Tech Titans: How Can a Chatbot Steal a Model's Secrets? with Steve Orrin, Federal CTO at Intel

Steve Orrin (0:00)

And it's that transition that we're seeing a lot of people struggle with, of how do I actually get value out of this AI? And it comes back to this notion that, you know, you can't just say, well, I'm going to sprinkle AI pixie dust on every problem in magic. AI is going to solve it all for me. There was a great example, now it's almost six, eight months ago, where a bank was attacked, where a deep fake, along with a chat bot, was used to trick a financial analyst to transfer $25 million out.

Joel (0:33)

And so what's your official role?

Steve Orrin (0:35)

So I'm the federal CTO for Intel Corp.

Joel (0:37)

And what does that mean?

Steve Orrin (0:39)

So as federal CTO, my job is to represent all of Intel's technologies, capabilities, and solutions for the federal ecosystem. So that's the federal government, the system integrators and manufacturers that service, we call them the defense industrial base, and the federal portions of the OEMs, so like Dell Federal, HP Federal, and the like. And my job is really to help the government adopt the technology, plan for what's coming next in the technology world, understand the nuances of different architectures and ecosystems, and really as an advisor to the federal government to help them achieve their mission and enterprise goals, whether it be performance, security, how to take full advantage of AI.

Because for the most part, the government doesn't buy direct from Intel. They buy through the channel and through the ecosystem. They buy planes from Lockheed, they buy servers from Dell and HP, and it's Intel ingredients inside. And so we work closely with the government and with that channel to help them get the best out of the systems they're purchasing and plan for the next gen of architectures. You see the hype, you see, well, yes, you apply AI and it will solve all your problems. Well, no, that's not actually true.

And we're starting to see some of that where there was large uptick in acquisition of AI technologies, both the hardware, the software, the solution sets. And the next question that a lot of people are asking now, after I've been playing with this for a couple of years, is, okay, where's the value? Where's my return on investment? How much utilization am I actually getting out of this infrastructure? And that's in business terms, the crossing the valley of death problem. We have all these great exercises and demos and really cool widgets that we can use online. How does that translate into something that saves me money, executes the mission faster, saves lives, whatever the industry is? And it's that transition that we're seeing a lot of people struggle with, of how do I actually get value out of this AI? And it comes back to this notion that you can't just say, well, I'm going to sprinkle AI pixie dust on every problem in magic. AI is going to solve it all.

Joel (2:43)

You can say that, yeah.

Steve Orrin (2:45)

You can say that. It doesn't work that way.

And it comes back to fundamentals. A, are you asking the right questions? In other words, are you defining the problem correctly in order to be able to apply the right set of technologies? And not every AI, sometimes you don't even need AI, simple machine learning would solve it. And then the other question is, do I have the right data to drive that outcome? You know you want to do things, but when you actually dive into it, and whether it be a security use case or certain type of sensors, I want to be able to detect this new kind of threat, I want to be able to detect this kind of cancer, the question you have to ask is, do I have sufficient data to train a model to be able to augment what we're doing today? And oftentimes you find you don't have either the volume or the diversity of data to get you where you need to go. You could use a great little experimental lab, but that doesn't necessarily translate to something that can go scale. And so a lot of the challenges we're seeing people have are in getting it out of the experiment phase and into something real, they're just not seeing the return on their investments there. And I think that goes back to poor planning or buying into all the hype as opposed to what is practically achievable and where do we start first? So going back to your question, I think there's absolutely opportunities for AI in government and in broader private sector and industry. It's picking the right Goldilocks use cases to tackle, that has the right data, that you can answer a question, and that at the end of the day when you're done, it provides value to the organization or to whoever you're servicing.