FCAI research: the need for new AI
FCAI research aims to tackle the key shortcomings of existing AI through the joint methodological goal of AI-assisted decision-making, design and modeling. Currently, about 70 professors with their groups actively advance the FCAI research and impact agenda.
In order to maximize the positive impact of AI, we work together with top experts in an increasing number of scientific disciplines, business domains, and societal initiatives. We leverage the resulting insights to inspire new AI methods.
FCAI research in a nutshell:
Ambitious and well-defined objectives to tackle key shortcomings in current AI.
Goal-driven Research Programs (R1–R7) to reach these overarching objectives by creating
Tools for AI-assisted decision-making, design and modeling demonstrated in
Concrete solutions through Highlight Programs (HA–HF).
Fundamental AI research and reach across a variety of scientific disciplines and application areas.
Our research is applied in the form of software. FCAI delivers open-source tools for everyone, so that they can easily make use of new ideas and results.
We work in Teams focused on various methods and Virtual Laboratories.
Jump to:
Research Programs →
Probabilistic
Simulators
Deep learning
Privacy and security
Interactive
Autonomous
Society
Highlight Programs →
Modeling tools
Healthcare
Service assistant
Atmospheric
Materials
Sustainability
Special Interest Groups →
Health
Language
Edge
Computer vision
Neuroscience
Children
Federated learning
Quantum computing
Inverse problems
Energy
Joint methodological goal:
AI-assisted decision-making, design and modeling
As a concrete step towards AI systems that are data-efficient, trustworthy and understandable we jointly focus on creating ‘Real AI’ tools for AI-assisted decision-making, design and modeling. We develop AI techniques needed for systems that can help their users make better decisions and design better solutions across a range of tasks from personalized medicine to materials design.
To work in the real world in collaboration with the user, these AI tools require a set of algorithms that can operate with limited data and in a trustworthy and understandable manner. A core insight in developing such AIs is that they need to have world models for understanding the world and interacting with it, and user models for understanding the user and interacting with them.
AI-assisted Virtual Laboratories
We believe AI-assistance will transform the way research and development work is carried out in a broad range of disciplines. This requires a coordinated effort in identifying the central tasks and challenges of the research process itself, and development of collaborative AI methods for assisting in them.
FCAI is driving this in the form of the Virtual Laboratory concept, developing the basis of AI-assisted support for research carried out in laboratories combining automated physical measurements and computational simulations. We are building the foundations and the AI methods needed for this, focusing in particular on techniques that can be applied across multiple fields of science. The first pilot Virtual Laboratories are being established in 2022 in drug design, sustainable mobility and atmospheric science.
Research Programs
FCAI runs seven Research Programs (R1–R7) with multiple research groups contributing to each. The programs do fundamental AI research and reach across a variety of scientific disciplines with groups from multiple fields working together.
The Research Programs are carefully designed to achieve our scientific goal: Real AI that is data-efficient (O1), trustworthy and ethical (O2), as well as understandable (O3). The methodological aim is to develop AI techniques needed for systems which can help their users make better decisions and design better solutions across a range of tasks.
Research Programs
Agile probabilistic AI (R1)
Research Program 1 Agile probabilistic AI contributes to objectives O1, O2 and O3 by developing an interactive and AI-assisted process for building new AI models with practical probabilistic programming. The models will work as explainable, verifiable, uncertainty-aware, reliable tools to build and check the behavior of AIs.
Coordinating professor: Professor Aki Vehtari, Computer Science (Aalto University)
Simulator-based inference (R2)
Research Program 2 Simulator-based inference contributes to objectives O1 and O3 with new methods needed for real AIs to have efficient and interpretable reasoning capabilities. This requires cross-breeding modern machine learning and simulator-based inference.
Coordinating professor: Professor Jukka Corander, Statistics (University of Helsinki, University of Oslo and Sanger Institute)
Next-generation data-efficient deep learning (R3)
Research Program 3 Next-generation data-efficient deep learning contributes to objectives O1 and O3 by developing methods that harness the power of deep learning. These methods include semi-supervised learning, few-shot learning for making use of auxiliary sources of training data, and learning models that can be reliably used in simulator-based inference. The goal is to achieve high-quality results with scarce training data and only limited human supervision.
Coordinating professor: Professor Arno Solin, Computer Science (Aalto University)
Privacy-preserving and secure AI (R4)
Research Program 4 Privacy-preserving and secure AI contributes to objectives O1 and O2 with security and privacy research. We develop realistic adversary models to build effective tools and techniques that practitioners can use to build dependable AI systems.
Coordinating professor: Professor Antti Honkela, Computer Science (University of Helsinki)
Interactive AI (R5)
Research Program 5 Interactive AI contributes to objectives O2 and O3 by developing methods for collaborative forms of AI: their ability to infer human beliefs and abilities from observations and predicting the consequences of their actions on humans. These are AIs with which people can naturally work and solve problems, and which demonstrate the ability to better understand our goals and abilities, take initiative more sensitively, align their objectives with us, and support us.
Coordinating professor: Professor Antti Oulasvirta, HCI, Cognitive Science (Aalto University)
Autonomous AI (R6)
Research Program 6 Autonomous AI contributes to objectives O1, O2 and O3 by addressing the fundamental challenges of long-term autonomous operation. In particular, the program examines how learning and planning can be performed to ensure safe operation over long time horizons.
Coordinating professor: Professor Ville Kyrki, Intelligent machines (Aalto University)
AI in society (R7)
Research Program 7 AI in society contributes to objectives O2 and O3 by focusing on social and ethical dimensions of AI. It deals both with the preconditions of trustworthy and socially acceptable AI and the consequences of uses of AI. The program brings together AI research and human sciences to better understand how AI works in organizations and society.
Coordinating professor: Professor Petri Ylikoski, Science and Technology Studies (University of Helsinki)
Highlight Programs
The Highlight Programs demonstrate the significant scientific impact that the fundamental AI research can produce in various other disciplines and showcase how this can directly translate into high socio-economic impact. Together with the Research Programs, our Highlights foster collaboration of scientists across a number of fields ranging from material sciences to health and environmental studies.
Highlight programs
Easy and privacy-preserving modeling tools (HA)
Highlight A Easy and privacy-preserving modeling tools has the main objective to measure and maximize the impact of FCAI research on the process of probabilistic AI development.
Coordinating professor: Professor Arto Klami (University of Helsinki)
Applications of AI in healthcare (HB)
Highlight B Applications of AI in healthcare creates AI tools to tackle real-world problems in healthcare together with expert collaborators from the respective fields.
Application 1: AI for genetics
Application 2: Computational vaccines
Application 3: Healthcare resource allocation
Coordinating professor: Professor Pekka Marttinen (Aalto University)
Intelligent service assistant for people in Finland (HC)
Highlight C was merged with the Research Program 7 AI in society in 2022.
Atmospheric AI (HD)
Highlight D Atmospheric AI is focused application of interactive AI in atmospheric and Earth system research, building a virtual atmospheric laboratory to combine ML/AI models with physical and chemical models.
Coordinating professor: Professor Kai Puolamäki (University of Helsinki)
AI-driven design of materials (HE)
Highlight E AI-driven design of materials develops AI technology for accelerated materials design and characterization.
Coordinating professors: Professor Milica Todorovic (University of Turku) and Research Professor Mikko Mäkelä (VTT)
AI for sustainability (HF)
AI is one of the main enablers of sustainability breakthroughs. Highlight F AI for sustainability aims to harness research results from the other Research and Highlight Programs for the benefit of the three pillars of sustainable development: ecological, social, and economic.
Coordinating professor: Professor Laura Ruotsalainen (University of Helsinki)
FCAI Special Interest Groups (SIGs)
Artificial intelligence is interdisciplinary by its nature, and we want to closely interact with researchers across different fields. FCAI Special Interest Groups (SIGs) gather people around a common theme of interest reaching beyond FCAI Research Programs and Highlights.
What are FCAI SIGs?
FCAI SIGs are formed by groups of people around a common theme of interest, e.g. a specific sub-field of AI, or an application area of AI. FCAI SIGs offer important contributions to the FCAI research and impact agenda and take advantage of FCAI Research Program results.
What is the benefit of joining FCAI SIGs?
FCAI SIGs gather and explore AI-related themes and ideas. They provide you easy access for collaboration, and a dynamic forum of networks, across a number of fields and applications. Working with like-minded people who have diverse expertise will generate new ideas around AI, and speed up their iteration to converge into focused efforts.
Our hope is that diverse SIG activities lead to increased collaboration not only within the SIGs, but also at the intersections of SIGs and the FCAI Research Programs, helping in identifying new opportunities for collaboration, leading to joint papers and joint research project proposals, and in getting more people involved with the FCAI Research Programs, or eventually even in the planning of new FCAI Research Highlights.
FCAI supports SIGs in the organization of meetings, seminars, workshops, visits of foreign experts, communications, and other activities leading towards a more integrated ecosystem. In addition, FCAI provides help in identifying links between the SIG communities, the ongoing FCAI Research Programs, and the overall FCAI ecosystem.
How to get involved?
If you are interested in proposing a new SIG, please see the details of how to proceed here.
Current FCAI SIGs
Coordination: Professor Aleksei Tiulpin (University of Oulu), Professor Simo Särkkä (Aalto University) and Mark van Gils (Tampere University)
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FCAI SIG: Language, speech and cognition
Coordination: Professor Jörg Tiedemann (University of Helsinki)
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Coordination: Professor Sasu Tarkoma (University of Helsinki), Professor Ella Peltonen (University of Oulu), Dr. Xiaoli Liu (University of Helsinki)
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FCAI SIG: AI for computer vision
Coordination: Professor Moncef Gabbouj (University of Tampere), Professor Guoying Zhao (University of Oulu), Professor Juho Kannala (Aalto University)
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Coordination: Professors Hanna Renvall (Aalto University & Helsinki University Hospital), Lauri Parkkonen (Aalto University)
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Coordination: Associate Prof. Marianne Kinnula, Prof. Netta Iivari, and Postdoc Sumita Sharma (University of Oulu); Prof. Nitin Sawhney (Aalto University), and Prof. Markku Turunen (Tampere University)
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FCAI SIG: Quantum computing for AI
Coordination: Postdoc Ahmad Farooq (Aalto University)
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FCAI and FAME SIG: AI for inverse problems and imaging
Coordination: Professor Jussi Tohka (University of Eastern Finland), Doctoral Researcher Siiri Rautio (University of Helsinki)
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Coordination: Professor Zhengmao Li (Aalto University)
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