Fredrik Heintz, Johan Håstad, Danica Kragic - WASP-sweden.org
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Fredrik Heintz, Johan Håstad, Danica Kragic
Tuesday, January 15th
13.00-14.30 Introduction to AI, ML and the research in WASP AI
Danica Kragic, Johan Håstad & Fredrik Heintz
14.30-15.00 Coffee
15.00-15.30 Introduction to WASP Graduate School, Fredrik Heintz
15.30-17.00 Parallel discussions PhD students and Supervisors
18.00- WASP Student activity Restaurant Bistrot, Diagonalen 8
The old students take out the new students to dinner.
18.00- Dinner (for those not at the WASP Student activity)
Wallenberg AI,
Autonomous Systems
and Software Program
3.5 billion SEK till 2026
$ 400 million
Recruitment program
Internationally competitive offers
Graduate School
Ambitious program, Industrial PhDs
Internationalization
Arenas
Wallenberg Autonomous Systems
and Software Program
• Start: 2015, September 6
• Largest individual research program in Sweden ever: 1815
MSEK for 10 years
• Knut and Alice Wallenberg foundation(1315 MSEK),
Universities (200 MSEK), Industry (300 MSEK)
Wallenberg AI, Autonomous Systems
and Software Program
2017, November 14:
• 1000 MSEK more for AI from KAW (plus in-kind from
universities and industry)
2018, March:
• 295 MSEK more from KAW until 2026 with 150 MSEK
for AI including collaboration with NTU Singapore
(plus in-kind from universities and industry)
• Total budget now 3.5 billion SEK (with 2.6 billion from
KAW)
AI initiative Two main focuses: - AI/ML,DL,X ML: Machine Learning, DL: Deep Learning X: X for other AI and for eXplainable AI (XAI) Lead by Danica Kragic - AI/Mathematics Theoretical basic questions of AI in the broadest sense Lead by Johan Håstad
AI initiative - components • 5 Wallenberg Professors in AI Total 100 MSEK. • 14 recruitment packages a 15 MSEK within AI/ML,DL,X. Total 210 MSEK. • 14 recruitment packages a 10 MSEK in AI/Mathematics. Total 140 MSEK. • 40 PhD students in AI/ML,DL,X. Total 160 MSEK. • 40 PhD students in AI/Mathematics. Total 140 MSEK. • 20 Industrial PhD students in WASP's current model. Total 48 MSEK. • Reinforcement of calculation infrastructure. Total 70 MSEK. • A career program 2021-2026. Total 70 MSEK. • Overall collaboration. Total 25 MSEK. • At the disposal of the Board 2023-2026. Total 60 MSEK. • Operations, guests, management, and coordination. Total 77 MSEK. • Post doc program with NTU Singapore Total 50 MSEK. • WASP-AI (funding from KAW) Total 1150 MSEK.
Participating universities
WASP WASP-AI
Five member universities
Within WASP-AI two partially:
ÖrU and UU in AI
UmU UmU
UU in Math
Additional individual
researchers UU
KTH ÖrU KTH
CTH LiU CTH LiU
LU LU
WASP Vision - Excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. (The word “industry” in its English meaning, for example including the financial industry.) Mission - Build a world leading platform for academic research that interacts with leading companies in Sweden to develop knowledge and competence for the future.
WASP Board
Mille Millnert Sara Mazur Alf Isaksson
Linköping University KAW ABB AB, Global Research
Chair WASP Vice Chair WASP Manager Control
Pontus de Laval Anders Palmqvist
Saab AB, CTO Nicolas Moch Chalmers, Vice President
SEB AB, Head of Information,
Strategy & Architecture
Annika Stensson-Trigell Viktor Öwall
KTH, Vice President Ulf Nilsson LTH , Dean
Linköping University, DeanWASP Main Instruments • A research program with the best researchers in the field. • Recruitment of both leading and young researchers. (Nine in place; more than 50 in total) • A graduate school in close interaction with Swedish industry. (At least 300 PhD with at least 100 industrial PhDs) • Arenas for research and demonstration in collaboration with industry and other parties. • Internationalization; Stanford, Berkeley, NTU
Structure of Research Program
Original WASP research ambitions stand
• A number of industrially motivated visions
• Today partly covered matrix belowWASP-AI first steps WASP announcements 2018: Broad and fundamental announcement for: • Five Wallenberg Chairs in AI • Five WASP (Assistant) Professorships in Mathematics for AI • 15 university PhDs in Mathematics for AI • 15 industrial PhDs (of course industrial perspective) • Guest professors Announcement more focused on ML: • Six WASP (Assistant) Professorships in AI/MLX • Projects for PhDs in AI/MLX (around 15 PhDs)
AI4X • Feb 12 Industry • Feb 27 Entertainment and Education • Mar 13 Health • Mar 27 Services and Finance • Apr 11 Society and Environment Report available
Infrastructure Computational infrastructure for AI 70 MSEK Directive and time plan (Oct 1) in place Group: Anders Ynnerman, LiU, Chair Matts Karlsson (NSC), Erwin Laure (PDC), Erik Elmroth (UmU), Michael Felsberg (LiU), Johan Eker (Ericsson, LU)
WASP Main Instruments • A research program with the best researchers in the field. • Recruitment of both leading and young researchers. (Eight in place; more than 50 in total) • A graduate school in close interaction with Swedish industry. (At least 300 PhD with at least 100 industrial PhDs) • Arenas for research and demonstration in collaboration with industry and other parties. • Internationalization; NTU Singapore, Stanford, Berkeley
Strategic Recruitments Plan to recruit more than 50 new professors on different levels 18 professors in Autonomous Systems and Software 14+14=28 assistant professors in WASP-AI 5 Wallenberg Chair in AI Eight recruitments in place Clear strengthening of Software in Sweden Will now strengthen AI and Machine Learning
Graduate School Joint effort to raise knowledge level in Sweden in interaction with Swedish industry. Very positive response for first batch; 62 PhD students started 2106 24 university PhD students 23 industrial PhD students 15 affiliated PhD students Second batch of PhD students; 52 started 2018 25 university PhD students 21 industrial PhD students 6 affiliated PhD students First batch of AI PhD students; at least 62 will start latest 2019 20 university AI/MLX PhD students 17 university AI/Math PhD students 14 industrial PhD students 11 affiliated PhD students Now 170+ PhD students in WASP
Graduate School
Joint effort to raise knowledge level in Sweden in close
interaction with Swedish industry.
The students should become knowledgeable researchers in the
area of WASP
The students should form a strong sense of forming a
community.
The students should get to know Swedish industry.
The students should form a strong and valuable international
academic-industrial network.WARA – Research Arenas
Open arenas with rich possibilities
Build on unique Swedish relation academia-industry
SAAB, Axis, Ericsson, …
WARA-PS:
Public Safety / Search and Rescue
Ongoing planning for more
demonstratorsInternationalization MoUs with Stanford, Berkeley, and NTU Singapore
IMAG International Management and Affiliations Group (IMAG) Main contact for general and research questions is Anders Ynnerman Main contact for questions related to PhD activities and student exchange is Fredrik Heintz (e.g. semester abroad) Anders Ynnerman Fredrik Heintz Karl-Henrik Johansson Bo Wahlberg
Internationalization
Concrete projects with NTU Singapore
Workshop in Singapore in March,
Workshop in Stockholm in October
Joint creative work
Research Area Project Title Lead PIs
AI Big Data Adversarial Machine Learning in Big Data Era Bo An Christos Dimitrakakis
Sensor Fusion Scalable Multi-Robot Sensor Fusion, Localization, Navigation, and Control Hu Guoqiang Fredrik Gustafsson
Visualisation Visualization for understanding and developing machine learning Jianmin Zheng Anders Ynnerman
Cloud Management beyond the edge Dusit Niyato Erik Elmroth
AI Big Data Co-evolutionary Reinforcement Learning for Multi-Agent Systems Chew Lock Yue Mikael Johansson
Computer Vision Deep reinforcement learning of driving using virtual paths Kai-Kuang Ma Michael FelsbergWallenberg AI,
Autonomous Systems
and Software Program
3.5 billion SEK till 2026
$ 400 million
Recruitment program
Internationally competitive offers
Graduate School
Ambitious program, Industrial PhDs
Internationalization
ArenasAI/MLX
PM-AI/MLX, GS-AI/MLX Danica Kragic, KTH Fredrik Heintz, LiU Thomas Schön, UU Amy Loutfi, ÖU Helena Lindgren, UmU Fredrik Kahl, CTH
PM-AI/MLX, GS-AI/MLX • Danica Kragic: Robotics, AI, CV • Fredrik Heintz: AI, AS • Thomas Schön: ML, signal processing, systems identification • Amy Loutfi: ML/DL, Robotics • Helena Lindgren: cognitive science, interaction design • Fredrik Kahl: CV, ML/DL
AI/Math
PM-AI/Math Johan Håstad ,KTH Sandra di Rocco, KTH Tobias Ekholm, UU Anders Rantzer, LU Holger Rootzén, CTH
PM-AI/Math, areas • Johan Håstad: complexity theory • Sandra di Rocco, algebraic geometry • Tobias Ekholm, geometry and topology • Anders Rantzer, system theory and optimization • Holger Rootzén, mathematical statistics
“Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs.” John McCarthy, Stanford “Artificial intelligence (AI) refers to systems that display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals.” EU Communication 25 April 2018
What is AI?
Goal
Intelligent agent
Observe
Agent
Environment
InfluenceRussell & Norvig: Artificial Intelligence: A
Modern Approach
Part I Artificial Intelligence Part IV Uncertain Knowledge and Reasoning
1 Introduction 13 Quantifying Uncertainty
2 Intelligent Agents
14 Probabilistic Reasoning
Part II Problem Solving
3 Solving Problems by Searching 15 Probabilistic Reasoning over Time
4 Beyond Classical Search 16 Making Simple Decisions
5 Adversarial Search 17 Making Complex Decisions
6 Constraint Satisfaction Problems Part V Learning
Part III Knowledge and Reasoning
18 Learning from Examples
7 Logical Agents
8 First-Order Logic 19 Knowledge in Learning
9 Inference in First-Order Logic 20 Learning Probabilistic Models
10 Classical Planning 21 Reinforcement Learning
11 Planning and Acting in the Real Part VII Communicating, Perceiving, and
World
Acting
12 Knowledge Representation
22 Natural Language Processing
23 Natural Language for Communication
24 Perception
25 RoboticsDigitalization, Big Data and AI
Digitalization Big Data AI
first wave second wave third wave
Digitalization AI
Well defined problems Hard to define problems
Predictable situations Unanticipated situations
Structured data Unstructured data
General solutions Adaptable solutions
Rationalizes Amplifies
Evolutionary Revolutionary
… …Hybrid systems
The bigger system / picture
ML
Data Model
Code
Hidden technical debt in Machine Learning Systems, Culley et. al. (NIPS 2015)WASP-AI focus areas
• Focus in academic part is on foundations of ML
• Representation learning and grounding
• Sequential decision-making and reinforcement
learning
• Learning from small data sets, GANs and
incremental learning
• Multi-task and transfer learningCalls and announcements so far
Industrial PhD Timetable • 2018-04-11 Information & Q/A Stockholm 2018-05-11 Application deadline 2018-06-30 Decision 2018-08-01 Earliest start 2019-01-01 Latest start • Status • 59 applications
Industrial PhD students
Industry University Project title
ABB KTH Autonomous learning of control architectures for real-time industrial
robot automation in a dynamic environment
Arm Lund University Representation learning and robust SLAM in mobile applications
AstraZeneca KTH Geomatric Deep Generative Models for de novo Molecular Design
AstraZeneca AB KTH Activity prediction from diverse data sources via machine learning
AstraZeneca AB KTH AI-supported endpoints in chronic kidney disease
Elekta Instrument AB Uppsala University Real-time image guided radiotherapy
Ericsson AB KTH Actualizing Reinforcement Learning Agents in Mobile Networks
Imint Image Intelligence AB KTH Personalized Video Enhancement
SAAB KTH Deep Learning For Streaming Sensor Data
SEB KTH Generative models and reinforcement learning in finance
Sectra AB Linköping University Orchestrating machine learning development in complex medical
imaging environments
Sony Mobile Lund University Incremental learning agents and human interaction
Tobii AB KTH Handling device and user variations by adaptive neural nets
Univrses AB KTH Industrial-grade multitask learning for autonomous driving
Volvo Car Corporation KTH Safe Autonomous Driving Under Dynamic Road Conditions
Astra Zeneca KTH lmproved Synthesis Planning Through calibrated Predictions
Scanica CV AB KTH Perception based trajectory suggestion with machine learningCALL FOR COLLABORATION PROJECTS
Open to the partner universities Chalmers University of Technology, Linköping
University, Lund University, KTH Royal Institute of Technology, Umeå University, as
well as Örebro University.
Four to seven projects are expected to be funded, dependent on size and quality.
The collaboration projects should be in the area of artificial intelligence with a specific
focus on machine learning, more specifically:
1. Representation learning and grounding: deep learning, probabilistic models,
statistical relational learning, auto-encoders
2. Sequential decision making and reinforcement learning
3. Learning from small data sets, GANs and incremental learning
4. Multi-task and transfer learning
First round: June 3rd, 2018, 23:59 CEST
Second round: September/October 30th, 2018, 23:59 CESTProjekt Universitet
Statistical and Adversarial Learning in Continuous System Control LU, KTH
Reinforcement Learning in Continuous Spaces with Interactively Acquired Knowledge-based LU, ÖU
Models
How to Inject Geometry into Deep Learning - Theoretical Foundation and New Chalmers, LiU
Computational Methods
Probabilistic models and deep learning - bridging the gap UU, Chalmers
Robot learning of symbol grounding in multiple contexts through dialog KTH, ÖU
Under-Supervised Representation Learning KTH,
Chalmers
Beyond supervised learning for semantic analysis of visual data KTH, LiU
Exploration and uncertainty in generative networks for supervised learning and KTH,
reinforcement learning Chalmers
Deep Probabilistic Neural Networks for Survival Analysis UU, KTH
Data-driven foundations for robust deformable object manipulation Chalmers,
KTHAI and Mathematics AI in general and ML in particular is conquering the world. We do not fully understand how it works. Can Mathematics help? First explain and then hopefully improve.
What kind of Mathematics? Analysis? Geometry? Complexity teory? Algebra? Combinatorics? Whatever might be useful.
Bottom up approach We let departments/researchers propose areas/projects that they need to argue are mathematics relevant to AI. Hopefully we are bold enough to accept also risky projects. The main goal is not to contribute to AI in the short run.
WASP-AI focus areas
• Focus in academic part is on foundations of ML
• Representation learning and grounding
• Sequential decision-making and reinforcement
learning
• Learning from small data sets, GANs and
incremental learning
• Multi-task and transfer learningWASP-AI-Math Understanding ML is important. We do want to keep a broader perspective and we are open to other sub-areas of AI as well.
Calls and announcements so far
AI/Math PhD students, 1 Advisor Student Univ Short title Persson Aronsson CTH Quantum deep learning Chacholski Jin KTH Topological data analysis Chacholski Tobari KTH Topological data analysis Tucker Andersson UU Optimally lean neural networks Jonasson Zetterqvist CTH Learning with noisy labels Turova Ekström LTH Multi-scale neural netwoks Larsson Wallin UmU Evolution of deep neural networks Hult Lindhe KTH Generative Models Goranko Ahlsén SU Multi-agent systems Mostad Johansson CTH Deep learning and satistical model choice
AI/Math PhD students, II Advisor Student Univ Title Linusson Restadh KTH Combinatorics of causality Sladoje Bengtsson UU Robust joint learning Olsson Persson LTH Optimization methods for deep networks Jonsson Osipov LiU Infinite domain constraint satisfaction Lang Wikman CTH Deepest learning using stochastic PDE
AI/Math Assistant professors • UmU: Compressive sensing and statistical learning with sparsity • UU: Networks and models underlying intelligence • KTH : Algebraic topology and mathematical statistics in AI • KTH: Probability and combinatorics in AI • CTH: Optimal transport, big data and ML • LU: Numerical optimizataion for large-scale ML All in their appointment processes
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