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Tema: AI

#0761: Senior Researcher

Gjest: Valeriya Naumova

Senior Researcher of Simula Research Laboratory


Med Vert Silvija Seres

In this episode of #LØRN Silvija talks to Director, Simula Consulting, and Senior Researcher in Simula Research Laboratory, Valeriya Naumova. Simula Consulting’s mission is to constantly redefine what’s possible through digitalization and bridging the gap between academic discoveries and real-world challenges. Because a lot can be done with the technology we have today.

Full transcript

With Valeriya Naumova and Silvija Seres

Velkommen til Lørn.Tech - en læringsdugnad om teknologi og samfunn. Med Silvija Seres og venner.

SS: Hello and welcome to Lørn in collaboration with smart collaboration Norway. My name is Silvija Seres and my guest today is Valeriya Naumova who is heading the similar consulting company, associated with a similar research laboratory. Welcome, Valeriya.

VN: Hi, thanks Silvija for having me.

SS: It’s lovely to have you again. We spoke almost two years ago about AI and the basics of AI and how people should learn. Also how do you guys work with AI in a similar research laboratory where you are a researcher. Since then a lot of things have happened. Also with this new company that we are going to be talking about. And your work on applying AI has had quite a lot of opportunities, to say the least. Not least with corona and this app for tracking infections. So we’ll talk about practical AI and what is new. OK?

VN: OK, lovely.

SS: So basically to start, maybe we could hear a little bit about you. Who is Valeriya and how did she turn out this way?

VN: So I’m originally from Ukraine, my background is from applied mathematics and essentially I studied in Austria and already worked in machine learning and data modeling projects. And then by doing that kind of visited quite a lot of countries and had a special passion for Scandinavia, so I was very happy when I saw a similar job back in 2014. And I was fortunate enough to get it in Norway and have changed some jobs since. I have changed some similar jobs, but still have a lot of passion and I am fascinated by advances in technologies, machine learning, data-driven modeling, working in demand. Both from a theoretical perspective, but also an industrial perspective on how advances can be applied to science and business needs and societal challenges. So that’s the story of me coming to Norway.

SS: Can I ask you on a personal note, what do you miss most from Ukraine?

VN: I’m not so sure actually. I feel quite comfortable here. I left Ukraine ten years ago so it has been a long time. Of course, family is the one thing I miss quite a lot.

SS: I have that with Serbia. Basically family, food, and certain landscapes.

VN: A few words about Simula as well. I have a special passion for that institute. I was on their board for a long time and know many of the people that work there. Tell us a few words about Simula and then tell us a bit about the consulting part.

VN: So Simula is a research institute established in 2001, owned by the ministry of research and education, but it’s organized as a private non-profit. So Simula combines the best of both academic and industrial words. Simula is focused on basic research and the education of young researchers. But at the same time, the company gives a lot of flexibility in making decisions or drive with more industrial projects. It’s also one of the opportunists on applying our skills and knowledge, thinking, and research to address societal challenges and business needs as we plan to do in similar consulting. Which are a new consulting company established this year and I’m really happy to be in charge of it. The idea of the company is to deliver our services to companies and essentially to bridge the gap between academic discoveries and cutting-edge technology and cutting-edge knowledge. And real-world challenges that are present in quite a lot of business branches and industries. Essentially showing people that a lot can be done with current technology and using researchers from different domains.

SS: Ok. So basically, Simula was very much focused on this blue-sky thinking and research data related one way or another. Fantastic research on applied mathematics for health, solutions, etc. But now you are trying to help the industrial and public world understand how they can use these better and apply them faster. Cause there is a knowledge gap rights know first of all.

VN: Exactly. And essentially showing them this technology and what it can deliver. For instance, you can show how you can apply artificial intelligence but not just use it as a passport on machine learning. And help them to succeed and strive by using technology we call deep-tech by bringing advances from similar research.

SS: So just for our listeners who are maybe working for an SME or have a project in the public sector and know they need to do something related to artificial intelligence or big data and data analytics, but have no idea how to get started. How do they get started?

VN: There are different ways and there are different courses for beginners or more advanced levels that describe what AI is and what machine learning can do. They can read up and get in touch with different companies and get in touch with different service providers. This is one of the advantages that I find in smart innovation Norway and particularly in the classes of AI because it combines different companies, services, service providers, and institutions which allows them to work together and being able to help each other. In particular, there was a workshop organized which essentially was a matching event, kind of a speed date, where different companies were put together to understand each other.

SS: And you were a part of this speed dating?
VN: Yes. It was extremely useful to get to know what people are doing, what their challenges are, and what type of problems they’re looking at. It’s needed to apply machine learning and other challenges and needs.

SS: I think it is a great idea. First of all to have this very efficient way to have a way to get people to expose their challenges and capabilities to see how you inspire them to start tomorrow. Something you say that applies to every situation is that you try to do something that seems insurmountable. Go talk to somebody who knows it and talking to experts like you get people from the starting point. It is a long road, but you have to start somewhere and there is help to be found.

VN: And I think that’s a great point on AI clusters. It allows not just to have the discussion, but also organized workshops and in detail discussion which allows companies who are willing to start advanced technologies to start and see the wat forward.

SS: Yes very good. Now if we talk a little bit more about other companies. Is there anyone from the cluster you could highlight as a potential global story in AI?

VN: There are a lot of candidates that have great ideas. I don’t have a specific company. I believe there is a lot of potential from all of them. A lot of them have good ideas, drive and focus. They seem to be very knowledgeable in working in this domain. So it will be exciting to see where we will be in one or two years' time.

SS: Very cool. So basically I think in AI development now it’s the applications that will change the world. Not necessarily the deep research into deep learning or neuro-networks. It’s how we find unique application areas that we know how to model better than anyone else in the world. Do you see any specific opportunities for Norway in this game?
VN: Yes. I think we discussed in the previous podcast what Norway does uniquely well. I still think there are well-preserved data sets that form the foundation for applied machine learning. In particular, working quite a lot in biomedicine and healthcare where we see these huge datasets preserved. And they make a perfect case for applying machine learning. For example, are we working a lot with the cancer register in Norway and looking at the data from the last 50 years trying to identify personalized screening intervals for females to prevent cervical cancer. This is one of the application areas. Having this access to data from around two million females and their screening history makes a big difference. In other countries, it isn’t so easy to get access to this type of data. So healthcare seems to have extremely well-preserved data, which is a rare case for other countries. In other domains, it also seems like data is available in much easier terms than in other countries.

SS: Why do you like AI? What motivates you about machine learning and its application?
VN: I have been working quite a lot with data-driven and machine learning in healthcare. It is to see how application helps people with different diseases or for instance for prevention or treatment of specific diseases. This is extremely rewarding for me. That’s my main motivation. I see the power of machine learning and you get great results. In many cases, you can already do things as a human would. You see the power and potential, which fascinate me.

SS: What do you think are the most interesting dilemmas?

VN: I have been working a lot with the “Smittestopp” app. One of the dilemmas is the development of first of all ethical and sustainable GDPR-based technology. And this assures where again machine learning or artificial intelligence is facing how to develop ethical algorithms and at the same time preserving the privacy of the data. We have seen a lot of examples where chatbots have a racist attitude or decisions in banks, for instance, mortgage applications having racist attitudes or being gender-biased. So we see more and more examples of this in the algorithms. So how do you ensure that this doesn’t happen? That’s one of the big dilemmas. Another dilemma is the open signs. It’s essentially open sharing of the data, knowledge, and results. Making it as open as possible, but at the same time making it as open as possible, but you need restrictions. We also need to think about privacy in some sense, and sometimes data cannot be shared with everyone.

SS: I think to me the word privacy is beginning to be a bit tenuous or tiring. Of course, we need control over our data but most people don’t understand how much can be done with your data and they have this vague fear. It’s important to educate the population but also the regulators and have a dialog about what we want to do with this data. I think with the “Smittestopp” discussion, which is an app for tracking corona. It used some geographical information and related information. A lot of people got worried about what happened to this data. At the same time, a lot of these people don’t care about their data on Facebook, Instagram, Google, Alexa, etc. That data goes far deeper and broader than “Smittestopp”. There are very few discussions because we can’t live without Facebook and Instagram, but we can't live without “Smittestopp”. My thing is that I think we need to explain to people why we want this data and what we are going to do with it and the value it brings.

VN: I agree. I also think that a lot of apps know everything about you, but as you said there is no discussions about data people share without when thinking about it. When “Smittestopp” was gathering data to contribute to the common project of corona and it has been ensured that the data wasn’t given publicly.

SS: Or sold to anyone in other contexts.

VN: But that’s the education of the public and understanding what that data is already shared and available publicly so that we can better control this situation.

SS: I’d like to ask a bit more about “Smittestopp”. It was a very cross-functional team, and I think whatever you apply to your AI team will be very cross-functional. How do you make the most out of these types of teams?
VN: Essentially “Smittestopp” had an additional challenge in that everything was done from home. No one met for any meeting before July. Everything was done from home and it was a really steep learning turf for us because it has been a very interdisciplinary project, involving people from epidemiology to software and app-developers and risk-analysts. And also developing a project as a software-developers which is meant for mass usage very fast but also being sustainable and reliable. That was very challenging but very rewarding. I’m very grateful to all the people who contributed because everyone showed the best of themselves. Working crazy hours and supporting each other at the same time to motivate and not pay attention to some negative press. Rather deliver and work on what we can do and help people to combat this disease.

SS: I was working on providing stories about the BigMed project, which is the Norwegian research council also doing kind of AI applications on medical data sets, genetic data sets and the biggest problems they had was legal, not mathematical or medical. And I think there also is this other challenge about access to datasets where we haven’t defined well enough role-based approach to data access. The people who need to understand corona patterns need certain access to data. This will be completely essential so that we can collect data in a sharable way.

VN: I agree that role-based data access will be more and more common. But in many cases, before you can provide this, you need to ensure the security and integrity of the data. This is quite a labor-intense task and there will be more work on developing how this data sharing is more automated.

SS: So as a data scientist person as well, one thing I’m missing in the AI discussions. We talk about ethics, data centers, and network capacity. But all of that is just a static commodity unless we can connect the data that lives in those data centers. We still live in a world of fragmented silos. I think connecting those silos are necessary to create unique value with these data sets we have access to, to fulfill their potential.

VN: That still remains quite far away from reaching reality, but of course more people are talking about connecting datasets. But it usually takes more time than the whole project lasts.

SS: You are now building a company. What are your guiding principles? What advice would you give to people in similar situations? What do you think is important? You have a huge opportunity, but it is still a daunting task, as every entrepreneur will know.

VN: I would admit that for me it’s challenging because my whole career has been about research and I don’t have an entrepreneurship background. And it was a lot of learning from a different mindset. My understanding is that the team is the most important for the company and how I can assure that everyone is healthy and productive. Don’t be afraid of failure. Especially working in the tech sector, failing is accepted and essentially you have to learn from this and carry on. And finally, I would say that innovating at a fast pace, which is partially connected to the failure-aspect, being ahead up to five years is what helps companies and being ahead of competitors from my half a year perspective of being in the industrial sector.

SS: You have some really interesting potential role models there when I ask who inspires you. You mentioned Demis Hassabis, the CEO of DeepMind. I think that Mustafa who is one of his partners is also now on a number of boards, including The Economist. In Silicon Valley what we see is that scientists are becoming full pedigree entrepreneurs. It’s very difficult in my opinion; I’m on the same journey. In science we have this mindset that you have the time, make the most of the most important and then we’ll see how you build on that. It’s almost a personality change. Do you have any experiences related to that? Do you find it a different game?
VN: It is in some sense because I also continued doing science with multiple projects. I see what you mean with 20/80 and then being very excellent or being in depth. Essentially the switch between being an entrepreneur and a scientist is challenging because you work differently. When you’re working in a company and trying to get new clients you have to be very proactive, you have to be driven, motivated and energetic at the same time. You have to be much more focused and for instance, disappear from the rest of the world for weeks or days.

SS: Can you say a little bit more about why you mentioned Demis Hassabis as your role model?

VN: One of the aspects is that he is mentioned as a superhero in artificial intelligence and DeepMind is essentially a forefront development scene in this field. One of the very appealing aspects of DeepMind is that they’re much pro-open data and open science in general. So much of the publications and results of DeepMind are shared and the public can get access to their advances. Another aspect that excites me or motivates me is the way he organizes DeepMind. He essentially combines this research mindset and the research focus on blue-sky thinking and research with the drive and motivation of startups. Essentially what he says is that if you could have had a possibility to achieve everything you would want in academia, he would have done it. He tried to create a company that allows him to do this cutting-edge technology and research that isn’t possible in academia because of bureaucracy and different kinds of pace of development in DeepMind. And another aspect is that he is optimistic and positive about the future of AI and how it can address challenges that society faces like climate change.

SS: What do you see as the unique advantages of being both an entrepreneur and a scientist?
VN: Maybe bringing more insides into the problems you discuss with companies and seeing the potential in their data or workflows. We can provide help by using technologies.

SS: What opportunities do you see for Norway? It is a small country and we often forget how small. What do we do uniquely well?
VN: What I find uniquely well done in Norway is the adoption of technologies. During the covid pandemic, we have seen new technology like ordering apps in restaurants, which has been adopted fast. The level of adoption is extremely fast and more difficult in larger countries. This is an advantage for Norway.

SS: I think the way we regulate the AI-driven future is also very much looking towards the past and adjusting past laws to something that will be different. And that will be a whole new area opened up for possible innovation. Where do you learn? Where do you go to keep updated?

VN: There are different sources. I read on a daily basis. I really love Medium which is a website created by the developer of Twitter. Essentially it aims to serve similar purposes to Twitter, but with longer articles. It is an example of social journalists where more professional and less professional people can contribute with different insights and stories. This is a platform that contains a lot of different topics. It’s a really useful platform if you want to know about new advances described in Layman’s terms and in an easy and accessible way. For keeping updated with advances or the latest state of art are reading papers from conferences in machine learning. That is a must. And also referring to big tech company's blogs about their recent achievements.

SS: Towards the end, I want to ask you two things. One is your perspectives from the corona crisis in terms of digital work, and the other is any sustainability issues you are particularly keen on?

VN: The first one is of course under lockdown and working from home offices with digital meetings online, we learned what can be done and what works. As mentioned before, the whole “Smittestopp” project was developed from home. I think what corona taught us or gave us a better insight into, was to achieve a better balance between office, home office and maximizing productivity and efficiency. But at the same time allowing people to choose what is best for them. Another thing is our need for social interactions and physical meetings. Therefore we should be careful about the way we communicate and how we take care of the team and people. And asking how everyone feels and what their needs are. If you are talking about sustainable things I would probably again refer to Demis Hassabis because I love his speech on the use of AI and how he sees how it helps to address different challenges. In particular climate change and inequalities and he says he would have been quite pessimistic about the challenges without AI. Because there are two ways to go; the one is for instance to change how people behave, and it has to be quite drastic. To be more collaborative, to be less selfish, and to be more transparent and work towards a common goal on a global scale. At the same time, it doesn’t seem like this is going to happen. Another way is to increase or improve technology that allows addressing several challenges within climate change. This seems more likely.

SS: I personally deeply disagree here. I think he has a terrible view on humanity. That’s one of my main worries about letting people like that run some of the most powerful companies in the world where they say step aside all of you politicians, philosophers, and the normal people because we with the technology will fix everything. I think there is one role for humans, and that is to keep technology a tool, not letting it become a master as Demis wants. My main worry is that people like him become a little too powerful and because we are not able to move fast enough with sustainability, they become the composers of our future and there is very little role for human responsibility in that future.

VN: I tend to disagree with you because he doesn’t exclude one or another direction. He says that technology should complement, but be prioritized. It doesn’t seem like the current population wouldn’t be able to prioritize human behavior.

SS: I think that’s one of the journeys that Silicon Valley, and Cambridge, need to make because I think they’re incredibly technocratic and we are missing the MLK that believes in people and their ability to use the technology and take on responsibility. I think Norway is one of few places where this can be played out because we trust the systems and are used to “dugnader”. So we can be moved in the right directions fast. It will be an interesting ten years ahead and it will be played out fast. That’s why public and private companies should learn about AI if they want to keep up.

VN: I agree, and education is something that’s still lacking and needs to be at a bigger scale for different levels of knowledge.

SS: And that’s where we providers of lifelong education need people like you that can provide cases like “Smittestopp” and cervical cancer application. People will remember the food optimization and you must keep telling the stories. Do you have a life motto of some sort?
VN: Yes, several quotes that help me to keep going. I think the best is that “I never lose, I either win or learn”. That is appealing to me. Everything that happens is for the best and it is important to keep going forward. I also like “keep calm and carry on”. That has helped through the corona.

SS: I like your Maxwell quote as well.

VN: Yes, it is essentially saying that “the pessimist complains about the wind, the optimist expects it to change and the leader adjusts the sails”.

SS: I think the adjustments of the sails with AI and the new regulations are perhaps the most important thing we should do going forward. Thank you for helping to do the good work and thank you for also participating where we are trying to demystify some of the good work related to the innovation class in AI and industrial applications.

VN: Thank you Silvija for having me.

Read full transcript

Who are you, personally and professionally?
I am leading a new daughter company of Simula Research Laboratory, Simula Consulting, and also a researcher at Simula Research Laboratory. I am very fascinated by advances in technologies and, in particular, how machine learning or data-driven models can help to address societal challenges and business needs. 

What does your organization sell, and why do people buy from you?

Simula Consulting is a new daughter company of Simula Research Laboratory which provides high-quality R&D consulting services in five core competence areas of Simula Research Laboratory: software engineering, communication systems, machine learning, cybersecurity, and scientific computing. 

Are there any interesting dilemmas?

- How to develop ethically sustainable and GDPR-based technology 
- Open-source sharing of data, knowledge, and results 
- What are the limits of AI / ML 

Your 3 best growth tips for other similar companies?

  1. Innovate at a fast pace 
  2. Don’t be afraid of a failure, learn from it, and continue to carry on
  3. Your team is the most important asset, take care of them 

Your own most important job projects in the last year?

I would say the Smittestopp project, which started in March and still continues.

Who inspires you?
I am inspired by several tech companies and their CTOs like UNACAST, UNLOC, Kolonial. I also would like to mention one person who inspires me a lot: Demis Hassabis, CEO of DeepMind.  

What is relevant knowledge for the future?

Modern disciplines like AI, ML combined with more “traditional” disciplines like mathematics, computer science, statistics, to have a better understanding of new technologies and how deep tech is going to change the world. 

What do we do uniquely well in Norway?

Agile support for start-ups (risk-free) and entrepreneurs, at least compared to other EU countries. Good in implementing new technologies in a fast and efficient way, e.g., app-based restaurant ordering system. 

Key new perspectives from Covid?

Under the extreme conditions of lockdown with home office and digital meetings only, we learned what works and not. I hope in the future we can achieve a much better balance between home office/office/travels to maximize our efficiency and productivity.

Your 3 best management tips?

  1. Keep calm and carry on» from Simula’s CEO Aslak Tveito
  2.  Always take care of your team and people
  3.  Don't be afraid of being vulnerable 

Any important sustainability perspectives?
We need to focus on tech development to help to address big societal challenges in addition to political and behavioral actions. 

A favorite quote or life motto?

“I never lose. Either I win or learn.” – Nelson Mandela.

 

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RESEARCH

This is what you will learn:

Machine learning 
Research and academia  
The Smittestopp project 
Innovate at a fast pace

I personally learned a lot as we had to work in interdisciplinary teams from epidemiologists to software and app developers, lawyers, and risk analysts. Understanding how to combine all pieces in one sustainable and reliable mass production has been very challenging but also extremely rewarding.

- Valeriya Naumova

Recommended literature:

I like reading Medium about the latest advances in AI/ML in layman’s terms, leadership, etc. For the latest advances in AI, conferences such as RecSys, NeurIPS, COLT, ICML, or blogs by tech companies like Google.

This is Simula Research Laboratory

Simula Consulting is a new daughter company of Simula Research Laboratory which provides high-quality R&D consulting services in five core competence areas of Simula Research Laboratory: software engineering, communication systems, machine learning, cybersecurity and scientific computing. The Simula Consulting’s mission is to constantly redefine what’s possible through digitalization, bridging the gap between academic discoveries and real-world challenges. Their consultants are drawn from the diverse teams of highly-skilled scientists that comprise the heart of Simula Research Laboratory.