The document discusses the advancements in machine intelligence, particularly IBM Watson, which defeated top human players in the game show Jeopardy. It explores various aspects and capabilities of human and machine intelligence, such as problem-solving and decision-making, and emphasizes the importance of human creativity in technological advancement. It concludes with insights on the future of intelligent systems and the collaboration between humans and machines.

1. Aug 16, 2014 ISS Open Day 2014
Knowledge Engineering Group
Problem Solving 
Human vs. Machine Intelligence

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 Watson scores landslide victory over the best players on
the TV game show Jeopardy, Feb 2011
 Watson earned $77,147, versus
$24,000 for Ken Jennings, and
$21,600 for Brad Rutter
Human Intelligence Machine Intelligence
Watson took 25 IBM scientists 4 years, and around $30 million to create.
Man vs. Machine: IBM Watson
?Do you
also know
?

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What experts say
 Can Watson decide to create Watson? … While computers can
calculate and construct, they cannot decide to create. We are
far from there. … Our ability to create is what allows us to
discover and create new knowledge and technology.
 Pradeep Khosla, Carnegie Mellon University
What Watson’s creators say
 I see human intelligence consuming machine intelligence,
not the other way around.
 David Ferrucci, IBM's lead researcher on Watson
Human Machine Intelligence

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 Human intelligence has been taken as the gold standard of
machine intelligence.
 Turing Test [Alan Turing, 1950]
The Gold Standard & AI Dream
The Turing test of artificial intelligence proposes a simple
game where a hidden computer A and a person B converse
with another person C.
If C is unable to distinguish which he is conversing with
then the computer can be said to be able to “think”.
Modern computer still
cannot pass the test !
image: www.rutherfordjournal.org

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 There are many aspects to describe human intelligence.
 An important one is the capability of problem solving using
knowledge
 Using existing knowledge to
solve new problem
 Learning of new knowledge
from new experience
 Discovery of knowledge
 Machine intelligence is expected to do the same.
Human Intelligence
image: jeeda-lovenpassion.blogspot.com

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Problem Solving
 Typical (but COMPLEX) problem solving tasks:
 Planning
 Decision
 Classification / categorization
 Prediction / forecasting
image: undsci.berkely.edu

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Problem Solving: examples
 Planning
 new housing development
 MRT stations / lines
 a course timetable for school
 … …
 Decision / optimization
an optimal investment decision
a good university to attend
… …
image: techchai.com
image: www.do2learn.com

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Problem Solving: examples
 Classification / categorization
 Classifying customer groups for adopting
suitable campaign / promotion strategies
 Categorizing customer complaints for effective responses /
follow-up actions
 … …
image: www.marketing.savant.com
image: cacm.acm.org

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Problem Solving: examples
 Prediction / forecast
 weather forecasting
financial market trends
demand of electricity
… …
image: www.paloalto.com

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√ Faster
√ Cheaper
? Better
 Computer / software systems for problem solving and
decision making, with the support of
 Advance IT technologies
 Internet, Mobile, Cloud, Data storage, …
 Data / information
 Past records, relevant reference, …
Intelligent Systems
 Why?
 Humans performing the same tasks would be considered as being
“intelligent”
Automated Problem Solving

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 An intelligent system (IS) is an embodiment
of machine intelligence. IS can typically solve problems …
 in well defined domain
 for well specified tasks
 with well described performance targets
 Intelligent Systems make use of …
 intelligent algorithms and domain knowledge
and/or
 machine learning techniques and sources of data
Machine Intelligence
Provided
by human
Developed
by human
Created by
human and/or
machine
Built by
human

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 A big picture of intelligent system (at data / information level)
Intelligent System
Intelligent
algorithms
Data
Domain
knowledge
human or
machine
intelligence
User
Developer
Intelligent
system

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 A complex task that requires the developer with …
 competencies in knowledge and skills of exploiting intelligent
systems techniques, and
 experience and knowledge of problem domains
 Knowledge engineering (KE), is an important discipline of
Artificial Intelligence (AI). KE comprises:
Methodologies,
Techniques, and
Practical approaches
for the successful development of intelligent systems
Developing Intelligent Systems
image: www.123rf.com

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 Knowledge Based System for Marketing (Local retailer)
 Indoor Location Prediction on Mobile Devices (SAP)
 Intelligent Cab Management System
 Length of stay prediction (Local Hospital)
 Prediction of Colorectal cancer recurrence (TTSH)
 Customer Complaints Handling (Local Bank)
 Insurance Quote Comparison (Local Insurer)
 Career Management system (Armed Forces)
 JWT World Wide IT Intelligent System (JWT)
 Regulatory Knowledge based system (Sennheiser)
 Storm water drainage design (Engineering)
 Weather forecasting system (NEA)
 Intelligent System for evaluating musical chord progression (NIE)
Many more in different areas …
Systems Developed by KE Students
Business
Service /
finance
Healthcare
/ medicine
Urban develop
/ environment
Enterprise /
organization
Arts /
Music
image: www.projectinsight.net

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 Intelligent system for a local leading department store
[KE24 batch, Jan 2014]
 Using data mining techniques and
other intelligent techniques
 Business analytics
 Perform spending analysis on
their card member base
 Build models using past promotion
 Generate a list of card members who are likely respond to
advertising for promotion
Systems Developed by KE Students

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 Indoor Location Intelligence [KE24 batch, Jan 2014]
 Realizing next place prediction in a shopping mall
 Studying the historical data to
work out patterns of the paths and
recognizing the behavior of the
shoppers using the data extracted
from WiFi signal of the shoppers
 An extended application that
relates indoor geographic or location
contexts to business data as part of
the decision making process.
Systems Developed by KE Students

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 In November 2013, IBM announced Watson as an
application development platform in the Cloud.
 IBM also recently announced
its intention to open Watson
to corporate developers, to
advance a new generation of
apps infused with Watson‘s
cognitive computing intelligence.
 Nearly 2,000 individuals and organizations have contacted IBM to
share ideas for building cognitive applications that redefine how
businesses and consumers make decisions.
IBM Watson: recent news
Users can also be contributors!

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 Learn and interact naturally with people to extend what
either humans or machines could do on their own.
 Help human experts make better decisions by penetrating
the complexity of Big Data.
 Human and machines working together.
Cognitive Computing Systems

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 Humans work together with
machines that are more intelligent
 Understanding natural languages
 Computing with words
 Perception-based reasoning
 Competitive & collaborative
 Learning & adapting to changing environment
 … …
Towards the Future
The gold standard is also evolving !
yesterday today tomorrow
image: darleneglasgow.wordpress.com
image: www.doyouknow.inimage: www.genetic-programming.org
image: informatics.indiana.edu

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Thank you!