ACM ByteCast - Steve Nouri - Episode 30

Episode Date: October 20, 2022

In this episode of ACM ByteCast, Rashmi Mohan hosts Steve Nouri, Founder of AI4Diversity, Founding Member of Hackmakers, and Chief AI Evangelist at Wand. He’s an award-winning technical leader, data scientist, academic, entrepreneur, and global leader on artificial intelligence. Nouri sits on the Forbes Technology Council, is a committee member at the International Organization for Standardization (ISO), and was named ICT Professional of the Year Gold Disruptor in 2019 by the Australian Computer Society (ACS). With more than 1 million followers on LinkedIn, he is one of the most influential voices in AI and Data Science. Steve describes his journey to computing, which started in his teens with computer games, and past work experiences including leading data projects at Data61, Australia’s leading digital research network. He speaks about the importance of building your brand online and how it can create more opportunities for computing professionals. Steve and Rashmi also discuss his Hackmakers hackathons, created during the height of the COVID-19 pandemic. Finally, he shares his big hopes for AI4Diversity, the growing non-profit organization he founded, with more than 10,000 volunteers from various backgrounds that engage and educate diverse communities about AI to benefit global society. Links: AI4Diversity Hackmakers

This is ACM ByteCast, a podcast series from the Association for Computing Machinery, the world's largest educational and scientific computing society. We talk to researchers, practitioners, and innovators who are at the intersection of computing research and practice. They share their experiences, the lessons they've learned, and their own visions for the future of computing. I am your host, Rashmi Mohan. Talking about artificial intelligence and its impact on humanity has been a trending

topic across the world. We are either touting its miraculous benefits to every aspect of our life, or predicting the doomsday scenarios that it could bring our way. Love it or hate it, there's no ignoring it. Our next guest might arguably be the definitive voice on all things related to AI. At last count, a few hundred connections away from 1 million followers on LinkedIn and a LinkedIn Top Voice 2020 finalist, he is definitely a force to be reckoned with. Steve Noorie is an award-winning technical leader, a data scientist, an academic,

an entrepreneur having founded multiple ventures, and a global leader on artificial intelligence. He is on the Forbes Technology Council, a committee member at the International Organization for Standardization, or ISO, and the founder of the nonprofit organization, AI for Diversity. Steve, welcome to ACM ByteCast. Thank you for having me, Rashmi. Great. We're super excited to have this conversation with you. I'd like to lead with a question that I ask all my guests, Steve. If you could please introduce yourself and talk about what you currently do, as well as what brought you

into this field of work in computing. Yeah, I mean, my journey to computing and I guess data science is a little bit of a boring one, which I, as a child, I started with playing with computer games when I was 10 years old. I think my dad has a plan for it because he bought a computer not for gaming. And I ended up playing a lot of interesting computer games of the time, probably early 90s, which was not the highest quality of the graphics, but it was nonetheless interesting. So it just evolved from playing computer games and then trying to understand how to make computer games. To be honest, I was thinking like, how can we produce programs that can do all of those graphics and sound? When I was like 14 or 15, I went to a computer sort of

educational course and there was a person sitting besides me, was doing programming. And I was just watching. It looked like magic. He was just writing a couple of sentences with numbers. And then finally, after, you know, executing it, you would see some shapes with colors and screen. So I was like, wow, that is is magic that's what i actually wanted all the time to learn so when he left i started playing with his uh with his codes so i started you know changing those numbers and things that i could understand like um you know it was easier to play with like colors it was like a red i changed it to maybe green. And then, you know, I could see which one of those shapes are changing colors. So essentially it was like a streamlined jumping into programming when I was 14 or 15. And from there, I was always fascinated with computer coding. I did codings in

many different languages, starting from C, C++, and then Visuals, Microsoft Visual Basic, and C Sharp. And I did my bachelor's in software engineering as well. As I said, it was so straightforward to what I'm doing right now. When I hear people have very interesting background of doing something totally different and accidentally finding out about data science, they sometimes envy. I was a project manager for a project like hospital information system. We were collecting a lot of data, a lot of data related to patients, medicines, procedures. And I always wanted to understand if I can do something more with those data. It seems that we have some ways of querying these data, but it's not as dynamic. It's not as complex to extract all the interesting nuggets in those data.

So I started doing research around 20 years ago to understand what in the world is used to extract those patterns and more interesting insights. That's where I kind of got into data analytics, I guess, and did my master's in data analytics. Back then, the terminology data science was not, I guess, coined or it was not as popular. So we were using knowledge extraction or data analytics in the kind of same way. Yeah, long story short, I have worked for many companies, public and private sectors, mostly government and public sector since then as a lead data scientist and head of data science, managing teams, delivering products, doing research. It was an interesting rollercoaster ride of working in startups, very fast-paced,

and then working for public sector, which is much, much different from that sort of environment. And these days, I mostly focus on the projects that I'm running myself in the private sector. I have founded two organizations, Hackmakers and AI for Diversity, and also advising a couple of companies. As you just mentioned, I'm an advisory board for Harvard Business Review. I'm also a council member for Forbes and do a couple of little things here and there and enjoying, I guess, the geek world of the technology and data. Yeah, that's fascinating. Not the word of choice that I would use in terms of little,

but an incredible journey. And I don't think you need to envy anybody, Steve. I think those of us that find that passion early on, I think it's an incredible journey for you to be able to build on that interest from playing computer games to actually, you know, being so deeply involved in data science. And, you know, one of the things that, and I know you've had such a diverse sort of career, you know, almost seems like a lot of it is, you know, self-motivated, a lot of self-learning where you're looking at code from what a friend was doing to then saying, hey, I want to actually understand how I can extract more value from this data. I know you also worked in the Australian government's research institution, CSIRO. So I was wondering if you could maybe talk a little bit about that phase, Steve. What kind

of areas of research did you pursue? Yes. You're actually right about being self-motivated. I just randomly find something that I care about and just my brain locks into it. And I have no way out until I find what I want to learn science for Data61, which is a sort of a business subsidiary of CSIRO, focusing on data science and AI. I was responsible for a great team of researchers, software engineers, product managers. And we had three major projects back then. One of them was a little bit, I guess, more entrepreneurial, which was a product. We were trying to understand what are the traits of the students that make them to be successful finding a job during or after graduation. So we made a product as a website. And then we asked, you know, we tried to match students with jobs in startups or, you know,

smaller companies. And by doing that, we started collecting data and use those data to understand those traits. There was a couple of studies that published articles and studies that published, and we also collaborated very closely with universities in that regard. Apart from that, there was another interesting research. That one was a little bit, I guess, more different in the topic,

but at the same time, interesting. So it was a team of researchers that we were working on understanding or predicting the financial outcome of the companies using the public data, which is not necessarily indication of their, you or their income. For example, what is the current state of their website and the language they use in their website, the technology that they have used in their website, or the information that anything that we could find

and scrape it from the internet that is available in a public domain. It is interesting that we were able to predict the company's sort of financial situations in terms of being successful or not just using this public data with a pretty decent accuracy. I think it was around 60% or something around that, which is not too bad because essentially we didn't have the direct indications of the work that they were doing. And it helped us to also help the government when government wants to give away grants or help these companies. sometimes they don't have access to a lot of information about them. And that'd be somehow a helpful tool for decision

makers. And it's like, these two, I guess, were the main interesting research projects. There was a couple of other things that the team were doing, but I'm very proud of these ones because we could be able to finalize it during the time that I was there. Yeah, no, they sound like great projects. And also to me, it sounds like, of course, the government was interested in because it was a good way for them to understand, you know, where might be the areas where they should be sort of investing or at least marketing the new grants that they might have available. But also seems like the targets that you were or the target audience that you were reaching out to probably were willing participants, right? Because there is a fair amount of benefit that they get, whether it's the entrepreneurs that are looking for sort

of funding, or the students that are looking for jobs. Did you find that it was easy enough to be able to gather this data with the participants? Yeah, 100%. For example, for students, it was even more than willing. They were very excited about being part of this platform because we found that it's much more important for us to deliver value before asking for any data or any help. So that's why I said there was a little bit of entrepreneurial sort of a mindset behind it. A lot of, I guess, government research is directly going into databases and the information that is available or sending surveys. And that's just, it means that there's nothing for you in it at all for the research, which is, you know, eventually it would go to the public, but there is no immediate, I guess,

benefit for the person. We made a website to actually match these people to like an internship or graduate roles. And that's where they could get a lot of benefit because they could just literally use it as a tool to, you know, find a job. And this information would just be something they would fill in the form to, you know, find the perfect match. So it was very, very, very easy. There was a lot of excitement from our students' side, and academics were also very helpful. At the same time, startups were also very supportive of this project because they could find higher-quality students

matched with their requirements. So I guess that's a sort of a best scenario, like everybody's winning and getting the value out of this relationship. Yeah, no, for sure. That sounds like, you know, definitely a match made in heaven. And one thing I wanted to tap into,

you were talking about the entrepreneurial nature of these projects that you were working on. You've been an academic, you know, you've been a teacher in some part in your career, and you continue to be somebody who enjoys sharing knowledge, right, based on your online interactions. So did you feel like you had that spirit of entrepreneurship and being able to sort of apply your research to build like products or build value right from the beginning? Like, how did you approach this whole idea of these projects or even your work with Data61? Yeah, first of all, about the teaching, I was always fascinated about the power of teaching

in terms of learning. I remember very, very early days when I was probably 17, I started teaching to students at high school. So back then, you know, my programming skills was a little bit beyond what is expected for a high school student. So I was able to leverage that to, you know, help others. As I said, like I could understand like what is in it for me in terms of getting the returns. And that was the motivator for me. I could see that I'm helping others, but at the same time I'm learning and it helps me, motivates me to stay on top of the trends, update myself, push myself forward. So from there, I guess I was always passionate about teaching. And

when I went back to university to teach advanced machine learning, I exactly thought about the same scenarios like, okay, so how can I do something that would help students get ready for a job? And then at the same time time make myself interested and excited. And that particular course was literally the most advanced course for postgraduate students. It's called Advanced Machine Learning. And me coming from, you know, sort of outside the academia, it was also interesting for students to come to my class and ask the questions about how the real life sort of a situation is for a data professional how they're going to leverage it in their in their work later and a lot of questions that made them you know more engaged and that's

a boring sort of course which is which is very deep into machine learning and math and stats just became more interesting and engaging for them, I guess. I hope so, I guess. And as you mentioned, also, when I was at Data61 and CYR, I always wanted to think, how can I take those studies further? I really enjoy the interaction with people, the impact of the work that I'm doing, and also, I guess, being more closer to the consumers or the users of those projects. I think that has been always obvious. is obvious so yeah it uh from there it was just like my mindset always was like um which one of these projects can be spin off from cyra and data 61 which is i think that's an interesting one that we were able to spin off that project the website from cyra and that became a commercial product that was sort of acquired by another company, which is a very successful, I guess, outcome for a research project to go forward and become an impactful business.

And kudos to the team and all the people that helped during this project, the managers, the directors, and all the team members to make it happen. That's such a great story. Thank you for sharing that. One of the other things that you mentioned, which I think is immensely valuable, is that to you, teaching is a method of learning. And I agree. I think most of us also feel that way, that the more you teach a certain topic, the better

your understanding of it grows and you sort of, you know, the finesse with which you're able to communicate and really get to the meat of the topic improves with every sort of every subsequent conversation that you have around that topic. I know that you currently, you know, are such a prolific content creator, if you will. You share a diverse pool of content online on LinkedIn. You talk about startups that are making waves in AI or, you know, tools for anybody who wants to enter the field. And you focus on ways to sort of build diversity in, you know, in the AI field. I'm wondering what sparked that, right? Was it a part of this whole, like,

I love to teach, and so I'm going to share information with folks and help them, enable them to sort of break into this field? Yeah, I mean, that's actually pretty much aligned with my teaching at university. There was a mixture of things that has happened at that point of time, at that stage of my life, I wanted to grow my network professionally. And also I was teaching data science at university and my students were really enjoying the content and they were asking me for some sort of recommendations about future studies in different relevant topics or how they can get more hands-on with those theoretical parts of the course. So I was always researching about what is available, what is the libraries available in Python or what is the new platform, Just because I had to answer those questions.

And, you know, as a teacher, as a lecturer, you try to be the know-all and try to answer as much question as possible. So then I was like, okay, so I'm doing this research. I'm sharing it with my own student. They are loving it. I would like to try to see if I can use the, I guess, social media and share it with a broader audience

because that's the reason that social media exists. You can, I guess, get more audience around the world. And I just started doing that for some time. And being consistent was very important. I was literally sharing things once or twice a week at the point of time that I started like six years ago. And at that point, it seems that data science was still a little bit of mystery. I don't know if it is still a mystery or not, but a lot of people wanted to enter into this field. They didn't know how to do it and they didn't know where to find the right material. There was a little bit of hype around AI and data science.

You know, there was a lot of interest in learning and also it was a very high paid sort of a job. So all of them together and that coincidence of me being available, sharing it, it seems that I was one of the pioneers of the, I guess, learning materials in data science and AI on LinkedIn. And that kind of helped me to get a lot of visibility through that short time frame. And then later, when I kind of my focus shifted from teaching to actually doing things and those entrepreneurial projects, then I started mixing my content with the latest innovations and the latest projects in AI and data science, the innovative projects that have impact, especially whatever is using AI to deliver some social impact or pushing forward the technology. And that also opened up another

front. And a lot of people started engaging with my post, not necessarily wanting to learn about AI in the sense of doing it. They wanted to learn, they wanted to understand what AI is doing to their current or future. And they wanted to know how can they leverage AI without being an AI engineer or a data professional. So that was the sort of the story. And through this six years of nonstop being available, sharing things that people like it, and that's essentially what I liked for myself and what I enjoyed and am interested to learn. I kind of got a lot of people supporting

my content and I appreciate all of their support. And it seems that this is sort of my brand right now that everybody knows what I'm going to share every day. People are excited, probably interested, hopefully, that see my post about the future of AI or maybe a bit of nuggets about how to learn a particular, I guess, tool or maybe a new platform. ACM ByteCast is available on Apple Podcasts, Google Podcasts, Podbean, Spotify, Stitcher and TuneIn. If you're enjoying this episode, please subscribe and leave us a review on your favorite platform. Yeah, and that's such an amazing and interesting trajectory, Steve, because like you said, you know, started out with sharing content for people to learn. But I'm sure as you started to share more content around, hey, these are the new innovative applications that are coming up in this space. I

mean, those are of interest to you. But I'm sure that also drove commentary on people sort of giving you, you know, views that probably aligned with yours as well as use views that sort of opposed yours, right. And that's a part of the journey that you talk about, you know, teaching that leads to learning, I'm sure that, you know, enhanced your own sort of knowledge about each of these areas that you were talking about. So the other thing that I want to touch upon, which I think you briefly mentioned is talking about personal brand, because really at one, almost 1 million followers on LinkedIn, this is a very, very significant part of your identity now. And as a practitioner using the power of LinkedIn, and understanding how to leverage this wide reach for the benefit

of others and yourself, it's pretty amazing how you've been able to do it. But I would love to understand, what do you think is the biggest sort of driver for this? I mean, I know you have, you're personally motivated to share, but what would you say is a good, you know, reason why anybody who's sort of navigating their career should think about it? Why should anybody consider building their brand online, you know, from a technical perspective? Of course, I mean, like, everybody can have their own motivation of why they want to be in a social media platform or why they want to use it to make their own personal brand. But what I've learned through these years is that having a personal brand

will sort of give you another dimension from being an employee. Because as an employee, you are sort of bound to your employer and all of your identity is within your interaction with your employer. And that is fine, but you can always have more than that. You can always have leverages in terms of, let's say, finding a new job, finding a better job or connecting with like-minded people that will bring more opportunities. And this is for everyone. Just literally everyone should think about what are the other opportunities out there for them to grow, to connect to people, to find better, maybe better, more interesting careers in the future. A social media platform like LinkedIn specifically provides huge amount of opportunities. That is, you know, sometimes people used to tell me back then when I started, oh, LinkedIn is just a placeholder for your resume. Like you need to just come back here when you're looking for a job or you want to update your resume. That is

supposed to be LinkedIn. I mean, I understand probably when it started, that kind of started as a resume placeholder or every six months you would share your new certificate or whenever you change job, you just change your title and everybody will just say congratulations. It evolved from there to being a content platform now. After Microsoft acquired LinkedIn, they had a huge push to make it a content creation platform, essentially similar to YouTube, to TikTok, whatever other platforms that are out there, but in a professional manner. So you need to just make sure that you stick to certain criteria, which is not very different from the other platforms, but you might want to share the content that are relevant to your career or would help you to get connected to your future customers or employers.

That's what I can understand. But at the same time, another takeaway is that by adding value to your audience without expecting something direct and, I guess, short term, you would accumulate huge amount of, I guess, opportunities that you can always go back to, I guess, leverage in any sense. Like if you want to be an entrepreneur in 10 years from now, even if you don't know what is exactly going to look like, it is better to start connecting with people and start adding value and make people to be aware of you and have a positive image of you. Because the moment that you actually need someone to listen to you, it is too late to start. I mean, we can always argue it's never too late to start, but I'm going to just go out of the, let me say,

it is too late to start when you need it because things happen in sort of a very, very step-by-step and long-term fashion. You need to be consistent. You need to think about a long-term gain to be able to do it. And then when you already have your business and you want to talk about an interesting product that you want to release tomorrow, that is probably a little bit late for you. Any students that are learning any courses, they need to start sharing more on LinkedIn they need to be more active

they always ask me like I mean like I'm not a professional I haven't learned this topic yet I'm not an expert I don't feel that I need to talk about it because there are lots of professors in here there are lots of professionals with years of years of experience. And I don't have that, I guess, the right to talk about any topic. And that is, that cannot be any wronger than this. Like it is totally incorrect understanding of social media. You can literally talk about your learning journey. Like I'm just going to make it very straightforward and easy. If you go to a class and they teach you math and stats, you can literally come to this platform and say, I learned about this particular equation today. I found it very interesting.

This is the background. This is what it is going to be used for. And maybe a couple of links to the places to learn more about it. And that's it. This is how you can start. motivational source of motivation from a movie or something that you experience in real life, at work, at university, at college, all of them can be great materials and people would appreciate it. The other aspect of sharing on social media is that always you would get people coming back to you with some sort of feedback. Some of them might not be very, I guess, helpful.

Some of them might look like a little bit too aggressive. That is the reality, you know, that's the reality. You would learn from it anyway. Whenever you have an stance, whenever you have an opinion, you know, putting a line in the sand, there would be people that would agree with you and some people would disagree. And that is a good position to be. If you want to be known and you want to have your own, I guess, identity, then you need to have an opinion. If we're all afraid of putting our opinions out there just because we will be judged and we might get negative feedback, people might disagree with us, then the other way is just to be indifferent and stay quiet.

I cannot see any benefit of just becoming invisible in the world because somebody might not be happy with our stance. I think that's such an incredibly valuable viewpoint, Steve, because I think that's probably what holds many of us back, right? The fear of being judged. But also the other piece that you brought up, which is you don't need to be working on something pathbreaking or something that has never been said before in order to share, you know, what your viewpoint is. Like you said, if I learned something, I think my journey of learning that same math equation that maybe 10 other people have learned might just be slightly different. And I think putting your journey out there, your, you know, the way you learn,

as well as maybe some of the challenges that you face, might help someone else, you know, feel more validated that, okay, you know, I'm not completely off the mark. And, you know, there are other people who have had this similar experience. And I think of that particularly, especially when it comes to diversity. And, you know, there's a lot of commentary on how, especially folks that come from, you know, a slightly diverse background in tech, whether that's gender or age or whatever that might be, are hesitant to participate in these forums because they don't feel like they belong. And I think the more of us that do that, I think it just makes the entire environment a little bit more welcoming. That's totally 100% true. Even for people with diverse background, this is the best place to find the others that are supporting you or similar to you.

Like this is even if I'm a minority in my geographical location, I can still find people with a similar background on social media. It's not too difficult. And, you know, it's out of like maybe 500 million people on LinkedIn, you would be able to find a couple of thousand that totally, you know, have the same experience or similar experience. They would be supportive. You would find people that would, that are helpful. They teach you something. They would be able to give you opportunities. So that's just thinking about, I guess, the half full of the cup is the best way to go forward. I think you don't need the 500 million people on LinkedIn to be able to leverage the value of LinkedIn. It just literally starts from the people that you know at university, the people that are

maybe are available in your geographical location, and the people that have the at university, the people that maybe are available in your geographical location, and the people that have the same journey as you are having right now. The students, maybe, if you are learning anything about, let's say, computer science, then that's where you will shine. Yeah, no, an extremely refreshing perspective. And to sort of reiterate what you had said earlier is, you know, your career itself is a marathon, not a sprint. And so investing in it in the long term for the long term, in terms of, you know, thinking about where it might go, but starting to invest in building that brand for yourself is super important. And this is a great way to do that. I did want to talk about your two entrepreneurial

ventures as well that you're currently involved with, Steve. One of them is called Hackmakers. What brought about the interest in hackathons? I mean, it's obviously very, very popular. It's an exciting thing for engineers to participate in. What did you find valuable? And what is the value proposition that you feel companies that engage with you get out of it? What is the ROI that they're looking for? It is another interesting story. I was the head of data science at ACS, Australian Computer Society in Australia. And then what happened is just during the COVID, like probably the first or second month of the COVID, there was news about Australia will have probably too many COVID cases very soon and we will not have enough ICU beds for those people in need.

So there was a conversation about how can we flatten the curve and how can we probably stay at home even more, take care of ourselves, make sure that we would not hit that particular problem of demand and supply. And at that point, we came up with an idea of running a hackathon. Like let's have a hackathon. People stay at home during the weekend. I think it was probably around the Easter holidays where everybody was like, not sure what's going to happen during the Easter holidays. Are people going to go out and socialize and sort of make the situation go worse? Or are we going to all together decide to stay home and make sure

that it doesn't go wrong? And we thought maybe that is a good idea for people to stay at home, especially the techies, because I believe the hackathons are techies' playground. So for techies, maybe that's a good way to spend time during the holidays since they are locked up at homes and they can not do anything fun. essentially, we had two goals of keeping people interested, happy at home, and also at the same time, delivering some solutions for this particular problem of the COVID in early days. It went super popular. We got within 10 days of the project timeframe was literally 10 days from the moment that we started advertising and talking about it to the moment we delivered this hackathon. And in 10 days, we're able to get around 2000 participants in majorly Australia and New Zealand. And that was the aha moment, the beginning of thinking about running hackathons. So later on, when I sort of

left ACS, I was thinking like, I need to, this is my passion. I loved it. I want to do it more, more frequently. And it seems that this is something that probably during COVID people would be more interested. So I literally started with the team, with my team to run hackathons like every couple of months as a fun side hustle for us. Like this was not a, it wasn't supposed to be a job. It was supposed to be just a fun thing to do. But then during such a short time, it became very popular and it got a lot of interest of these large corporates to be part of it as a partner, as a sponsor from Microsoft, Google, Oracle, IBM, and you name it. They were reaching out to us. We were reaching out to them. That was a very good collaboration happening there that sort of showed us that there is a value of doing it better

and I guess more frequently. So then it became a startup. We got funded by the government. We also got some investors and we are building the platform right now. It is an innovation, sort of an end-to-end innovation platform, which will enable people to go through phases to be able to come up with some interesting innovative solutions for problems from the ideations, from coming up with different ways of tackling a problem systematically and then collaborating to deliver a POC or an MVP in a platform that would allow you to collaborate in a virtual environment. That is the goal. We're in an early stage of delivering that. Right now, there is something interesting happening.

We are going to do it in a Web3 fashion, in a decentralized way, which means that we're going to open it to the public to contribute to these projects and to own it. That is probably something that will revolutionize the whole community aspect of the Internet in future, and we would like to be one of the pioneers of leveraging that sort of mindset of Web3. That's, I guess, the next step for us.

We have done hackathons with a couple of great companies and organizations around the world. And the last hackathon was the World Innovation Day, which was the second time we did it with the help of international organizations like UNESCO and all the big tech companies also usually will come in to deliver such an interesting project that would help the global community come together and think about innovative ways to tackle the problems that we are having all together around the world, which is identified by United Nations as global goals. So it was a very successful event. I guess around 4 000 people participated and we are hoping to continue that

fashion and always try to innovate the way that we're doing it ourselves we learned a lot in the last couple of um years i think we've been running it for two years already. And I guess this is an evolving community project for us. Well, I mean, congratulations, that kind of impact and the, you know, the amazing work that you're doing is definitely going to have like far reaching impact. I think the, you know, the fact that you're also now opening it up for contributions will only mean that, you know, it'll be so much richer with the newer ideas that come in for you to be able to grow it. And also, you know, it sounds like a lot of the projects that you've been working on recently is more for, you know, tech for good, which is always, you know, something that's very warm and close to my heart, for sure. We are, you know, I could go on with this conversation, Steve, but we are sort of running out of time. But I would love to ask you for our final bite. What are you most excited about in the field of AI over the next, say, five years?

That's a very, I guess, difficult question to answer because I'm excited about a lot of things. A lot of things happening, very good projects in different directions, the applications of AI in industries. One thing that I'm very excited is one of the projects that we are pushing forward, AI for Diversity. And it is essentially a community-led project. It's a non-for-profit community-led project that would bring together different groups of people with different backgrounds, people with different socioeconomical backgrounds, with a cultural background, with different gender. Everything that can sort of bring this diversity together would be ideal for us.

We have started it around six, seven months ago. We already have more than 10,000 people signed up from all over the world to be part of this initiative. This initiative essentially is planning to educate and enable people with different backgrounds, but also is going to help us understand how can we ensure the fairness and responsible AI, the notion of responsibility for AI. And it doesn't mean the AI is going to be responsible. We're going to make a responsible AI that is transparent, accountable. People are making it in a fashion that it's fair and explainable. And if something goes wrong, we are able to understand it, mitigate the risk. And if not, there is a way to actually for people to intervene

and make sure that it will not impact anyone in a negative way. So that is one topic very close to my heart. And very soon we will start opening the chapters in different countries, you know, check it out and participate. I know I for certain will. Steve, this has been an amazing conversation. Thank you so much for taking the time to speak with us at ACM ByteCast. Thank you very much, Roshmi. ACM ByteCast is a production of the Association for Computing Machinery's Practitioners Board. To learn more about ACM and its activities, visit acm.org. For more information about this and other episodes, please visit our website at learning.acm.org. That's learning.acm.org slash B-Y-T-E-C-A-S-T.