go-ai

Georgia Tech Team Takes Second Place at ICRA Robot Teleoperation Contest

dgivens8 — Tue, 29 Jul 2025 12:58:25 +0000

Georgia Tech Team Takes Second Place at ICRA Robot Teleoperation Contest dgivens8 Tue, 07/29/2025 - 08:58

An algorithmic breakthrough from School of Interactive Computing researchers that earned a Meta partnershipdrew more attention at the IEEE International Conference on Robotics and Automation (ICRA).

Meta announced in February its partnership with the labs of professors Danfei Xu and Judy Hoffman on a novel computer vision-based algorithm called EgoMimic. It enables robots to learn new skills by imitating human tasks from first-person video footage captured by Meta’s Aria smart glasses.

Xu’s Robot Learning and Reasoning Lab (RL2) displayed EgoMimic in action at ICRA May 19-23 at the World Congress Center in Atlanta.

Lawrence Zhu, Pranav Kuppili, and Patcharapong “Elmo” Aphiwetsa — students from Xu’s lab — used Egomimic to compete in a robot teleoperation contest at ICRA. The team finished second in the event titled What Bimanual Teleoperation and Learning from Demonstration Can Do Today, earning a $10,000 cash prize.

Teams were challenged to perform tasks by remotely controlling a robot gripper. The robot had to fold a tablecloth, open a vacuum-sealed container, place an object into the container, and then reseal it in succession without any errors.

Teams completed the tasks as many times as possible in 30 minutes, earning points for each successful attempt.

The competition also offered different challenge levels that increased the points awarded. Teams could directly operate the robot with a full workstation view and receive one point for each task completion. Or, as the RL2 team chose, teams could opt for the second challenge level.

The second level required an operator to control the task with no view of the workstation except for what was provided to through a video feed. The RL2 team completed the task seven times and received double points for the challenge level.

The third challenge level required teams to operate remotely from another location. At this level, teams could earn four times the number of points for each successful task completed. The fourth level challenged teams to deploy an algorithm for task performance and awarded eight points for each completion.

Using two of Meta’s Quest wireless controllers, Zhu controlled the robot under the direction of Aphiwetsa, while Kuppili monitored the coding from his laptop.

“It’s physically difficult to teleoperate for half an hour,” Zhu said. “My hands were shaking from holding the controllers in the air for that long.”

Being in constant communication with Aphiwetsa helped him stay focused throughout the contest.

“I helped him strategize the teleoperation and noticed he could skip some of the steps in the folding,” Aphiwetsa said. “There were many ways to do it, so I just told him what he could fix and how to do it faster.”

Zhu said he and his team had intended to tackle the fourth challenge level with the EgoMimic algorithm. However, due to unexpected time constraints, they decided to switch to the second level the day before the competition due to unexpected time constraints.

“I think we realized the day before the competition training the robot on our model would take a huge amount of time,” Zhu said. “We decided to go for the teleoperation and started practicing.”

He said the team wants to tackle the highest challenge level and use a training model for next year’s ICRA competition in Vienna, Austria.

ICRA is the world’s largest robotics conference, and Atlanta hosted the event for the third time in its history, drawing a record-breaking attendance of over 7,000.

Subtitle

Summary sentence

A Georgia Tech team earned second place in the ICRA Robot Teleoperation Contest for their EgoMimic algorithm, which allows robots to learn skills by mimicking human tasks from first-person video.

Summary

Students from Georgia Tech's Robot Learning and Reasoning Lab earned second place and a $10,000 cash prize in a robot teleoperation contest at the 2025 International Conference on Robotics and Automation in Atlanta. The RL2 lab announced a partnership with Meta in February on a novel computer vision-based algorithm called EgoMimic. It enables robots to learn new skills by imitating human tasks from first-person video footage captured by Meta’s Aria smart glasses.

Dateline

Wed, 06/11/2025 - 12:00

Associated importer

Keywords

cc-research; ic-ai-ml; ic-robotics

artificial intelligence (AI)

go-ai

go-researchnews

Research Horizons

student competition

Categories

Computer Science/Information Technology and Security

Robotics

Student Competition WInners (academic, innovation, and research)

Mercury ID

682761

Source updated

Thu, 06/12/2025 - 07:52

Georgia Tech to Build $20M National AI Supercomputer

admin — Wed, 16 Jul 2025 18:58:41 +0000

Georgia Tech to Build $20M National AI Supercomputer admin Wed, 07/16/2025 - 14:58

The National Science Foundation (NSF) has awarded Georgia Tech and its partners $20 million to build a powerful new supercomputer that will use artificial intelligence (AI) to accelerate scientific breakthroughs.

Called Nexus, the system will be one of the most advanced AI-focused research tools in the U.S. Nexus will help scientists tackle urgent challenges such as developing new medicines, advancing clean energy, understanding how the brain works, and driving manufacturing innovations.

“Georgia Tech is proud to be one of the nation’s leading sources of the AI talent and technologies that are powering a revolution in our economy,” said Ángel Cabrera, president of Georgia Tech. “It’s fitting we’ve been selected to host this new supercomputer, which will support a new wave of AI-centered innovation across the nation. We’re grateful to the NSF, and we are excited to get to work.”

Designed from the ground up for AI, Nexus will give researchers across the country access to advanced computing tools through a simple, user-friendly interface. It will support work in many fields, including climate science, health, aerospace, and robotics.

“The Nexus system's novel approach combining support for persistent scientific services with more traditional high-performance computing will enable new science and AI workflows that will accelerate the time to scientific discovery,” said Katie Antypas, National Science Foundation director of the Office of Advanced Cyberinfrastructure. “We look forward to adding Nexus to NSF's portfolio of advanced computing capabilities for the research community.”

Nexus Supercomputer — In Simple Terms

Built for the future of science: Nexus is designed to power the most demanding AI research — from curing diseases, to understanding how the brain works, to engineering quantum materials.

Blazing fast: Nexus can crank out over 400 quadrillion operations per second — the equivalent of everyone in the world continuously performing 50 million calculations every second.

Massive brain plus memory: Nexus combines the power of AI and high-performance computing with 330 trillion bytes of memory to handle complex problems and giant datasets.

Storage: Nexus will feature 10 quadrillion bytes of flash storage, equivalent to about 10 billion reams of paper. Stacked, that’s a column reaching 500,000 km high — enough to stretch from Earth to the moon and a third of the way back.

Supercharged connections: Nexus will have lightning-fast connections to move data almost instantaneously, so researchers do not waste time waiting.

Open to U.S. researchers: Scientists from any U.S. institution can apply to use Nexus.

Why Now?

AI is rapidly changing how science is investigated. Researchers use AI to analyze massive datasets, model complex systems, and test ideas faster than ever before. But these tools require powerful computing resources that — until now — have been inaccessible to many institutions.

This is where Nexus comes in. It will make state-of-the-art AI infrastructure available to scientists all across the country, not just those at top tech hubs.

“This supercomputer will help level the playing field,” said Suresh Marru, principal investigator of the Nexus project and director of Georgia Tech’s new Center for AI in Science and Engineering (ARTISAN). “It’s designed to make powerful AI tools easier to use and available to more researchers in more places.”

Srinivas Aluru, Regents’ Professor and senior associate dean in the College of Computing, said, “With Nexus, Georgia Tech joins the league of academic supercomputing centers. This is the culmination of years of planning, including building the state-of-the-art CODA data center and Nexus’ precursor supercomputer project, HIVE."

Like Nexus, HIVE was supported by NSF funding. Both Nexus and HIVE are supported by a partnership between Georgia Tech’s research and information technology units.

A National Collaboration

Georgia Tech is building Nexus in partnership with the National Center for Supercomputing Applications at the University of Illinois Urbana-Champaign, which runs several of the country’s top academic supercomputers. The two institutions will link their systems through a new high-speed network, creating a national research infrastructure.

“Nexus is more than a supercomputer — it’s a symbol of what’s possible when leading institutions work together to advance science,” said Charles Isbell, chancellor of the University of Illinois and former dean of Georgia Tech’s College of Computing. “I'm proud that my two academic homes have partnered on this project that will move science, and society, forward.”

What’s Next

Georgia Tech will begin building Nexus this year, with its expected completion in spring 2026. Once Nexus is finished, researchers can apply for access through an NSF review process. Georgia Tech will manage the system, provide support, and reserve up to 10% of its capacity for its own campus research.

“This is a big step for Georgia Tech and for the scientific community,” said Vivek Sarkar, the John P. Imlay Dean of Computing. “Nexus will help researchers make faster progress on today’s toughest problems — and open the door to discoveries we haven’t even imagined yet.”

Subtitle

Summary sentence

Summary

The National Science Foundation has awarded Georgia Tech and its partners $20 million to build a powerful new supercomputer that will use artificial intelligence to accelerate scientific breakthroughs. Called Nexus, the system will be one of the most advanced, AI-focused research tools in the U.S. Nexus will help scientists tackle urgent challenges such as developing new medicines, advancing clean energy, understanding how the brain works, and driving manufacturing innovations.

Dateline

Tue, 07/15/2025 - 12:00

media@gatech.edu

Contact

Siobhan Rodriguez
Senior Media Relations Representative
Institute Communications

Associated importer

Keywords

National Science Foundation

NSF

Georgia Tech

supercomputer

Nexus

artificial intelligence

scientific breakthroughs

hpc

clean energy

brain research

medicine development

manufacturing innovation

AI infrastructure

scientific discovery

persistent scientific services

quantum materials

climate science

health research

aerospace

robotics

flash storage

data transfer

U.S. researchers

AI workflows

ARTISAN

Coda Data Center

HIVE supercomputer

NCSA

University of Illinois

national collaboration

tech partnerships

research support

AI talent

scientific community

research access

AI tools

AI-centered innovation

go-researchnews

go-ai

News room topics

Campus and Community

Science and Technology

Categories

Artificial Intelligence

Computer Science/Information Technology and Security

Institute and Campus

Research

Mercury ID

683161

Source updated

Wed, 07/16/2025 - 14:56

Georgia Tech to Build $20M National AI Supercomputer

admin — Tue, 15 Jul 2025 15:05:37 +0000

Georgia Tech to Build $20M National AI Supercomputer admin Tue, 07/15/2025 - 11:05

Called Nexus, the system will be one of the most advanced, AI-focused research tools in the U.S. Nexus will help scientists tackle urgent challenges such as developing new medicines, advancing clean energy, understanding how the brain works, and driving manufacturing innovations.

Nexus Supercomputer — In Simple Terms

Built for the future of science: Nexus is designed to power the most demanding AI research — from curing diseases to understanding how the brain works to engineering quantum materials.

Blazing fast: Nexus can crank out over 400 quadrillion operations per second — the equivalent of everyone in the world continuously performing 50 million calculations every second.

Massive brain + memory: Nexus combines the power of AI and high-performance computing, with 330 trillion bytes of memory to handle complex problems and giant datasets.

Storage: Nexus will feature 10 quadrillion bytes of flash storage, equivalent to about 10 billion reams of paper. Stacked, that’s a column reaching 500,000 km high — enough to stretch from Earth to the Moon and a third of the way back.

Supercharged connections: Nexus will have lightning-fast connections to move data almost instantaneously, so researchers do not waste time waiting.

Open to U.S. researchers: Scientists from any U.S. institution can apply to use Nexus.

Why Now?

This is where Nexus comes in. It will make state-of-the-art AI infrastructure available to scientists all across the country, not just those at top tech hubs.

Like Nexus, HIVE was supported by NSF funding. Both Nexus and the HIVE are supported by a partnership between Georgia Tech’s research and information technology units.

A National Collaboration

Georgia Tech is building Nexus in partnership with the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign, which runs several of the country’s top academic supercomputers. The two institutions will link their systems through a new high-speed network, creating a national research infrastructure.

Tech companies, whose technologies will power the system, will also play a role.

What’s Next

Summary sentence

Summary

Dateline

Tue, 07/15/2025 - 12:00

media@gatech.edu

Contact

Siobhan Rodriguez
Senior Media Relations Representative
Institute Communications

Associated importer

Keywords

go-ai

News room topics

Campus and Community

Science and Technology

Categories

Artificial Intelligence

Computer Science/Information Technology and Security

Institute and Campus

Research

Mercury ID

683139

Source updated

Tue, 07/15/2025 - 11:05

Georgia Tech to Build $20M National AI Supercomputer

admin — Tue, 15 Jul 2025 14:11:41 +0000

Georgia Tech to Build $20M National AI Supercomputer admin Tue, 07/15/2025 - 10:11

Nexus Supercomputer — In Simple Terms

Built for the future of science: Nexus is designed to power the most demanding AI research — from curing diseases, to understanding how the brain works, to engineering quantum materials.

Blazing fast: Nexus can crank out over 400 quadrillion operations per second — the equivalent of everyone in the world continuously performing 50 million calculations every second.

Massive brain plus memory: Nexus combines the power of AI and high-performance computing with 330 trillion bytes of memory to handle complex problems and giant datasets.

Storage: Nexus will feature 10 quadrillion bytes of flash storage, equivalent to about 10 billion reams of paper. Stacked, that’s a column reaching 500,000 km high — enough to stretch from Earth to the moon and a third of the way back.

Supercharged connections: Nexus will have lightning-fast connections to move data almost instantaneously, so researchers do not waste time waiting.

Open to U.S. researchers: Scientists from any U.S. institution can apply to use Nexus.

Why Now?

This is where Nexus comes in. It will make state-of-the-art AI infrastructure available to scientists all across the country, not just those at top tech hubs.

Like Nexus, HIVE was supported by NSF funding. Both Nexus and HIVE are supported by a partnership between Georgia Tech’s research and information technology units.

A National Collaboration

What’s Next

Summary sentence

Summary

Dateline

Tue, 07/15/2025 - 12:00

media@gatech.edu

Contact

Siobhan Rodriguez
Senior Media Relations Representative
Institute Communications

Associated importer

Keywords

National Science Foundation

NSF

Georgia Tech

supercomputer

Nexus

artificial intelligence

scientific breakthroughs

hpc

clean energy

brain research

medicine development

manufacturing innovation

AI infrastructure

scientific discovery

persistent scientific services

quantum materials

climate science

health research

aerospace

robotics

flash storage

data transfer

U.S. researchers

AI workflows

ARTISAN

Coda Data Center

HIVE supercomputer

NCSA

University of Illinois

national collaboration

tech partnerships

research support

AI talent

scientific community

research access

AI tools

AI-centered innovation

go-researchnews

go-ai

News room topics

Campus and Community

Science and Technology

Mercury ID

683137

Source updated

Tue, 07/15/2025 - 10:10

Brain-Inspired AI Breakthrough Spotlighted at Global Conference

dgivens8 — Thu, 26 Jun 2025 16:46:41 +0000

Brain-Inspired AI Breakthrough Spotlighted at Global Conference dgivens8 Thu, 06/26/2025 - 12:46

Researchers at Georgia Tech have taken a critical step forward in creating efficient, useful and brain-like artificial intelligence (AI). The key? A new algorithm that results in neural networks with internal structure more like the human brain.

The study, “TopoNets: High-Performing Vision and Language Models With Brain-Like Topography,” was awarded a spotlight at this year’s International Conference on Learning Representations (ICLR), a distinction given to only 2 percent of papers. The research was led by graduate student Mayukh Deb alongside School of Psychology Assistant Professor Apurva Ratan Murty.

Thirty-two of Tech’s computing, engineering, and science faculty represented the Institute at ICLR 2025, which is globally renowned for sharing cutting-edge research.

“We started with this idea because we saw that AI models are unstructured, while brains are exquisitely organized,” says first-author Deb. “Our models with internal structure showed more than a 20 percent boost in efficiency with almost no performance losses. And this is out-of-the-box — it’s broadly applicable to other models with no extra fine-tuning needed.”

For Murty, the research also underscores the importance of a rapidly growing field of research at the intersection of neuroscience and AI. “There's a major explosion in understanding intelligence right now,” he says. “The neuro-AI approach is exciting because it helps emulate human intelligence in machines, making AI more interpretable.”

“In addition to advancing AI, this type of research also benefits neuroscience because it informs a fundamental question: Why is our brain organized the way it is?,” Deb adds. “Making AI more interpretable helps everyone.”

Brain-inspired blueprints

In the brain, neurons form topographic maps: neurons used for comparable tasks are closer together. The researchers applied this concept to AI by organizing how internal components (like artificial neurons) connect and process information.

This type of organization has been tried in the past but has been challenging, Murty says. “Historically, rules constraining how the AI could structure itself often resulted in lower-performing models. We realized that for this type of biophysical constraint, you simply can’t map everything — you need an algorithmic solution.”

“Our key insight was an algorithmic trick that gives the same structure as brains without enforcing things that models don't respond well to,” he adds. “That breakthrough was what Mayukh (Deb) worked on.”

The algorithm, called TopoLoss, uses a loss function to encourage brain-like organization in artificial neural networks, and it is compatible with many AI systems capable of understanding language and images.

“The resulting training method, TopoNets, is very flexible and broadly applicable,” Murty says. “You can apply it to contemporary models very easily, which is a critical advancement when compared to previous methods.”

Neuro-AI innovations

Murty and Deb plan to continue refining and designing brain-inspired AI systems. “All parts of the brain have some organization — we want to expand into other domains,” Deb says. “On the neuroscience side of things, we want to discover new kinds of organization in brains using these topographic systems.”

Deb also cites possibilities in robotics, especially in situations like space exploration where resources are limited. “Imagine running a model inside a robot with limited power,” he says. “Structured models can help us achieve 80 percent of performance with just 20 percent of energy consumption, saving valuable energy and space. This is still experimental, but it's the direction we are interested in exploring.”

“This success highlights the potential of a new approach, designing systems that benefit both neuroscience and AI — and beyond,” Murty adds. “We can learn so much from the human brain, and this project shows that brain-inspired systems can help current AI be better. We hope our work stimulates this conversation.”

Subtitle

Summary sentence

Researchers at Georgia Tech have developed an algorithm that helps AI models develop internal organization just like the human brain — boosting efficiency by 20 percent.

Summary

Researchers at Georgia Tech have developed an algorithm that helps AI models develop internal organization just like the human brain — boosting efficiency by 20 percent.

Dateline

Thu, 06/26/2025 - 12:00

Contact

Written by Selena Langner

Contact: Jess Hunt-Ralston

Associated importer

Keywords

go-neuro

cos-neuro

go-ai

go-researchnews

Categories

Artificial Intelligence

Computer Science/Information Technology and Security

Research

Student and Faculty

Student Competition WInners (academic, innovation, and research)

Student Research

Mercury ID

682905

Source updated

Thu, 06/26/2025 - 12:46

Middle Schoolers’ Feedback Informs New Approach to AI-based Museum Exhibits

dgivens8 — Fri, 30 May 2025 18:14:41 +0000

Middle Schoolers’ Feedback Informs New Approach to AI-based Museum Exhibits dgivens8 Fri, 05/30/2025 - 14:14

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.”

Summary sentence

Partnering with Chicago's Museum of Science and Industry, Researchers at Georgia Tech are creating accessible museum exhibits that explain artificial intelligence (AI) to middle school students.

Summary

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.

Dateline

Fri, 06/21/2024 - 12:00

Contact

Nathan Deen

Communications Officer I

School of Interactive Computing

Associated importer

Keywords

go-ai

artificial intelligence

middle school

AI education

go-researchnews

Research Horizons

Categories

Community

Education

Exhibitions

Georgia Tech Arts

Music and Music Technology

Mercury ID

675196

Source updated

Wed, 07/17/2024 - 10:05

Teaching AI to Collaborate, not Merely Create, Through Dance

dgivens8 — Fri, 30 May 2025 18:14:41 +0000

Teaching AI to Collaborate, not Merely Create, Through Dance dgivens8 Fri, 05/30/2025 - 14:14

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.

Summary sentence

Georgia Tech and KSU faculty are putting a collaborative AI system they’ve developed to the ultimate test: the world’s first collaborative AI dance performance.

Summary

Georgia Tech and KSU faculty are putting a collaborative AI system they’ve developed to the ultimate test: the world’s first collaborative AI dance performance.

Dateline

Thu, 05/02/2024 - 12:00

Contact

Megan McRainey
megan.mcrainey@gatech.edu

Associated importer

Keywords

artificial intelligence (AI)

go-ai

go-neuro

Categories

Art Research

Digital Media and Entertainment

Georgia Tech Arts

Mercury ID

674495

Source updated

Fri, 05/10/2024 - 14:03

AI: Am I? Georgia Tech Experts Weigh in on the Transformative Power of AI

admin — Mon, 19 May 2025 13:33:41 +0000

AI: Am I? Georgia Tech Experts Weigh in on the Transformative Power of AI admin Mon, 05/19/2025 - 09:33

AI solutions have the power to become our silent partners in ways that could drastically improve our daily lives — and are already doing it. Yet, in a world where algorithms can sift through data with a precision no human can match, uneasiness stirs.

Georgia Tech researchers are confronting the paradoxes, pitfalls, and potential of artificial intelligence. Here, some of them shed light on the emerging role of AI in our lives — and answer questions about how humans and machines will coexist in the future.

We asked Georgia Tech AI experts key questions about the technology, its use and misuse, and how it might shape our shared future. Here’s what they had to say.

Click here to read the story.

Summary sentence

At Georgia Tech, at the crossroads of innovation and ethics, scientists and engineers are weighing in on a topic that has dominated headlines and internet searches of late: artificial intelligence.

Summary

In a world where algorithms can sift through data with a precision no human can match, uneasiness stirs. Am I being replaced? Am I being helped? Am I being paranoid? Experts from across Georgia Tech weigh in on AI.

Dateline

Fri, 10/27/2023 - 12:00

Associated importer

Keywords

go-ai

go-researchnews

Mercury ID

670729

Source updated

Fri, 10/27/2023 - 14:36

SKYSCENES Dataset Could Lead to Safe, Reliable Autonomous Flying Vehicles

admin — Mon, 19 May 2025 13:31:53 +0000

SKYSCENES Dataset Could Lead to Safe, Reliable Autonomous Flying Vehicles admin Mon, 05/19/2025 - 09:31

Is it a building or a street? How tall is the building? Are there powerlines nearby?

These are details autonomous flying vehicles would need to know to function safely. However, few aerial image datasets exist that can adequately train the computer vision algorithms that would pilot these vehicles.

That’s why Georgia Tech researchers created a new benchmark dataset of computer-generated aerial images.

Judy Hoffman, an assistant professor in Georgia Tech’s School of Interactive Computing, worked with students in her lab to create SKYSCENES. The dataset contains over 33,000 aerial images of cities curated from a computer simulation program.

Hoffman said sufficient training datasets could unlock the potential of autonomous flying vehicles. Constructing those datasets is a challenge the computer vision research community has been working for years to overcome.

“You can’t crowdsource it the same way you would standard internet images,” Hoffman said. “Trying to collect it manually would be very slow and expensive — akin to what the self-driving industry is doing driving around vehicles, but now you’re talking about drones flying around.

“We must fix those problems to have models that work reliably and safely for flying vehicles.”

Many existing datasets aren’t annotated well enough for algorithms to distinguish objects in the image. For example, the algorithms may not recognize the surface of a building from the surface of a street.

Working with Hoffman, Ph.D. student Sahil Khose tried a new approach — constructing a synthetic image data set from a ground-view, open-source simulator known as CARLA.

CARLA was originally designed to provide ground-view simulation for self-driving vehicles. It creates an open-world virtual reality that allows users to drive around in computer-generated cities.

Khose and his collaborators adjusted CARLA’s interface to support aerial views that mimic views one might get from unmanned aerial vehicles (UAVs).

What's the Forecast?

The team also created new virtual scenarios to mimic the real world by accounting for changes in weather, times of day, various altitudes, and population per city. The algorithms will struggle to recognize the objects in the frame consistently unless those details are incorporated into the training data.

“CARLA’s flexibility offers a wide range of environmental configurations, and we take several important considerations into account while curating SKYSCENES images from CARLA,” Khose said. “Those include strategies for obtaining diverse synthetic data, embedding real-world irregularities, avoiding correlated images, addressing skewed class representations, and reproducing precise viewpoints.”

SKYSCENES is not the largest dataset of aerial images to be released, but a paper co-authored by Khose shows that it performs better than existing models.

Khose said models trained on this dataset exhibit strong generalization to real-world scenarios, and integration with real-world data enhances their performance. The dataset also controls variability, which is essential to perform various tasks.

“This dataset drives advancements in multi-view learning, domain adaptation, and multimodal approaches, with major implications for applications like urban planning, disaster response, and autonomous drone navigation,” Khose said. “We hope to bridge the gap for synthetic-to-real adaptation and generalization for aerial images.”

Seeing the Whole Picture

For algorithms, generalization is the ability to perform tasks based on new data that expands beyond the specific examples on which they were trained.

“If you have 200 images, and you train a model on those images, they’ll do well at recognizing what you want them to recognize in that closed-world initial setting,” Hoffman said. “But if we were to take aerial vehicles and fly them around cities at various times of the day or in other weather conditions, they would start to fail.”

That’s why Khose designed algorithms to enhance the quality of the curated images.

“These images are captured from 100 meters above ground, which means the objects appear small and are challenging to recognize,” he said. “We focused on developing algorithms specifically designed to address this.”

Those algorithms elevate the ability of ML models to recognize small objects, improving their performance in navigating new environments.

“Our annotations help the models capture a more comprehensive understanding of the entire scene — where the roads are, where the buildings are, and know they are buildings and not just an obstacle in the way,” Hoffman said. “It gives a richer set of information when planning a flight.

“To work safely, many autonomous flight plans might require a map given to them beforehand. If you have successful vision systems that understand exactly what the obstacles in the real world are, you could navigate in previously unseen environments.”

For more information about Georgia Tech Research at ECCV 2024, click here.

Summary sentence

Georgia Tech researchers created a new benchmark dataset of computer-generated aerial images that could allow autonomous flying vehicles to operate reliably and safely.

Summary

Few aerial image datasets exist that can adequately train the computer vision algorithms that would pilot autonomous flying vehicles. Judy Hoffman, an assistant professor in Georgia Tech’s School of Interactive Computing, worked with students in her lab to create SKYSCENES. The dataset contains over 33,000 aerial images of cities curated from a computer simulation program.

Dateline

Thu, 09/26/2024 - 12:00

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Nathan Deen

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drones; UAV; unmanned aerial vehicles

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Source updated

Thu, 09/26/2024 - 15:12

Computing Framework Could Reveal Signs of Neuro Disorders Hidden within Brain Data

admin — Mon, 05 May 2025 13:58:41 +0000

Computing Framework Could Reveal Signs of Neuro Disorders Hidden within Brain Data admin Mon, 05/05/2025 - 09:58

A Georgia Tech doctoral student’s dissertation could help physicians diagnose neuropsychiatric disorders, including schizophrenia, autism, and Alzheimer’s disease. The new approach leverages data science and algorithms instead of relying on traditional methods like cognitive tests and image scans.

Ph.D. candidate Md Abdur Rahaman’s dissertation studies brain data to understand how changes in brain activity shape behavior.

Computational tools Rahaman developed for his dissertation look for informative patterns between the brain and behavior. Successful tests of his algorithms show promise to help doctors diagnose mental health disorders and design individualized treatment plans for patients.

“I've always been fascinated by the human brain and how it defines who we are,” Rahaman said.

“The fact that so many people silently suffer from neuropsychiatric disorders, while our understanding of the brain remains limited, inspired me to develop tools that bring greater clarity to this complexity and offer hope through more compassionate, data-driven care.”

Rahaman’s dissertation introduces a framework focusing on granular factoring. This computing technique stratifies brain data into smaller, localized subgroups, making it easier for computers and researchers to study data and find meaningful patterns.

Granular factoring overcomes the challenges of size and heterogeneity in neurological data science. Brain data is obtained from neuroimaging, genomics, behavioral datasets, and other sources. The large size of each source makes it a challenge to study them individually, let alone analyze them simultaneously, to find hidden inferences.

Rahaman’s research allows researchers and physicians to move past one-size-fits-all approaches. Instead of manually reviewing tests and scans, algorithms look for patterns and biomarkers in the subgroups that otherwise go undetected, especially ones that indicate neuropsychiatric disorders.

“My dissertation advances the frontiers of computational neuroscience by introducing scalable and interpretable models that navigate brain heterogeneity to reveal how neural dynamics shape behavior,” Rahaman said.

“By uncovering subgroup-specific patterns, this work opens new directions for understanding brain function and enables more precise, personalized approaches to mental health care.”

Rahaman defended his dissertation on April 14, the final step in completing his Ph.D. in computational science and engineering. He will graduate on May 1 at Georgia Tech’s Ph.D. Commencement.

After walking across the stage at McCamish Pavilion, Rahaman’s next step in his career is to go to Amazon, where he will work in the generative artificial intelligence (AI) field.

Graduating from Georgia Tech is the summit of an educational trek spanning over a decade. Rahaman hails from Bangladesh where he graduated from Chittagong University of Engineering and Technology in 2013. He attained his master’s from the University of New Mexico in 2019 before starting at Georgia Tech.

“Munna is an amazingly creative researcher,” said Vince Calhoun, Rahman’s advisor. Calhoun is the founding director of the Translational Research in Neuroimaging and Data Science Center (TReNDS).

TReNDS is a tri-institutional center spanning Georgia Tech, Georgia State University, and Emory University that develops analytic approaches and neuroinformatic tools. The center aims to translate the approaches into biomarkers that address areas of brain health and disease.

“His work is moving the needle in our ability to leverage multiple sources of complex biological data to improve understanding of neuropsychiatric disorders that have a huge impact on an individual’s livelihood,” said Calhoun.

Summary sentence

A Georgia Tech doctoral student’s dissertation could help physicians diagnose neuropsychiatric disorders, including schizophrenia, autism, and Alzheimer’s disease.

Summary

Ph.D. candidate Md Abdur Rahaman’s dissertation studies brain data to understand how changes in brain activity shape behavior.

Dateline

Thu, 04/24/2025 - 12:00

Contact

Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu

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Biotechnology, Health, Bioengineering, Genetics

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682026

Source updated

Mon, 05/05/2025 - 09:58

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SKYSCENES Dataset Could Lead to Safe, Reliable Autonomous Flying Vehicles

What's the Forecast?

Seeing the Whole Picture

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