Welcome to How AI Transform Business, a new series featuring insights from conversations with Microsoft partners who are combining deep industry knowledge with AI in novel ways and, in doing so, creating leading-edge intelligent business solutions for our digital age.
Our first episode features eSmart Systems, which is in the business of creating solutions to accelerate global progress towards sustainable societies. Headquartered in the heart of Østfold county, Norway, eSmart Systems develops digital intelligence for the energy industry and for smart communities. The company is strategically co-located with the NCE Smart Energy Markets cluster and the Østfold University College and thrives in a very innovative environment. When it comes to next-generation grid management systems, or efficiently running operations for the connected cities of the future or driving citizen engagement, the company is at the forefront of digital transformation.
We recently caught up with Davide Roverso, Chief Analytics Officer at eSmart Systems. Davide has many interesting things to share about where and how AI is being applied in the infrastructure industry. Among other things, he talks about how utilities companies are forced to fly manned helicopters missions over live electrical power lines today, just to perform routine inspections, and how – using AI – it is possible to have safer and more effective inspections that do not expose humans to this sort of risk.
Davide Roverso, Chief Analytics Officer, eSmart Systems, in conversation with Joseph Sirosh,
Chief Technology Officer of Artificial Intelligence in Microsoft’s Worldwide Commercial Business.
Video and podcasts versions of this session are available via the links below. Alternatively – just continue reading a transcript of their conversation below.
Joseph Sirosh: Davide, would you tell a little about eSmart Systems and yourself?
Davide Roverso: eSmart Systems is a small Norwegian startup, was established in 2013. The main area in which we work is building SaaS for the energy and utilities sector. So basically, it was founded by a group of people that had been working together for over 20 years in the energy and utilities space. They were first working a lot on power exchange software, and delivered power exchange to California, among others. And then, about 2012, they went for a kind of exploration trip to the US, to Silicon Valley and that area, and they visited Google and Amazon and Microsoft and Cloudera and tried to find what were the new biggest trends. And they came back home with a clear idea that they had to focus on cloud and AI. And of course, they used that in their core business and that was power and utilities.
So that’s how eSmart Systems started.
JS: And so, you have an analytics team, or now is it an AI team?
DR: We have 10 data scientists, so more than 10% of the company is data scientists, so we have a big focus on AI. When I started in eSmart Systems about three years ago we were just two, so I built quite a good group since then. And we use machine learning in a lot of different areas. Two main areas are specifically time series analysis and predictions, and the other is more on analyzing images – we use that for inspecting, for instance, power lines with drones.
JS: You must have a lot of interesting projects. So, tell me, in the power and utilities industry, where is AI used?
DR: Well, we mainly work with the DSOs, distribution system operators, which are kind of responsible for distributing power to end users. Up to few years ago they were basically operating blind because the last lowest voltage network is not instrumented. But since the introduction of smart meters, every home now – well in most of the European countries they are rolling out smart meters and the same in most of the US – every home now basically has a sensor. So now, suddenly they have much more data they can use to more intelligently steer the grid. So, there AI we use mostly to make predictions of loads and consumption from different types of customers, both household and industry customers.
And this is very important information, especially now, with the large introduction of distribution energy resources – all the renewables that are coming online. A lot of people are installing solar panels on the roofs. A lot of end users are now what we call prosumers, so they both produce and consume electricity, so there’s a two-way flow of power and data. So, there are lots of opportunities to optimize this new kind of smart grid that is becoming more and more widespread now.
JS: Very interesting. So, what are some of the most exciting AI applications that you have seen now in the power industry and in what you are doing?
DR: We are developing some very exciting applications in the space of inspections. We are combining AI with drones. Of course, the electrical infrastructure is relatively old and requires quite a lot of maintenance and inspections. And, so far, these inspections have been mostly done manually, so periodically people actually walk along the lines and climb up the poles and check infrastructure. And the last few years they have started using helicopters, and they fly helicopters – quite dangerous missions because they have to be quite close to the power lines, and every year there are reports of near incidents. So, it is quite an expensive process, but it is, of course, necessary, and even more necessary as the infrastructure ages even more.
So, the idea here is to use drones to have a cheaper, more effective inspection. And here, it is very exciting to use all the new technology that we have today for this kind of image intelligence that we have, with deep networks and convolutional neural networks. So, recognizing infrastructure, recognizing different types of faults and anomalies.
|“It is very exciting to use all the new technology that we have today… with deep networks and convolutional neural networks, [for] recognizing infrastructure, recognizing different types of faults and anomalies.”
JS: And so, how do you use the cloud?
DR: Our systems are basically deployed in the cloud. So, the smart meter / smart grid systems, they collect data from smart meters and upload everything in the cloud. And all the analysis – all the machine learning and AI – happens in the cloud. And the same for the drones. Well, there are different missions. If it’s kind of a periodic inspection, then time is not the big issue, you can analyze the images in batch, and then we use cloud for that. So, we upload – it can be hundreds of thousands of images – and process them in the cloud.
JS: So, what is the advantage that cloud brings you, cloud and AI together?
DR: It is scalability. Regardless of how many drones or how many pictures our customers are sending to the systems, we are able to serve those.
JS: Near instantly being able to provision as many resources as you want. Okay, that’s very good.
DR: Also, edge is very important, it’s not just the cloud, the intelligent…
JS: Intelligent cloud and intelligent edge.
DR: Because if you’re on a mission for finding a fault or outage as quickly as possible then you need intelligence on the edge. And you also need that if you want to have autonomous drones, of course. Because today, we still don’t have fully autonomous drones – we still have pilots that remotely pilot the drones – but of course, the longer-term vision is to have fully autonomous drones.
JS: So, have you developed a prototype of autonomous drones that can follow power lines?
DR: Yes, to follow power lines and then position itself in the optimum spots to take the correct pictures for the detailed inspection. So the drone is not doing the detailed inspection – that happens in the cloud – but is using edge AI to localize the components, the assets that we need to inspect and take the right pictures and then move on to the next.
JS: Is AI scary?
DR: Not today. But it can be, in the future, you know. Your probably read Bostrom’s book “Superintelligence” that came out in 2014, I think. So, he envisioned like a superintelligence that will take over, and we will not even notice that because it will come so fast we won’t realize. But this is a long time away. But anyway, today there are philosophical and ethical questions that are important to ask ourselves. And there are big institutes both in the UK and in the US that focus on that, so that’s important. But todays technologies can be weaponized in a way, so there is that kind of scary side of it, of using AI without ethical controls, for autonomous weapons. So, there are some initiatives there. In my opinion, there should be an international agreement on how to control autonomy.
JS: But all technologies are the same way, I would think.
DR: Of course.
JS: What are some of the most exciting AI developments you have seen recently?
DR: Well, of course, all the developments around visual intelligence as I call it – so all the analysis of images, segmentation, detecting objects, and things like that with deep neural networks, and convolutional neural networks – it’s very exciting. And one very exciting development is, of course, self-driving cars. That, for me, is very exciting, and I use it a lot as an example in my presentations because it both showcases vision development / technological development but also its an application that basically touches almost everyone. Everyone drives a car, at least in the developed world, so it’s one of the applications that will come – that we will feel – much more quickly than other ones. But, of course, all the developments around language and speech recognition, and all these new intelligent systems and bots that are coming, it’s very exciting developments. From the research point of view, I like a lot of what is happening around the games and gaming in AI. You know, we both started working on AI in the nineties, and at that time, well since the beginning, AI has been applied to games – from checkers, and then chess, Deep Blue beating Kasparov in ’97, and then, more recently, of course, AlphaGo, and AlphaZero, even more exciting and now the latest one with Open AI playing Dota 2 – so, it’s a very nice way of developing new concepts. It doesn’t have direct applications in the real world, but it develops kind of fundamental capabilities that real world systems are going to need.
JS: Any thoughts about the applications of AI outside of the power industry, some of the most exciting other areas that you might be able to go into?
DR: Yeah, well – basically all the work that we are doing both around images and inspections is applicable to other…
JS: … all types of inspections. Yeah, one thing I heard sometime recently was about inspecting for lightning strikes on aircraft. And they were looking to see if you can use AI to identify, because today again somebody has to climb the airplane and go look at spots and see if there has been a lightning strike.
DR: Or inspecting like pipelines, or railways – any kind of infrastructure.
JS: Or even assets, even just counting assets, is one thing I heard, which was interesting.
DR: Almost limitless amount of applications.
JS: Very exciting. Any concluding thoughts on AI and its applications?
DR: Well, it’s very exciting times. I’ve been working in AI for 30 years and finally we see a lot of traction, and we see an explosion of applications and interest and money nonetheless coming into AI. And real applications that are both helpful and exciting.
JS: And do you think AI is being democratized – made available to software developers much more easily?
DR: Yeah, definitely. Today, basically anyone can experiment with AI. Maybe it’s still difficult to make an application that is production-ready if you are not a data scientist because you can fall in many places – you can make a lot of mistakes if you don’t know what you’re doing. But you can experiment and generate something useful in a much easier way than before. So, there’s been a lot of progress around that and there is going to be more progress – I cannot even say in the years to come, just weeks!
JS: Wonderful, it’s been a pleasure talking to you.
DR: Thank you, it’s been a pleasure.
“It’s very exciting times. I’ve been working in AI for 30 years and finally we see a lot of traction, and we see an
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The AI / ML Blog Team