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In this episode of The Speaker Show, Maria Franzoni interviews Elin Hauge.
Artificial intelligence is probably the most disruptive technological development in human history, and it is ubiquitous; from face recognition on your smartphone to advanced public surveillance, from food production to medical research, from online shopping to autonomous cars. According to Elin, understanding the basics of artificial intelligence should be as compulsory for any leader in 2022 as having a computer.
Elin Hauge has built bridges between data-driven technologies and business value for more than 20 years, with AI as one of her key capabilities. She helps leaders to build relevant competence in artificial intelligence, understand responsible human-machine interaction, and outline sustainable practices around digital technologies.
In this fascinating episode, we discuss:
- Artificial Intelligence
- Digital is not (necessarily) sustainable
- Digital technologies
Maria Franzoni (00:17): Hello and welcome back to The Speaker Show with me your host Maria Franzoni. In today’s show, we are talking about artificial intelligence and the metaverse. The Speaker Show is brought to you by Speakers Associates, the global speaker bureau for the world’s most successful organizations, providing keynote speakers for events, conferences, and summits. Artificial intelligence is probably the most disruptive technological development in human history and it’s ubiquitous from face recognition on your smartphone to advanced public surveillance from food production to medical research from online shopping to autonomous cars. According to my guest, understanding the basics of artificial intelligence should be as compulsory for any leader in 2022, as having a computer, my guest has built bridges between data driven technologies and business value for more than 20 years with AI as one of her key capabilities, she helps leaders to build relevant competence in artificial intelligence, understand responsible human machine interaction and outline sustainable practices around digital technologies. Please welcome my guest Elin Hauge. Elin, it’s lovely to see you. You’re looking radiant today. How are you?
Elin Hauge (01:30): Thank you very much. I’m feeling really good this morning. I’ve been looking forward to talking to you today.
Maria Franzoni (01:36): Thank you. That’s really nice. Not many people say that. So I appreciate that. So listen, before we get cracking on understanding a bit more about your topic and your expertise and how you help organizations. Tell me how you personally got interested in artificial intelligence.
Elin Hauge (01:51): Well, I think this started when I was in high school because I’ve always been interested in the stem subjects. So when I was going to choose my profession, it was, should I become a medical doctor or should I do something else? And then I ended up not becoming a medical doctor, but studying medical physics. And that’s when it started really this interest of connecting data with how the world works. And so when I finished my master’s degree in engineering, I started working in the consulting industry. And then of course I wasn’t in the lab killing rats and studying cells. But I was talking to business leaders about what kind of outcome, but it’s all about how do you use those data? And then I decided that if I was going to continue working in the consulting industry or in business in general, I needed to have a few more business subjects. So I went on to study at the work business school. And I took a master’s degree in operational research, which is really about understanding how data in a value chain can be used to understand what’s going on and how to support the management on their decisions.
Elin Hauge (03:10): And basically that’s what 20 something years ago, we didn’t talk about machine learning at that time, but we did talk about optimization now, 20 something years later, we talk about machine learning, but the topics and the subjects are really almost the same. We have just added a few more new technologies to it. And so, yeah, that’s the red thread from my started my career to where I am today.
Maria Franzoni (03:38): That’s fascinating. You were sort of like a bit ahead of your time, weren’t you really sort of, you know, who could foresee that this would become such a hot topic? I mean, it is such a hot topic. Now everybody is talking about artificial intelligence. So what’s your position? Have you got a unique take on the topic?
Elin Hauge (03:58): Well, let me just comment on this notion of being a bit ahead. I just remember this morning and that 10 years ago, I was talking to my boss then working in the insurance industry about the use of psychographic data in customer relationship management in insurance. And he said, nah, the union is not going to accept this. It’s too complicated. Nah, now it’s going to happen. Yes. Well, 10 years later, that’s where we are. So my unique take, I’ve been working in the consulting industry for a few years now. So basically through my entire career, I’ve been either on the buying side or the delivering side of consultancy.
Elin Hauge (04:43): And I have been one of those who have been both buying and selling those pink clouds with a little bit of fairy dust storm. And then over the maybe last couple of years, I started looking back and said, okay, what, what actually happened? What were the consequences of what we were selling? And so my take on it now is let’s be pragmatic because there are so many opportunities around how we can use data to create a better understanding and an insight into the business and the organization and the world and the climate and whatever. And we can use these data combined with new technologies and machine learning, and let’s call it artificial intelligence to optimize and to support human decision making. But my focus is very much on the pragmatic what’s here right now. What’s
Maria Franzoni (05:46): Here right now. What’s under our nose is what can we do? That is so important because often some speakers will talk about things that are untouchable. You can’t do anything is there’s not, it’s not practical. It’s not pragmatic. Sorry I interrupt you. Carry on. No
Elin Hauge (05:59):
Maria Franzoni (06:10): Yeah. So starting where we are, start from where you are in order to get to. And I love the fact that you are a bit of a, a futurist. I might have to ask you at the end, what you think is gonna happen in the next 10, 20 years, because you do seem to have that visioned well done. So just explain what you think artificial intelligence is really all about.
Elin Hauge (06:30): Well, to me, it is about applying mathematical recipes. What do we call algorithms to a large amount of data that we have created somehow to me, this is an extension of the big data we talked about 10 years ago. The digital transformation we have talked about over the last decade and was still so much in the middle of, and this AI thing basically here and now it’s about how do we actually utilize those data for something useful with the technologies we have available. So it’s basically about using mathematics to identify patterns in the data. We have to somehow predict a future outcome or future behavior that simple and that complicated.
Maria Franzoni (07:21): I like that. And I like mathematical recipes. I love that. I’m gonna use that. An algorithm is a mathematical recipe that just makes it so it makes it visual for me, actually. I quite like that. So we talk about bias as well. You talk about bias being a huge problem. How is it a problem for us when we are dealing with data? When we’re dealing with AI,
Elin Hauge (07:44): We tend to blame the algorithms. We tend to blame the technology, but that is the wrong path. Now the source here lies in what I just said. AI is about applying mathematical recipes to a large amount of data. The data that’s where the problem is because how did that amount of data come to life? Or how, where does it come from? Who created it? We did, we as humans created those data through our behavior over years, maybe decades. And when we use mathematical algorithms or recipes to find patterns in those data, we’re actually training the mathematical algorithms to replicate human behavior, human ways of thinking human decision making, which means we are also optimizing human stupidity to a large extent. And that’s where bias is a problem because it’s our biases that are just ingrained in those data. And there are so many examples of that.
Elin Hauge (09:03): The Amazon case of recruitment, we see it in, in legislation or law enforcement. We can see it in credit scoring. There are so many examples where we say, well, this isn’t really fair. This isn’t really good because there’s a bias here, the gender ethnicity, whatever kind of bias we don’t really like. Well it’s because we, as humans did this first and then we trained the algorithm to just do what we did. Wasn’t good. No, but the problem is we are just standing in front of a huge mirror. And what we see is the mirror image of ourselves, but not the prettiest version of ourselves.
Maria Franzoni (09:49): Wow. That’s a bit scary. Isn’t it automating human stupidity. That’s incredibly scary. And the fact that it’s a mirror is interesting. Can, can it be solved?
Elin Hauge (10:00): Yes, it can be solved at least to a large extent, not completely, as long as we humans continue to behave in, not so fair, rational, clever ways. We will continue to create those biases. Right. But we can mitigate it to a large extent by being aware of the problem, by being aware of the fact that it is our own human behavior, that is creating those biases. And then by looking at the data in advance and actually working through the data properly, compliance, transparency, you know, all these big words that might feel a bit complicated, but really that is what we need to do. We need to understand our data and need to be sure that the data we actually feed to the mathematical recipes is worth feeding to the algorithms. Right? And this is also where the coming legislation from the European union points out the need for governance, for transparency, for compliance, because it is really about preventing that we use bad data to put in in Tableau way.
Maria Franzoni (11:18): Okay. Wow. That’s a big topic, isn’t it? And I suppose it’s a big deal and, and it’s, it’s, it is fascinating, but something else you talk about that I would like to know more about is you say that digital is not necessarily sustainable. So first of all, I want to know what you mean by digital, not being sustainable and also why you’re focusing on digitalization here and it, it and not AI. So you’ve sort of moved. Tell me about that.
Elin Hauge (11:45): Well, I haven’t really moved. It’s more about not being so specific that people don’t really listen to me because the topic is still the same. So why do I talk about digital not being sustainable? Well, we tend to think that anything digital is sustainable because these data just floating wirelessly around in the air, right? Not leaving any footprints, but that’s wrong. They are indeed leaving footprints. And when I talk about sustainability here, my focus is primarily two things. So one is the electricity needed to actually operate anything digital. And that’s one of the reasons why I’m not only talking about artificial intelligence, but also other digital technologies such as blockchain and cryptocurrencies virtual reality gaming metaverse yes. So a bit more than just artificial intelligence. And the other side of it is that we tend to forget that anything digital has a hardware end somewhere or even a hardware beginning because we have smartphones.
Elin Hauge (12:59): We have computers, we have a whole lot of sensor technologies. We have smart watches sensors in the lighting and the heating and the cooling of the rooms. We have cars full of sensors. They are everywhere cameras, but also service data centers, gaming computers, monitors, TVs, all these hardware elements related to our digital transformation. They have a huge environmental cost for our planet. And we forget that in 2021, the world produced nearly 58 million metric tons of electronic waste. That is more than a total weight of the Chinese wall in one year. And about half of that is smartphones.
Elin Hauge (13:52): And we are not really recycling very much of it, a bit, a tiny bit of it is recycled, but for now we are not really good at recycling electronic waste. And where does it end up? Well, it ends up in dump sites in Africa, for example, in Ghana, on Agbogbloshie, which is the largest dump site in the world for electronic waste. And it becomes a really toxic waste and let’s move back to the manufacturing process. So actually as much as 80% of the environmental footprint of electronic devices is related to the manufacturing process, which is a really dirty process, actually requires a lot of electricity. A lot of clean water creates a lot of waste. It involves a lot of precious metals, and we’re also digging a lot of big holes in this planet to produce simple, simple things as smartphones, right? So anything digital requires that we are actually doing mining several places around the world.
Elin Hauge (14:58): So when I hear people talking about how AI can heal the world, and there will be no mining and there will be no child labor and there will be no poverty that that is so wrong because anything digital has a hardware end and that hardware end is created using metals and those metals, they are dug out of the ground. The garbage is handled by to a large extent children. So right now 18 million children are in immediate risk or health risk, severe health risk because of working in this informal electrical waste management industry. Why? Because the HandsOn nimble is easier for them to pick a part of smartphone than for a grown man. Houston, we have a problem.
Maria Franzoni (15:52):
Elin Hauge (16:15): Well, I think also every business leader can do something. And so one of the things like maybe step one is start by requiring that all your data in any data center is processed during using green power. So at least let’s help the data center providers in their transition towards renewable energy. That’s not enough, but it’s an important first step. The other thing is create a cultural computational efficiency, start creating awareness in your company about the fact that every email you send leaves, a footprint, every email you don’t delete and just leave in your inbox, creates a footprint. Every data point created leaves a footprint, maybe as a marketing department, you shouldn’t send all those newsletters to all those people. If they actually never really care that much, or maybe you shouldn’t save all those data as much as 90% of the data we collect, they’re never used that in three months.
Elin Hauge (17:26): So maybe we don’t need to con to save all those data all that time, because it’s just stacking up in enormous amounts. Maybe you don’t need to run your algorithms that many times during training, just because you wanted to check if it worked or if it got any better, maybe you should think about what is good enough. Can we be smarter in how we develop and how we train our algorithms? Can we be smarter in how we build our code so that it runs more efficiently? There are so many things one can do to create this computational efficiency and include that in the ESG reporting. I mean that simple and that complicated, but still something you can actually do right now. There are tools in the market open source as well for that can help you calculate what is the actual footprint of your calculations.
Elin Hauge (18:26): And then the third part, make sure that you reuse the hardware for as long as you can. And when you need to change it, then do it in a safe and secure way, and maybe find a partner that can actually take over part of the value chain of the hardware, such as the smart phones of the company. There are companies out there such as an Norwegian company text step that can actually help you with the value chain. And when your company are done with those smartphone after two or three years, companies like text step, they take over, they fix them and they sells them to a next generation of users because they’re still usable, right? Everybody saves money. We save the planet at better the list. And another generation of users can get a smartphone a little bit cheaper, everybody wins. So those are three examples. We could probably talk about a few more, but at least let’s start somewhere. And this is something that all leaders can do right now. Just do it.
Maria Franzoni (19:32): Fantastic. I love that. And actually you’re making me think about, you know, I’ve got a lot of data that I keep you know, in the cloud that I probably don’t need to. So I might have a little bit of a, a clear and a call and often you get overwhelmed by the amount of stuff you keep and you keep it. And you’re right. In terms of, you know, after three months, a lot of that data isn’t used you are absolutely right. And obviously I’m a small part of this problem, but if everybody like me did something about it, it would all Mount up. That’s that’s phenomenal. Thank you. That’s really made me think so apart from this and apart from AI and many other topics, you’ve, you actually cover quite a range of topics. So what, tell me about the kinds of audiences and what they’re asking for, for you to speak about and how you make sure it’s relevant to them.
Elin Hauge (20:21): So typically I speak to leaders in various industries that need to understand more about data driven technologies, such as artificial intelligence and a metaverse very often, I start by educating them a little bit in a nice way, but even if the consulting industry has done a lot to tell about technologies to the market, people are still not really completely understanding what this is about. So my role is number one to educate number two is to raise the awareness around the flip side of digital transformation. So maybe not so more, much more talking about the amazing opportunities and the pink clouds. Well, I do talk about opportunities, but I try to be a bit more right here right now. What can we actually do? But I very often find my role to be, to talk about what is the flip side, what is the challenge that we are getting ourselves into on behalf of this planet, through our digital transformation. And then we’re talking about sustainability, which we’ve already talked quite a bit about, but also the issue of responsibility or ethics. But I prefer to talk more about responsibility rather than philosophical issue of ethics.
Elin Hauge (21:53): Yes.
Maria Franzoni (21:55): Great. And
Elin Hauge (21:55): Then, yeah,
Maria Franzoni (21:57): I’ve written down that, that the flip side of digital transformation, I think that’s such a good line because we, we always, everybody always talks about the positive and it’s rare to have someone talking about, hang on a minute, let’s look at the other side. So I think that is a really unique take that you, you have. So Elin, you have this ability to think about what’s coming next, what’s coming next for you in terms of what you are doing in terms of your work and your topic areas.
Elin Hauge (22:25): Well, I think my role is to always be a little bit ahead and challenging our beliefs. So when the market moves and the leaders understand more, they embrace technologies and maybe also embrace more the issues of sustainability and responsibility. I would continue moving a few steps ahead. There will always be new issues and metaverses is one such a good example. We just started talking about it in the market. I do get requests around. So what is this metaverse can you tell us what is going to be? Well, actually, my answer is no, I can’t because nobody knows, but I can tell you a little bit about what are the components, what are the visions that are being painted for this metaverse what are the huge challenges that we are also seeing? And what do I think is going to happen in next few steps? And I think that’s also where we are going to see artificial intelligence moving, because yes, there’s many use cases that we still haven’t utilized within artificial intelligence, but scientists are aware of those.
Elin Hauge (23:37): So it’s more about actually getting it done. What is the next level? Well, there, there’s a lot of research going on within AI, but it’s mostly just the next few steps of how can we do things a little bit better? The real breakthrough will be when we have a completely new generation of processing power, because then we can do so much more. But that is still a few years ahead talking about quantum computing and then the whole metaverse discussion levels up the discussion around AI as well, because it is just the next league, basically of data or processing of opportunities and challenges. It’s like multiplying everything.
Maria Franzoni (24:33): Okay. Sounds a bit scary to me, but it also sounds fascinating what a fascinating world you live in. Elin. It’s been an absolute pleasure. I hope you’ve enjoyed yourself. And thank you so much.
Elin Hauge (24:44): Well, thank you for inviting me.
Maria Franzoni (24:46): You’re welcome and thank you for listening to The Speaker Show. And if you’ve enjoyed this episode, please make sure you leave a rating on apple podcasts. You can keep up with future episodes on the Speakers Associates website, which is speakersassociates.com or your favorite podcast app. And if you would like to invite Elin to speak at your next conference or event, please contact Speakers Associates in plenty of time to book her, because I know she’s very busy and you don’t want to be disappointed. So I will see you all next week bye bye for now.
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Maria Franzoni is an established and recognised speaking industry expert and one of the most experienced speaker bookers in Europe.
As well as working with speakers, Maria also hosts live shows and podcasts. She currently hosts The Speaker Show podcast for Speakers Associates.