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Profil: AI och MaskininlärningFredrik Heintz, IDA, Linköpings [email protected]@FredrikHeintz
http://deeplearningskysthelimit.blogspot.se/2016/04/part-2-alphago-under-magnifying-glass.html
Image Classification
Speech Recognition
Artificial Intelligence
Interaction
• Human-AI collaboration
• Social and ethical aspects
• Multi-agent systems
Reasoning
• Inference
• Prediction
• Decision making
• Planning
Learning
• Deep learning
• Bayesian learning
• Reinforcement learning
Robotics / Cyber-PhysicalSystems
Decision Support Systems
AGI
“Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs.” John McCarthy, Stanford
Why now?
• Software is eating the world
• The digital and the analog are becoming one
• Computer power and GPUs
• Massive amounts of labeled data
• Improved algorithms due to increasing research inacademia and industry
1997
• Safety guarantees
• Decision making in complex situations
• Integration in transportation infrastructure
• Handle all operational environments
• Manipulation
• Human-robot collaboration
• Robustness and extended operation
• From single task to general purpose
IoT
IBM Watson
• Extracting and leveraging semantics
• From single questions to continuous dialogue
• Online and stream reasoning
• Proactive decision support
• Combine model-based and data-driven approaches
Types of Machine Learning
• Supervised learning
– Given input-output examples f(X)=Y, learn the function f().
• Unsupervised learning
– Given input examples, find patterns such as clusters
• Reinforcement learning
– Select and execute an action, get feedback, update policy (what action to do in which state).
https://www.techleer.com/articles/203-machine-learning-algorithm-backbone-of-emerging-technologies/
https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/stanford-algorithm-can-diagnose-pneumonia-better-than-radiologistshttps://arxiv.org/abs/1711.05225
NIH released data set with 112 120 chest X-ray images with 14 labeled diagnoses. 4 Stanford radiologists annotated 420 images for indications of pneumonia.After 1 month of training cheXNet outperformed all the radiologists.
Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy. Explaining and Harnessing Adversarial Examples. ICLR 2015https://arxiv.org/abs/1412.6572
Machine learning is still brittle…
Generative Adversarial Networks (GANs)
Kevin McGuinness. Deep Learning for Computer Vision: Generative models and adversarial training (UPC 2016).http://www.slideshare.net/xavigiro/deep-learning-for-computer-vision-generative-models-and-adversarial-training-upc-2016
Explainable AI
https://www.darpa.mil/program/explainable-artificial-intelligence
Generative Adversarial Networks (GANs)
• Sample efficient learning
• Online learning
• Learning with guarantees
• Learning explainable models
• Statistical-relational learning
• Transfer learning
“Weak human + machine + superior process was greater than a strong computer and, remarkably, greater than a strong human + machine with inferior process.”
Garry Kasparov
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Vision
A principled approach to building collaborative intelligent autonomous systems for complex missions.
Autonomous Systems at AIICS, Linköping University
Micro UAVsweight < 500 g, diameter < 50 cm
Yamaha RMAXweight 95 kg, length 3.6 m
PingWing
LinkMAV
LinkQuad weight ~1 kg, diameter ~70cm
Delegation-Based CollaborationDelegation
AdjustableAutonomy
Mixed-InitiativeInteraction
Delegate(A, B, task, constraints)
Delegate(GOP, UAV, task, constraints)Delegate(UAV, GOP, task, constraints)Important: Safety, security, trust, etc.
By varying the task and constraints parameters the degree of autonomy allowed can be controlled.
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Research ProgramThe best researchers in the field
Graduate SchoolAmbitious program, Industrial PhDs
Demonstrator ArenasDemonstrations with external parties
Recruitment Program Internationally competitive offers
Ten year program 3000+ MSEK~300 million Euro
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Extends WASP with 1000 MSEK for AI. Two tracksAI/ML: Machine Learning, Deep Learning,
eXplainable AI (XAI)AI/Math: Theoretical questions related
to AI in a wide senseAI4X Conferences in the spring:
Feb 12: IndustryFeb 27: Education & EntertainmentMar 13: HealthMar 27: Finance & ServicesApr 11: Society & Environment
WASP AI
http://wasp-sweden.se/ai/ai4x/
Profilregler
• Minst 36 hp inom profilen
• Varav 30 hp på avancerad nivå
• Obligatoriska kurser
– TDDC17 Artificiell intelligens
– TDDE01 Maskininlärning
– TDDE19 Avancerad projektkurs: AI och maskininlärning
TDDD41 – Data Mining – Clustering and Association AnalysisTDDE16 – Text Mining TDDE19 – Språktekno-logiTBMI19 – Medicinska informationssystem TSKS11 – Algoritmer och Tjänster i Stora Nätverk
TDDE05 – AI-robotikTSFS06 – Diagnos och övervakning TSRT07 – Industriell ReglerteknikTSRT62 – Modellbygge och simuleringTSBB09 – BildsensorerTSRT14 – Sensorfusion TSRT78 – Digital signalbehandling
TDDC17 – Artificiell intelligensTDDD08 – LogikprogrammeringTDDD20 – Konstruktion och analys av
algoritmerTDDD48 – Automatisk planeringTDDE01 – MaskininlärningTDDE07 – Bayesianska metoderTDDE13 – MultiagentsystemTDDE15 – Avancerad maskininlärning TDDE19 – Avancerad projektkurs TBMI26 – Neuronnät och lärande system TSBB06 – Multidimensionell signalanalysTSBB08 – Digital bildbehandlingTSBB17 – Visuell detektion och igenkänning
Datadrivet beslutsfattande Autonoma system
Profilkurser
Work in the Age of Automation?
