Testimonials
Are AI and machine learning doing what they intend to do? In other words: do they solve the desired problems?
- Programme
- Software Science
Machine Learning and AI
Machine Learning and AI have a lot of success players and tangible examples. Just think about the self-driving cars or self-flying drones. According to Nils Jansen, many people believe that AI can solve all problems. However, there are still huge problems that remain. ‘People are still better than machines in solving a lot of problems. The question is: are AI and machine learning doing what they intend to do? In other words: do they solve the desired problems? Our research focusses on those questions.
We can, for example, predict the weather, based on certain data. What we also know, is that there are things that can change the outcome. We intend to become more robust against all possible uncertainties that occur in the real world.’
Reinforcement Learning
Reinforcement Learning is in fact a very particular machine learning technique. What it does mainly, is exploring the environment. Nils Jansen clarifies: ‘The problem is that the agent, for example a self-flying drone or self-driving car, often tries out every possible action and then observes what the possible outcome is. The most famous example is Alpha Go. A couple of years ago, the AI computer that played the game Go beat the best human player in the world. It can thus be very powerful, however: it is also extremely safety critical. It can harm itself and its environment, because it tries all possible actions.’ Thus, contrary to what most people think, the new technologies are not always better and smarter, but also create new safety challenges.
Making it safe again
The European Research Council is part of the EU and offers big research funding programs. Nils applied for a starting grant beginning of this year, so he could do more research into the safety problems of Reinforcement Learning. And he succeeded: ‘I was granted € 1.5 Mio to invest in making Reinforcement Learning and AI safer. We use mathematical models to precisely capture the uncertainty that happens in the real world. Our group tries to measure what will happen in the worst case, but then more reliable. For instance: we assume the wind will have a certain speed and direction, this is based on historical data. What we add to this, is that we precompute the behavior which is safe, based on the incomplete data we have. Amazon will use drones to deliver packages in a couple of years. At this moment a drone has data which it can use to prevent crashing into a building, like data about the wind. However: those are not reliable enough. Therefore we precompute the worst thing that could happen. After that, we use this information to make a decision with an acceptable amount of risk.’ The funding helps Nils Jansen and his team working on the reliability of AI and machine learning. And eventually, that makes the world a safer place for all of us.
This article was written and published by TechGelderland.nl. Photo credits: Linda Verweij
The consumption of our ICT sector increases by 30-40% each year. I want to do something about that.
- Nationality
- Dutch
- Programme
- Software Science
Can you introduce yourself?
I am Bernard van Gastel, and I studied Informatica, the predecessor of the current Computing Science. I teach two sustainability courses, two technical courses on operating systems and the New Devices Lab course. In the latter course, you design a device that you will actually build yourself.
Why did you choose to study/work in this field? What makes this field so interesting?
I love my field because you can clearly see its societal impact. There are current problems, and together with societal stakeholders, we look for solutions. For example, I have advised on how Rijkswaterstaat should handle its data, or how Logius (the government's ICT environment) can sustainably procure their services.
What are you currently doing your own research on?
Sustainable ICT is my field. I look at the energy consumption of software, how we can measure it, predict it, and reduce it. This is because the consumption (and therefore our emissions) of our ICT sector increases by 30-40% each year. I want to do something about that.
What advice do you have for students making their study choice?
Experience the programme: talk to students and try to join for a day.
What is the best part of working with students?
The creative ideas that students have. These keep my perspective fresh and help me continue to question whether things could be done differently.
Choose primarily based on what you find interesting and not on what you think you need for a particular job.
- Previous education
- Computing Science (Radboud Universiteit)
- Programme
- Software Science
- Study end date
Where do you work now and what does your job entail?
I work as a software developer at AMIS Conclusion in Nieuwegein. Here, I work in a team on an application that helps prevent insurance fraud.
Why did you choose to work in this field?
I knew I wanted to do something with software, but not yet exactly in what way or in what sector. AMIS is a software company that works with several other companies. This allows me to get a better idea of what is possible.
What did you learn during your studies that you now use in your work?
Too many to mention! I found the Master's a lot more challenging than the Bachelor's. It taught me much better how to work well. I now know how to motivate myself and how to make sure I finish something by a certain time without having to go on nights. I have also become better at figuring things out on my own and taking initiative. I don't really use the specialist knowledge of software, although my broad background knowledge of computers helps me to quickly understand the specific frameworks used at work.
How did you experience this programme at Radboud University? In your opinion, what made this programme special?
The lecturers are committed, approachable, and expert in their field. This allows you to gain very specialised knowledge.
What advice do you have for students choosing a Master's programme?
Choose primarily based on what you find interesting and not on what you think you need for a particular job. Almost none of my colleagues have studied computer science, but have come in from another field or have not studied at all.
Every job uses certain techniques and practices and requires a certain amount of domain knowledge, all of which are not in your studies. So when you start working, you spend at least 6 to 18 months mainly learning new things. That doesn't get less by doing a Master's. When I was looking for a job, I came across only a few vacancies where a Master's is a requirement.
Lastly, I would recommend thinking as early as possible about where you would like to work and then try to drop by there. Almost all companies like to tell about themselves and will respond positively if you ask to come and meet them. It is perfectly normal not to apply immediately, but to get to know them a little first. This way, you can get a better idea of whether you do indeed want to work at the company, and if you do end up applying, it helps if you already know a few names and faces.
The next big programming language isn't going to develop itself! You need people for that, and Software Science gives the right background.
- Previous education
- BSc Computing Science (Radboud University)
- Programme
- Software Science
What do you like about the programme/specialisation and why? How has the programme/specialisation challenged you (in relation to your previous education)?
The Software Science Master's is really about everything regarding software development, ranging from mathematical theory to more practical programming. I like this variety, even though the mathematical courses are a lot more challenging (for me at least). Luckily there are lots of courses to choose from.
What do you think about the atmosphere in class?
Most courses have only a small group of people coming to the lectures. Combined with having lectures in a smaller room, this gives an almost cosy vibe where it's easy to feel part of the group. Interactivity is easier at such a small scale, and all questions, remarks and conversations (with the teacher or with fellow students, or both) are very much welcome.
What do you find most challenging in your Master’s (specialisation)? Have you encountered any obstacles?
I'm not that good at the theoretical courses, and they make up a large part of the specialisation electives. A couple of those courses have required a lot of work for me to understand the material, and I wasn't always interested in the contents. But for most of them I'm happy I did them in hindsight.
Are you currently doing an internship? Or what is your thesis about?
I'm doing my internship at TOP Software, a small company on the university campus. I'm working on a project to generate LLVM code from Clean bytecode, in order to generate both WebAssembly and native executables from that. My internship is about integrating a garbage collector for WebAssembly specifically. That means I'm simultaneously working with Clean, Clean's ABC bytecode, LLVM IR, WebAssembly and C. That's quite a lot of things to juggle, but it's right up my alley.
Why do you think it is important that there are people with this degree? What are your plans once you have received your Master's degree?
The next big programming language isn't going to develop itself! You need people for that, and Software Science gives the right background. After my degree I hope to find work related to programming language development somewhere.