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http://www.idt.mdh.se/kurser/computing/

Research Thinking and Writing Toolbox 

Gordana Dodig CrnkovicSchool of Innovation, Design and Engineering, Mälardalen University, Sweden

http://www.idt.mdh.se/personal/gdc/

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Course Wrap-up

This last lecture will put the whole course in the perspective.

What have we learned?

How to see all lectures, those separate puzle pieces as a whole?

Zoom out! And zoom in! And zoom out again!

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Course Wrap-up

In the lecture by Jeannette M. Wing, we learned about the important skill of computational thinking: automation of abstraction.

Five deep questions of Computing:

P = NP?

What is computable?

What is intelligence?

What is information?

(How) can we build complex systems simply?

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Course Wrap-up

Concerning writing tool box, we learned from Paper Writing

and Publication lecture by Jan about useful practices of paper

writing and publication.

In the lecture on Academic English, Diane emphasized

importance of making clear who says what and giving credit to

other people – use of sources and problem of plagiarism. This is a

very important issue. Be careful when you write your Notebook and

Home Exam!

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Course Wrap-up

COMPUTATIONAL NETWORKS (ALBERT-LÁSZLÓ BARABÁSI)

Although for many decades questions related to

complexity were driven by statistical physics, in the new era of

interdisciplinary and multidisciplinary science, the sciences and

methods involved are much wider.

But despite the necessary multidisciplinary approach to tackle the

theory of complexity, scientists remain largely compartmentalized in

their separate disciplines. Will it take five years or five decades to

make significant advances?

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Course Wrap-up

COMPUTATIONAL NETWORKS (ALBERT-LÁSZLÓ BARABÁSI)

Will it take five years or five decades to make significant advances?

Once we get a first glimpse of some universal order, it will take no

time to unfold the whole construction.

At that point we will have a chance to understand the key to

nature’s secret code for multitasking — the one that orchestrates

the actions of uncountable components into a magic dance of order

and ultimate elegance.

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Course Wrap-upComputational Intelligence

Origins of Intelligence

Brain, Perception and Neurorobotics

Origins of Brain. Blue Brain Project.

Multimodal sensory integration in human neocortex (Baran)

shows how knowledge production can be understood on the level of

networks of neurons in the brain, how different sorts of signals (input

information) from the environment and from the body get processed

in the brain so that we acquire an integrated picture of the world.

One of the central issues of perception and cognition in general is

synchronization. This knowledge about Perception and Neurorobotics

is used for application to a theoretical framework for multimodal

information processing for robots (vision, speech, gestures ).

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Course Wrap-up

Computing Nature– All of the nature computes.

If we understand processes of dynamical changes in the nature as

computation,

many processes on different levels of organization – from on the

quantum level to the processes on the level of biological systems

can be modelled computationally.

Computational Universe- a universe of all possible programs

(Wolfram)

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Course Wrap-up

Computational tools of Software Engineering

(Ivica/Sasi): Software is a key and strategic factor in present-

day and future technology and it must be dependable as

more or less all of our vital functions depend on it. How is

dependability assured? Source of errors: hardware,

wired/wireless networks, environment, malicious faults. Fault

tolerance and redundancy mechanisms are used to increase

dependability. Understanding requires ability to zoom in and

zoom out (when solving problems in SE but even in general).

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Course Wrap-up

Intelligence, Artifactual and Biological, Symbolic, Sub-

symbolic and Agent-based: thinking is based on human abilities

we call intelligence, so it is important to understand intelligence,

natural and artifactual; the ways we reason, acquire knowledge and

construct knowledge. Examples of computational thinking tools from

AI: computational models of logical reasoning, constraint solvers,

deduction tools, believe and decision networks, decision trees, graph

searching tools, artificial neural networks (ANN) and Agent-Based

Models (ABM) http://aispace.org/mainTools.shtml

See also: http://artint.info/html/ArtInt.html Artificial Intelligence -

foundations of computational agents - free online book!

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Course Wrap-up

Robotics and Morphological Computing

- physical matter performing natural computation

Computational Biology and Bioinformatics

-biological organisms understood as information processing systems

Computational Design and Material Computation

- using somputational ideas in applications such as architecture (A.

Menges)

Lessons Learned - Highlights

Thinking The Biggest PictureAnd the Smallest. Abstraction

Already our perception* of the world implies

many steps of simplification. Our perception of

an apple is not an apple, it just (for us)

represents an apple; it is only (simplified)

information about an apple.

When we think, we use concepts (ideas) and

relationships between them (established by

various processes of perception and thinking),

and those are also abstractions and

simplifications.12Robert Fludd's depiction of

perception (1619). wiki

* Perception is the conscious mental registration of a sensory stimulus: recognition and interpretation of sensory stimuli based chiefly on memory.

Lessons Learned - Highlights

Thinking The Biggest PictureAnd the Smallest. Abstraction

“Thinking tools” are methods or approaches that

have been developed for understanding and solving

different sorts of problems in different domains

(fields) which we use when we think within those

fields.

“Making distinctions” is one of the most

fundamental thinking tools, it helps us find out what

the world is and how it looks like. Information is "a

difference which makes a difference." (Bateson,

1979). Information is the stuff we use to construct

knowledge. 13Robert Fludd's depiction of perception (1619). wiki

Lessons Learned - Highlights

Thinking The Biggest PictureAnd the Smallest. Automation

Not only our present-day computational machinery contains

layers of automated abstractions – from hardware, assembler

and higher-level languages to program specification and

software design etc.

Even nature in its physical computation shows layered

structure, with quantum computing, molecular computing,

bioinformatical computing, and cognitive computing

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Lessons Learned - Highlights

Different types of thinking tools

In the same way as a mechanical tool has

specific role and can solve specific problem so

is the case with thinking tools, models and

abstractions.

"It is tempting, if the only tool you have is a

hammer, to treat everything as if it were a

nail.“ Abraham Maslow

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Lessons Learned - Highlights

Different types of thinking toolsOur conceptual world is a construction which consists of our

understanding of what the world is, how things in the world

relate to each other and how humans relate to the world

including other humans.

This is also how we solve problems (and hope for the best)

One night Nasrudin (Mullah Nassr Addin) was looking something desperately

under the street lamp. His neighbors saw him and asked him what did he loose.

He answered: "my key!" So his neighbors started to look for it as well all around

the street lamp. After one hour and even more nobody found anything! So

finally they asked him: "well do u remember where did you loose it your key?".

Nasrudin answered: "yes, it must be somewhere there in the dark far from the

street lamp." They asked him: "If you lost it there, why are you looking for it

here then under the street lamp?" Nasrudin: "because under the street lamp I

can see!" 16

Lessons Learned - Highlights

Thinking The Biggest Pictureand the Smallest. The Role of Sciences

Even though everything is related to everything else in some way or the other, we are

lucky that for the majority of practical purposes we can implement separation of

concerns and we do not need to take into account infinitely many factors in order to

understand things “good enough” for a purpose.

Example: if a person has a toothache, dentist will solve the problem – no need to visit

nephrologists (kidney specialist) or cardiologist (heart specialist), even if our body

makes one whole and all belongs together in one single body.

In other words: many problems are local (But far from all! We know about errors able to

“propagate” through several levels of organization of a system.)

Lesson learned: When modelling ..

… Make everything as simple as possible, but not simpler! (Einstein) 17

Sciences help us explore and understand worlds that do not belong to our everyday experiences.

Those worlds are often strange and unexpected and our everyday thinking tools simply do not apply!

The Unnatural Nature of Science L Wolpert

Infinity Zoom – Beyond our Universe : From the outer universe to the microscopic world

http://www.youtube.com/watch?v=LVLRDBFyrTk

But how can we know what can be simplified and what can not?

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Lessons Learned - Highlights

Thinking The Biggest Pictureand the Smallest. The Role of Sciences

The universe as we know it today is really huge.

Our knowledge about the universe is produced by

different sciences, with their different tools, within their

specific domains.

Of course we do not expect instruments from one

research field, say microscop from microbiology apply

to a different field, say observation of objects in

astronomy.

And yet, there are things in common!

Mathematical principles, structures and patterns.

Computational processes.

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Lessons Learned - Highlights

Thinking The Biggest Pictureand the Smallest. The Role of Sciences

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Lessons Learned – Highlights: Diversity

One of the lessons learned in this course is about the necessity of diversity.

Computational Thinking – diversity of computational tools and topics, diversity of applications

Paper Writing and Publication – diversity in writing and publication practices

Academic English – diversity of voices: who is speaking?

Perception and Neurorobotics – diversity of sensory inputs in the brain and their integration.

An Introduction to Computer Ethics – shows the diversity of ethical approaches.

And so on.

The fact that we move on different scales reveals one important feature of

nature:

MORE IS DIFFERENT!

http://www.physics.ohio-state.edu/~jay/880/moreisdifferent.pdf

P. W. Anderson, More Is Different

Science, New Series, Vol. 177, No. 4047 (Aug. 4, 1972), pp. 393‐396

Today we want to understand emergent properties which are the system

properties of the whole system which its parts do not possess.

For example:

Meaning of a word is an emergent property that no letter of the word possess.

“A” “P” “P” “L” “E”

Lessons Learned - Highlights

More is Different

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A complex system is a system composed of interconnected parts

that as a whole exhibits properties not evident from the

properties of its individual parts.

Examples of complex systems: ant colonies, human economies

and social structures, climate, nervous systems, cells and living

organisms, including human beings, eco-systems, societies, as

well as energy- or ICT infrastructures.

Lessons Learned - Highlights

Thinking Complex Systems - Multidisciplinarity

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Some characterize our time as “Complex Systems Era” and talk

about “Complexity Revolution” which is clearly based on

dramatically increased computational resources that scientists

have today.

Lessons Learned - Highlights

Thinking Complex Systems - Multidisciplinarity

See http://en.wikipedia.org/wiki/Complex_systemBook: Unifying themes in complex systems: Proceedings from the third International ... AvAli A. Minai,Yaneer Bar-Yam http://www.google.com/books?id=mOna7SjN08sC&pg=PA421&dq=Complex-Systems+era&lr=&hl=sv&cd=3#v=onepage&q=Complex-Systems%20era&f=false

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An example:

The Helsinki School of Economics (HSE), the Helsinki University of

Technology (TKK) and the University of Art and Design Helsinki (TAIK)

have merged to form Aalto University on January 1, 2010.

Nowadays we have to think in many levels and from many points of view –

this means we must be ready to think multi-disciplinary: understand

results from several fields and be able to present results from our own

research so that people from other fields can understand.

Lessons Learned - Highlights

Thinking Complex Systems - Multidisciplinarity

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The principle of charity requires interpreting a speaker's statements to be

rational and, in the case of any argument, considering its best, strongest

possible interpretation.

The goal of this methodological principle is to avoid attributing irrationality,

logical fallacies or falsehoods to the others' statements, when a coherent,

rational interpretation of the statements is available.

Principle of Charity:Understanding Other People’s Thinking

http://en.wikipedia.org/wiki/Principle_of_charity

Two General Remarks on Thinking: General Remark 1

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We make maximum sense of the words and thoughts of others when we

interpret in a way that optimizes agreement. Davidson

When is Principle of Charity good? When we want to learn from thoughts of

others and we show respect for the thoughts of others.

From the ethical point of view charity is a good principle.

From the point of view of learning from others it is also useful.

Why is this principle not always used ? When are we searching for the most

unfavorable interpretations? Sometimes in order to check how far a statement

will reach, one might search for situations where it does not hold. Also in

discussions, opponents might tend to interpret each others words in least

favorable ways.

Principle of Charity

Two General Remarks on Thinking: General Remark 1

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Fundamentally all thinking is based on

computational processes on many different levels

of abstraction:

This course presents information processing

(computational) view of problem modeling and

solving.

Two General Remarks on Thinking: General Remark 2

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Question to answer in your Class Notes

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What do we see with our ‘info-computational spectacles’ on, that we don’t see otherwise?

Observe: Thinking is a process! Knowledge is a structure.

If you want to see the knowledge-based approach, see: A Methodology of Human Knowledge course by George Kampis http://www.jaist.ac.jp/~g-kampis/Human_Knowledge.html

Assignments

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DEADLINES

May 31  CLASS-NOTES

May  31   TAKE-HOME EXAM

Important to have in mind: Jan’s lecture on writing and Diana’s on the correct use of sources.

You are welcome to share your reflections and discuss topics from the course via mail.