Store and Apple devices. Building ARCollab on Apple’s peer-to-peer network framework ensures the functionality of the AR components. It also lessens the learning curve, especially for experienced AR users.

ARCollab overcomes traditional surgical planning practices of using physical heart models. Producing physical models is time-consuming, resource-intensive, and irreversible compared to digital models. It is also difficult for surgical teams to plan together since they are limited to studying a single physical model.

Digital and AR modeling is growing as an alternative to physical models. CardiacAR is one such tool the group has already created. 

However, digital platforms lack multi-user features essential for surgical teams to collaborate during planning. ARCollab’s multi-user workspace progresses the technology’s potential as a mass replacement for physical modeling.

“Over the past year and a half, we have been working on incorporating collaboration into our prior work with CardiacAR,” Mehta said. 

“This involved completely changing the codebase, rebuilding the entire app and its features from the ground up in a newer AR framework that was better suited for collaboration and future development.”

Its interactive and visualization features, along with its novelty and innovation, led the Conference on Human Factors in Computing Systems (CHI 2024) to accept ARCollab for presentation. The conference occurs May 11-16 in Honolulu.

CHI is considered the most prestigious conference for human-computer interaction and one of the top-ranked conferences in computer science.

M.S. student Harsha Karanth and alumnus Alex Yang (CS 2022, M.S. CS 2023) co-authored the paper with Mehta. They study under Polo Chau, an associate professor in the School of Computational Science and Engineering.

The Georgia Tech group partnered with Timothy Slesnick and Fawwaz Shaw from CHOA on ARCollab’s development.

“Working with the doctors and having them test out versions of our application and give us feedback has been the most important part of the collaboration with CHOA,” Mehta said. 

“These medical professionals are experts in their field. We want to make sure to have features that they want and need, and that would make their job easier.”

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Two children are playing with a set of toys, each playing alone. That kind of play involves a somewhat limited set of interactions between the child and the toy. But what happens when the two children play together using the same toys?

“The actions are similar, but the choices and outcomes are very different because of the dynamic changes they’re making with the other person,” says Brian Magerko, Regents’ Professor in Georgia Tech’s School of Literature, Media, and Communication. “It’s a thing that humans do all the time, and computers don’t do with us at all.”

Welcome to the next frontier of artificial intelligence (AI) — not just generating but collaborating in real-time.

Magerko and his colleagues, Georgia Tech research scientist Milka Trajkova and Kennesaw State University Associate Professor of Dance Andrea Knowlton, are putting a collaborative AI system they’ve developed to the ultimate test: the world’s first collaborative AI dance performance.

Dance Partner

LuminAI is an interactive system that allows participants to engage in collaborative movement improvisation with an AI virtual dance partner projected on a nearby screen or wall. LuminAI analyzes participant movements and improvises responses informed by memories of past interactions with people. In other words, LuminAI learns how to dance by dancing with us.

The National Science Foundation-supported project began about 12 years ago in a lab and became an art installation and public demo. LuminAI has since moved into a different phase as a creative collaborator and education tool in a dance studio.

“We’re looking at the role LuminAI can play in dance education. As far as we’re aware, this is the first implemented version of an AI dancer in a dance studio,” says Trajkova, who was a professional ballet dancer before becoming a research scientist on the project.

To prepare LuminAI to collaborate with dancers, the research team started by studying pairs of improvisational dancers.

“We’re trying to understand how non-verbal, collaborative creativity occurs,” Knowlton says. “We start by trying to understand influencing factors that are perceived as contributing to improvisational success between two artists. Through that understanding, we applied those criteria to an AI system so it can have a similar experience with co-creative success.”

“We’re working on a creative arc,” adds Trajkova. “So instead of the AI agent just generating movements in response to the last thing that happened, we’re working to track and understand the dynamics of creative ideas across time as a continuous flow, rather than isolated instances of reaction.”

Students from Knowlton’s improvisational dance class at Kennesaw State spent two months of their spring semester working routinely with the LuminAI dancer and recording their impressions and experiences. One of the purposes the team discovered is that LuminAI serves as a third view for dancers and allows them to try ideas out with the system before trying it out with a partner.

The classroom experiment will culminate in a public performance on May 3 at Kennesaw State’s Marietta Dance Theater featuring the students performing with the LuminAI dancer. As far as the research team is aware the event is the world’s first collaborative AI dance performance.

While not all the dancers embraced having an AI collaborator, some of those who were skeptical at first left the experience more open to the possibility of collaborating with AI, Knowlton says. Regardless of their feelings toward working with AI, Knowlton says she believes the dancers gained valuable skills in working with specialized technology, especially as dance performances evolve to include more interactive media.

Refined Movement

So, what’s next for LuminAI? The project represents at least two possible paths for its learnings. The first includes continued exploration about how AI systems can be taught to cooperate and collaborate more like humans.

“With the advent of generative AI these past few years, it’s been really clear how great a need there is for this sort of social cognition,” says Magerko. “One of the things we’re going to be getting off the ground is sense-making with large language models. How do you collaborate with an AI system – rather than just making text or images, they’ll be able to make with us.”

The second involves the body movements LuminAI has been cataloging and analyzing over the years. Dance exemplifies highly refined motor skills, often exhibiting a level of detail surpassing that found in various athletic disciplines or physical therapy. While the tools designed to capture these intricate movements—through cameras and AI—are still nascent, the potential for harnessing this granular data is significant, Trajkova says.

That exploration begins on May 30 with a two-day summit being held at Georgia Tech to discuss its application for transforming performance athletics, with interdisciplinary participants in dance, computer vision, biomechanics, psychology, and human-computer interaction from Georgia Tech, Emory, KSU, Harvard, Royal Ballet in London, and Australian Ballet.

“It’s about understanding AI's role in augmenting training, promoting wellness as well as diving deep in decoding the artistry of human movements. How can we extract insights about the quality of athlete’s movements so we can help develop and enhance their own unique nuances?” Trajkova says.

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From her home more than 800 miles away, Georgia Tech online master's student Jasmine Tata is monitoring fish in aquariums at Georgia Tech.

Tata is a New York-based QA analyst and project manager. She started the Online Master of Science in Computer Science (OMSCS) program in Fall 2022 and joined FishStalkers last year.

The student-led research program is part of the School of Biological Sciences' McGrath Lab. Its researchers use machine learning, computer vision, and other technologies to better understand the evolution of animal behaviors.

One of the lab's research projects studies Lake Malawi cichlids to explore connections between observed behavior and brain function.

The FishStalkers are vital to the project. They collect video, depth, and other data from individual fish using Raspberry Pi single-board computers. This information, coupled with open-source code they developed, allows the group to track, monitor, and classify the behaviors of a fish as it builds and maintains its bower, which is a sand structure these cichlids use to attract mates.

Along with monitoring the research tanks, Tata's contributions include improving the automated collection and analysis of data streaming from the Pis. She's also helping to adapt the data pipeline to work with yellow-head, orange-cap, and other cichlid species.

[RELATED: Georgia Tech's OMSCS Program Celebrates 10th Anniversary]

"I've enjoyed learning more about new problems in a relatively unfamiliar field. In a pure computer science-focused lab, I never would experience the frustrations of data collection that come with biological subjects," said Tata.

"The fish builds bowers on its own schedule, and data collection must accurately capture this, regardless of weekends or holidays."

Tata says her experience with FishStalkers has given her new ideas about presenting data to non-technical team members. The team uses a spreadsheet integrated with data collection scripts running on the Raspberry Pis. The spreadsheet allows someone without technical knowledge to pause, upload data, or start new trials simply by toggling a dropdown.

"This has given me a lot of ideas about how to meet people where they are in terms of technical skills when it comes to user interface design and has encouraged me to learn more about human-computer interaction," said Tata.

Tata learned about the FishStalkers research group when its founder, Breanna Shi, reached out through the OMSCS Slack study channel. Shi developed the group through Georgia Tech's Vertically Integrated Projects (VIP) program as a mentorship program.

"Given their real-world computer science experience, I wanted to see if there were OMSCS students interested in collaborating on FishStalkers projects and assisting in the mentorship of undergraduate researchers," said Shi. 

Shi is a third-year Ph.D. student studying bioinformatics with minors in machine learning and higher education. She created FishStalkers as a mentorship program because she recognized that undergraduate and masters-level students could feel less valued or isolated in research environments.

"The FishStalkers model empowers all its researchers with the respect and responsibility as a full team member. Whether it's your first week as a FishStalker or your last, you will complete tasks that benefit the research team and yourself," said Shi.

[RELATED: Women-Centered Mentorship Provides Empowerment to Conquer Ph.D.]

Tata's experience in the business world made her a good fit for the FishStalkers program. Shi says Tata contributes valuable insight to the group as a mentor because most students approach the program from a purely academic viewpoint.

"Jasmine, like other OMSCS students, works full-time and attends the OMSCS program part-time. Her roles as a project manager and a software QA analyst allow her to contribute a unique perspective to the FishStalkers group," said Shi.

In addition to sharing her experience mentoring two OMSCS students this semester, Tata has helped Shi overcome some of the inherent challenges of long-distance collaboration. These include creating a sense of interpersonal connection among in-person and remote research team members.

Group meetings host a virtual link to enhance the online research experience. Every member provides progress updates during the sessions. The researchers also virtually check in and out of their research hours in a shared group chat and describe the work completed during their check-out.

"FishStalkers also runs a monthly lab-buddy program where a researcher is paired with a new buddy each month to schedule a 30-minute meeting to chat and learn about each other's work," said Shi.

"These strategies benefit OMSCS students in our group and provide a positive research environment for junior researchers. We seek to incorporate innovative strategies to create an accessible research environment for all students interested in participating in our research," said Shi.

FishStalkers has been such a success that Shi is expanding the model. This fall, Shi will work with OMSCS Executive Director David Joyner and OMSCS Associate Director of Research Nick Lytle to connect OMSCS students with interdisciplinary research projects in labs across campus.

"My role will be to establish relationships between data collectors and data analyzers to provide a service to non-technical labs across campus and a valuable research experience for OMSCS students," said Shi.

"We will be building from my existing work in image processing in the McGrath Lab and expanding to other labs with data analysis needs. I am very excited to have the experience of growing as a collaborator."

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