0:00if if you get through it and it's been a big success so far. So, I know things like that are going to be part of how we create um this upskilling in the economy. But overall, my guess is that this is going to create way more jobs. I I just think it's um it's going to be a very dynamic period. Yeah, I think your point about um about workforce is resonates with a lot of folks here here at this table because I think um sometimes when we think about the I revolution, we're not thinking about the welders and the fiber technicians and the construction workers that all go
0:29into into building the infrastructure that supports it. Um and I know the program that you guys launched, America's Workforce Academy, I think I think shows a different uh and important dynamic about how the private sector can be integrally involved in the in the training and skilling of of individual Americans. And it's something that I think if you roll back, you know, two or three decades was something that was not even in the card. So, um, it's incredible work. I think I think one thing that I wanted to to kind of close with, which is is interesting to a lot of folks here, is um, is this this
0:58question of of open source and, you know, you've you've announced your plan to to open source Muse 1.2, which is the leading sort of American um, um, open source LLM at the moment. So are you committed to doing to more than this in the future or how do you kind of how do you kind of think about the role that open source will play in in our uh in our ecosystem?
1:17Yeah, I mean to me open source is one important part of making sure that the AI future and the prosperity is broadly shared by a lot of people. So the probably the biggest part of what we need to do is build these kind of agents that I talked about and make sure that they're distributed to um to to billions of people. That way the the power and the capability of AI um should be something that everyone has not um not just something that's restricted to to a small number of people. But the ethos of
1:45open source is also very aligned with this philosophy and is something that Meta's focused on for a long time as well. It basically puts the power in individual developers hands. So that way they're not just dependent on a small number of these uh frontier labs for what is an increasingly critical part of the applications and technology that they're building. So I think that that's a very important part of what we do. Um
2:12at Meta, you know, we we build both open and closed models. We've had a long history of building open source software. We've felt that it's it's worked well. When you look at issues like cyber security, I think it's just very well established over decades that open source systems end up being more secure because um there's more scrutiny on them, right? It's basically if more people can see how the thing works and can measure it and understand it um understand what where it works well, understand where the defects are, patch
2:41it, then that leads to um systems that are more hardened over time. And I think that that's going to be the case here as well. So I I think that this is an important part of having a secure and safe future is both having the checks and balances that come from having an ecosystem that isn't just centered around a small number of frontier labs but that where you know everyone from governments to startups to individuals can access models that they control. Um and having that system kind of having the underpinning of it be something that
3:09everyone can scrutinize I think is going to be more likely to lead to a safe future as well. So um we will continue doing some open source models. Um we kind of evaluate it on a model bymodel basis to understand whether the capabilities of what we're building are appropriate to open source at that point. But our philosophy in general is that we think that this is um this is a really important ecosystem to make sure that we foster and um you know beyond just what we release ourselves. We're very strong advocates that um open
3:38source shouldn't be something that um that people try to restrict or lock down. And I think it's a very important part of creating a good future for everyone.
3:45Amazing. Well, thank you so much for your time and I encourage all the ministers here to certainly look into America's Workforce Academy. I think it's one of a shining example in the US of how uh how we can come together to to train our our workers for the jobs of the future. So, thank you so much, Mark. It was terrific you could join us.
4:02Great. Our uh next guest is Michael Crowe. Michael Crowe is the president of Arizona State University um and has massively expanded the reach of the public university model and its capacity to flexibly meet the education and workforce objectives of students and employers alike. So, let's give uh Michael a quick second to join the the chat.
4:30Should be there. Hey, hey, how are you? I don't know if you heard our our introduction. We tried to talk you up as I did. I did. Well, thank you, Michael. So, yeah. Great to join us. And, you know, I think just kind of kick things off. I mean, I think it'd be great to share with the group um from your perspective, [clears throat] you know, how has has ASU restructured its own model to close this gap between what employers need and what graduates are actually prepared to do?
4:55Well, thank you, Michael. I mean, it's really been important for us. You know, we've got about 600 billion of the new capital investments occurring right here in Arizona for the re-industrialization efforts and the AI efforts in the country. And we've been working for some time to conceptually rethink the university in its entirety. So, we're a $1.2 billion a year research university, which is quite large. Uh, and here's the things that we've done specifically to restructure. One, we're now design driven versus tradition driven. We're
5:25designed as an institution to work towards the outcomes that are beyond the university, not just inside the university. Second, we've figured out how to scale uh how to have elite performance by drawing in students and learners from all backgrounds. We've increased our number of graduates by eightfold, our technically trained people by 20fold, 25fold, our research by tenfold, all with the same staff.
5:51That is we've learned how to be scalable. uh we have embraced 200,000 degree seeeking students and 500,000 learners. The third thing that we've worked on is intellectual agility. Uh restructuring our schools, 40 new transdisiplinary schools, each of which have connections to industry, even in the humanities and other areas, new methods of learning, new tools for learning, accelerated our research. Uh I think another thing that we've done in terms of our model is to link all levels of learning working with technical
6:21schools, working with community colleges, working with uh high schools, uh finding ways to connect all of the state-of-the-art research that our faculty are doing in new areas of industrial development with all of these other uh uh teaching and uh training organizations. So we broke down the elitist barriers of the university. Uh I think the next thing we've done is to become partnership empowered. We're the largest supplier of college graduates to Intel on a global basis. We're working
6:48with TSMC, Applied Materials, DECA, hundreds if not thousands of other companies where we work with them to listen to what kinds of uh of uh individuals do they need. And then I think the last thing I would mention two things is that we have greatly accelerated what we call our learning technology enhancements. How do we expand the power of the faculty member?
7:10Not replace like Mark Zuckerberg was saying, we're not trying to replace anyone. We're trying to enhance every outcome conceivable. And we do this with tools that enhance and accelerate learning at all levels from technician level to technologist level to engineer level to scientist or or researcher level. And then the last thing that we've done is uh AI empowerment across all things. And so those are huge design changes for us in our design model and they've allowed us to close the gap
7:40between employer needs. We have about 13,000 13,000 employer uh uh organizations that come to our university to find our our graduates.
7:49We're the largest producer of engineers in the United States for instance. And then what we've done is we've we've found a way to use all of these changes to greatly accelerate our product. So what I mean one thing that that always comes up is um is kind of what what do sort of you know institutions like government you know we we spend a lot of time talking to ourselves about how we can prepare you know people in our countries to be ready for the workforce of the future and what do you think that we get wrong and traditional
8:18institutions get wrong uh about preparing students for for the next generation of opportunities.
8:24So one thing is there's there's they're they're too rigid. The government policies are generally too rigid. They're too slow. They're non- adaptive. Uh they uh aren't innovative enough. They don't stimulate innovation enough. They allow us. So what we have now is a clock speed problem. So we have industrial transformation occurring at warp speed. We have social transformation occurring at just below warp speed using the Star Trek analogy.
8:48And then we have the universities operating at human speed. And human speed has got to accelerate. We've got to find ways to accelerate. And we operate with no ministerial control at this university. We operate as what's called a public enterprise. It allows us to partner in real time speed. It allows us to create new degree programs, new pathways, new technologies. And so what we have is a speed mismatch. And we've got to find a way to uh not slow things down. And in general, institutions and
9:16governments get get wrong this notion of of that you can't do things more more effectively or more broadly uh or what have you. AI has been a an unbelievably empowering force for us to accelerate learning, to enhance learning, to deepen learning, and to speed learning.
9:33One one question I have for you is I I think what we we're all observing is that AI is moving at a um at a at warp speed, I guess, if we're using your your terms. And and I think a lot of a lot of things that we think and worry about or even like help put together are things like credentiing and training pathways and we say okay you want to think about what's going to be the job that someone's going to want to have a year from now. So let's build a program to get them ready for that that takes a year to make the program. Then you then you institute the program then you get the first graduate and by then AI's run
10:03off. So, how do you sort of view this sort of um this this sort of how do you keep pace with where AI is going with the types of credentiing and the types of program training programs we need for our citizens?
10:14Well, this is an area that we spend a lot of time on and exactly the problem that you talk about. And so, we're not trying to produce a person capable of doing job X or job Y. We're trying to produce people capable of being what we call master learners. They're capable of learning anything. They're capable of adjusting to anything. They have fundamental learning that allows them to learn. And this is true for technicians who have to become technologists and technologists who have to become engineers and engineers who have to become research grade scientists or engineers just in these technical areas.
10:42But it's true throughout the rest of the institution. So our focus has been greatly enhancing the learning. We don't want a person graduating without technical skills or without programming skills. We don't want a technician who's not ready to become a technologist or a technologist who's not ready to become an engineer or a scientist. We have to create these master learners. That's really what we need to focus on.
11:03Great. And I guess in in closing uh you know what what what is the most exciting opportunity that AI is going to present and enable for the the next generation of uh and the future of all of our workforces.
11:16So we have 8,000 uh projects that are AI driven. We've trained 4,000 of our faculty in AI tool development. We have about 95% of our students are using AI multiple times per day. Uh, and so we're already there. What we're trying to do now is to move forward with what we call the agentic self. How do we help every learner, whether they're in a technical school, whether they're in a community college, a university, a graduate program, a PhD program, how do we find a
11:46way for the agentic tools to be personalized to the individual so that you can enhance their ability to adapt, their ability to adjust, their ability to expand their footprint. So we've experimented with these projects and we have huge huge huge opportunities in this notion of personalized agentic selves for every learner and we've got these things underway right now.
12:06Amazing. Well, thank you so much for joining us. To all the ministers here, if you're ever in Arizona, you should definitely stop and visit. I would argue this is probably one of the most innovative uh institutions in the United States, if not the world, on on how to approach higher education. Uh thank you so much for your time and uh I'm glad you could join us.
12:25Um, so now we're going to turn to uh the second closed door session of the ministerial and we'll take up pillars two and three. So we can uh leave take a second to let the press uh out of the room.
