Like many universities, Georgia Tech has been grappling with how to offer students the training they need to prepare them for a recent sea change in IT job markets — the arrival of generative AI (genAI).
Through a partnership with chipmaker Nvidia, Georgia Tech’s College of Engineering built a supercomputer dubbed AI Makerspace; it uses 20 Nvidia HGX H100 servers powered by 160 Nvidia H100 Tensor Core GPUs (graphics processing units).
Those GPUs are powerful — a single Nvidia H100 GPU would need just one second to handle a multiplication operation that would take the school’s 50,000 students 22 years to achieve. So, 160 of those GPUs give students and professors access to advanced genAI, AI and machine learning creation and training. (The move also spurred Georgia Tech to offer new AI-focused courses and minors.
Announced two weeks ago, the AI Makerspace supercomputer will initially be used by Georgia Tech’s engineering undergraduates. But it’s expected to eventually democratize access to computing resources typically prioritized for research across all colleges.
Computerworld spoke with Matthieu Bloch, the associate dean for academics at Georgia Tech’s College of Engineering, about how the new AI supercomputer will be used to train a new generation of AI experts.
The following are excerpts from that interview:
Tell me about the Makerspace project and how it came to be? “The Makerspace is really the vision of our dean and the school chair of Electrical and Computer Engineering (ECE), Arijit Raychowdhury, who really wanted to put AI in the hands of our students.
“In 2024 — in the post ChatGPT world — things are very different from the pre-ChatGPT world. We need a lot of computing power to do anything that’s meaningful and relevant to industry. And in a way, the devil is out of the box. People see what AI can do. But I think to get to that level of training, you need infrastructure.
“The name Makerspace also comes from this culture we have at Georgia Tech of these maker spaces, which are places where our students get to tinker, both within the classroom and outside the classroom. The Makerspace was the idea to bring the tools that you need to do AI in a way that’s relevant to do meaningful things today. So, right now, where we’re at is we’ve partnered Nvidia to essentially offer to students a supercomputer. I mean, that’s what it is.
“What makes it unique is that it’s meant for supporting students. And right now it’s in the classroom. We’re still rolling it out. We’re in phase one. So, the idea is that the students in the classroom can work on AI projects that are meaningful to industry — problems that are interesting, you know, from a pedagogical perspective, but they don’t mean a whole lot in an industry setting.”
Makerspace’s Nvidia H100 Tensor Core GPUs
Georgia Tech School of Engineering
Tell me a bit about the projects they’ve been working on with this. “I can give you a very concrete example. ChatGPT is a very typical, a very specific form of AI called generative AI. You know, it’s able to generate. In the case of ChatGPT, [that means] text in response to prompts. You might have seen a generative model that generates pictures. I think these were very popular and whatnot. And so these are the kind of things our students can do right now, …generate anything that would be, say, photo realistic.
“You need a pretty hefty computing power to train your model and then test that it’s working properly. And so that’s what our students can do. Just to give you an idea of how far we’ve come along, before we had the AI Makerspace, our students were relying largely on something called Google CoLab. CoLab is Google making some compute resources freely accessible for use. They’re really giving to us the resources they don’t use or don’t sell to their be clients. So it’s like the crumbs that remain.
“It’s very nice of them [Google] to do that, but you could only work with very [limited resources], say for training on something like 12,000 images. Now you can, for instance, train a generative model on a data set with like one million images. So you can really scale up by orders of magnitude. And then you can start generating these photo-realistic pictures that you could not generate before. That’s the most visual example I can give you.”
Can you tell me a little bit about the genAI projects the students are working on? How good is the technology at producing the results they want? “It’s a complicated question to answer. I mean, it has many layers. We’ve just launched it, like literally, the AI Makerspace was open officially two weeks ago. So right now it’s really used at scale in the classroom. The students in that class are learning how to do machine learning. [The students] have to get the data. [They] have to learn how to train a model. The students have homework projects, which consists of this fairly sophisticated model that they have to train, and that they have to test.
“Now we have a vision beyond that, what we call phase two of the Makerspace. We’re doubling the compute capacity. The idea now is that we’re going to open that to senior design projects. We’re gonna open that to something we call vertically integrated projects, in which are students essentially doing long-term research with faculty advisors over multiple years. Our students are going to do many things — certainly all of [the] engineering [school].
“We’ve given incentives to a lot of faculty to create a lot of new courses throughout the College of Engineering for AI and ML for what matters to their field. For instance, if you’re an electrical engineer, there’s a lot of hardware to it, you know you have a model for that. How do you make the model smaller so that you can put it in hardware? That’s one very tangible question that the students would ask. But if they’re, say, mechanical engineers, they might use it differently. Maybe for them what generative AI could do is help them generate 3D models, think about structures that they would not think about naturally. And you can decline that model. The Makerspace is a massive tool. But how the tool is used is really a function of the specific domain. The goal, of course, is for Makerspace to be available beyond engineering.
“It’s already being used by our College of Computing, and we’re hoping that our co colleagues in, say, the College of Business will see the value, because they haven’t used AI yet — perhaps for financial models, predicting whether to sell or buy a stock. I think the sky is a limit. There’s no one use of AI through Makerspace. It’s an infrastructure that provides the tools. And then these tools find declinations in all different areas of expertise.”
Why is it important to have this technology at the school for students to learn about AI? “The way we’ve come to articulate this is as follows: We’re not deliverers in doomsday scenarios, where AI is going to generate terminators that are going to eradicate humanity. Okay, that’s not how we’re thinking about it.
“AI is definitely going to change things. And we think that AI is certainly going to displace a few people. I think the humans enhanced by AI will start displacing humans who don’t use AI.
“I think the way a lot of the discussion has been shaped since ChatGPT was released to the world, in universities there’s sometimes a lot of fear. Are students cheating on their essays? Are students cheating on this cheating on that? I had these discussions with my colleagues in computing. We have an intro to computing class, where they’re cheating to write their code, which I think is not the right approach to it. But, the devil is out of the box. It’s a tool that’s here, and we have to learn how to use it.
“If I can give you my best analogy: I drive my car. I don’t know how my car really works. I mean, I was never a mechanical or electrical engineer. I sort of know what it takes [for a car to run], but I’m unable to fix it. But that doesn’t mean I can’t drive it. And I think we’re at that stage with AI tools, where one needs to know how to use them because you don’t want to be the person riding a bicycle when everybody else has a car.
“Not everyone needs to be a mechanic, but everyone needs a car. And so I think we want every student at Georgia Tech to know how to use AI, and what that means for them would be different depending on their specialty, their major. But these are tools, and you need to have played with them to really start mastering them.”
In what way has AI expanded Georgia Tech’s curriculum? “We were lucky in the sense that [we’re] building that infrastructure from new. But thinking about AI, Georgia Tech has been doing it for decades. Our faculty is very research focused. They do state-of-the-art research and AI…was always there in the background — the roots of AI. We had a lot of colleagues who actually were doing machine learning without saying it in these terms.
“Then when deep learning started appearing, people were ready to grasp that. So, we were already thinking about doing it in the labs, and the integration in the curriculum was already slowly happening. And so what we decided to do was to accelerate that so the Makerspace…accelerates the other mechanisms we’ve had to give incentives to faculty, to rethink the curriculum with AI and Ml in mind.”
So what AI courses have you launched? “I can give you two examples that we’ve launched, which are, you know, very new. But I I think I’ve been quite successful already. One is we’ve officially launched an AI minor.
“The great thing about this AI minor [is that it] is a way for students to take a series of courses with a coherent and unified team, and they get credit for that on their diploma and their transcript. This minor was designed as a collaboration right now between the College of Engineering and the College of Liberal Arts.
“Then we have the ethics and policy piece. Students need to take a specially designed course on AI Ethics and AI policy. We’re thinking very holistically. AI is a technology play, but if you just train engineers to do the technology piece alone, maybe then the doomsday-Terminator scenario is a likely outcome.
“We want our students to think about the use of AI because it’s technology that can have many uses [and problems associated with it]. We talk about deep fakes. We’re worried about it for all sorts of political reasons.
“The other thing we’ve done in the College of Engineering is essentially incentivized faculty to create new undergraduate courses related to AI and ML but relevant to their own disciplines. I literally [just made the announcement] and the has college approved 10 new courses or significantly revamped courses. So, what that means is that we have courses on machine learning for smart cities, civil environmental engineering, and a course in chemical processes in chemical and bioengineering, where they’re using AI and ML for completely different things. That’s how we’re thinking of AI. It’s a tool. So the courses need to embrace that tool.”
Are students already using genAI to assist in creating applications — so software engineering and development? “Officially or unofficially? I don’t have a good answer, because the truth is, I don’t know. But what I know is that our students are using it with or without us. You know they are using generative AI because I’m willing to bet they all have a subscription to ChatGPT.
“Now in the context of the Makerspace, this is a resource you can start doing all sorts of things. Our students are using it to write lines of code absolutely.”
So what would you say is the most popular use right now of the AI Makerspace? “We haven’t officially launched it at scale for very long, so I can’t attest to that. It’s been used largely in the classroom setting for the kind of homework students could not even dream of doing before.
“We’re going to launch it and use it over the summer for an entrepreneurship program called Create X, that students can use to take ideas and go through prototype and potentially think about building startups out of these. So that’s going to be primary use over the summer, and we’re testing it over these few weeks in the context of a hackathon in partnership with Nvidia, where teams come with big problems that they want to solve. And we want to accelerate their science, to use Nvidia’s words, to by teaching them how to use that Makerspace.”
CPUs and Processors, Education Industry, Generative AI, Natural Language Processing
Like many universities, Georgia Tech has been grappling with how to offer students the training they need to prepare them for a recent sea change in IT job markets — the arrival of generative AI (genAI).
Through a partnership with chipmaker Nvidia, Georgia Tech’s College of Engineering built a supercomputer dubbed AI Makerspace; it uses 20 Nvidia HGX H100 servers powered by 160 Nvidia H100 Tensor Core GPUs (graphics processing units).
Those GPUs are powerful — a single Nvidia H100 GPU would need just one second to handle a multiplication operation that would take the school’s 50,000 students 22 years to achieve. So, 160 of those GPUs give students and professors access to advanced genAI, AI and machine learning creation and training. (The move also spurred Georgia Tech to offer new AI-focused courses and minors.
Announced two weeks ago, the AI Makerspace supercomputer will initially be used by Georgia Tech’s engineering undergraduates. But it’s expected to eventually democratize access to computing resources typically prioritized for research across all colleges.
Computerworld spoke with Matthieu Bloch, the associate dean for academics at Georgia Tech’s College of Engineering, about how the new AI supercomputer will be used to train a new generation of AI experts.
The following are excerpts from that interview:
Tell me about the Makerspace project and how it came to be? “The Makerspace is really the vision of our dean and the school chair of Electrical and Computer Engineering (ECE), Arijit Raychowdhury, who really wanted to put AI in the hands of our students.
“In 2024 — in the post ChatGPT world — things are very different from the pre-ChatGPT world. We need a lot of computing power to do anything that’s meaningful and relevant to industry. And in a way, the devil is out of the box. People see what AI can do. But I think to get to that level of training, you need infrastructure.
“The name Makerspace also comes from this culture we have at Georgia Tech of these maker spaces, which are places where our students get to tinker, both within the classroom and outside the classroom. The Makerspace was the idea to bring the tools that you need to do AI in a way that’s relevant to do meaningful things today. So, right now, where we’re at is we’ve partnered Nvidia to essentially offer to students a supercomputer. I mean, that’s what it is.
“What makes it unique is that it’s meant for supporting students. And right now it’s in the classroom. We’re still rolling it out. We’re in phase one. So, the idea is that the students in the classroom can work on AI projects that are meaningful to industry — problems that are interesting, you know, from a pedagogical perspective, but they don’t mean a whole lot in an industry setting.”
Makerspace’s Nvidia H100 Tensor Core GPUs
Georgia Tech School of Engineering
Tell me a bit about the projects they’ve been working on with this. “I can give you a very concrete example. ChatGPT is a very typical, a very specific form of AI called generative AI. You know, it’s able to generate. In the case of ChatGPT, [that means] text in response to prompts. You might have seen a generative model that generates pictures. I think these were very popular and whatnot. And so these are the kind of things our students can do right now, …generate anything that would be, say, photo realistic.
“You need a pretty hefty computing power to train your model and then test that it’s working properly. And so that’s what our students can do. Just to give you an idea of how far we’ve come along, before we had the AI Makerspace, our students were relying largely on something called Google CoLab. CoLab is Google making some compute resources freely accessible for use. They’re really giving to us the resources they don’t use or don’t sell to their be clients. So it’s like the crumbs that remain.
“It’s very nice of them [Google] to do that, but you could only work with very [limited resources], say for training on something like 12,000 images. Now you can, for instance, train a generative model on a data set with like one million images. So you can really scale up by orders of magnitude. And then you can start generating these photo-realistic pictures that you could not generate before. That’s the most visual example I can give you.”
Can you tell me a little bit about the genAI projects the students are working on? How good is the technology at producing the results they want? “It’s a complicated question to answer. I mean, it has many layers. We’ve just launched it, like literally, the AI Makerspace was open officially two weeks ago. So right now it’s really used at scale in the classroom. The students in that class are learning how to do machine learning. [The students] have to get the data. [They] have to learn how to train a model. The students have homework projects, which consists of this fairly sophisticated model that they have to train, and that they have to test.
“Now we have a vision beyond that, what we call phase two of the Makerspace. We’re doubling the compute capacity. The idea now is that we’re going to open that to senior design projects. We’re gonna open that to something we call vertically integrated projects, in which are students essentially doing long-term research with faculty advisors over multiple years. Our students are going to do many things — certainly all of [the] engineering [school].
“We’ve given incentives to a lot of faculty to create a lot of new courses throughout the College of Engineering for AI and ML for what matters to their field. For instance, if you’re an electrical engineer, there’s a lot of hardware to it, you know you have a model for that. How do you make the model smaller so that you can put it in hardware? That’s one very tangible question that the students would ask. But if they’re, say, mechanical engineers, they might use it differently. Maybe for them what generative AI could do is help them generate 3D models, think about structures that they would not think about naturally. And you can decline that model. The Makerspace is a massive tool. But how the tool is used is really a function of the specific domain. The goal, of course, is for Makerspace to be available beyond engineering.
“It’s already being used by our College of Computing, and we’re hoping that our co colleagues in, say, the College of Business will see the value, because they haven’t used AI yet — perhaps for financial models, predicting whether to sell or buy a stock. I think the sky is a limit. There’s no one use of AI through Makerspace. It’s an infrastructure that provides the tools. And then these tools find declinations in all different areas of expertise.”
Why is it important to have this technology at the school for students to learn about AI? “The way we’ve come to articulate this is as follows: We’re not deliverers in doomsday scenarios, where AI is going to generate terminators that are going to eradicate humanity. Okay, that’s not how we’re thinking about it.
“AI is definitely going to change things. And we think that AI is certainly going to displace a few people. I think the humans enhanced by AI will start displacing humans who don’t use AI.
“I think the way a lot of the discussion has been shaped since ChatGPT was released to the world, in universities there’s sometimes a lot of fear. Are students cheating on their essays? Are students cheating on this cheating on that? I had these discussions with my colleagues in computing. We have an intro to computing class, where they’re cheating to write their code, which I think is not the right approach to it. But, the devil is out of the box. It’s a tool that’s here, and we have to learn how to use it.
“If I can give you my best analogy: I drive my car. I don’t know how my car really works. I mean, I was never a mechanical or electrical engineer. I sort of know what it takes [for a car to run], but I’m unable to fix it. But that doesn’t mean I can’t drive it. And I think we’re at that stage with AI tools, where one needs to know how to use them because you don’t want to be the person riding a bicycle when everybody else has a car.
“Not everyone needs to be a mechanic, but everyone needs a car. And so I think we want every student at Georgia Tech to know how to use AI, and what that means for them would be different depending on their specialty, their major. But these are tools, and you need to have played with them to really start mastering them.”
In what way has AI expanded Georgia Tech’s curriculum? “We were lucky in the sense that [we’re] building that infrastructure from new. But thinking about AI, Georgia Tech has been doing it for decades. Our faculty is very research focused. They do state-of-the-art research and AI…was always there in the background — the roots of AI. We had a lot of colleagues who actually were doing machine learning without saying it in these terms.
“Then when deep learning started appearing, people were ready to grasp that. So, we were already thinking about doing it in the labs, and the integration in the curriculum was already slowly happening. And so what we decided to do was to accelerate that so the Makerspace…accelerates the other mechanisms we’ve had to give incentives to faculty, to rethink the curriculum with AI and Ml in mind.”
So what AI courses have you launched? “I can give you two examples that we’ve launched, which are, you know, very new. But I I think I’ve been quite successful already. One is we’ve officially launched an AI minor.
“The great thing about this AI minor [is that it] is a way for students to take a series of courses with a coherent and unified team, and they get credit for that on their diploma and their transcript. This minor was designed as a collaboration right now between the College of Engineering and the College of Liberal Arts.
“Then we have the ethics and policy piece. Students need to take a specially designed course on AI Ethics and AI policy. We’re thinking very holistically. AI is a technology play, but if you just train engineers to do the technology piece alone, maybe then the doomsday-Terminator scenario is a likely outcome.
“We want our students to think about the use of AI because it’s technology that can have many uses [and problems associated with it]. We talk about deep fakes. We’re worried about it for all sorts of political reasons.
“The other thing we’ve done in the College of Engineering is essentially incentivized faculty to create new undergraduate courses related to AI and ML but relevant to their own disciplines. I literally [just made the announcement] and the has college approved 10 new courses or significantly revamped courses. So, what that means is that we have courses on machine learning for smart cities, civil environmental engineering, and a course in chemical processes in chemical and bioengineering, where they’re using AI and ML for completely different things. That’s how we’re thinking of AI. It’s a tool. So the courses need to embrace that tool.”
Are students already using genAI to assist in creating applications — so software engineering and development? “Officially or unofficially? I don’t have a good answer, because the truth is, I don’t know. But what I know is that our students are using it with or without us. You know they are using generative AI because I’m willing to bet they all have a subscription to ChatGPT.
“Now in the context of the Makerspace, this is a resource you can start doing all sorts of things. Our students are using it to write lines of code absolutely.”
So what would you say is the most popular use right now of the AI Makerspace? “We haven’t officially launched it at scale for very long, so I can’t attest to that. It’s been used largely in the classroom setting for the kind of homework students could not even dream of doing before.
“We’re going to launch it and use it over the summer for an entrepreneurship program called Create X, that students can use to take ideas and go through prototype and potentially think about building startups out of these. So that’s going to be primary use over the summer, and we’re testing it over these few weeks in the context of a hackathon in partnership with Nvidia, where teams come with big problems that they want to solve. And we want to accelerate their science, to use Nvidia’s words, to by teaching them how to use that Makerspace.”
CPUs and Processors, Education Industry, Generative AI, Natural Language Processing Read More Computerworld
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