research at the cybernetics lab ima/zlw & ifu rwth aachen ... · 08.12.2016 s. jeschke term:...

www.ima-zlw-ifu.rwth-aachen.de

Research at the Cybernetics Lab

IMA/ZLW & IfU –

RWTH Aachen University

December 2015, Aachen


IMA/ZLW & IfU

Faculty of Mechanical Engineering

RWTH Aachen University

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S. Jeschke

apl.-Prof. Dr. habil. Ingrid Isenhardt

Deputy Head of Institute

Prof. Dr. rer. nat. Sabina Jeschke

Head of InstituteDr. rer. nat.Frank Hees

Vice Deputy Head of Institute

IMAInstitute of Information Management in Mechanical Engineering

Prof. Dr.-Ing. Tobias Meisen

Managing DirectorJunior Professorship

ZLWCenter for Learning and Knowledge Management

Prof. Dr. phil. Anja Richert

Managing Director

Junior Professorship

IfUAssociated Institute forManagement Cybernetics

Dr. rer. nat. René Vossen

Managing Director

Prof. Dr.-Ing. em. Klaus Henning

Senior Advisor

Traffic and Mobility

Dipl.-Inform. Christian Kohlschein(temporary)

Production Technology

Dr.-Ing. Christian Büscher

KnowledgeEngineering

Sebastian Stiehm M. Sc.

Didactics in STEM Fields

Valerie Stehling M. A.

Agile Mana-gement & eHumanities

Dr.-Ing. ChristianTummel

Engineering Cybernetics

Dipl.-Wirt.-Ing. Sebastian Reuter

CognitiveComputing & eHealth

Dipl.-Inform. Christian Kohlschein

Economic and SocialCybernetics

Dr. phil. Kristina Lahl

Administration IT & Media

Technology

PublicRelations

The Multidisciplinary Institute Cluster IMA/ZLW & IfU

… Organisational Chart

Innovation Research &Futurology

Stefan Schröder M. Sc.

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ca. 65 scientists

The Multidisciplinary Institute Cluster IMA/ZLW & IfU

… Data and Facts

… at the Faculty of Mechanical Engineering of the RWTH Aachen University

! Cross-disciplinary teams work together on interdisciplinary projects

Team = ca. 215 employees

50 % from Engineering and Natural Sciences50 % from Humanities and Economic Sciences

Gender-Mix: ca. 50:50

ca. 20 employees for technical service and administration

ca. 130 student workers

Culture-Mix: ca. 90:10 (heavily under construction)

ca. 85 % third-party funding

Financial structure in a nutshell

ca. 7 million € annual turnover

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Term: „governance“, to navigate

Born around 1940

1948: “Cybernetics or control and communication in the Animal and in the machine” (Norbert Wiener)

until 1953: Macy-Conferences

FeedbackloopCircular explanations for systems behavior, self-

regulation(Forrester, Ashby)

AutopoiesisSystem capacity to maintain

and stabilize itself (Maturana, Varela)

DecentralizationDecentralized navigation,

bottom up processes(Stafford Beer)

Central terms in the field of intelligent distributed systems

The central elements of Cybernetics

EmergenceSpontaneous new properties,

swarm behavior (Wolfram, Gell-Mann)

Complex Systems

Multi-component systems in complex interactions

(Stafford Beer)

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And how do these systems work?

Artificial Intelligence: from GOFAI to Connectivism, about 1980---

Top-Down / symbolic AI

Bottom-Up / subsymbolic AI

Connectivisminteraction as basis

of intelligence

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Knowledge on demand /knowledgeaquisition

GOFAIGood old fashionedArtificial Intelligence; based on high-level "symbolic“ knowledgerepresentations

Knowledge storage/

knowledgeretrieval

Two competing movements? --- From top-down to button-up design

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… leading to the 4th industrial (r)evolution...

Breakthroughs - A new era of artificial intelligence

Communication technologybandwidth and computational power

Embedded systemsminiaturization

Semantic technologiesinformation integration

Google Car2012

Systems of “human-like” complexity

Artificial intelligencebehavior and decision support

1st dimension: complexity level

Watson 2011

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… leading to the 4th industrial (r)evolution...

Breakthroughs - Everybody and everything is networked

Team Robotics

Swarm Robotics

Smart Grid

Smart Factory

Car2Infra-structure

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Artificial intelligencebehavior and decision support

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… towards a networked world

And how do these systems work?

Power revolutionCentralized electric power infrastructure; mass production by division of labor

1st industrial revolutionMechanical production systematically using the power of water and steam

today

Digital revolutionDigital computing and communication technology, enhancing systems’ intelligence

Information revolutionEverybody and everything is networked – networked information as a “huge brain”

around 1750 around 1900 around 1970

Towards intelligent and (partly-)autonomous systems AND systems of systems

?? Steering -Controlling ??

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Power RevolutionCentralized electric power infrastructure; mass production by division of labor

1st Industrial RevolutionMechanical production systematically using the power of water and steam

Today

Digital RevolutionDigital computing and communication technology, enhancing systems’ intelligence

Information RevolutionEverybody and everything is networked – networked information as a “huge brain”

Characteristics of Industrial Revolutions:

The vendor change

Around 1750 Around 1900 Around 1970

Latest version of Google’s self driving car (Huffington Post, 28.5.2014)

Google: First autonomic car with street license, 2012

Ford 021C concept car 2012, designed by Newson now at Apple (1999)

Apple Inc.

Innovations in 4.0

The vendor change around „cars“

Tesla X 2015, other Teslassince 2006; Forbes: “most innovative enterprise”

Sony announced autonomous car in 2015, based on their experience in visual sensors

Car specialists? – No. Connectivity & data

specialists. Energy & sensor

specialists.

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Power RevolutionCentralized electric power infrastructure; mass production by division of labor

1st Industrial RevolutionMechanical production systematically using the power of water and steam

Today

Digital RevolutionDigital computing and communication technology, enhancing systems’ intelligence

Information RevolutionEverybody and everything is networked – networked information as a “huge brain”

Characteristics of Industrial Revolutions:

The vendor change

Around 1750 Around 1900 Around 1970

Latest version of Google’s self driving car (Huffington Post, 28.5.2014)

Google: First autonomic car with street license, 2012

Ford 021C concept car 2012, designed by Newson now at Apple (1999)

Apple Inc.

Innovations in 4.0

The vendor change around „cars“

Tesla X 2015, other Teslassince 2006; Forbes: “most innovative enterprise”

An autonomous car is more like a computer on wheels than a car which includes one or many computers.

Sony announced autonomous car in 2015, based on their experience in visual sensors

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The creative artificial mind

Industry 4.0 does not only change the “routine” jobs

The typical assumption…

… that job changes in 4.0 are mainly addressing blue collar jobs and/or routine jobs does not hold true.

White collar jobs

… are under massive change due to the enhancement in

AI, here the impact often hits “middle class jobs”

Social robots

… will become capable of taking over even complex

tasks with personal presence as in health or home care

From „blue collar – low qualified“ to „white collar – middle class“...

but probably, this is just a transition phenomenon

High qualified jobs

… as e.g. health professionals face already the taking over

through AI in certain fields by Watson, Google Flu, etc.

Decentralized platforms

… with automated consensus models (e.g. blockchain) take over complex administrative

tasks e.g. in judiciaries

Virtual and augmented environments

… allowing for new international players, even in tasks requiring humans

and presence

Autonomous systems

… as autonomous cars and more envanced production

technology will change the blue collar – low qualified as well

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From the history of autonomous vehicles

2009: Truck robot platoons – distributed intelligence

The KONVOI project (several institutes from RWTH & industry partners)

2005-2009 automated / partly autonomous

transportation e.g. by electronically coupling trucks to convoys

several successful tests with trucks: Chauffeur, KONVOI, SARTRE (EU), Energy-ITS (Japan), …

expected improvements:beyond safety, reduction of fuel consumption and gained road space

!

Adv. driver assistance system for trucks short distances between vehicles of

approx. 10m at a velocity of 80 km/h Energy-ITS: 4m ! (2013)

KONVOI: Car2infrastrcuture components! Model of multi agent systems

Connectivity…

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Organization forms on demand – individualized by client – initialized by product

!Product agitates as “super-agent”: Plans production and transportation steps Requests services from agents Negotiates with other products for agent-resources

Heterogeneous player modeled as multi agent concept Models from biology and social sciences Based on autopoiesis & embodiment theory

Transport unit

Production unit

Virtual service providerFa

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Changes already „under construction“

With decentralized models towards lot size 1

Konvoi 2005-2009, RWTH with partners

(partly) autonomous driving via convoys

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Advantage of decentralized control structures

Intralogistics goes mobile: The Festo Logistics League

Competitions robocup:

2012: 0 points in World Cup

2013: 4th in World Cup

2014: Winner of the GermanOpen

2014: Winner of the World Cup



Critical factors for success: Totally decentralized No ”hard coded components“ Strong cooperation Re-planning during tasks

Mobile transportation robots from flexible routing

!

Competencies: localization & navigation computer vision adaptive planning multi agent strategies sensory & hardware

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Lend the robots a face

Into Service Robotics: The next step – the “Oscars”

Transform mobile robotic experiences into the field of service robotics

!

1. Investigating “new” human machine Interfaces and interaction schemes Simple, intuitive Schematic eyes following you “natural eyes behavior”: randomly

looking around, showing interest by blinking, looking bored, …

!Performing service robot tasks Distribute brochures and serving drinks Path planning, room exploration, …

!

2. Investigating the “Uncanny Valley”: when features look almost, but not exactly, like natural beings, it causes a response of revulsion among the observers (Mori 1970)

3. Investigating diversity specific reactions (gender, age, culture) to artificial systems and in particular robots

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Data-driven learning – supervised

Supervised learning in high-pressure die casting

? Can we predict the result of a HPDC (high-pressure die casting) process –by using historical data? – YES, WE CAN! 2015

HPDC process measurements

Historic dataProcess and quality data

Prediction modelModelling and

training

Visualization of predictionInline and web-based

(result NIO|IO with reason)

IOIO

NIO (Outbreak)

NIO (Blowhole)

NIO (Cold shot)

Setting: The die casting equipmentin the research wing was separatedfrom the quality check. Thus, the forms were checked with a delay in time and a considerable spacialshift. Under these conditions, „reproducibility“ of the resultscould not be reached...

Not the original

… in cooperation with

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Supervised learning in individualized production (1)

? Can we use supervised learning to improve very flexible production processes towards lot size 1? – YES, WE CAN!

Injection Moulding / synthetics (at IKV):

For lot size 1, real experiments are expensive Current simulations in the field help to optimize

the results but do not feedback with the process At IKV, a large amount of training data is available High quality process simulations available

Automating the moulding process alongparameters coming from geometry, weight, etc.

Hot Rolling /metals (at ibf):

Coils or slabs depend highly on the costumer So far, the rolling schedule is driven by

experience rather than by automated design Process simulations of mill trains are “physically

incomplete”, real experiments are expensive

Automating the rolling schedule alongparameters as the number of millings, pressureapplied, and temperature

! The BRAIN PROJECT - by the cluster of excellence (since 9/2016)

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Sulfur (S) > 0.04% and heat treatment fragile structure

Phosphorus (P) > 0.04% reduced plasticity

Chrome (Cr) > 16%, Molybdenum (Mo) > 13%, Nickel (Ni) > 56% no findings

…

Data-driven learning – unsupervised

Unsupervised learning in a somewhat “corrupt” database

!Finding hidden relations in our data, we were not aware of, e.g., understanding failures or bad quality of products and processes 2015


Searching for hidden relations in data by

subgroup mining

Unsupervised cleaning

Setting: The company was facing a certain muddle in theirexpert knowledge base. If all the information providedwas to be true – then certainphenomenons would not occur. But did.

The suspicion was thatsome information might beoutdated by now, but the specialists still believe them...

But – which one?

Data about chemical compositions

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Unsupervised learning for laser cutting processes

?Can we use unsupervised learning to identifying a group of desired process results in a highly complex process - YES, WE CAN!

! Part of B1 - by the cluster of excellence (since about 1/2016)

Sheet Metal Cutting (at NLD):

challenge: reaching a maximum assurance of the cut quality reliable process simulations are available (QuCut, developed at NLD) thus, a large amount of training data can be generated

Automating the cutting process along parameters coming from

five optics design parameters (beam quality, astigmatism, focal position, beam radius in x and y directions (elliptical laser))

and eight process criteria (roughness values at different depths)

[all pictures/movies:NLD, W. Schulz, 2016]

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… using k-means clustering

Results of cluster analysis:• Identification of desired simulation runs (i.e. blue cluster)• Further analytics reveals process parameters that lead to desired results

multidimensional visualization

K=5 clusters... ... transferred into parallel coordinates(reduced to the 8-dim. roughness space)

k-means clustering groups n observations into k clusters. Using k-means clustering on simulation data allows to… analyze multidimensional relationships between process criteria discover hidden structures in experimental results gain knowledge about the structure in the data

2nd paper currentlyunder review at wgp

?Can we use unsupervised learning to identifying a group of desired process results in a highly complex process - YES, WE CAN!

! Part of B1 - by the cluster of excellence (since about 1/2016)

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Deep learning combined with reinforcement learning

Deep learning to deal with variations

? Can we make a robot really understand its task and perform movements that solve this task adapting to dynamic and variable circumstances of the real world?


Setting: An industrial robot with a veryelastic joint is to grab objects from a distinct location a and pitch them into a hole at location b. Here, the shapes of the objects possess a certain variation – like „real bananas“. Traditional programming is inflexible –slightest variations leads to failure of the task.Can we make the robot learnto grab and put different kindof objects?

An industrial robot... training for „crooked cylinders in the box“

Pretraining to reduce the amount of experience needed to train

visuomotor policies Enhance security for robot and environment as it

limits the motion to a certain “frame”

Neural network of 7 (4) layers (3 convolutional layers for vision, skipped here) 1 expected position layer that converts pixel-wise

features to feature points 3 fully connected layers to produce the torques.

Main training process

Model predictiveregulators

Optimization of the dynamics model

Max. of thereward

Generate trainingdata of the model

Neuronal Network

Dynamics model

Pretrainingprocess

Source: Levine, S., Wagener, N., & Abbeel, P. (2015, May). Learning contact-rich manipulation skills with guided policy search. In 2015 IEEE international conference on robotics and automation (ICRA) (pp. 156-163). IEEE.

Not the original

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Deep learning to replace teach-in programming

? Can we use deep learning to improve flexible motion patters for robots –all towards lot size 1? – YES, WE CAN!

! The CENSE PROJECT – by the cluster of excellence (since 5/2016)

Machine tools

Robot playing the „wire loop game“ (at IMA & WZL):

challenge: guiding a metal loop along a twisted wire (geometry unknown) without touching the loop to the wire

reliable simulations are available, which allows for first training in VR before experimenting in the real world

Automating the motion path along different wires

Getting rid of the teach-in programming as it is not flexible enough for indivudualized production

Transfer to the process of production and assembly Planning of welding lanes Applying of glue beads Assembly of limp components

1st paper currentlyunder review at CIRP

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Deep learning to replace teach-in programming

? Can we use deep learning to improve flexible motion patters for robots –all towards lot size 1? – YES, WE CAN!

! The CENSE PROJECT – by the cluster of excellence (since 5/2016)

Machine tools

Environment model

Environmental state

Q-Learning (special caseof R-Learning)

Deep neural network

Communication via web service connection

𝒙𝟏𝒙𝟐𝒙𝟑𝒙𝟒...𝒙𝒏

Feature vectors




Simulation environment

Next step: Improving topology towards CNN(convolutional neural network)

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Where the Story Goes: AlphaGo

!

Go originated in China more than 2,500 years ago. Confucius wrote about it. As simple as the rules are, Go is a game of profound complexity. This complexity is what makes Go hard for computers to play, and an irresistible challenge to AI researchers. [adapted from Hassabis, 2016]

Bringing it all together!

The problem: 2.57×10210 possible positions – that is more than the number of atoms in the universe, and more than a googol times (10100) larger than chess.

Training set30 million moves recorded fromgames played by humans experts

Creating deep neural networks12 network layers with millions ofneuron-like connections

Predicting the human move(57% of time)

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Learning non-human strategiesAlphaGo designed by Google DeepMind, played against itself in thousands of games and evolved its neural networks; Monte Carlo tree search

! Achieving one of the grand challenges of AI

March 2016:Beating Lee Se-dol (World Champion)AlphaGo won 4 games to 1.(5 years before time)

[Has

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is, 2

01

6]

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From discrete to continuous variance

From the paradigm „Individualization by building blocks“... Consumers chose from predefined options Processes are separately planned and realised for each option

2017

... Towards the paradigm „Individualization by continuous models“ Consumers taylor their individual (non-prodefined) product Processes are carried out in real-time, within a model

2030

Customer specifying car toindividual needs; at home; incl. „continuous features“

+ 20 cm, drivers seat

area

CAD & Design CAM

Manufacturing needed tools

part manufacturingAssembly

AI drivenfeedback loops

Delivery ofindividualized car

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Head of Institute:Univ.-Prof. Dr. rer. nat. Sabina JeschkeTel.: +49 [email protected]


Deputy Heads of Institute:

apl.-Prof. Dr. habil. Ingrid IsenhardtTel.: +49 [email protected]

Dr. rer. nat. Frank HeesTel.: +49 [email protected]

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1968 Born in Kungälv/Sweden

1991 – 1997 Studies of Physics, Mathematics, Computer Sciences, TU Berlin1994 NASA Ames Research Center, Moffett Field, CA/USA10/1994 Fellowship „Studienstiftung des Deutschen Volkes“ 1997 Diploma Physics

1997 – 2000 Research Fellow , TU Berlin, Institute for Mathematics2000 – 2001 Lecturer, Georgia Institute of Technology, GA/USA2001 – 2004 Project leadership, TU Berlin, Institute for Mathematics04/2004 Ph.D. (Dr. rer. nat.), TU Berlin, in the field of Computer Sciences2004 Set-up and leadership of the Multimedia-Center at the TU Berlin

2005 – 2007 Juniorprofessor „New Media in Mathematics & Sciences“ & Director of the Multimedia-center MuLF, TU Berlin

2007 – 2009 Univ.-Professor, Institute for IT Service Technologies (IITS) & Director of the Computer Center (RUS), Department of Electrical Engineering, University of Stuttgart

since 06/2009 Univ.-Professor, Head of the Cybernetics Lab IMA/ZLW & IfU, Department of Mechanical Engineering, RWTH Aachen University

2011 – 2016 Vice Dean of the Department of Mechanical Engineering, RWTH Aachen University

since 03/2012 Chairwoman VDI Aachen

since 05/2015 Supervisory Board of Körber AG, Hamburg

Prof. Dr. rer. nat. Sabina Jeschke

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Appendices

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… optimizing the classifiers

? Can we predict the result of a HPDC (high-pressure die casting) process –by using historical data? – YES, WE CAN! 2015


! Classification – “One classifier to rule them all?”Different classification models can be used to categorize the states of your system.


Observation(feature vector)

𝒚𝟏𝒚𝟐...𝒚𝒎

Probabilityof category

Classifier: kNN classifier decision trees random forest tree support vector machines naïve Bayes classifier …

73%

85%

98%Setting: The die casting equipmentin the research wing was separatedfrom the quality check. Thus, the forms were checked with a delay in time and a considerable spacialshift. Under these conditions, „reproducibility“ of the resultscould not be reached...


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Cyber-physical systems

… and related terms: Internet of Things & Industry 4.0

Cyber-physical systems OR Internet of things ?

• large-scale distributed computing systems of systems• Computation and “intelligence” is

not decoupled from environment

• Internet as large-scale network • embedded systems

(= intelligent components)

Shared

Vision

Core Technology

• Internet of Thingsdriven from computer sciences, Internet technologiesdriven by EC

• Cyber-physical systemdriven from engineering aspects driven by the NSF

• Internet of Thingsfocusing on openness and on the network - virtuality

• Cyber-physical systemfocusing on the physical system behind, often a closed-loop system

Distinct

Scientific Community

Philosophy, focus

For all practical purposes:• Today: more or less synonym• Industry 4.0 as a special field of application

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Creativity as basis for innovation

!“Creativity is a phenomenon whereby something new … is formed. The created item may be intangible (such as an idea, a scientific theory, a musical composition or a joke) or a physical object (such as an invention, a literary work or a painting).” [adapted from Wikipedia, last visited 5/3/2016]

„In 1917, Sigmund Freud offered a theory of the three mortifications of man:

1. the cosmological (Kopernikus, man isn’t the center of the universe),

2. the biological (Darwin, man descends from the ape)

3. and the psychological (Freud himself, man isn’t master of his own psyche).

In line with the rapid development of Artificial Intelligence, the addition of another mortification suggests itself:

4. the intellectual: there are more intelligent entities than man.“

[Dr. phil. Kristina Lahl, RWTH, Cybernetics Lab]

Rather, it appears to be a mixture between “wishful thinking” and a

lack of definition.

The frequently uttered assumption that AI can’t be creative does not hold true.

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neuromorphiccomputing

machinelearning

The “second way”

About the connection between birds and AI

“You don’t need to be a bird to fly.”

!

In traditional machine learning, “general purpose computers” are considered.

These computers have no strong similarities of biological brain structures.

Even if they work less effective than biological brains (so far), they have a enormous energy consumption.

!

In the “brain projects”, specialized computer architectures are developed, driven by biological paradigms.

These architectures are more efficient for certain tasks, but do not follow the “general purpose idea” any longer.

Hardware and software become strongly coupled. Thus, experimental changes become more complicated.

… however, it‘s ok to be a bird to fly!

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Limits of general purpose computers and machine learning

The limitations of the Moore’s law

Gordon Moore (born 1929) co-founder and Chairman Emeritus

of Intel Corporation before: director of R&D at Fairchild

Semiconductor education: Berkeley, Caltech, Hopkins

Moore's law(from a speech given by Moore, in 1965)

… the prediction that the number of transistors that can be placed on the same volume and for constant costs will double every 18 months!

(above: the current definition; the original used a doubling speed of one year)

5 – the magicboundary…

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Limits of general purpose computers and machine learning

Von Neumann architecture

John von Neumann (1903 – 1957) a Hungarian-American mathematician,

physicist (quantum mechanics), inventor, computer scientist, …

Manhattan project ENIAC project: “Great brain”, first pure

electronic computer, 1946 turing complete

!

Turing completeness:

colloquial: “a general-purpose computer can do what a turing machine can do”

And by Church’s lemma: “a turing machine can compute ‘every algorithm’”

thus, Turing completeness means: can compute everything as long as the algorithm is known

Von Neumann architecture is turing -complete

The von Neumann bottleneck:

The shared single bus between the program memory and data memory

limits the data transfer rate between CPU and memory

Resulting in an intellectual bottleneck: “only one thing at a time thinking”

Memory wall: CPU speed rises faster than the speed of transfer and in the memory

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Towards new approaches in data sciences

What is neuromorphic computing?

“Neuromorphic engineering, also known as neuromorphic computing, … describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.

Neuromorphic engineering is an interdisciplinary subject … to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.

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The “Brain projects”

! Brain Initiative

White House BRAIN Initiative (Brain Research through Advancing Innovative Neurotechnologies)

Established: 2013 by Obama Timeline: about 10 years

Directors: Cornelia Bargmann and William Newsome

Web: http://www.braininitiative.nih.gov/

total costs: private-public mix, about 100 mio $ in 2014, …

!

Human Brain Project (HBP)

EC FET Flagship

Established : 2013 Timeline: about 10 years

Director: Henry Markram Coordinator: EPFL Web: www.humanbrainproject.eu Partners: 100 all over Europe, in Germany:

Heidelberg, Jülich, …

total costs: 1.19 billion €, about half of it provided by the EC

? Worldwide, already a strong competition has been started.The biggest research activities in this field are … :

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Examples for neuromorphic chips

Intel, sensor image processing architecture

SpiNNaker, component of the Human Brain Project

!

SpiNNaker: Spiking Neural Network Architecture

Spiking: inculdes time and temporal coding novel computer architecture goal: to use 1 mio. ARM processors (currently

0.5 mio) in a massively parallel computing platform based on spiking neural networks

“One Million Chips Mimic One Percent Of The Brain”

By Steve Furber, Univ. of Manchester, one of the world’s best microprocessor designers

!

Intel Reveals Spin-based Neuromorphic Chip Design with up to 300 times lower energy computation

Involves the combined use of spintronics and memristors (memory resistors, not constant but depend on the history of current). In a cross-bar switch lattice, lateral spin valves act as neurons, and memristors act as synapses.

[a paper published by Intel in June 2012, Sharad et.al, 2012: arXiv:1206.3227]

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Timeline – results so far and the expectations in the future

? After Moore’s law: What might be the replacement?A pipeline of new technologies to prolong Moore’s magic

“For some time, making transistors smaller has no longer been making them more energy-efficient; as a result, the operating speed of high-end chips has been on a plateau since the mid-2000s.”

[economist, 2016]

!

The short history of transistors

• 2004 strained silicon• 2007 metal oxides used to beat the

effects of tunneling• 2012 finFET transistors• 2020 “gate-all-around“ transistors

Beyond changes in the design of transistors, more exotic solutions may be needed? For example materials beyond silicon. But in the end, we just “stave off the need for something radical”.

[Greg Yeric, ARM transistor designer, 2016]

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Is AI at its end because Moore is not holding much longer?

NO. New technologies will take over, however there may be a gap in time…

NO, because additionally, the future may lay in more and more powerful algorithms instead of pure computational speed and power.

Specialized chipsneuromorphic computing

New materials nanotubes, cadmiumtellurid,

graphene, molybdenite, …

Quantum computingmake direct use of quantum-

mechanical phenomenaNew geometries3D architectures, …


In a nutshell, the alternatives to the beaten paths

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Summary

The “new AI”: Cognitive Computing

“Cognitive computing (CC) makes a new class of problems computable. It addresses complex situations that are characterized by ambiguity and uncertainty; in other words it handles human kinds of problems. …To do this, systems often need to weigh conflicting evidence and suggest an answer that is “best” rather than “right”.Cognitive computing systems make context computable.”

“Cognitive computing systems [are] a category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally […]. These systems will learn and interact to provide expert assistance to scientists, engineers, lawyers, and other professionals in a fraction of the time it now takes.”

“Cognitive computing is the simulation of human thought processes in a computerized model…. involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works.”

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New forms of human machine interaction

About the role of emotion in human-maschine-interaction

!However, it took a while before emotions wereconsidered important in computer science.

Plato (ca. 400 BC)“Human behavior flows from three main sources: desire, emotion, and knowledge.”

Rosalind Picard (since 1997; MIT)“Computers that will interact naturally and intelligently with humans need the ability to at least recognize and express affect.”

Picard coined the term“affectice computing”

KISMET - MIT (1990-2000; Cynthia Breazeal)

Analysis and simulation of human-like emotions

Research on interaction between robotsand humans

Part of the “organic development”

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New forms of human machine interaction

Automatic Emotion Recognition: Project IMOTION (1)

?Did you ever yell at your GPS, e.g. when it told you the equivalent of “drive through that river ahead!”?

Well, WE do constantly. And so the story went on like this:

! Motivation and goal: Transform the GPS into an intelligent co-driver, i.e. get it to adapt to your emotions!

Solution: A machine-learning based system architecture. Did it work? Often! Was it fun? Hell, yeah!

Ingredients in a nutshell

Primary source of emotion: driver speech

Machine-learning algorithm: Support Vector Machine (SVM)

Training database: talk show based corpus (lots of yelling)

Test-bed: a driving simulator

Test persons: old and young

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Changes already „under construction“

Towards human-robot cooperation: hybrid teams

Towards hybrid teams and in-the-box production

© F.Welter Aachen © F.Welter Aachen

Audis collaborative robots in Ingolstadt, the “Cobots” pick up components and pass them to workers (02/2015)

New intelligence models: New AI for “context understanding”

New “body concepts” for robots New types of “sensible” robots, mainly “lightweight”

Real-time capability: New fast sensors allows avoiding accidents in close cooperation

PhD Ying Wang, RRWTH, IMA/ZLW & IfU, 2016

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Some even more “out of space” concepts

The new driver

!

„My colleague the robot…“

Again more: In a few years, automated driving might outcompete human drivers.Security issues, the demographic change, and the decreasing attractiveness of the job may add to a fast change.

DHL

DaimlerGoogle

www.cargocap.de

www.spektrum.de

Rolls Roycerail-bound caps

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The third dimension

The megacities of the future

At a certain point, due to purely mathematical reasons, extended 3-dimensional building structures can not longer be served by purely 2-dimensional (street) networks.

Source : National Geographic Magazine

!

“In 2030, 70% of all humans will live in cities. Already then, about 10% will live in megacities (i.e., more than 10 Mio people). Escalating…”

[freestyle translation, source pwc studies]

Below ground

Above ground

Source : Cargo sous terrain, CH

Third dimension

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“plastics instead of parcels?” -UPS moving from logistics to3D printing (tests since 2013)


The new construction

!

„Digital warehouses are replacing physical spare parts storages“[freestyle translation, source Logistik magazine]

„3D printing is on its way to leave the somewhat ‘restricted’ areas of spare part business, tool making etc. and is about to become a serious challenger for all traditional manufacturing models“.

[source Prof. Erman Tekkaya, TU Dortmund]

Harbor Rotterdam – 3D printer farm for metal printing (after piloting, now roll-out in 2016)

Water carbonatorsreaching high salesfigures

3D printing of house (sourceUniv. of Southern California 2013)

3D print of pasta –Barilla (tests since 2015)

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Service Innovations

Expectation management – from B2C to B2B and the Generation Y!

The costumer gets powerful.

And: he/she expects services in business (B2B) to work in the same comfortable way as at home (B2C).

And – more again: the digital native is entering the scene. This guy does not even know what a fax machine is used for. Everything outside the internet does not exist!

Business units – such as marketing, sales, customer support – communicate with each other, but also directly and autonomously with the customer.

Marketing Customer Support

Sales

Business Customer

Customer focusedmulti-channelcommunication

Traditional enterpriseCommunication

Enabled byservice-orientedbusiness models

Internal communicationbecomes more efficient

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!Services become available and experts become obsolete!

As information becomes more and accessible, experts lose information power. This observation is inline with all earlier changes along the information chain, starting with the book printing …

Expert systems losing ground: Experts and service providers lose their unique selling proposition

ExpertKnowledge

Tourist Guide Real Estate

Salesman

Travel Agent

Informed Customer Profound Decisions

Management of services: The new business model

Service InnovationsBusinesses in the customer’s pockets: the „Service Society“

Information and knowledge

transparency

expertise“replaced” by information

Smartphonesingle point of contact for all information

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CarrierRequest

Delivery Customer Delivery Service End Consumer

Requests and Status Updates

!

Logistics 4.0 or „Logistics as a Service“ (LaaS)

The terminology is based on concepts as Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) …. up to Anything-as-a-Service (XaaS).It is sometimes referred to as "on-demand XY”, without hosting or owning the necessary infrastructures and tools.

The philosophy behind it is: “Just do it – I don’t care how!”

ManagingPlatforms

MobileApps

Service Innovations

… dealing to „Logistics as a Service“ (LaaS)

B2B relations still lacking generic services like in B2C relations

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Service Innovations

From production to services: The shift towards a sharing economy

! Uncertain future of patents… IPR?

Sharing economy: “A common premise is that when information about goods is shared (typically via an online marketplace), the value of those goods may increase for the business, for individuals, for the community and for society in general.”

[Wikipedia, 2015]

„The sharing economy endangers traditionalbusiness models. E.g. carsharing could choke offthe demand of new cars…“

[http://www.haufe.de/ 2015]

!

“…already today, about 25% of the Germans can becounted as `social-innovative co-consumer`“

[Heinrichs, Leuphana, 2015: Auf dem Weg in eine

neue Konsumkultur?]

research at the cybernetics lab ima/zlw & ifu rwth aachen ... · 08.12.2016 s. jeschke term:...

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