artificial intelligence by aleksandra pizurica

Ar#ficial Intelligence Lecture in the scope of the Honors Program

Quetelet Colleges March 14, 2017

Aleksandra Pizurica

A. Pizurica, Quetelet Colleges 2017 : ArBficial Intelligence

² AI around us: recent progress

² Measuring intelligence: Beyond Turing test

² Can we trust robots? AI for the people

² Deep learning: Principles and state-‐of-‐the-‐art

² Some of UGent research: ApplicaBons in art invesBgaBon

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Overview

A. Pizurica, Quetelet Colleges 2017 : ArBficial Intelligence 3

AI entering all spheres of our life


High demand for AI professionals

IEEE The InsBtute, vol. 40, no. 2, June 2016


Main parts of modern AI

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Problem solving Game playing (SEARCHING)

Knowledge representaBon

Planning (LOGIC)

Reasoning under uncertainty (BAYESIAN NETWORKS)

RaBonal decisions & AcBng (PROBABILITY + UTILITY)

Learning (NEURAL & BELIEF

DEEP NETS)

natural language processing – computer vision – robotics



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Planning (LOGIC)




DEEP NETS)

game tree

route planning tree search



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Planning (LOGIC)




DEEP NETS)

planning graphs

acBon schema

knowledge diagrams



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Planning (LOGIC)




DEEP NETS)

belief propagaBon



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Planning (LOGIC)




DEEP NETS) decision networks



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Planning (LOGIC)




DEEP NETS)



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Planning (LOGIC)




DEEP NETS)


The foundaBons of AI

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Autonomous smart vehicles •  Driverless cars (e.g. Google car) •  “highway pilots” for hands-‐free driving •  PredicBons: 75% autonomous cars by 2040

State of the art in AI

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A. Davies: AI Is All Around Us, IEEE The Ins1tute, June 2016.

Examples: •  Released in 2016: BMW 750i xDrive

can park itself with no one behind the wheel

•  2015 Infinity Q50S and 2015 Mercedes-‐Benz S65 AMG engaging the breaks when car comes close to another object or pedestrian

•  Jan 2016 Toyota releases plans to invest $50 million in AI program



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O. Levander, IEEE Spectrum, February 2017


Autonomous planning and scheduling in space explora#on § Beginnings: NASA’s remote agent program (1999) § Lunar Atmosphere and Dust Environment Explorer (LADEE), 2013 § Mars exploraBon (two NASA’s rovers landed on Mars in 2014); Aurora launch scheduled in 2018 (ESA).


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LADEE approaching Lunar orbit ArBst’s concept (hkps://www.nasa.gov/)


Computer aided diagnosis § E.g. in radiology the computer output is already rouBnely used as a "second opinion" in assisBng radiologists' image interpretaBons

RoboBc surgery


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Da Vinci surgery robot, designed by Xi Spine, eye, hip & knee, cancer & tumor operaBons


Go game

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•  A strategy board game, originaBng from ancient China (more than 5500 years old).

•  Played on 19x19 grid of lines with pieces called stones.

•  The aim: surround more territory than the opponent. The strategy includes akacking the opponent's weak groups.

•  Simple rules, but highly complex game, far more complex than chess! (b ≈ 250, d ≈ 150)

Source: Wikipedia hkps://en.wikipedia.org/wiki/Go_(game)


AlphaGo

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AlphaGo is a computer program developed by Google DeepMind

•  In October 2015, the first Computer Go program that defeated a professional human Go player

•  January 2016: defeated European Go champion Fan Hui (2 dan master) 5:0

•  March 2016: AlphaGo defeated a 9-‐dan master Lee Sedol, 4:1

The method was published in Nature, Jan 2016 issue:


AlphaGo vs. human professional

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D. Silver et al: Mastering the game of Go with deep neural networks and tree search, Nature, January 2016.


Some recently featured topics

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From IEEE Computa1onal Intelligence Magazine, November 2014

Heterogeneous vehicle rouBng

OpBmizing supply chain networks

Planning of aircrar trajectories

…


ApplicaBons: Asteroid exploraBon

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From IEEE Computa1onal Intelligence Magazine, October 2013

Autonomous Asteroid ExploraBon by RaBonal Agents


ApplicaBons: Rover missions

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From IEEE Computa1onal Intelligence Magazine, October 2013


ApplicaBons: Smart Grid

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From IEEE Computa1onal Intelligence Magazine, August 2011

ComputaBonal Intelligence for the Smart Grid


ApplicaBons: Smart Internet of Things

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From IEEE Computa1onal Intelligence Magazine, August 2013


ApplicaBons: EmoBon analysis

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Communica1ons of the ACM, December 2014

ComputaBonally Modeling Human EmoBon


ApplicaBons: Stock market predicBon

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A. HedayaB Moghaddama, M. H. Moghaddamb, M. Esfandyari. Stock market index predicBon using arBficial neural network, Journal of Economics, Finance and Administra1ve Science, 21 (2016) 89–93.



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Intelligence Technology for Robots That Think From IEEE Computa1onal Intelligence Magazine, August 2013

Measuring Intelligence Beyond the Turing test


What is AI?

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Four categories of AI definiBons

Systems that think like humans Systems that think ra#onally

Systems that act like humans Systems that act raBonally


Intelligent Agents

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The Turing test (Alan Turing, 1950) was designed to provide a saBsfactory operaBonal definiBon of intelligence

Suggested major components of AI: § Knowledge representaBon (store what it hears or knows) § Automated reasoning (use the stored info to draw conclusions) § Machine learning (adapt to new scenarios; detect and extrapolate pakerns) § Language processing (e.g., communicate in English or another language)

Extension -‐ total Turing test includes video to test perceptual abiliBes

The Turing test

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Importance of the Turing test

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The experts in AI do not give much importance to actually passing the Turing test. Rather, its main importance is in defining the major components of the field


Beyond the Turing test

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TesBng math and geometry

TesBng commonsense knowledge

TesBng inference and world knowledge

AI Magazine, Spring 2016 issue


Beyond the Turing test

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PotenBals of AI systems: •  GeneraBng and verifying quickly huge numbers of plausible hypotheses •  Maintaining global repository of knowledge (access to huge amounts of

papers, experiments, reports etc.)

Discovery as a search problem: Deep exploraBon of knowledge space

AI Magazine, Spring 2016 issue

AI for the people Can we trust robots?

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AIXI – An opBmal agent model for maximizing an environmental reward signal

Communica1ons of the ACM, September 2014 Exploratory Engineering in ArBficial Intelligence



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IEEE Spectrum, June 2016

IEEE The Ins1tute, June 2016 Communica1ons of the ACM, September 2016


AI for rather than instead of people

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PrioriBes for robust and beneficial AI: •  OpBmizing AI’s economic impact •  Law and ethics research

§  Liability & law for autonomous vehicles §  Machine Ethics §  Autonomous Weapons §  Privacy

Robust and Beneficial ArBficial Intelligence: “Because of the great poten1al of AI, it is important to research how to reap its benefits while avoiding poten1al piQalls”

Interest in human-‐centered approach: •  RehabilitaBon •  Assist with disabiliBes •  Facilitate learning

AI Magazine, Winter 2015

Deep learning Principles and state-‐of-‐the-‐art

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Example: Image classificaBon

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Example from: Stéphane Mallat, "Scakering Invariant Deep Networks for ClassificaBon” (Caltech database)


Challenges in object classificaBon

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Samoyed White wolf

At a pixel level, images of two samoyeds can be quite different depending on the pose and background, whereas images of a wolf and a samoyed can appear similar


A long standing dream of pakern recogniBon was: replace hand-‐engineered features with representa1on learning

Learning vs. feature engineering

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Deep Learning: Yann LeCun, Yoshua Bengio & Geoffrey Hinton

Finding the right features is difficult, requires expert knowledge, tuning …

Classical machine learning


RepresentaBon learning §  Feed machine with raw data §  AutomaBcally discover representaBons

Deep learning §  RepresentaBon learning with mulBple layers §  Simple but non-‐linear modules at each level §  Results in a hierarchy of representaBons

Key ideas §  Layers of features not designed by programmers §  Learning features from data with a general method

RepresentaBon learning

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Image features

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LeCun-‐Bengio-‐Hinton, Deep Learning, Nature 2015.


Image features

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Image features

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Why mulBple layers?

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A mulBlayer neural network can distort the input space to make the classes of data linearly separable.

C. Olah (hkp://colah.github.io/)


Supervised learning

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NVIDIA GPUs -‐ The Engine of Deep Learning (hkps://developer.nvidia.com/deep-‐learning)

In a typical deep-‐learning system there may be hundreds of millions of weights and hundreds of millions of labeled examples with which to train a machine


OpBmizaBon is highly non-‐convex

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J. Shlens and G. Toderici, Deep Learning for Image and Video Processing, ICIP 2016 tutorial


Feed forward pass

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At each layer: weighted sum of inputs followed by a nonlinearity. Typically, recBfied linear unit (ReLU) is used used:


BackpropagaBon

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Error derivaBves propagate from top to to bokom by applying a simple rules


Deep learning with CNN

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ConvoluBonal Neural Networks (CNN) are biologically-‐inspired variants of mulB-‐layer percepBons



InspiraBon from neuroscience

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Feedforward model contd.

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V1 -‐ primary visual cortex; V2 -‐ visual area II; V4 -‐ visual area IV; PIT -‐ posterio inferotemporal cortex; AIT -‐ anterior inferotemporal cortex;

Si -‐ simple cells at layer Vi Ci -‐ complex cells at layer Vi

T. Serre et al : A Theory of Object RecogniBon: ComputaBons and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex


Feedforward model

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T. Poggio and E. Bizzi, GeneralizaBon in vision and motor control, Nature, 2004.

•  Valid for the rapid categorizaBon tasks

•  It is believed that first 100-‐200 milliseconds of visual percepBon involves mainly feedforward processing

•  Human observers can dicriminate a scene that contains a parBcular prominent object (e.g., animal, vehicle) arer only 20ms of exposure


From images to text

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Vision Deep CNN

Language generaBng RNN

RNN – Recurrent neural network (its hidden units maintain a ‘state vector’ that implicitly contains informaBon about the history of the inputs)



Deep learning with CNN

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A women is throwing a frisbee in a park A dog is standing on a hardwood floor A stop sign is on a road with a mountain in the background

A likle girl is si~ng on a bed with a teddy bear

A group of people si~ng on a boat in the water

A giraffe standing in a forest with trees in the background



State-‐of-‐the-‐art in deep learning

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Some of our research ApplicaBons to art invesBgaBon

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Ghent Altarpiece: RestoraBon treatment

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Ghent Altarpiece: RestoraBon treatment


AutomaBc detecBon of paint losses

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Virtual restoraBon

InpainBng method from: T. Ruzic and A. Pizurica, IEEE Transac1ons on Image Processing, 2015.


Virtual restoraBon

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A. Pizurica, L. Platisa, T. Ruzic, et al. (2015): Digital Image Processing of the Ghent Altarpiece: Supporting the Painting’s Study and Conservation Treatmant. Signal Processing Magazine, 9(2): 583-594.


Digital painBng analysis D1

D2

D3

C1 C2

C3

B1

B2

A3

A2

A1

A4

Crack detecBon and virtual restoraBon

Painter style characterizaBon

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A. Pižurica, L. Platisa, T. Ruzic, et al. (2015): Digital Image Processing of the Ghent Altarpiece: Supporting the Painting’s Study and Conservation Treatmant. Signal Processing Magazine, 9(2): 583-594.


Other high-‐dimensional data Hyperspectral images

•  Hundreds of spectral bands à huge data sets!

•  Much richer informaBon, but a huge challenge for processing

Extract most interesting information from massive data

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MulBmodal data fusion

Best Paper Challenge award: 2014 IEEE GRSS Data Fusion Contest

visible RGB

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W. Liao, F. Van Coillie, A. Pizurica, S. Gautama and W. Philips (2014): Fusion of thermal infrared hyperspectral and VIS RGB data using guided filtering and supervised fusion graph, IGARSS’14.


•  Understanding the mechanisms behind deep learning § The number of layers, input and output neurons and filter responses are determined through experiments that require expert knowledge

§ Rigorous mathemaBcal models are sBll lacking

•  Unsupervised learning § There are much more unlabeled data; humans learn by observing too

•  CombinaBon of learning and complex reasoning §  Includes efficient inference methods in graphical models

•  PrioriBes for beneficial AI -‐ building AI for the people § OpBmizing AI’s economic impact § Law & ethics research

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Some future prospects of AI research