I can go all the way back to maybe my first program, I remember the first time I ever had a keyboard and started coding, was maybe at the age of seven or eight was back in the day using Basic, and I just fell in love. I had this book and by using that, I could affect Quiz Game as an introduction. And I wrote the first one and after that, like, you know, started coding over and over, like the same things. Then I stopped coz there was no internet. My family's mainly in the medical field. So I was thinking of one day I'm gonna be a doctor one day. But then I fell in love again with math and started looking into the engineering track instead of like the medical track in high school and focused more on that. And then I went to the American University in Cairo, where I majored in computer science and one of the stories that you always did like folks back then in 2002, when I was just joining the university as a freshman. First week with a friend, or begin a friend amid a stranger asked me like "what you wanna do it after you graduate?" And I said, "I'm gonna go work for Microsoft in U. S". And he was like, "Are you crazy that nobody's hiring from this university for various reasons back then." But the idea was like, that was absolutely impossible. Fast forward to three years I get an e-mail from our career advisement office, and I basically applied for Microsoft. You know, they came back to hire from Egypt, and that's where I'm from. So after going through all the series of crazy filtering interviews on the phone and in-person interviews, they offered work with Microsoft. After graduation, I went to the visual studio team, and I spent a couple of years there and then moved to the Bay Area to work on Hopping Lacombe back then. Then I got the bug of machine learning. I can't remember the exact moment where I saw the Qatar or some solution that is so elegant. You know, this solid data is the new code and how you can use data today. So some of the problems we've been facing that a lot of old systems kind of actually find a good solution. And I started tinkering around them, playing around with some of the problems. We had a work, and it was obvious to me that this is my passion. I'm gonna follow it. So I started working on some of my code decision processes for Improving the bear strategies for machine and what not? And then I just noticed that I want more. I want way more. So I, you know, got into the anti-spam team. I lit a couple of teams working on any of the little ability may transfer agents between like, you know that Jim Miller camp of your common Yeah. Come on, hold it, Players. And you basically busy owning ATA And you know, you have, like, filter spam different stages of email into the variance alarm. So I got into that and did it a bit more. But I wanted more. So I went to them where my team was either the team they're working on infrastructure and some modeling for the personalization and recommendations that you get on any newsfeed I think that's including the home page, whether you're on your computer or in your phone and this is where I really learned a lot. I was surrounded by people who are entire day-to-day is just getting to the best, performance metrics and assistant performance, serving machine learning systems and understanding user behavior and the modeling. I learned that time. We were had a couple of friends, we had an idea and we just wanted to go and do something about it. And it was, how can we use augmented intelligence, AI to more productive at work. We started with co-founders, we started looking at the meetings as a starting point for where we're actually spending a lot of time. And how can we automate all the desks? So you turn talk into action and you can start thinking of like, kind of bring the meeting into the enterprise workflow. So you are now our conference. Our agents could have easily joined this, recorded the whole thing, captured those action items and other tasks, but also give us our conditions over chat like, Hey, I heard you say this. What? You'd like to schedule the meeting next week, and if you say yes, it will just scheduled the meeting for you. So really integrating into like the enterprise book for what is lost in meetings and bringing that to the ecosystem that you're working on, whether you're using, like a desk manager or the calendar or any other system like CIM So bridging the gap between what happens inside the meeting room or in the conference line and what you actually use to work. So we got a quite bright cisco a few months ago and now I'm running the AI for contact center businesses and context interest solutions. Cisco is a big player in that space and there's a lot to be done here. We're implementing the agents, making them more productive and making supervisors more productive, helping customers who also call on the phone or chat or, you know, interact to get support and other, aspects of the context center. So there's a lot AI can do there, and we're very excited, actually building that here.

I will actually answer the second part first. we at Cisco have a very cool culture of work and happen anywhere. We believe in the collaboration tools that we built. And most of the time, we're actually are working with teams across the globe across the US, across multiple places. So they can work from their home, office or while traveling. They work remotely. That's a big cultural value we have here that you can do work wherever you want cause we believe in the kind of like seamlessness. The main responsibilities when you think of what I can bring to our business is really about the direction and the way you introduce, change management and figuring out the technical strategy. So mainly, you know, if you think of where is it that you would have an impact on the lives and who are you helping? And seeing across the spectrum. How can you actually introduce AI and where would it mean that you know what customers need. And of course, coming up with innovative solutions for these areas. So for us, if you think about it, there's a lot of trends that are happening. As you know, that deep learning has been negatively taking. Should running like, uh, solutions into a direction where things like why more data gonna be more effective And you're solving way more problems that were or in a more efficient way. And then more on the Heidi effective ways than ever before and we're looking at, like, across the board. Or, you know, from the moment a call comes in or any interaction is initiated by the customer or even, you know, maybe before that as well. But I'm just gonna go through the, like, the path that you can see that for yourself going through. You have a problem where you're calling, banks or your airlines for something. How can we make your experience over time a great experience? What if we could just get you the best person to talk to right away. We can solve the problem or the team to talk right away for the concern problem. How can we recommend the agent to actually give context that could say you're browsing something on the website and you saw a product and you want to ask a question about the product? How can you escalate from the chat while preserving the context and find the right person who can help you with that? There's more that should be done. So one of the suffering we always looking to like, How can we take the call and make sure that we have analytics that would have you so problems fast? So there's a lot of knowledge that comes into a contact center, you know, the product catalogs and like them, a lot of knowledge. It has to do with the Catalogs and documents. Maybe they're searchable maybe they're not. I brought the ideas like how to expose this on the fly to the agents so they can be like superhuman right, that you don't have to go on like hold and neck. Put the caller on hold and figure out how can I solve this problem? That might be AI like psychics. That gives you the answer right away and just give that answer to the caller immediately. So there's a lot to be done on. As I said that before, the idea is to figure out the direction we're to invest, what kind of technologies we will use working off, you know, model into needs. And also we are aspiring to figure out the new one novel ways of solving problems. So it's not just the applied piece of it. There's a lot to be done as well.

This is a big question. I have been asked this question multiple times in different you know, situations whether it is software engineering, tools or programming languages or what kind of computer you use. There's always a question about choices. I believe in a couple of things. First one, there's no silver bullet, right? So there's no ultimate, perfect winner. There's always a situation of thing that you have to consider all the factors to figure out what works for me. So I continue just as an analogy. People ask online forums. Hey, should I use Go or Java? And the answer should be like for what? This a necessity or maybe a fit, you know, test first. If you want to have something that satisfies these preconditions or criteria, then you use this one versus that. So you have to figure out that first, what are you optimizing for right? But let's go back to the second point. I believe, more in a guiding decision-making framework rather than the choices themselves. Meaning that I would, you can figure out with the team what are the principles on top of which we can make decisions versus making the decisions because, in the future, new decisions or decision-making points are gonna emerge. And you really want to have a framework of resolving any conflicts or any competing interests. So let's say, for example, one of the decisions or decision-making framework guidelines we look at it like, Is it secure? Right. So if you're using a program, is it secure as prospective privacy of the customers you're collecting data. Most of the cases, that's true. So if you're using that program, like our framework TensorFlow versus PyTorch yeah, we know that they're coming from a reputable like projects, and it's not like a big or short. There's no issue there to use one of the two. But the idea is that you come up with all of these different guidelines or principals kind of ways of judging the decision making versus the actual process. I can walk you through a couple just thinking or given example. So security is top of my mind usually, especially for software engineering generally speaking, not just machine learning. The second one is gonna be something that will allow me an iteration velocity that will get us somewhere fast first, just to deliver something as MVP, minimal viable product. So, really, how can How fast can I reiterate this thing? If it's gonna be something, take a lot of time to iterate using the tool, then that is gonna be something that will slow me down every single time I need to use it. So you want to use something over bait evidence and accelerating your research or escalating your developing over time, right? So after that, you should think of all of the different criteria. Like does it skate? You know, does it save me like money and cost, like basically efficiency and so on? So these are just a couple of examples. And they change, depending on whether this is a short term thing or a long term thing to the other factors in both, I hope this gives you an answer. I can tell you about the specifics of what we used, but they're the choices themselves are circumstantial. And I don't believe in the silver bullet so I hope that is a better answer than just giving the exact concrete names and brands that people usually want to know. But I'm happy also to go into the details of what we have been using.