www.ima-zlw-ifu.rwth-aachen.de
AI in 45 minutes –How Artificial Intelligence Shapes our Future of Production
Aachener ERP-Tage 2016Planung und Regelung 4.0 –Das Zusammenwachsen von ERP und MES
June 16th, 2016
Univ.-Prof. Dr. rer. nat. Sabina Jeschke
IMA/ZLW & IfU
Faculty of Mechanical Engineering
RWTH Aachen University
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Outline
I. Introduction
The rise of AI… and its relation to 4.0
Entering the scene: intelligent self-learning systems
II. The basics of machine learning and their applications
Data-driven methods: supervised and unsupervised learning
Trial-and-error driven methods: neuroevolution
Probabilistic engines
Deep learning – a powerful tool for “both sides”
Where the story goes: AlphaGo and other stories
Machines getting creative
III. The brain projects
To be or not to be …a bird!
The death of Moore’s law
The limitations of the Von Neumann architecture
Neuromorphic computing
IV. Summary and Outlook
The concept of cognitive computing
The embodiment theory and its implications for your “colleague the robot”
The END!
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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
Watson 2011
Google Car2012
Systems of “human-like” complexity
Artificial intelligencebehavior and decision support
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Communication technologybandwidth and computational power
Embedded systemsminiaturization
Semantic technologiesinformation integration
… leading to the 4th industrial (r)evolution...
Breakthroughs - Everybody and everything is networked
Artificial intelligencebehavior and decision support
Team Robotics
Swarm Robotics
Smart GridSmart
Factory
Car2Infra-structure
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The fourth industrial (r)evolution
“Information Revolution”
Everybody and everything is networked. - Big Data & Cyber-Physical Systems
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
Weidmüller, Vission 2020 - Industrial Revolution 4.0Intelligently networked, self-controlling manufacturing systems)
“Internet of Things & Services, M2M or Cyber Physical Systems are much more than just buzzwords for the outlook of connecting 50 billions devices by 2015.”
Dr. Stefan Ferber, Bosch (2011)
Vision of Wireless Next Generation System (WiNGS) Lab at the University of Texas at San Antonio, Dr. Kelley
„local“ to „global“
„local“ to „global“
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Communication technologybandwidth and computational power
Embedded systemsminiaturization
Semantic technologiesinformation integration
… 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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Outline
I. Introduction
The rise of AI… and its relation to 4.0
Entering the scene: intelligent self-learning systems
II. The basics of machine learning and their applications
Data-driven methods: supervised and unsupervised learning
Trial-and-error driven methods: neuroevolution
Probabilistic engines
Deep learning – a powerful tool for “both sides”
Where the story goes: AlphaGo and other stories
Machines getting creative
III. The brain projects
To be or not to be …a bird!
The death of Moore’s law
The limitations of the Von Neumann architecture
Neuromorphic computing
IV. Summary and Outlook
The concept of cognitive computing
The embodiment theory and its implications for your “colleague the robot”
The END!
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Towards machine learning
Machines and learning
?Can machines learn?Can they learn to predict future states and to do tasks optimized and in the right way? And if so, how can they do it?
This is what this talk is about!
! Let us take a look into a first example of data-driven learning!
How do machines learn?
A –Learning by observations and explanations
Data-driven learning
B –Learning by doing
Trial-and-error learning
! Later…
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Data-driven learning - supervised
A first example – learning from guided observations
! Do you remember your childhood heroes – “The Mario Brothers” by Nintendo?
So let us write down our observations (and gather some training data)
pos_x on_ground action status
563 yes jump (B) alive (1)
571 yes jump (A) alive (1)
580 yes walk right dead (0)
582 no jump (A) dead (0)
… … … …
walk right
jump (B)
We want to learn general rules how to survive in this situation - by using data –and visualize it in a decision tree
pos_xon_groundyes
no
> 560 and < 575action
jump (B)
jump (A)confidence (c) = 50%
c = 100%
c = 75%c = 75%
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Data-driven learning - supervised
Supervised learning down-to-earth
? Can we predict the result of a HPDC (high-pressure die casting) process –by using historical data? - YES WE CAN!
… in cooperation with
We extended the prediction model by integrating mechanical vibration (using solid-borne sound sensors) weather data.
Acoustic measurementsFourier transformation &
feature extraction
Weather dataTemporal correlation of
weather (and circumstances)
Extended modelk-nearest clustering and
random forest tree
HPDC process Historic dataProcess and quality data
Prediction modelModelling and
training
Visualization of predictionInline and web-based
(result NIO|IO with reason)
IONIO (Outbreak)
NIO (Blowhole)
NIO (Cold shot)
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Data-driven learning - unsupervised
A second example – what if we do not tell what is right
? What if we do not know if an observation belongs to a specific category?Or, if an observation is good or bad?
Finding the hidden structure in data!“Although it may seem somewhat mysterious to imagine what a machine could possibly learn given that it doesn't get any feedback from its environment, it is possible to find patterns in image data using probabilistic techniques.”
Zoubin Ghahramani, Professor of Information Engineering at the University of Cambridge, Machine Learning
Batch of unlabeled picturesCleansing, preprocessing and
hierarchical clustering
Unsupervised. Human factor is reduced to modeling. (however a certain bias survives…)
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Data-driven learning - unsupervised
Unsupervised learning “down-to-earth”
! Finding hidden relations in our data, we were not aware of, e.g. understanding failures or bad quality of products and processes
Data about chemical compositions of steel (identified as low quality - example)
Searching for hidden relations in databy applying subgroup mining
Sulfur (S) > 0.04% and heat treatment fragilestructure
Phosphorus (P) > 0.04% reduced plasticity Chrome (Cr) > 16%, Molybdenum (Mo) > 13%,
Nickel (Ni) > 56% no findings …
[Ruiz, 2014]
… in cooperation with
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Learning by doing – reinforcement learning
The next step: Using rewards to learn actions
? Remember Mario: What if the machine could learn, how to solve a level? Why not use a some kind of intelligent trial-and-error?
Reinforcement learning (R-learning) is inspired by behaviorist psychology –maximizing the expected return by applying a sequence of actions at a current state.
can be applied to broad variety of problems
[SethBling, 2015]
Neuroevolution of augmenting topologies (NEAT)
Genetic algorithms on top of neural networks
At each state the system decides what action to do
Actions are rewarded if Mario do not die in return
Level progress by evolving neural networks
[Stanley, 2002]
Human factor is “very small” reduced to very general, mainly
formal specifications of the neural network…
However, human still influences the underlying representation model
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Learning by doing
Reinforcement learning “down-to-earth”
[MiorSoft (reexre), 2014][TU Delft, 2012]
… for learning and optimization of motions
… as “pro-training” for human-machine interaction
[Intelligent AutonomousSystems, 2015]
Should Google have crashed 10.000 cars before coming up with first „ok-solutions“ for autonomous driving?
Avoiding “nonsense solutions” by using simulation environments
[UC Berkeley, 2015]
… for learning and executing complete assembly tasks
[UC Berkeley, 2015]
! Obviously: Super-Mario can easily be extended towards intralogistics scenarios…
Coupling to embodiment theory
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“Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection” [Levine et.al., Google, 02/2016]
Learning by doing
The “Kindergarten for robots”
! Transferring human/biological learning processes into all areas -combined with the power of cooperation, “collective learning”
A new approach:object manipulation by “trial-and-error” approach is goal-centric (not insight-oriented!) two components:
1. a grasp success predictor, which uses a deep convolutional neural network (CNN) to determine the success potential a given motion
2. a continuous servoing mechanism, that uses the CNN to continuously update the robot’s motor commands (feedback loop)
trained using a dataset of over 800,000 grasps collected using a cluster of 14 similar (but not
identical !!) robotic manipulators
object manipulation “up to today”
humans and animals: fast feedback loop between perception and action; even very complex manipulation tasks can be performed without advance planning
robotic manipulation: relies heavily on advance planning and analysis;with relatively simple feedback, such as trajectory following (results often slow and unstable, non-adaptive)
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Deep learning
The age of deep learning (deep neural networks)
!“Today, computers are beginning to be able to generate human-like insights into data…. Underlying … is the application of large artificial neural networks to machine learning, often referred to as deep learning.” [Cognitive Labs, 2016]
Deep Q-Networks (also "deep reinforcement learning“, Q refers to the mathematical action-prediction-function behind the scenes….): Learning directly from high-dimensional sensory input
[nature, 2015]
AI starts to develop strategies to beat the game Signs of “body cousciousness” …
[Minh, 2015]
Human factor practically zero.
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Deep learning
Deep learning “down-to-earth”
! … a variety of practical applications
Central part of “cognitive computing”
Handwriting recognitionAnomaly recognition
Natural language processingAutomated translation
Face/picture/object recognitionImportant feature for autonomous driving etc.
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Linguisticpreprocessing
Generation of possiblecandidates
Evaluation of candidates
Probabilistic engines
The new probabilistic engines
? Back to Watson: how is this guy running the (Jeopardy!) show??
DeepQA architecture Purely based on natural language processing (NLP) Approx. 100 different AI/linguistic methods come into play Without any specific semantic representation (“as-is”)
90 IBM-Power-750 servers For each: a 3.5 GHz POWER7
processor, with 8 cores, and 4 threads per core
In total: 2.880 POWER7 threads 16 terabytes of RAM
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Probabilistic engines
Probabilistic engines “down-to-earth”
! Watson: from playing Jeopardy! towards becoming some kind of a “medical doctor”…
today, only 20% of the medical knowledge is evidence based (basis of individualized medicine)
also, amount of medical information is doubling every 5 years: physicians can’t read all the journals
Data: all types, 1. structured data from electronic medical record
databases and 2. unstructured text from physician notes and
published literature
How can we deal with these challenges?
Goal of Watson: help physicians in diagnosing and treating patients by analyzing large data
acting as a huge preprocessor for all kind of medical information
potential to transform health care into individual medicine
currently tested by several clinics, e.g. Mayo, MD Anderson, Cleveland, and Sloan-Kettering
“IBM's Watson is better at diagnosing cancer than human doctors”
Example “p53”: Watson identified possible treatments for protein p53 linked to many cancers
Example “Google Flu” (another engine): already now, doctors integrate the results of GoogleFlu (spreading and direction of contagious illnesses) as it is much faster and more precise as the results of the best medical centers in the world)
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Deep learning
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 artificial intelligence (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)
Dat
a-d
rive
n le
arn
ing
Re
info
rce
me
nt
lear
nin
g
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
sab
is, 2
01
6]
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!“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]
Deep learning
Microsoft Visual Storytelling (SIS): machines becoming creative
Visual-Storytelling by Microsoftbased on deep neural networks (convolutional neural
networks)
DII (descriptions for images in isolation): Traditional storytelling software
SIS (stories for images in sequence): new approach towards storytelling, including
Based on SIND - Sequential Image Narrative Dataset: 81,743 unique photos in 20,211 sequences, aligned to both descriptive (caption) and story language.
[Margaret Mitchell / Microsoft, 04/2016, together with colleagues from Facebook]
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!“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]
Van Gogh’s Starry Nightinterpreted by Google DeepDream
based on deep neural networks
“Do Androids Dream of Electric Sheep?”
(science fiction novel by American writer Philip K. Dick, published in 1968)
Computational creativity (artificial creativity) … is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts. [adapted from Wikipedia, last visited 5/3/2016]
„Can machines be creative?“ by Iamus, a
computer cluster composing classical
music by genetic algorithms, concert for
Turings 100th birthday [youtube]
Deep learning
Google DeepDream: machines becoming creative
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Outline
I. Introduction
The rise of AI… and its relation to 4.0
Entering the scene: intelligent self-learning systems
II. The basics of machine learning and their applications
Data-driven methods: supervised and unsupervised learning
Trial-and-error driven methods: neuroevolution
Probabilistic engines
Deep learning – a powerful tool for “both sides”
Where the story goes: AlphaGo and other stories
Machines getting creative
III. The brain projects
To be or not to be …a bird!
The death of Moore’s law
The limitations of the Von Neumann architecture
Neuromorphic computing
IV. Summary and Outlook
The concept of cognitive computing
The embodiment theory and its implications for your “colleague the robot”
The END!
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Summary of part I
What a zoo! Get me out of here… :)
? Relax. Order is half of life.
A –Learning by observations and explanations
Data-driven learning
B –Learning by doing
Trial-and-error learning
WANT BETTER RESULT? – Just shake it!!
C–Using “biological brain structures”
Machine learningNeuromorphic
computing
neuroevolution
genetic algorithmsneural networks
deep learning
SARSA
Q-LearningProbabilistic engines
supervised…
un-supervised…
reinforcement learning
k-nearest clustering
random forest trees
decision trees
Monte Carlo tree search
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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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Towards new approaches in data sciences
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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Towards new approaches in data sciences
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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Towards new approaches in data sciences
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, …
Towards new approaches in data sciences
In a nutshell, the alternatives to the beaten paths
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Outline
I. Introduction
The rise of AI… and its relation to 4.0
Entering the scene: intelligent self-learning systems
II. The basics of machine learning and their applications
Data-driven methods: supervised and unsupervised learning
Trial-and-error driven methods: neuroevolution
Probabilistic engines
Deep learning – a powerful tool for “both sides”
Where the story goes: AlphaGo and other stories
Machines getting creative
III. The brain projects
To be or not to be …a bird!
The death of Moore’s law
The limitations of the Von Neumann architecture
Neuromorphic computing
IV. Summary and Outlook
The concept of cognitive computing
The embodiment theory and its implications for your “colleague the robot”
The END!
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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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Shadow Dexterous Hand
Summary
From embodiment … to humanoids
Zykov V., Mytilinaios E., Adams B., Lipson H. (2005) "Self-reproducing machines", Nature Vol. 435 No. 7038, pp. 163-164Bongard J., et al., Resilient Machines Through Continuous Self-Modeling, Science 314, 2006Lipson H. (2005) "Evolutionary Design and Evolutionary Robotics", Biomimetics, CRC Press (Bar Cohen, Ed.) pp. 129-155
Robonaut 2- NASA
The Bongard robot – learning through embodiment [Bongard, 2006; Lipson, 2007]
Embodiment theory:„intelligence needs a body“
The existence of a body (incl. sensors and actuators)are basic prerequisites to build experience and finally the development of intelligence.
Embodiment theory:„different bodies = different intelligences“
… leading to humanoids / humanoid components
Asimo Honda
KIT, Dillmann, SFB 588
Atlas 2016 – Boston Dynamics
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Summary
When do you start embracing artificial intelligence?
!Waiting for Google to take over? –Google is addressing fields as gaming, mobility, language …Because there, they do get the data they need.They do not have the data for production lines. – So far….
[Blomberg, 2016]
!
Production engineering is still somewhat hesitating and waiting,
but you have the data and we have the domain experts (from production engineering as well as data science),
so - let’s get started!
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Summary
… in five steps!
4.0: Revolution of (distributed) artificial
intelligence
Innovation –a question of culture
AI today
4th Industrial Revolution
Cognitive Computing and Embodiment
AI tomorrow
www.ima-zlw-ifu.rwth-aachen.de
Thank you!
Univ.-Prof. Dr. rer. nat. Sabina JeschkeHead of Institute Cluster IMA/ZLW & IfUphone: +49 [email protected]
Co-authored by:
Prof. Dr.-Ing. Tobias MeisenInstitute Cluster IMA/ZLW & [email protected]
Dr.-Ing. Christian BüscherResearch group leader „Production Technology“[email protected]
Thorsten Sommer, M. Eng.Team „Knowledge Engineering“[email protected]
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1968 Born in Kungälv/Schweden
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 Institute Cluster IMA/ZLW & IfU, Department of Mechanical Engineering, RWTH Aachen University
since 10/2011 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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References
[Bloomberg, 2016] Why 2015 Was a Breakthrough Year in Artificial Intelligence, http://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence, last visited 18.02.2016
[Cognitive Labs, 2016] http://cognitivlabs.com/the-age-of-neural-networks/, last visited 18.02.2016
[Hassabis, 2016] AlphaGo: using machine learning to master the ancient game of Go, https://googleblog.blogspot.de/2016/01/alphago-machine-learning-game-go.html, last visited 18.02.2016
[Intelligent Autonomous Systems, 2015] Collaborative assembly with phase estimation (ISRR 2015), https://www.youtube.com/watch?v=4qDFv02xlNo, last visited 18.02.2016
[Minh, 2015] Mnih, Volodymyr; et al. (2015). “Human-level control through deep reinforcement learning”, 518: 529–533.
[MiorSoft (reexre), 2014] Neuroevolution - Car learns to drive, https://www.youtube.com/watch?v=5lJuEW-5vr8, last visited 18.02.2016
[nature, 2015] http://www.nature.com/nature/journal/v518/n7540/fig_tab/nature14236_SV1.html, last visited 18.02.2016
[Ruiz, 2014] Ruiz, Paula Andrea Rotes; et al. (2014). “An Interactive Approach for the Post-processing in a KDD Process”. In: Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World”, pp 93-100
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