When it comes to manufacturing innovation, the “valley of death” — the gap between the lab and the industry floor where even the best discoveries often get lost — looms large.

“An individual faculty’s lab focuses on showing the innovation or the new science that they discovered,” said Aaron Stebner, professor and Eugene C. Gwaltney Jr. Chair in Manufacturing in the George W. Woodruff School of Mechanical Engineering. “At that point, the business case hasn't been made for the technology yet — there's no testing on an industrial system to know if it breaks or if it scales up. A lot of innovation and scientific discovery dies there.”

The Georgia Tech Manufacturing Institute (GTMI) launched the Advanced Manufacturing Pilot Facility (AMPF) in 2017 to help bridge that gap. 

Now, GTMI is breaking ground on an extensive expansion to bring new capabilities in automation, artificial intelligence, and data management to the facility. 

“This will be the first facility of this size that's being intentionally designed to enable AI to perform research and development in materials and manufacturing at the same time,” said Stebner, “setting up GTMI as not just a leader in Georgia, but a leader in automation and AI in manufacturing across the country.”

AMPF: A Catalyst for Collaboration

Located just north of Georgia Tech’s main campus, APMF is a 20,000-square-foot facility serving as a teaching laboratory, technology test bed, and workforce development space for manufacturing innovations.

“The pilot facility,” says Stebner, “is meant to be a place where stakeholders in academic research, government, industry, and workforce development can come together and develop both the workforce that is needed for future technologies, as well as mature, de-risk, and develop business cases for new technologies — proving them out to the point where it makes sense for industry to pick them up.”

In addition to serving as the flagship facility for GTMI research and the state’s Georgia AIM (Artificial Intelligence in Manufacturing) project, the AMPF is a user facility accessible to Georgia Tech’s industry partners as well as the Institute’s faculty, staff, and students.

“We have all kinds of great capabilities and technologies, plus staff that can train students, postdocs, and faculty on how to use them,” said Stebner, who also serves as co-director of the GTMI-affiliated Georgia AIM project. “It creates a unique asset for Georgia Tech faculty, staff, and students.”

Bringing AI and Automation to the Forefront

The renovation of APMF is a key component of the $65 million grant, awarded to Georgia Tech by the U.S. Department of Commerce’s Economic Development Administration in 2022, which gave rise to the Georgia AIM project. With over $23 million in support from Georgia AIM, the improved facility will feature new workforce training programs, personnel, and equipment. 

Set to complete in Spring 2026, the Institute’s investment of $16 million supports construction that will roughly triple the size of the facility — and work to address a major roadblock for incorporating AI and automation into manufacturing practices: data.

“There’s a lot of work going on across the world in using machine learning in engineering problems, including manufacturing, but it's limited in scale-up and commercial adoption,” explained Stebner. 

Machine learning algorithms have the potential to make manufacturing more efficient, but they need a lot of reliable, repeatable data about the processes and materials involved to be effective. Collecting that data manually is monotonous, costly, and time-consuming.

“The idea is to automate those functions that we need to enable AI and machine learning” in manufacturing, says Stebner. “Let it be a facility where you can imagine new things and push new boundaries and not just be stuck in demonstrating concepts over and over again.”

To make that possible, the expanded facility will couple AI and data management with robotic automation.

“We're going to be able to demonstrate automation from the very beginning of our process all the way through the entire ecosystem of manufacturing,” said Steven Sheffield, GTMI’s senior assistant director of research operations.

“This expansion — no one else has done anything like it,” added Steven Ferguson, principal research scientist with GTMI and managing director of Georgia AIM. “We will have the leading facility for demonstrating what a hyperconnected and AI-driven manufacturing enterprise looks like. We’re setting the stage for Georgia Tech to continue to lead in the manufacturing space for the next decade and beyond.”

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Hepatic, or liver, disease affects more than 100 million people in the U.S. About 4.5 million adults (1.8%) have been diagnosed with liver disease, but it is estimated that between 80 and 100 million adults in the U.S. have undiagnosed fatty liver disease in varying stages. Over time, undiagnosed and untreated hepatic diseases can lead to cirrhosis, a severe scarring of the liver that cannot be reversed. 

Most hepatic diseases are chronic conditions that will be present over the life of the patient, but early detection improves overall health and the ability to manage specific conditions over time. Additionally, assessing patients over time allows for effective treatments to be adjusted as necessary. The standard protocol for diagnosis, as well as follow-up tissue assessment, is a biopsy after the return of an abnormal blood test, but biopsies are time-consuming and pose risks for the patient. Several non-invasive imaging techniques have been developed to assess the stiffness of liver tissue, an indication of scarring, including magnetic resonance elastography (MRE).

MRE combines elements of ultrasound and MRI imaging to create a visual map showing gradients of stiffness throughout the liver and is increasingly used to diagnose hepatic issues. MRE exams, however, can fail for many reasons, including patient motion, patient physiology, imaging issues, and mechanical issues such as improper wave generation or propagation in the liver. Determining the success of MRE exams depends on visual inspection of technologists and radiologists. With increasing work demands and workforce shortages, providing an accurate, automated way to classify image quality will create a streamlined approach and reduce the need for repeat scans. 

Professor Jun Ueda in the George W. Woodruff School of Mechanical Engineering and robotics Ph.D. student Heriberto Nieves, working with a team from the Icahn School of Medicine at Mount Sinai, have successfully applied deep learning techniques for accurate, automated quality control image assessment. The research, “Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results,” was published in the Journal of Magnetic Resonance Imaging.

Using five deep learning training models, an accuracy of 92% was achieved by the best-performing ensemble on retrospective MRE images of patients with varied liver stiffnesses. The team also achieved a return of the analyzed data within seconds. The rapidity of image quality return allows the technician to focus on adjusting hardware or patient orientation for re-scan in a single session, rather than requiring patients to return for costly and timely re-scans due to low-quality initial images.

This new research is a step toward streamlining the review pipeline for MRE using deep learning techniques, which have remained unexplored compared to other medical imaging modalities.  The research also provides a helpful baseline for future avenues of inquiry, such as assessing the health of the spleen or kidneys. It may also be applied to automation for image quality control for monitoring non-hepatic conditions, such as breast cancer or muscular dystrophy, in which tissue stiffness is an indicator of initial health and disease progression. Ueda, Nieves, and their team hope to test these models on Siemens Healthineers magnetic resonance scanners within the next year.

Publication
Nieves-Vazquez, H.A., Ozkaya, E., Meinhold, W., Geahchan, A., Bane, O., Ueda, J. and Taouli, B. (2024), Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results. J Magn Reson Imaging. https://doi.org/10.1002/jmri.29490

Prior Work 
Robotically Precise Diagnostics and Therapeutics for Degenerative Disc Disorder

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Editorial for “Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results”

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New research from Georgia Tech is giving scientists more control options over generative artificial intelligence (AI) models in their studies. Greater customization from this research can lead to discovery of new drugs, materials, and other applications tailor-made for consumers.

The Tech group dubbed its method PRODIGY (PROjected DIffusion for controlled Graph Generation). PRODIGY enables diffusion models to generate 3D images of complex structures, such as molecules from chemical formulas. 

Scientists in pharmacology, materials science, social network analysis, and other fields can use PRODIGY to simulate large-scale networks. By generating 3D molecules from multiple graph datasets, the group proved that PRODIGY could handle complex structures.

In keeping with its name, PRODIGY is the first plug-and-play machine learning (ML) approach to controllable graph generation in diffusion models. This method overcomes a known limitation inhibiting diffusion models from broad use in science and engineering.

“We hope PRODIGY enables drug designers and scientists to generate structures that meet their precise needs,” said Kartik Sharma, lead researcher on the project. “It should also inspire future innovations to precisely control modern generative models across domains.” 

PRODIGY works on diffusion models, a generative AI model for computer vision tasks. While suitable for image creation and denoising, diffusion methods are limited because they cannot accurately generate graph representations of custom parameters a user provides.

PRODIGY empowers any pre-trained diffusion model for graph generation to produce graphs that meet specific, user-given constraints. This capability means, as an example, that a drug designer could use any diffusion model to design a molecule with a specific number of atoms and bonds.

The group tested PRODIGY on two molecular and five generic datasets to generate custom 2D and 3D structures. This approach ensured the method could create such complex structures, accounting for the atoms, bonds, structures, and other properties at play in molecules. 

Molecular generation experiments with PRODIGY directly impact chemistry, biology, pharmacology, materials science, and other fields. The researchers say PRODIGY has potential in other fields using large networks and datasets, such as social sciences and telecommunications.

These features led to PRODIGY’s acceptance for presentation at the upcoming International Conference on Machine Learning (ICML 2024). ICML 2024 is the leading international academic conference on ML. The conference is taking place July 21-27 in Vienna.

Assistant Professor Srijan Kumar is Sharma’s advisor and paper co-author. They worked with Tech alumnus Rakshit Trivedi (Ph.D. CS 2020), a Massachusetts Institute of Technology postdoctoral associate.

Twenty-four Georgia Tech faculty from the Colleges of Computing and Engineering will present 40 papers at ICML 2024. Kumar is one of six faculty representing the School of Computational Science and Engineering (CSE) at the conference.

Sharma is a fourth-year Ph.D. student studying computer science. He researches ML models for structured data that are reliable and easily controlled by users. While preparing for ICML, Sharma has been interning this summer at Microsoft Research in the Research for Industry lab.

“ICML is the pioneering conference for machine learning,” said Kumar. “A strong presence at ICML from Georgia Tech illustrates the ground-breaking research conducted by our students and faculty, including those in my research group.”

Visit https://sites.gatech.edu/research/icml-2024 for news and coverage of Georgia Tech research presented at ICML 2024.

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From weather prediction to drug discovery, math powers the models used in computer simulations. To help these vital tools with their calculations, global experts recently met at Georgia Tech to share ways to make math easier for computers.

Tech hosted the 2024 International Conference on Preconditioning Techniques for Scientific and Industrial Applications (Precond 24), June 10-12. 

Preconditioning accelerates matrix computations, a kind of math used in most large-scale models. These computer models become faster, more efficient, and more accessible with help from preconditioned equations.

“Preconditioning transforms complex numerical problems into more easily solved ones,” said Edmond Chow, a professor at Georgia Tech and co-chair of Precond 24’s local organization and program committees. 

“The new problem wields a better condition number, giving rise to the name preconditioning.”

Researchers from 13 countries presented their work through 20 mini-symposia and seven invited talks at Precond 24. Their work showcased the practicality of preconditioners. 

Vandana Dwarka, an assistant professor at Delft University of Technology, shared newly developed preconditioners for electromagnetic simulations. This technology can be used in further applications ranging from imaging to designing nuclear fusion devices.

Xiaozhe Hu presented a physics-based preconditioner that simulates biophysical processes in the brain, such as blood flow and metabolic waste clearance. Hu brought this research from Tufts University, where he is an associate professor.

Tucker Hartland, a postdoctoral researcher at Lawrence Livermore National Laboratory, discussed preconditioning in contact mechanics. This work improves the modeling of interactions between physical objects that touch each other. Many fields stand to benefit from Hartland’s study, including mechanical engineering, civil engineering, and materials science.

A unique aspect of this year’s conference was an emphasis on machine learning (ML). Between a panel discussion, tutorial, and several talks, experts detailed how to employ ML for preconditioning and how preconditioning can train ML models.

Precond 24 invited seven speakers from institutions around the world to share their research with conference attendees. The presenters were: 

• Monica Dessole, CERN, Switzerland
• Selime Gurol, CERFACS, France
• Alexander Heinlein, Delft University of Technology, Netherlands
• Rui Peng Li, Lawrence Livermore National Laboratory, USA 
• Will Pazner, Portland State University, USA 
• Tyrone Rees, Science and Technology Facilities Council, UK
• Jacob B. Schroder, University of New Mexico, USA

Along with hosting Precond 24, several Georgia Tech researchers participated in the conference through presentations. 

Ph.D. students Hua Huang and Shikhar Shah each presented a paper on the conference’s first day. Alumnus Srinivas Eswar (Ph.D. CS 2022) returned to Atlanta to share research from his current role at Argonne National Laboratory. Chow chaired the ML panel and a symposium on preconditioners for matrices.

“It was an engaging and rewarding experience meeting so many people from this very tight-knit community,” said Shah, who studies computational science and engineering (CSE). “Getting to see talks close to my research provided me with a lot of inspiration and direction for future work.”

Precond 2024 was the thirteenth meeting of the conference, which occurs every two years. 

The conference returned to Atlanta this year for the first time since 2005. Atlanta joins Minneapolis as one of only two cities in the world to host Precond more than once. Precond 24 marked the sixth time the conference met in the U.S. 

Georgia Tech and Emory University’s Department of Mathematics organized and sponsored Precond 24. The U.S. Department of Energy Office of Science co-sponsored the conference with Tech and Emory. 

Georgia Tech entities swarmed together in support of Precond 24. The Office of the Associate Vice President for Research Operations and Infrastructure, College of Computing, and School of CSE co-sponsored the conference.

“The enthusiasm at the conference has been very gratifying. So many people organized sessions at the conference and contributed to the very strong attendance,” Chow said. 

“This is a testament to the continued importance of preconditioning and related numerical methods in a rapidly changing technological world.”

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Artificial intelligence and machine learning techniques are infused across the College of Engineering’s education and research.

From safer roads to new fuel cell technology, semiconductor designs to restoring bodily functions, Georgia Tech engineers are capitalizing on the power of AI to quickly make predictions or see danger ahead.

Explore some of the ways we are using AI to create a better future on the College's website.

This story was featured in the spring 2024 issue of Helluva Engineer magazine, produced biannually by the College of Engineering.

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It’s tempting to think that the artificial intelligence revolution is coming — for good or ill — and that AI will soon be baked into every facet of our lives. With generative AI tools suddenly available to anyone and seemingly every company scrambling to leverage AI for their business, it can feel like the AI-dominated future is just over the horizon.

The truth is, that future is already here. Most of us just didn’t notice.

Every time you unlock your smartphone or computer with a face scan or fingerprint. Every time your car alerts you that you’re straying from your lane or automatically adjusts your cruise control speed. Every time you ask Siri for directions or Alexa to turn on some music. Every time you start typing in the Google search box and suggestions or the outright answer to your question appear. Every time Netflix recommends what you should watch next.

All driven by AI. And all a regular part of most people’s days.

But what is “artificial intelligence”? What about “machine learning” and “algorithms”? How are they different and how do they work?

We asked two of the many Georgia Tech engineers working in these areas to help us understand the basic concepts so we’re all better prepared for the AI future — er, present.

Read the full crash course on the College of Engineering website.

This story was featured in the spring 2024 issue of Helluva Engineer magazine, produced biannually by the College of Engineering.

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A team’s success in any competitive environment often hinges on how well each member can anticipate the actions of their teammates.

Assistant Professor Christopher MacLellan thinks teachable artificial intelligence (AI) agents are uniquely suited for this role and make ideal teammates for video gamers.

With the help of funding from the U.S. Department of Defense, MacLellan hopes to prove his theory with a conversational, task-performing agent he co-engineered called the Verbal Apprentice Learner (VAL).

“You need the ability to adapt to what your teammates are doing to be an effective teammate,” MacLellan said. “We’re exploring this capability for AI agents in the context of video games.” 

Unlike generative AI chatbots like ChatGPT, VAL uses an interactive task-learning approach. 

“VAL learns how you do things in the way you want them done,” MacLellan said. “When you tell it to do something, it will do it the way you taught it instead of some generic random way from the internet.”

A key difference between VAL and a chatbot is that VAL can perceive and act within the gaming world. A chatbot, like ChatGPT, only perceives and acts within the chat dialog.

MacLellan immersed VAL into an open-sourced, simplified version of the popular Nintendo cooperative video game Overcooked to discover how well the agent can function as a teammate. In Overcooked, up to four players work together to prepare dishes in a kitchen while earning points for every completed order.

How Fast Can Val Learn?

In a study with 12 participants, MacLellan found that users could often correctly teach VAL new tasks with only a few examples.

First, the user must teach VAL how to play the game. Knowing that a single human error could compromise results, MacLellan designed three precautionary features:

• When VAL receives a command such as "cook an onion," it asks clarifying questions to understand and confirm its task. As VAL continues to learn, clarification prompts decrease.
• An “undo” button to ensure users can reverse an errant command.
• VAL contains GPT subcomponents to interpret user input, allowing it to adapt to ambiguous commands and typos. The GPT subcomponents drive changes in VAL’s task knowledge, which it uses to perform tasks without additional guidance.

The participants in MacLellan’s study used these features to ensure VAL learned the tasks correctly. 

The high volume of prompts creates a more tedious experience. Still, MacLellan said it provides detailed data on system performance and user experience. That insight should make designing a more seamless experience in future versions of VAL possible.

The prompts also require the AI to be explainable.

“When VAL learns something, it uses the language model to label each node in the task knowledge graph that the system constructs,” MacLellan said. “You can see what it learned and how it breaks tasks down into actions.”

Beyond Gaming

MacLellan’s Teachable AI Lab is devoted to developing AI that inexperienced users can train.

“We are trying to come up with a more usable system where anyone, including people with limited expertise, could come in and interact with the agent and be able to teach it within just five minutes of interacting with it for the first time,” he said.

His work caught the attention of the Department of Defense, which awarded MacLellan multiple grants to fund several of his projects, including VAL. The possibilities of how the DoD could use VAL, on and off the battlefield, are innumerable.

“(The DoD) envisions a future in which people and AI agents jointly work together to solve problems,” MacLellan said. “You need the ability to adapt to what your teammates are doing to be an effective teammate.

“We look at the dynamics of different teaming circumstances and consider what are the right ways to team AI agents with people. The key hypothesis for our project is agents that can learn on the fly and adapt to their users will make better teammates than those that are pre-trained like GPT.”

Design Your Own Agent

MacLellan is co-organizing a gaming agent design competition sponsored by the Institute of Electrical and Electronic Engineers (IEEE) 2024 Conference on Games in Milan, Italy.

The Dice Adventure Competition invites participants to design their own AI agent to play a multi-player, turn-based dungeon crawling game or to play the game as a human teammate. The competition this month and in July offers $1,000 in prizes for players and agent developers in the top three teams.

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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.”

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Work done by Georgia AIM (Artificial Intelligence in Manufacturing) is translating into success stories across the state. Recently, these success stories framed another achievement: Helping to host Vice President Kamala Harris as she kicked off her Economic Opportunity Tour in Atlanta at the end of April.

The multi-state tour was designed to showcase ways the Biden-Harris administration has built economic opportunity, supported communities, and is investing in traditionally underserved areas. Georgia AIM is an example of this, as it helps to expand technology training, job opportunities and advances for manufacturing across the state. Along with Georgia AIM, the Georgia Minority Business Development Agency Business Center (Georgia MBC), and Southeast Business Hub, programs of Georgia Tech’s Enterprise Innovation Institute, also attended the event at the Georgia International Convention Center, near Hartsfield-Jackson Atlanta International Airport.

“This event was a great opportunity to introduce the vice president to the work of Georgia AIM,” said Donna Ennis, co-director of Georgia AIM, which works to drive adoption of AI in U.S. manufacturing. “We were founded as part of the federal government’s Build Back Better plan. It’s important for her to see how we are putting the grant to work to deliver equity in artificial intelligence for manufacturing in Georgia.”

Prior to the arrival of Vice President Harris, attendees could visit tables set up in the entry hall and learn about a number of organizations, from banks to nonprofits to governmental agencies, that are working to level the playing field for underserved Georgians. Attendees included representatives from the Environmental Protection Agency, Rise, and Brunswick Job Corps Center.

The Georgia AIM table, staffed by Ryan Scott, the community engagement manager, and Kyle Saleeby, research engineer with Georgia Tech Manufacturing Institute (GTMI), was a popular stop, thanks to the tabletop “cobot” showing how robotics can be used in manufacturing and an array of 3-D printed industrial materials.

The program featured a conversation with Harris and financial literacy and business advice podcasters Rashad Bilal and Troy Millings, from Earn Your Leisure. The podcast has an audience of about 2 million people, a majority of whom are Black. Harris spoke to the crowd of approximately 400 people about the administration’s focus on access to capital for minority small businesses and entrepreneurs.

“One of the compelling reasons for me to start this tour now,” Harris said, “is to ask all the leaders here for help in getting the word out about what is available to entrepreneurs and small businesses. Because we are in the process of putting a lot of money in the streets of America.”

Some of those funds have gone to Enterprise Innovation Institute programs, including $65 million for Georgia AIM.

Georgia senators Jon Ossoff and Raphael Warnock and Rep. Nikema Williams also spoke at the event. Prior to the event, they joined Harris at the Russell Innovation Center for Entrepreneurs(RICE), a partner project with Georgia AIM. RICE is developing a mobile lab with researchers at the University of Georgia College of Engineering that will showcase AI-based technologies to communities across the state.

“It was exciting to hear first-hand about the administration’s commitment to equity in small businesses and entrepreneurship,” Ennis said. “It dovetails perfectly with the commitment of the programs of the Enterprise Innovation Institute.”

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Naiya Salinas and her instructor, Deryk Stoops, looked back and forth between the large screen on the wall and a hand-held monitor.

Tracing between the lines of code, Salinas made a discovery: A character was missing.

The lesson was an important, real-world example of the problem-solving skills required when working in robotics. Salinas is one of a half-dozen students enrolled in the new AI Enhanced Robotic Manufacturing program at the Georgia Veterans Education Career Transition Resource (VECTR) Center, which is setting a new standard for technology-focused careers.

The set-up of the lab was intentional, said Stoops, who designed the course modules and worked with local industry to determine their manufacturing needs. Then, with funding from the Georgia Tech Manufacturing Institute's (GTMI) Georgia Artificial Intelligence in Manufacturing (Georgia AIM) project, Stoops worked with administrators at Central Georgia Technical College to purchase robotics and other cutting-edge manufacturing tools.

As a result, the VECTR Center’s AI-Enhanced Robotic Manufacturing Studio trains veterans in industry-standard robotics, manufacturing modules, cameras, and network systems. This equipment gives students experience in a variety of robotics-based manufacturing applications. Graduates can also finish the 17-credit course with two certifications that carry some weight in the manufacturing world.

“After getting the Georgia AIM grant, we pulled together a roundtable with industry. And then we did site visits to see how they pulled AI and robotics into the space,” said Stoops. “All the equipment in here is the direct result of industry feedback.”

Statewide Strategic Effort

Funded by a $65 million grant from the federal Economic Development Administration, Georgia AIM is a network of projects across the state born out of GTMI and led by Georgia Tech’s Enterprise Innovation Institute. These projects work to connect the manufacturing community with smart technologies and a ready workforce. Central Georgia received around $4 million as part of the initiative to advance innovation, workforce development and STEM education in support of local manufacturing and Robins Air Force Base.

Georgia AIM pulls together a host of regional partners all working toward a common goal of increasing STEM education, access to technology and enhancing AI among local manufacturers. This partnership includes Fort Valley State University, the Middle Georgia Innovation Project led by the Development Authority of Houston County, Central Georgia Technical College, which administers the VECTR Center, and the 21st Century Partnership.

“This grant will help us turn our vision for both the Middle Georgia Innovation Project and the Middle Georgia STEM Alliance, along with our partners, into reality, advancing this region and supporting the future of Robins AFB,” said Brig. Gen. John Kubinec, USAF (ret.), president and chief executive officer of the 21st Century Partnership.

Georgia AIM funding for Central Georgia Technical College and Fort Valley State focused on enhancing technology and purchasing new components to assist in education. At Fort Valley State, a mobile lab will launch later this year to take AI-enhanced technologies to underserved parts of the state, while Central Georgia Tech invested in an AI-enhanced robotics manufacturing lab at the VECTR Center.

“This funding will help bring emerging technology throughout our service area and beyond, to our students, economy, and Robins Air Force Base,” said Dr. Ivan Allen, president of Central Georgia Technical College. “Thanks to the power of this partnership, our faculty and students will have the opportunity to work directly with modern manufacturing technology, giving our students the experience and education needed to transition from the classroom to the workforce in an in-demand industry.”

New Gateway for Vets

The VECTR Center’s AI-Enhanced Robotics Manufacturing Studio includes FANUC robotic systems, Rockwell Automation programmable logic controllers, Cognex AI-enabled machine vision systems, smart sensor networks, and a MiR autonomous mobile robot.

The studio graduated its first cohort of students in February and celebrated its ribbon-cutting ceremony on April 17 with a host of local officials and dignitaries. It was also an opportunity to celebrate the students, who are transitioning from a military career to civilian life.

The new technologies at the VECTR Center lab are opening new doors to a growing, cutting-edge field.

“From being in this class, you really start to see how the world is going toward AI. Not just Chat GPT, but everything — the world is going toward AI for sure now,” said Jordan Leonard, who worked in logistics and as a vehicle mechanic in the U.S. Army. Now, he’s upskilling into robotics and looking forward to using his new skills in maintenance. “What I want to do is go to school for instrumentation and electrical technician. But since a lot of industrial plants are trying to get more robots, for me this will be a step up from my coworkers by knowing these things.”

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