Three-year pro­ject on test­ing of ma­chine learn­ing sys­tems launched

New research aims to develop methods and tools for the creation of safe and reliable AI systems. An interest group for Finnish companies is also planned.

VTT Technical Research Center of Finland and the University of Helsinki are, together with six Finnish companies, starting a three-year research co-operation in the context of the ITEA IVVES  (Industrial-grade Verification and Validation of Evolving Systems). Business Finland supports the project with 1,3 M€ for the University of Helsinki and 1,4 M€ for VTT.

The aim of the project is to develop methods and tools for the creation of safe and reliable AI systems.

“We can now significantly extend our research efforts on testing, continuous development, and maintenance of machine learning systems together with our partners in Finland, The Netherlands, Sweden, and Canada. We are also planning to set up an interest group for Finnish companies interested in the project,” says professor Jukka K. Nurminen from the department of Computer Science at the University of Helsinki.

“Making sure that AI solutions are, and stay, unquestionably reliable in real-life applications, also long after they have been installed, is an important topic that requires efforts - IVVES gives us the means to develop tools to address this properly” adds research professor Mark van Gils from the Data-Driven Solutions research area at VTT.

The University of Helsinki's work in the project is jointly headed by professors Tommi Mikkonen and Jukka K. Nurminen. At VTT, the project is headed by Johan Plomp (the country co-ordinator for the Finnish consortium) and research professor Mark van Gils.

Empirical Software Engineering research group

Artificial Intelligence at VTT

Update on 11 March: We’ve updated this news item by adding information about VTT’s role in this research project and including Mark van Gils’s comment.

New doctoral students starting work on multidisciplinary applications of AI

Eleven new doctoral students at the University of Helsinki are specialising in multidisciplinary applications of artificial intelligence. They were selected from the joint call of the 32 doctoral programmes of the university, in which AI-themed positions were available to applicants from all fields of science and scholarship.

As part of deliberate efforts to nurture multidisciplinary research, FCAI participated in selecting the students and is offering mentoring support.

The new doctoral candidates and their research topics:

Anton Björklund, Faculty of Science
Interpretable machine learning

Mattia Cordioli, Faculty of Medicine
A nationwide artificial intelligence assessment of cardiometabolic risk

Evgeni Grazhdankin, Faculty of Pharmacy
Harnessing machine learning and artificial intelligence to tackle lead compound discovery challenges

Tuomo Lehtonen, Faculty of Science
Computational aspects of structured argumentation

Siiri Rautio, Faculty of Science
Interpretable deep learning for computed tomography

Luisa Fernanda Rodriguez Carrillo, Faculty of Biological and Environmental Sciences
Movement ecology meets community ecology: does movement behaviour leave imprints into species distributions?

Santeri Räisänen, Faculty of Social Sciences
Practices of algorithmic knowledge production

Janine Siewert, Faculty of Arts
Synchronic and diachronic Low Saxon dialectometry

Tuisku Tammi, Faculty of Arts
Learning under uncertainty – an experimental approach to compare human and AI learning

Alexander Thesleff, Faculty of Law
Tekoälyteknologioiden ja laajentuvan tekijänoikeusteollisuuden vaikutus tekijänoikeuteen (‘The impact of artificial intelligence technologies and the expanding intellectual property industry on copyright’)

Tianduanyi Wang, Faculty of Pharmacy
Deconvoluting complex disease mechanisms via machine learning methods for finding targeted therapeutic approaches

FCAI supports the Finnish government’s new expert group on AI and digitalization

Sirpa Paatero, the Finnish Minister of Local Government, has set up a new expert group on artificial intelligence and digitalization. Several members of FCAI were appointed as the expert group members.

Professor Petri Myllymäki was appointed as a permanent member of the group, while Professor Sasu Tarkoma, who at FCAI leads its collaboration with the 6GFlagship, and Dr. Jaana Leikas, the chairperson of the FCAI Ethical Board, were appointed as non-permanent members of the group.

The chairperson of the new expert group on AI and digitalization is Heikki Mannila, the President of the Academy of Finland. The first public seminar of the group takes place on April 8. Read more at the Finnish government’s website (in Finnish).

Predictive models can guide the development of better vaccines and vaccination programmes

Researchers from the University of Helsinki in Finland, Simon Fraser University in Canada and Imperial College London combined genomic data, models of bacterial evolution and machine learning to predict how vaccines could be optimised for specific age groups, geographic regions and communities of bacteria.

The study, published in Nature Microbiology, focused on Streptococcus pneumoniae, which can cause serious bacterial infections such as pneumonia, sepsis and meningitis – known collectively as invasive pneumococcal disease (IPD). It is estimated that IPD causes around 1.6 million deaths per year worldwide.

S. pneumoniae is difficult to target with vaccines, because there are approximately 90 pneumococcus serotypes around the world and vaccine effectiveness varies between countries depending on which serotypes are present. When serotypes are removed from circulation by a particular vaccine, other serotypes of S. pneumoniae rise to take their place.

The researchers optimised a computer model to approximate the effect of vaccines targeting different serotype combinations. Analysis of vaccine effectiveness was then carried out on vaccination programme data from around the world. The much faster approximation method meant it was feasible to intelligently scan through a large subset among the possible vaccine compositions.

The study also found that disease rates could be reduced by up to 50 per cent by following up infant vaccination with a second vaccine in adulthood. The results highlight the need for vaccination programmes to be tailored to specific communities of bacteria and to consider vaccination at different ages. With growing concern at the threat of antimicrobial resistance (AMR) to common medicines, effective vaccination programmes have an important role to play in reducing rates of disease and the need for antibiotic treatments.

“With the power of the latest DNA sequencing technology, we are heading towards a future where large-scale genomic surveillance of major bacterial pathogens is feasible. The approach we describe in this study will play an important role in accelerating future vaccine discovery and design to help reduce rates of disease”, says professor Jukka Corander from the University of Helsinki, the University of Oslo and Wellcome Sanger Institute.

Publication: Caroline Colijn, Jukka Corander and Nicholas J. Croucher. Designing ecologically optimized pneumococcal vaccines using population genomics. Nature Microbiology 3.2.2020. DOI 10.1038/s41564-019-0651-y

AIX Forum gives the floor to people who need AI the most

The BrAIN seminar, an AIX Forum event, organized at the University of Helsinki’s Think Corner in January hosted almost a full house.

FCAI has received plenty of positive feedback for the new train of events that provides a meeting place for researchers of different disciplines, companies and public organizations interested in AI.

Last autumn, FCAI launched AIX Forum, a train of events aiming to find novel opportunities for high-impact AI applications in Finland and provide a meeting place for AI researchers, researchers of other disciplines, companies, and public organizations.

After six successfully organized AIX Forum events, Professor Petri Myllymäki, the Vice-Director of FCAI, says that FCAI has received plenty of positive feedback about the forum. “People have thanked us especially for the fact that the floor is given to those who need AI the most and not just to AI experts.”

The AIX Forum events are suitable especially for organizations that cannot find the needed AI-based solutions in the current AI toolkits, and who would be ready to commit to a long-term collaboration with FCAI researchers.

The mission of FCAI researchers is to develop AI applications that answer real-life needs in different areas of life, and the AIX Forum wants to present new, practical solutions for those needs. In the abbreviation AIX, the first two letters come from the term Artificial Intelligence while the letter X represents the changing application areas of AI. X can refer to any problem domain where AI could provide a solution and it may be related to an industrial or societal sector or a scientific field.

“So far we have already addressed themes like traffic, public services, cancer research, economics, health and maritime,” Professor Myllymäki says.

Most AIX Forum events consist of three parts. Typically, they start with brief pitch talks in which experts from different fields present a problem that could be solved by using AI. The talks are followed by panel discussions in which AI researchers and developers, together with the speakers, seek solutions for the presented problems. After that, there is some time for networking and peer support. “You may find help from the audience, from people who have struggled with similar issues in another field or another sector of the society,” Myllymäki explains.

The events are gaining popularity also among AI researchers. Myllymäki says that an increasing number of researchers at FCAI have understood that the broad scientific, societal, and economical impact, which the Academy of Finland flagships are supposed to deliver, calls for in-depth interdisciplinary collaboration. The AIX Forum is a platform that helps to recognize possibilities for such a collaboration. “We try to find issues that the next-generation AI methods developed at FCAI can help to solve,” Myllymäki says.

According to him, the AIX Forum provides researchers with clear research challenges that have a well-defined goal but that cannot be fully solved with the current AI methods. The pitch talks may also discuss a broader set of problems or a source of data.

“We are still developing AIX Forum and at this point we are testing out different kinds of formats and getting more experience. But we can see that people are increasingly interested in this series, as we already have many new events coming up in the early 2020.”

Find all the upcoming AIX Forum events here

”Machine learning excited me before I knew it was a thing”

Wherever you end up in working life, some kind of social skills and a capability to work together with people are needed, says Arno Solin. Photo: Matti Ahlgren / Aalto University

Arno Solin is excited about machine learning, as it offers a way to both delve deep into theory and solve real-life problems.

Dr. Arno Solin, Assistant Professor, stores a plastic bag full of electronic gadgets in his office wardrobe, since a student of his needed equipment for building a robot. Part of them have come a long way. "My father bought this soldering iron. I was too little to be in school at the time, or just barely in elementary school," Solin says and laughs.

Already as a child, Solin was interested in technology, physics and mathematics. At home, he would build robots and spaceships. The son of academicians, he saw firsthand the life of researchers – and wanted to become one himself. "After high school, I almost chose Political History as my major. But then I figured it would be easier to have history as a hobby than statistics and mathematics."

In addition to working as an Assistant Professor at Aalto University, Solin is a researcher of FCAI. FCAI is collaborating with the Alan Turing Institute, based in London, and Solin is taking part in this collaboration.

Solin thinks that FCAI’s power lies in the fact that together researchers are stronger. Individual researchers typically need to market their research on their own, but a background institution – such as FCAI – helps to make AI research more concrete for decision-makers and non-professionals. It also helps to build the brand of Finnish research internationally.

“Development of methods isn’t just about doing something at the university, but instead, there’s a greater background idea and clear goals in the long run.”

Machine learning combines theory with solutions to tangible problems

Solin studies machine learning, which is an application of artificial intelligence: the machine learns based on experience without being further programmed by humans. Machine learning makes use of statistics, for example.

In June 2019, the Academy of Finland granted the research project led by Solin funding allocated to the new generation of researchers. Researchers make use of statistical machine learning and the development of computer vision in their project entity.

“Development of methods isn’t just about doing something at the university, but instead, there’s a greater background idea and clear goals in the long run.”

Solin finds machine learning fascinating, as you get to combine theory with solutions to tangible problems. He concentrates on probability modeling: How do you model uncertainties? How does machine learning deduce results from new data? How can you help machines reach sensible deductions in the here and now?

"I think I was interested in machine learning already before I knew it was a thing."

In the Academy-funded project, researchers concentrate on sensing, comprehending and describing the environment via machine vision methods. These functions are a challenge in the development of any autonomous or augmented reality system, especially when the surrounding conditions are uncertain.

The project has the potential to develop methods that could solve many a practical problem. This potential fascinates him. New research results could help develop, for example, the functionalities of smartphones. Computation, especially, can be used to make them work better, to use the current data more efficiently. Smartphone cameras could become better for shooting at night, or provide better depth of field without bigger, better and more expensive sensors.

"Existing sensors, existing smartphones can improve and have more to offer simply by being able to arrive at conclusions more effectively from information detected by the device," Solin sums it up.

New knowledge can be applied to many other things. For example depth estimation can help create video games, or devices for the visually impaired to better grasp their surroundings. The research is mostly pure research in nature, but provides reliable and efficient methods for the needs of other disciplines. Through collaboration, they have been adapted in medicine and the evaluation of urban air quality.

The researcher must know how to communicate

Solin, Assistant Professor since 2018, is the co-author of a textbook in stochastic differential equations together with Simo Särkkä, Professor of Electrical Engineering, and he has taught several courses at Aalto University as well as Introduction to AI at the Open University.

Solin laughs when he claims his motives for sharing knowledge are partly self-interested. "Explaining to others, I learn myself. When you have to explain things from different angles, in different ways and afresh, you get a different take on familiar things."

There's no point in being an ace researcher, if you are not able to communicate your findings to others

Solin considers teaching and conveying your own particular expertise to others as an essential part of a researcher's work. It supports research and raises new generations to study and apply what they have learned.

Already as a child, at the Waldorf School of his native Turku, he learned to present things visually and with clarity, and to hold presentations. He got used to going to some trouble to make things clear to his audience.

Photo: Matti Ahlgren / Aalto University

"That is something I think should be valued more. There's no point in being an ace researcher, if you are not able to communicate your findings to others."

The Waldorf School has strongly shaped the kind of adult and researcher Solin has grown up to be.

In his opinion, the pedagogy supports the pupil's personal growth. Studies advance in the pupil's terms. Group sizes are small, which enables tuition that is more personal. The school encourages self-expression and social interaction.

"Wherever you end up in working life, some kind of social skills and a capability to work together with people are needed."

Solin hears people are amazed at how many international partners he has. He puts it down to his proclivity to work with other people. "Maybe it comes through, and then others like to work with me, too."

Authored by Anu Haapala / Aalto University
English translation by Susanna Bell

Machine learning in chemistry - algorithms help finding minimum energy paths and saddle points more effectively

Reproduced from J. Chem. Phys. 147, 152720 (2017), with the permission of AIP Publishing.

Machine learning brings new possibilities to many areas of research - also chemical research, which is shown in a recent dissertation written at the Finnish Center for Artificial Intelligence.

Olli-Pekka Koistinen, doctoral candidate at Aalto University, developed machine learning algorithms based on Gaussian process regression to enhance searches of minimum energy paths and saddle points, and tested how well the algorithms work.

In theoretical chemistry, finding minimum energy paths and saddle points is one of the problems that consume most time and computational resources. The bottleneck of the computation is the accurate evaluation of energy and forces for each atomic configuration, which typically needs to be performed at hundreds of points in the configuration space.

Algorithms utilizing machine learning can reduce the number of observation points and expensive energy evaluations to a fraction of what is required by conventional methods and thus speed up the computation.

Minimum energy paths lie on a potential energy surface that describes the energy of a particular system - a molecule, for example - in terms of particular parameters. Usually these parameters show the locations of the atoms. The local minimum points of the energy surface correspond to the stable states of the system. The minimum energy paths connect these points and describe possible reaction mechanisms.

“As an orienteer, I see this energy surface as a map. The stable atom configurations are shown as depressions on the map, and the minimum energy path is a route between two such depressions. It stays as low as possible all along the way. The highest point of the path is at a saddle point where you can get from one depression into another one staying as low as possible,” Koistinen explains.

Traditionally, researchers have searched for minimum energy paths and saddle points using iterative methods that proceed on an energy surface with small steps. With the help of machine learning and statistical models, previous observations can be utilized to model the energy surface, and the goal can be reached with significantly fewer iterations.

Therefore, machine learning offers a more effective and lighter and thus also cheaper and more ecological option. It can also open new possibilities for studying problems that have not been feasible with traditional methods. “This is another example of a research topic in which machine learning methods can be helpful,” Koistinen says.

Neural network for elderly care could save millions

Illustration by Matti Ahlgren / Aalto University

A deep neural network model helps predict healthcare visits by elderly people, with the potential to save millions.

If healthcare providers could accurately predict how their services would be used, they could save large sums of money by not having to allocate funds unnecessarily. Deep learning artificial intelligence models can be good at predicting the future given previous behaviour, and researchers based in Finland have developed one that can predict when and why elderly people will use healthcare services.

Researchers at the Finnish Centre for Artificial Intelligence (FCAI), Aalto University, the University of Helsinki, and the Finnish Institute for Health and Welfare (THL) developed a so-called risk adjustment model to predict how often elderly people seek treatment in a healthcare centre or hospital. The results suggest that the new model is more accurate than traditional regression models commonly used for this task, and can reliably predict how the situation changes over the years.

Risk-adjustment models make use of data from previous years, and are used to allocate healthcare funds in a fair and effective way. These models are already used in countries like Germany, the Netherlands, and the US. However, this is the first proof-of-concept that deep neural networks have the potential to significantly improve the accuracy of such models.

‘Without a risk adjustment model, healthcare providers whose patients are ill more often than average people would be treated unfairly,’ Pekka Marttinen, Assistant Professor at Aalto University and FCAI says. Elderly people are a good example of such a patient group. The goal of the model is to take these differences between patient groups into account when making funding decisions.

According to Yogesh Kumar, the main author of the research article and a doctoral candidate at Aalto University and FCAI, the results show that deep learning may help design more accurate and reliable risk adjustment models. ‘Having an accurate model has the potential to save several millions of dollars,’ Kumar points out.

The researchers trained the model by using data from the Register of Primary Health Care Visits of THL. The data consists of out-patient visit information for every Finnish citizen aged 65 or above. The data has been pseudonymized, which means that individual persons can not be identified. This was the first time researchers used this database for training a deep machine learning model.

The results show that training a deep model does not necessarily require an enormous dataset in order to produce reliable results. Instead, the new model worked better than simpler, count-based models even when it made use of only one tenth of all available data. In other words, it provides accurate predictions even with a relatively small dataset, which is a remarkable finding, as acquiring large amounts of medical data is always difficult.

‘Our goal is not to put the model developed in this research into practice as such but to integrate features of deep learning models to existing models, combining the best sides of both. In the future, the goal is to make use of these models to support decision-making and allocate funds in a more reasonable way,’ explains Marttinen.

The implications of this research are not limited to predicting how often elderly people visit a healthcare centre or hospital. Instead, according to Kumar, the researchers’ work can easily be extended in many ways, for example, by focusing only on patient groups diagnosed with diseases that require highly expensive treatments or healthcare centers in specific locations across the country.

The research results were published in the scientific publication series of Proceedings of Machine Learning Research.

Further information
Yogesh Kumar
Doctoral Candidate
Aalto University, Finnish Centre for Artificial Intelligence
yogesh.kumar@aalto.fi

Pekka Marttinen
Assistant Professor
Aalto-yliopisto, Finnish Centre for Artificial Intelligence
Phone +358 50 5124362
pekka.marttinen@aalto.fi

Helsinki and FCAI will host a new ELLIS unit for top AI research

Finnish artificial intelligence research received a significant acknowledgement. Finnish Center for Artificial Intelligence FCAI will host one of the new European units of top AI research, as the European Laboratory for Learning and Intelligent Systems will establish one of its first units in Finland.

The first ELLIS units were announced at the ELLIS assembly on 10 December as a part of the NeurIPS 2019 conference. From left to right: Yoshua Bengio, Bernhard Schölkopf, Nuria Olivier, Matthias Bethge, Max Welling, Director of FCAI Samuel Kaski, Sepp Hochreiter, and Yann LeCun.

ELLIS is a pan-European effort initiated in 2018 to secure the excellence of European machine learning research. It aims to ensure that Europe continues to be competitive with big economies, such as the US and China, and benefit from the newest findings of AI research. 

With the units, ELLIS wants to strengthen European AI research and collaboration between European researchers.

The unit will be founded in Aalto University and the University of Helsinki and hosted by the Finnish Center for Artificial Intelligence FCAI. Samuel Kaski, the Director of FCAI and Academy Professor at Aalto University, sees this as an excellent opportunity to boost basic AI research, which is the basis of all AI-related applications and impact. “Finland is very strong in AI research, and this new status is one indication of that.”

Professor Kaski believes that the ELLIS unit helps Finland to maintain its position as an attractive destination for top-level international researchers. It also gives current AI researchers in Finland more reasons to stay.

ELLIS aims to offer European researchers outstanding opportunities to carry out their research in Europe, and to nurture the next generation of young researchers in the important field of AI. All ELLIS units will arrange visits and events as well as provide funding for doctoral students in the ELLIS PhD programme.

The other cities selected to host a unit are Alicante, Amsterdam, Copenhagen, Darmstadt, Delft, Freiburg, Linz, Lausanne, Leuven, Oxford, Prague, Saarbrücken, Tel Aviv, Tübingen, Vienna, and Zürich.

Read more
Press release at the ELLIS Society homepage

Further information
Samuel Kaski
Professor, Aalto University
Director, FCAI
Phone +358 50 3058 694
samuel.kaski at aalto.fi

Click photo to see more photos from the ELLIS assembly.

Jaakko Lehtinen receives an ERC Consolidator Grant of nearly 2 million euros

The European Research Council Consolidator Grant goes to Professor Jaakko Lehtinen for a project to build a computer that 'sees' the real world much better than the current methods do.

Photo: Matti Ahlgren / Aalto University

Jaakko Lehtinen, Associate Professor at Aalto University’s department of Computer Science, will receive a 1.9 million euro Consolidator Grant from the European Research Council (ERC).

The ERC funding was awarded to Lehtinen’s project Learning Pixel-Perfect 3D Vision and Generative Modeling (PIPE) for five years. The significant grant allows him and his research group to focus on studying how to bring models based on machine vision, machine learning, and physics together. Lehtinen hopes that the new funding attracts top-level postdoctoral researchers to his group. ‘We are talking about things that haven’t been yet demonstrated in almost any way. In that sense, our goals are very ambitious,’ he says.

To give an example, a machine can spot a tiger by its stripes – not its cat-like shape. GAN models, which Lehtinen and his colleagues helped develop, can generate very realistic human faces, but we cannot ask those models to tell what a face shape is based on those photos.

In the ERC-granted research project, Lehtinen studies the most fundamental questions of machine vision research – how can we teach a machine to perceive the world as animals do? – and his work can be used to help improve AI applications like robots.

Current robots only function well in environments in which conditions never change. That means that the robots’ skills are not generalizable: If we teach a machine to clean an apartment, it will not be able to do the task if we ever rearranged the furniture, let alone clean someone else’s apartment.

Creating AI that can operate with humans in a real, complex world is one of the main goals of the Finnish Centre for Artificial Intelligence (FCAI) initiated by Aalto University, University of Helsinki, and VTT Technical Research Centre of Finland. Lehtinen is actively contributing to FCAI’s operations.

Findings answers to the research questions on which Lehtinen and his group focus helps to take game and film productions steps further. Currently, designing for example 3D environments of games is very laborious, slow, and expensive. ‘Should we succeed in all this, we will come up with something that has never been seen before.’

The ERC Consolidator Grants are awarded to outstanding researchers of any nationality and age, with seven to twelve years of experience after obtaining a doctoral degree, and a scientific track record showing great promise.

Lehtinen graduated as a Doctor of Science (in Technology) from the Helsinki University of Technology in 2007 after which he worked as a postdoctoral researcher at the Massachusetts Institute of Technology (MIT) for three years. Aalto University appointed him as an Associate Professor in 2012, and in addition, he works as a Principal Research Scientist at NVIDIA.

Further information

Jaakko Lehtinen
Associate Professor, Aalto University
jaakko.lehtinen at aalto.fi

Designing AI that understands humans’ goals better

Photo: Matti Ahlgren / Aalto University

To make a better smart assistant,  we need an AI that understands its user and does not constantly need detailed instructions

When researchers design machine learning systems, their goal is typically to automate certain functions. Instead of being fully autonomous, however, most of these systems work together with humans. In order to be truly helpful, they need to understand what goals people have.

Researchers at the Finnish Center for Artificial Intelligence (FCAI) have now taken important steps towards designing AI that understands people.

At first, the researchers taught the AI to build a model of its user - human or machine. Then, they taught it to adapt this model by following the user’s actions. In practice, the researchers developed machine learning methods which combine statistics with computation, and then tested the methods in practice and in simulations. They tested the algorithms in simple situations in order to make sure they understand what exactly happens in those situations and report about the events accurately.

In the first experiment, they designed an AI teacher for the learning AI.

‘This was difficult especially because the learning AI could decide what it wanted to learn,’ explains Samuel Kaski, the director of FCAI and professor at Aalto University. The researchers noticed that the AI learner achieved better learning results when the teacher understood what information the learner had already learned and adapted its teaching material to suit this  particular learner.

In the second experiment, human users were asked to find a particular target word by using an AI-based word-search engine. The engine presents the user one word at a time, and the user then  tells it whether the presented word is useful in finding the target word. If the user is looking for the word ‘football,’ for instance, they are likely to say that the first presented sport-related word is useful, if all the previous words have been related to food.

The results of this experiment showed that the AI could help the users in finding the target words faster if it understood that, by responding to the presented words in a certain way, the user wants to direct the AI towards the right words. In other words, the AI took into account the fact that the user is trying to teach it.

According to Professor Kaski, this topic is important, as the interaction between user and AI becomes much easier when the AI understands its user’s goals. ‘Then the human user does not need to explain in detail anymore what they expect from the AI helper.’

One of the main goals of FCAI is to develop AI that understands humans and is understandable. ‘So far, we can build AI systems that understand the users’ goals only in very simple situations, which means that designing truly helpful AI assistants calls for a lot of additional work,’ Kaski says.

The research article will be published at NeurIPS, the world’s largest and most prestigious machine learning conference that takes place in Vancouver, Canada on December 8 through December 14, 2019.

Link to the research article: https://aaltopml.github.io/machine-teaching-of-active-sequential-learners/

Further information

Samuel Kaski
Professor, Aalto University
Director, FCAI
Phone +358 50 3058 694
samuel.kaski@aalto.fi

Tomi Peltola
Postdoctoral Researcher, Aalto University, FCAI
Research Scientist, Curious AI
tomi@cai.fi

Finnish Center for Artificial Intelligence (FCAI) is a nationwide competence center for Artificial Intelligence in Finland, initiated by Aalto University, University of Helsinki, and VTT Technical Research Center of Finland. FCAI’s mission is to create a new type of AI that can operate with humans in the complex world and renew the Finnish industry. FCAI is one of the research flagships of the Academy of Finland.

Read also: Ten research papers from FCAI accepted to the prestigious NeurIPS conference

How ‘AI ready’ is Europe? How AI changes cancer research and treatment? These and many other topics were discussed at AI Day 2019

FCAI’s third annual AI Day organized on November 26 attracted hundreds of participants to discuss and hear about the newest trends in AI research.

Similar to the previous years, AI Day 2019 took place at Aalto University Campus in Otaniemi, Espoo. The event included two keynote sessions with eleven talks in total, over 90 poster and demo presentations, and the opening event of ‘Connecting the Dots’ AI exhibition by FCAI and Aalto Digi Platform.

FCAI’s Vice Director and HIIT Director Petri Myllymäki opened AI Day 2019 taking the time to present FCAI activities and research programmes. FCAI currently runs seven research programmes that do fundamental AI research on carefully selected high-impact areas.

Myllymäki said: “In addition to world-class research on new type of AI methods that will remove the biggest bottlenecks in wider application of AI, we need collaboration between AI researchers, other scientists, companies and public organisations in order to maximize impact for the Finnish industry and society at large - this is what the FCAI flagship is all about."

Petri Myllymäki opened AI Day 2019.

David Pool, Curious AI

The first keynote speaker David Pool, UK Managing Director at Curious AI, emphasized the increasingly important role of AI and machine learning for companies. He said that these days all big companies speak about artificial intelligence and machine learning, and he gave various examples of AI projects that have successfully moved from invention to implementation.

Pool talked about AI readiness across Europe and the region’s progress and potential, pointing out that Europe has six million professional developers – more than the US – but realises only about 12% of its full digital potential. According to Pool, Finland’s AI readiness is within the top 25% in the whole world, along with countries such as the US, Ireland, Sweden, and the United Kingdom.

A person in the audience asked Pool how can small countries like Finland compete with big economies, such as the US and China, and in his answer, Pool emphasized the role of data curation, effective data management, and Europe’s ability to focus on one or two areas of research.

Sampsa Hautaniemi

Jörg Tiedemann

Professor Sampsa Hautaniemi’s (University of Helsinki) spoke about AI in cancer research and treatment. Hautaniemi pointed out that, as not all patients respond to the same standard treatments, we need more personalized options, and in order to develop more personalized treatments, we need cancer research. AI methods are already part of cancer research and soon part of cancer treatment, too, he said.

Professor Jörg Tiedemann (University of Helsinki) spoke about language as the key factor of both human and artificial intelligence. Humans would not have reached their current level of intelligence without language that allows them to convey information to others in the way we can, and this, according to Tiedemann, is a motivation to study language in the context of AI.

Self-driving cars, health data usage, and beating poverty

The other keynote speakers were Tuomas Rintamäki, Matti Järvisalo, Antti Piirainen, Perttu Hämäläinen, Ella Peltonen, Juha Vesala, and Katja Hagman and Heli Hidén.

Rintamäki, Deep Learning Engineer at NVIDIA, explained in-depth what type of an AI infrastructure NVIDIA developes for self-driving cars. Professor Järvisalo (University of Helsinki) focused on the impact of automated reasoning, emphasizing the fact that AI and machine learning are not synonyms, and the importance of combining both symbolic fact-based and probabilistic views.

Piirainen is the Head of Communications at Findata and he introduced his organization’s work, a Social and Health Data Permit Authority that starts operating at the beginning of 2020. Findata will be a one-stop shop for secondary use of social and health data.

Professor Hämäläinen (Aalto University) presented simulation-based design in, for instance, games and user interfaces, while Dr Peltonen (University of Oulu, 6G Flagship) explained how we go towards edge-native AI. You can read more about the research conducted by Peltonen and her colleagues at FCAI and 6G Flagship here.

Vesala, postdoctoral researcher at the University of Helsinki, brought up legal perspectives of AI speaking about safeguarding and promoting innovation, creativity, and competition. Hagman and Hidén from the City of Espoo challenged the audience to think about ways how technology, ecosystem, knowhow, and AI could help to beat increasing poverty in families with children.

The opening event of ‘Connecting the Dots’ AI exhibition, which included networking, music, food and drinks, provided a festive way to close this year’s AI Day. The exhibition is open until January 15, 2020.

Author: Anu Haapala / Aalto University
Photos: Matti Ahlgren / Aalto University

Click the photo below to see more photos from AI Day 2019!

Every­one has their secrets – ma­chine learn­ing needs to re­spect pri­vacy

Photo: Susan Heikkinen / University of Helsinki

How can we teach artificial intelligence to make unbiased decisions? How can we protect citizens’ privacy when processing extensive amounts of data? Questions such as these need answers before the application of artificial intelligence and machine learning can be extended further.

In spring 2018, inboxes filled to the point of frustration with messages from businesses and organisations announcing their measures related to the entry into force of the EU’s General Data Protection Regulation, or GDPR by its common name.

The purpose of the regulation was to improve the privacy of citizens whose personal details are stored in various databases, a matter that is closely related to the research conducted by Associate Professor Antti Honkela at the University of Helsinki. At FCAI, Honkela heads a research programme focused on privacy-preserving and secure artificial intelligence.

He is specialised in machine learning that preserves privacy.

“Machine learning and artificial intelligence work best on massive repositories of data. These data often contain personal information that needs to be protected. The utilisation of machine learning must not jeopardise anyone’s privacy,” Honkela states.

Ap­plic­a­tion po­ten­tial in di­verse fields

Among other fields, machine learning could be used in medical research where extensive registers that contain medical records are employed as research data. Honkela himself has contributed to developing privacy-preserving machine learning techniques in targeting treatments to serious diseases.

“The aim has been to find a form of treatment best suited to individual patients. The same cure does not necessarily always work on a different cancer even though it might appear similar. The genome, for instance, can have an impact on the efficacy of various drugs. We have been developing techniques with which to work out answers from large datasets,” Honkela explains.

The utilisation of machine learning must not jeopardise anyone’s privacy

To have sufficient data at their disposal, researchers must be able to convince people that their research does not put the participants’ privacy at risk. It must be impossible to link sensitive information with individuals.

This is a problem Honkela is solving by developing methods in machine learning and statistics.

There is demand for machine learning that considers privacy also outside medical research. Potential for applications can be found in almost all areas of life, such as applications needed for research in various fields of science, the development of predictive text for mobile phones or banking systems.

We all have secrets

These days, the protection of privacy is a common topic. Honkela believes this is exactly as it should be.

“This is about a fundamental right. The Universal Declaration of Human Rights itself specifies that each human being has an inviolable right to privacy,” he notes.

Honkela says that society’s overall ability to function is based on people having secrets that stay safe. “Someone who says they have nothing to hide hasn’t thought it through,” he adds.

For instance, they could consider whether a company that provides medical insurance should have access to the genome data of its clients. Or whether a business looking to recruit new employees should be able to read personal messages written by applicants.

Those living in Western democracies may find it hard to grasp the potential consequences of a totalitarian state gaining access to the private data of their citizens.

Ma­chines must not dis­crim­in­ate

In addition to solving problems related to privacy, the broader application of machine learning in various sectors of life requires that consideration is given to how to make artificial intelligence unbiased.

“If machine learning is used in decision-making, we have to be certain that it doesn’t discriminate against anyone subject to those decisions,” Honkela points out.

Examples of discriminative decisions made by artificial intelligence have already been seen. Amazon, the online shopping giant, started using artificial intelligence to support staff recruitment. Eventually, the system was found to discriminate against female applicants.

“Perhaps previous data were used to train the machine. If more men have been hired earlier, the system may have interpreted this as something being wrong with women,” Honkela speculates.

Equality would also be a key feature in credit decisions made by banks or in granting various social welfare subsidies.

“For us researchers, there is still a lot to do in terms of the non-discrimination principle. For the time being, we haven’t reached a consensus even on the theoretical level on how to integrate it with machine learning,” says Honkela.

Authored by Anu Vallinkoski / University of Helsinki

How ecological is AI? How to compose with a neural network? Come to AI exhibition to find out!

A new exhibition by Aalto Digi Platform and FCAI combines science and arts and sheds light on the history, state of art, and future of AI.

Aalto Digi Platform and FCAI are hosting a science and art exhibition that takes its visitors to a journey in the world of Artificial Intelligence. The Connecting the Dots exhibition, taking place at Aalto University’s campus in Espoo, Finland, aims to increase our understanding about the history of AI, the current state of art, and what the future holds.

“AI is already part of our everyday life, but what we can do with it and how it works is still quite a mystery to most people. With this exhibition, we hope to give answers to questions they may have,” says Saara Halmetoja, the exhibition coordinator from Aalto Digi Platform.

The exhibition is suitable for people of all ages. Visitors can step into a sound environment in which they can control the surrounding voices with their movements; compete against AI in a music game; and compose together with a neural network, among other activities.

The exhibition dives into the core of AI research. Visitors will see how a machine learns to classify and predict things, as well as how it forms sounds, pictures, text, and molecules. The exhibition presents robotics and shows how AI adapts to real-world situations.

Connecting the Dots discusses the history and ecological aspects of AI. While AI can help the clothing industry in getting rid of textile waste, artificial neural networks – mimicking human brain’s neural networks – consume enormous amounts of energy.

The exhibition brings together the diverse fields of Aalto University from natural sciences, technology, and architecture to design and arts. The scientists, artists, and students behind the exhibition come from diverse backgrounds; some of them are “pure” AI researchers, while others use AI tools in their scientific or artistic work.

According to Halmetoja, “if only certain types of people take part in discussions about AI, we will end up designing discriminatory technology. Therefore, we need to make sure that everyone in the society participates in these discussions”

Entrance to the Connecting the Dots exhibition is free and the event is open during Dipoli’s normal opening hours from November 27 until January 15. For larger groups, such as school groups, the minimum age recommendation is 12 years old. If you want to visit the exhibition with a group of people, please contact Saara Halmetoja, the exhibition coordinator, in advance.

Connecting the Dots exhibition - Dipoli, Otakaari 24, 01250 Espoo, Finland - November 27, 2019 - January 15, 2020

Further information
Saara Halmetoja
Exhibition Coordinator
Aalto Digi Platform
Phone +358 50 5720730
saara.halmetoja@aalto.fi

Finnish Flagships join forces for next generation networks

Virtual reality applications benefit from edge-native artificial intelligence. Photo: University of Oulu

Experts of two Academy of Finland Flagships - the Finnish Center for Artificial Intelligence (FCAI) and 6G Flagship – are joining forces to harness the synergy between edge computing and Artificial Intelligence (AI), which are revolutionizing communication networks and becoming key components of next generation networks.

6G Flagship aims at developing 6G technologies that will bring to life the data-driven and hyper-connected future society while the mission of FCAI is to create real AI for real people in the real world – new type of AI that is able to operate with humans in the complex world.

Professor Sasu Tarkoma from the University of Helsinki, one of the organizations behind FCAI, has high expectations for the joint research approach.

“Edge computing provides a distributed platform, in which smart localized software meets advanced machine learning and AI, and privacy enhanced technologies,” Tarkoma says. “All this results in new applications and services, such as AR/VR applications, that react in real-time and can achieve a high level of privacy.”

The future internet, 5G and 6G networks, will be in operation in the 2020s and 2030s. In these networks, it will be crucial to optimize the local computational solutions to guarantee real-time connectivity and support the massive increase of data.

Dr. Ella Peltonen. Photo: University of Oulu

Computational solutions for future internet will improve radically with edge computing as devices in smart homes and vehicles, and even personal devices such as smartphones, participate in computation together with the network infrastructure. The result will be improved internet experience for users due to reduced delay, i.e. latency, among other factors. AI, on the other hand, empowers algorithms that are more effective. They enable novel applications in, for example, health care, smart cities, logistics, and transportation.

“There are a lot of globally important research questions and novel innovation and business opportunities, too,” says Dr. Ella Peltonen from 6G Flagship and the University of Oulu. “In the beginning, we focus especially on safer driving and smart traffic systems assisting the drivers, smart campus and endorsing learning in smart spaces, and in the future, smart hospitals and healthcare applications.”

Ella Peltonen is one of the speakers at FCAI’s AI Day on November 26, 2019. Check out the full program and sign up on November 18 the latest to make sure you get your tickets.

Further information:

Professor Sasu Tarkoma
University of Helsinki, FCAI
sasu.tarkoma@helsinki.fi
Phone +358 40 5062163

Dr. Ella Peltonen
University of Oulu, 6G Flagship
ella.peltonen@oulu.fi
Phone +358 50 68565

Com­puter vis­ion iden­ti­fies ripe fruit and coun­ter­feit drugs

Researchers are developing an application based on AI algorithms that works in regular smartphones and brings extremely accurate hyperspectral imaging within anyone’s reach.

When would this avocado be suitably ripe for making guacamole? Is the drug I bought on my travels to far-off places the real thing or a fake? How big an apple crop are we getting this year?

Soon, answers to questions like these will be easily obtained, as computer vision technology developed by researchers will be made available to consumers at a reasonable price.

Mikko Toivonen and Chang Rajani, doctoral students in computer science, together with Assistant Professor Arto Klami from the University of Helsinki and FCAI, have designed computer vision algorithms that can convert photos taken with a phone into extremely accurate hyperspectral images.

To take hyperspectral images using their solution only requires a camera phone and a peripheral device attached to the phone. Photos are converted by computer vision algorithms into hyperspectral images in a cloud service.

Hy­per­spec­tral im­ages re­veal more

Hyperspectral images are different from regular photographs because they reveal things unseen to the naked eye in the object photographed. The technique is not based on transillumination; rather, hyperspectral images interpret the wavelengths of light more accurately than regular photos.

“Normal photos use three colour channels, such as red, blue and green. In hyperspectral imaging, the light wavelength resolution is finer, comprising a hundred colour channels,” Klami explains.

“A simple three-colour camera is unable to distinguish the spectrum of, for example, chlorophyll. In a hyperspectral image taken outdoors, it’s easier to identify the bits with chlorophyll, that is, the areas with vegetation,” Toivonen says.

Many uses, ex­pens­ive equip­ment

Hyperspectral cameras date back years, and there are many uses for such imaging. The technology is used, among other fields, in geographical remote sensing and estimating yield sizes in agriculture. Hyperspectral images can also be used to identify counterfeit art and pharmaceuticals.

“Fake drugs are a problem, especially in developing nations. By using a mobile spectral imaging device, pharmacies and consumers could take a photo of a drug and check whether the pill corresponds with a reference spectrum supplied by the drug manufacturer,” says Klami.

However, the devices currently available are specialist equipment, with prices starting from several thousand euros. The less expensive technology developed by the University of Helsinki researchers could bring the solution to regular consumers.

The high cost of previous devices is partly because they produce hyperspectral images independently from start to finish. In the researchers’ version, images are converted from regular photographs taken with a smartphone equipped with a peripheral device, and the heavy lifting, or developing the hyperspectral images, is carried out by computer vision algorithms in a cloud service.

“The phone peripheral is cheap and compatible with basically all smartphones. Consumers don't need to buy a separate device,” Klami says.

Above is an RGB photo of avocados created from a hyperspectral image. Below are the spectra of the avocados' surfaces. The colour-coded squares indicate the areas where the spectra have been shot. The avocado on the right is clearly greener than the others, which can be seen as a spike in the blue spectrum curve at 550 nanometres. The spike indicates that the avocado in question is likely to be less ripe than the others.

Aim­ing for the con­sumer mar­ket in 2021

The seed for the idea on consumer market spectral imaging was planted a couple of years ago when Toivonen and Rajani were developing computer vision algorithms for hyperspectral imaging as part of their doctoral studies.

“I noticed that we could also make the algorithms create the image, instead of leaving this cumbersome task to the device taking the pictures,” Toivonen describes.

His long-standing interest in amateur photography helped him to make this observation. Prototypes for the smartphone peripheral have also been created with Toivonen’s personal 3D printer.

The invention is patent pending and next year begins the search for investors. The team’s goal is to make Toivonen’s home printer obsolete by 2021, replacing it with a spin-out company running the development of the peripheral device and mobile application.

Arto Klami, Mikko Toivonen and Chang Rajani. Photo: Susan Heikkinen

Find out more about hyperspectral imaging at the University of Helsinki / Helsinki Innovation Services (HIS) stand at Slush in Helsinki on 21-22 November 2019.

More information:

Arto Klami
Assistant Professor, FCAI & University of Helsinki
arto.klami@helsinki.fi
+358505823654

Mikko Toivonen
Doctoral student, University of Helsinki
mikko.e.toivonen@helsinki.fi
+358443303131

Chang Rajani
Doctoral student, University of Helsinki
chang.rajani@helsinki.fi
+358503115791

Unlock the Power of Artificial Intelligence

Artificial intelligence is shaping our reality at an unprecedented pace. To succeed in this brave new world, business and technology developers must understand how to best harness its potential. The Diploma in Artificial Intelligence program helps meet this need.

Artificial intelligence is everywhere. Most of us interact with AI on a daily basis, whether it's voice assistants on our smartphones, customer service chatbots on a website, Amazon recommendations, or search results on Google. In business, AI offers a virtually limitless number of opportunities – and disrupts more or less every process along the way. 

To succeed on the new playing field, organizations and individuals need new technological skills and a clear grasp of what artificial intelligence is and how to leverage its power. The Diploma in Artificial Intelligence program meets this need by giving you an in-depth understanding of the topic and helps you apply contemporary AI technologies.

"The program will give you the tools to increase your organization's AI maturity by leaps and bounds," says Teemu Roos, one of the program's instructors, Associate Professor at the Department of Computer Science, University of Helsinki, and Leader of AI Education at Finnish Center for Artificial Intelligence FCAI.

The Diploma in AI is a joint effort between Aalto PRO, University of Helsinki Centre for Continuing Education HY+, and FCAI.

"Our collaboration with FCAI has been excellent. We've joined forces to build AI expertise in the business community. Bringing together AI expertise from both the academia and industry, FCAI is a natural partner for us," says Jonni Junkkari, Solutions Director, Aalto PRO.

"We take a rather detailed and technical approach to some topics, but our main objective is to give the participants an overview of the AI development life cycle so that they know what's involved and what skills are needed. They may never again do any coding themselves, but it's easier to communicate with the actual developers when you understand the data acquisition challenges and technological limitations and have an idea of how to turn the technology into business," says Roos.

Tapio Kuusisto, Diploma in AI Alumni and Director, Architecture, Data & Analytics at Outotec, a Finnish minerals and metals processing technology and service company, agrees.

"I enrolled in the program to stay relevant professionally. AI is everywhere, and when your job involves enterprise or ICT architecture, you simply have to understand it better than the average Joe, if only to be able to understand what's what when people try to sell you their 'revolutionary' AI solutions and services," Kuusisto says.

Become an AI pioneer

The program consists of six two-day study modules with intensive in-class sessions and learning by doing. Topics include fundamentals of data and AI and cutting-edge AI technologies and applications ranging from analytics to modern deep learning models. There are also case presentations by leading organizations.

Roos promises that after completing the program, participants will feel empowered and on top of all things AI.  

"The program gives people a confidence boost, making them ready to learn even more and become AI pioneers in their organizations," Roos says.

The mandatory modules are complemented by elective modules, giving the participants the chance to select between a technical study track requiring programming skills or a business study track focusing on business applications. Individual exercises and project work enable the participants to apply their newly-acquired skills to their daily work right away and figure out what is really relevant for their organization. 

In his project work, Kuusisto focused on text analytics, which turned out to be a good choice.

"As an example, Outotec is often approached by startups offering text analysis applications. Now I know what to ask them, for example, about language capabilities. For us, Finnish and English are not enough. We need, for example, German, Portuguese, Swedish, and Spanish, too. The challenges related to text analysis, such as what is easy, what is difficult and so forth became quite clear," Kuusisto mentions. 

As AI is a hot topic and has been for a while, there are numerous books and online courses available on the subject. Nevertheless, both Roos and Kuusisto emphasize that participating in the Diploma in AI program is a whole different ballgame. Not only do you get personal sparring, but the six-month time span gives you time to reflect on what you're learning.

"Participating in a program like this gives you the impetus and forces you to set aside time for learning. It's definitely a plus," says Kuusisto.

Going the extra mile

The program is targeted at programmers and developers, product managers, business development managers and directors, deployment managers, software architects, and IT managers and directors.

"It's not a magic bullet, but it gives you a profound understanding of what's possible and what's not," Kuusisto says.

He also applauds the teachers for their high standard and varied approaches.

"We are very proud of this program, and it's a big investment for us. It's not often that you see such high-caliber academic researchers teaching a program like this," says Roos.

For Kuusisto, the program exceeded all expectations. It took some doing on his part, too.

"I really went the extra mile to reach the next level. I have never coded for a living but I believe that you should have an in-depth understanding of the things you're in charge for. That's why I decided to learn Python to see how AI applications are done. Soon I noticed that I can build machine learning solutions by myself."

When physician and AI work together, the patient benefits

The machine learning method by Iiris Sundin and her colleagues taken into account uncertainties of the world. This is important in decision-making. Photo Matti Ahlgren / Aalto University

Doctoral student Iiris Sundin learned in her studies that a machine learning model could make use of a physician's silent knowledge which usually is never written down. This kind of model predicts best how a given patient will react to specific treatment.

Artificial Intelligence opens up new avenues into health care, for example, but its potential is as of now not fully put to use. There are many reasons, but the most important one became clear to Iiris Sundin as she was starting her doctoral studies on machine learning: machine and Man must learn to work together.

"When I acquainted myself with the research of my advisor, Professor Samuel Kaski, on user modeling where the machine tries to understand the human, I realized the huge potential. Cooperation can mean other things than the human operating Excel or passively staring at projections on the screen," says Sundin, a doctoral student at the Department of Computer Science at Aalto University.

Samuel Kaski is the director of FCAI. Sundin's research combines Medicine, technology and many other interests of hers. She has also always wanted to work at something that benefits other people. On the other hand, she was interested in mathematics and programming.

The role of AI and machine learning in health care is researched a lot. Sundin's point of view is unique in having the machine make use of the doctor's knowledge to define the best possible care for the patient.

There are uncertainties in the world that must factor in when decisions are taken

Physicians have vast quantities of knowledge that is never written down and that is impossible to feed directly into a learning algorithm. Sundin and her colleagues have found out just how the machine could make use of such knowledge in e.g. figuring out the efficacy of a given type of medication.

Sundin and her colleagues have at their disposal, for example, data on gene specimens collected from cancerous cells, courtesy of FIMM, Institute for Molecular Medicine Finland. Researchers took a look at the mutations in the specimens and tried to predict which cancer medicine would work best for each patient.

We don't just discuss the average person, we acknowledge there are differences, and take it into account when reaching decisions.

It is crucial to remember that there are always uncertainties in the world, and thus, also a physician's knowledge of the effect of different mutations can be uncertain. The machine learning model devised by the research group takes this into account. The results show including expert knowledge in machine learning models and emphasizing data improve prognoses on how a given patient will react to a particular treatment.

So the model developed by researchers depicts realistically how sure people are of the different properties of real matters. "I would that such thinking were more common: we don't just discuss the average person, we acknowledge there are differences, and take it into account when reaching decisions. That way, things can be modeled in a more useful manner."

The researcher gets to tell something new about the world

Even though Sundin was fond of mathematics already as a child, and it comes naturally to her to see the world via mathematical thinking, art held a big role in her life, especially when she was young.

As a schoolgirl, she played the piano, sang in a choir, read books and attended art school. Now, she is into yoga, air acrobatics and camping. A researcher can be interested in many things, and creativity, for example, helps at work, too. "When you e.g. make posters for a conference, it is very useful if you can do some of the visual elements yourself."

The antics of engineering students were something Sundin grew up with. Both her parents graduated from the Helsinki University of Technology, and the family always celebrated First of May with parents’ college pals. All the adults sported the traditional tassel caps of engineering students. So engineering studies came naturally. "The degree of an engineer is really well-rounded and offers a good start. You can choose to be a researcher, industrial work or pretty much anything."

I realized researching was such great fun that I rather do more of the same.

Sundin completed her Master’s in Automation and System Technology. Although she never fancied herself a researcher, her interest in it was kindled when she was working on her dissertation. Sundin was modeling the properties of a drop of fluid on different surfaces.

Research offered a way to bring received mathematical ways of thinking and skills into the real world, and to tell something about that world. "I realized it was such great fun that I rather do more of the same."

”You succeed at work if you are open”

Sundin says she sometimes imagined researchers worked alone in their chambers. That is no longer the case, anyway. "You succeed better at work is you are open, socially adept and like to travel to conferences and network with the people there."

Research work is social. "You succeed better at work if you are open, socially adept and like to travel to conferences and network with the people there,” says Iiris Sundin. Photo Matti Ahlgren / Aalto University

It is crucial for a doctoral student to possess the wish and motivation to understand things. The point of further studies is to delve into one fairly narrow area of learning very profoundly, something which can be demanding. Therefore, it is important to have tenacity and the ability to stick to it. "It is needed, if you want to make it to the end."

Even though Sundin has hitherto worked in basic research, she feels the methods developed need to be taken to a practical level, once the foundation laid by basic research is robust enough.

In the future, she hopes to apply what she has learned and continue work for the benefit of people and also the environment. "I hope to work at some Finnish institute doing research that help Finland make smarter decisions when it comes to the environment."

Authored by Anu Haapala
English translation by Susanna Bell

Expertise from FCAI selected to the Finnish government’s Research and Innovation Council

Photo Anni Hanén / Aalto University

Samuel Kaski, Director of Finnish Centre for Artificial Intelligence (FCAI) and Professor at Aalto University, and Antti Vasara, CEO of VTT Finland, have been appointed to the Finnish Government’s Research and Innovation Council. The new council was elected on October 10.

FCAI is pleased that Finland sees the importance of artificial intelligence and the Finnish government displays trust in knowledge and research in general.

The Research and Innovation Council is an advisory body chaired by Prime Minister Antti Rinne that addresses issues relating to the development of research and innovation policy that supports wellbeing, growth, and competitiveness.

The vice chairs are of the council are Hanna Kosonen, Minister of Science and Culture, and Katri Kulmuni, Minister of Economic Affairs. The other three governmental members are Li Andersson, Minister of Education; Anna-Maja Henriksson, Minister of Justice; and Maria Ohisalo, Minister of the Interior.

The other members of the new council are Heidi Fagerholm (Head of Early Research and Business Development at Merck KGaA), Peppi Karppinen (Dean at the University of Oulu), Ilkka Kivimäki (Partner at Maki.vc), Petra Lundström (Director at Fortum), and Vesa Taatila (Rector and CEO at the Turku University of Applied Sciences).

The Finnish "Ele­ments of AI" on­line course trains em­ploy­ees of the European Union

Photo Matti Ahlgren / Aalto University

An extremely successful online course that is open to all on the basics of artificial intelligence, developed by the technology company Reaktor and FCAI, has been selected to be part of the voluntary continuing education for officials working for the European Union.

The kick-off for the course was held at an event organised on 18 September.

Artificial intelligence has the potential to provide solutions to a range of societal challenges, which is why its utilisation is an important focus area for the EU. Staff training is part of promoting the bloc’s AI strategy.

In Finland, a number of government ministries, the Tax Administration and more than 250 other organisations and companies have offered the course to their staff. 

Read more about the course

View a video on the course

Follow the phenomenon on social media: #elementsofai

Further information:

Ville Sinisalo, Reaktor
Phone +358 40 762 2019
ville.sinisalo@reaktor.com

Tanja Remes, University of Helsinki
Phone +358 50 415 0286
tanja.remes@helsinki.fi