Artificial Intelligence for Undergrads

Artificial
Intelligence
FOR UNDERGRADS
J. Berengueres

Artificial Intelligence for Undergrads
Version 2.2
Editor
Jose Berengueres
Edition
First Edition. August 24th, 2014.
Text Copyright
© Jose Berengueres 2014. All Rights Reserved.
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ii

1 | Introduction to AI
Bielefeld CITEC JUL 2014
Bielefeld University, Germany. Psychologists
at CITEC study Human Robot Interaction
What makes something intelligent?
1. Saliency - The ability to focus on the
important facts
2. Speed - Lack of speed has never been
associated with intelligence. Sun Tzu
3. Pattern recognition - the ability to recognize
situations and objects allows you to use past
experience to react and predict or adapt to
current and future situations... in summary, AI
is like having a cheat sheet to take advantage
of past events.
AI Formula, AI = 1 + 2 + 3
AI as an emerging property
AI as an emerging property of simple components, a
EC3 commodity. Examples,
1. Ant colony algorithm
2. Viola Jones face recognition
3. Norvig’s SpellChecker
To reinforce the ideas
1. Andrew Ng on Brain inspiration
2. Maja Rudinac on saliency on developmental
psychology
To d a y , t h e i i n A I i s
small... you are here
because you want to
change the small Cap
into a big Cap.

Ant Colony
http://www.youtube.com/watch?v=SMc6UR5blS0
4
1 | Introduction to AI - Are Ants Smart?
The ants + the
pheromone evaporation
turn random guesses into a
superb optimization algorithm
necessary for survival of the
fittest colony
------
ACO or Ant Colony
Optimization was discovered
by Marco Dorigo in the 90’s
------
(but they never managed to sell it
commercially, too much
tweaking required)
Wow! What is
your IQ?
------

Universite Libre de Bruxelles. Belgium. Marco
Dorigo is a pioneer in applying ant to AI.
5
1 | Introduction to AI - Are Ants Smart?
Hi I am Marco
Dorigo’s pet. This is our
Lab in Brussels
------

http://vimeo.com/12774628
http://www.youtube.com/watch?v=AY4ajbu_G3k
6
1 | Introduction to AI - Viola Jones
Wait! Where did
I see this strategy
before!
------
AI that works is about
ag g regating simple
“f eatu re s”. T h e tr i ck i s
how to aggregate large
number of features
The more features the better as long they are better than 0.0001% of
100% random. (Theorem, see Berengueres & Efimov on Etihad case)

How to Write a Spelling Corrector
(Abridged from http://norvig.com/spell-correct.html by Peter
Norvig)
“In the past week, two friends (Dean and Bill) independently
told me they were amazed at how Google does spelling
correction so well and quickly. Type in a search like [speling]
and Google comes back in 0.1 seconds or so with Did you
mean: spelling. (Yahoo and Microsoft are similar.) What
surprised me is that I thought Dean and Bill, being highly
accomplished engineers and mathematicians, would have
good intuitions about statistical language processing
problems such as spelling correction. But they didn't, and
come to think of it, there's no reason they should: it was my
expectations that were faulty, not their knowledge.
I figured they and many others could benefit from an
explanation. The full details of an industrial-strength spell
corrector are quite complex (you con read a little about it here
or here). What I wanted to do here is to develop, in less than a
page of code, a toy spelling corrector that achieves 80
or 90% accuracy at a processing speed of at
l e a s t 10 words per second.”
7
1 | Introduction to AI - Spell This!
HELLO
------
I don’t know so
lets minimize the
CANDIDATE FREQUENCY P. ERROR
HELLO 23423 LOW
HELO Not in Dict HIGH
PELO 4 HIGH
HELO
------
PELO
errors
Who is
right?
? ?

(Abridged from http://norvig.com/spell-correct.html by Peter Norvig)
So here, in 21 lines of Python 2.5 code, is the complete spelling corrector:
Import re, collections
def words(text): return re.findall('[a-z]+', text.lower())
def train(features):
model = collections.defaultdict(lambda: 1)
for f in features:
model[f] += 1
return model
NWORDS = train(words(file('big.txt').read()))
alphabet = 'abcdefghijklmnopqrstuvwxyz'
def edits1(word):
splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
deletes = [a + b[1:] for a, b in splits if b]
transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1]
replaces = [a + c + b[1:] for a, b in splits for c in alphabet if b]
inserts = [a + c + b for a, b in splits for c in alphabet]
return set(deletes + transposes + replaces + inserts)
def known_edits2(word):
return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS)
def known(words): return set(w for w in words if w in NWORDS)
def correct(word):
candidates = known([word]) or known(edits1(word)) or known_edits2(word) or [word]
return max(candidates, key=NWORDS.get)
The code defines the function correct, which takes a word as input and returns a likely correction of that word. For example:
>>>
import re, collections
>>> correct('speling')
'spelling'
>>> correct('korrecter')
'corrector'
8
Oh My
Pythons!
Genius!

9
Exhaustivity
is the enemy of
fun

1 | Introduction to AI - The Case of Machine Translation
Machine Translation
(Abridged from http://www.foreignaffairs.com/articles/139104/
kenneth-neil-cukier-and-viktor-mayer-schoenberger/the-rise-of-
big-data by Kenneth Neil Cukier and Viktor Mayer-
Schoenberger FROM FP MAY/JUNE 2013 ISSUE )
“Consider language translation. It might seem obvious that
computers would translate well, since they can
store much information and retrieve it quickly.
Linguistics Models
But if one were to simply substitute words from
a French-English dictionary, the translation would be
atrocious. Language is complex. A breakthrough came in the
1990s, when IBM delved into statistical machine translation. It
fed Canadian parliamentary transcripts in both French and
English into a computer and programmed it to infer which
word in one language is the best alternative for another. This
process changed the task of translation into a giant problem
of probability and math. But after this initial improvement,
progress stalled.”
Google
“Then Google barged in. Instead of using a relatively small
number of high-quality translations, the search giant
harnessed more data, but from the less orderly Internet --
“data in the wild,” so to speak. Google inhaled translations
from corporate websites, documents in every language from
the European Union, even translations from its giant book-scanning
project. Instead of millions of pages of
texts, Google analyzed billions. The result is that its
translations are quite good -- better than IBM’s
were--and cover 65 languages. Large amounts of
messy data trumped small amounts of cleaner data.”
Reflection
In later chapters we
will see how Big
Data is connected to
this ideas. This case
ilustrates the debate
of wether modeling
the world vs. taking
10
Hi, I am
Keneth
----

1 | Introduction to AI - The Case of Machine Translation
the sensory input as model is better. By now it should be clear
what works. In the case of humans another factor plays into
play... the emotion predictor.
11

The Games
Computers Play
In two-player games without
chance or hidden
information, AIs have
achieved remarkable
success. However, 19-by-19
Go remains a challenge.
Abridged from: http://
spectrum.ieee.org/robotics/artificial-intelligence/ais-have-
mastered-chess-will-go-be-next
TIC-TAC-TOE
Game positions: 104
Computer strength: PERFECT
OWARE
Game positions: 1011
CHECKERS
OTHELLO
Computer strength:
SUPERHUMAN
9-BY-9 GO
Computer strength: BEST
PROFESSIONAL
CHESS
Computer strength:
SUPERHUMAN
XIANGQI (CHINESE
CHESS)
Computer strength: BEST
PROFESSIONAL
SHOGI (JAPANESE
CHESS)
Computer strength: STRONG
PROFESSIONAL
19-BY-19 GO
Computer strength: STRONG
AMATEUR
12
1 | Introduction to AI - Monte Carlo Tree Search

13
We sto p p e d t r y i n g to p la y g o
with masters’ rules and started
using Monte Carlo with back
pro pagat ion an d pr u n in g... a
somehow brute force approach”
(S e e a l s o ED Sh aw a ppro ach to A I for
sto ck sp e cu lat io n v s. M e d allio n fu n d)
Amazon book and More Money than Go d
That’s what I call
intelligence free AI
This reminds me
of Searle’s Chinese Box
Medallion Fund
made millions using
the same strategy to bet
on Wall Street
-----
MMTG

“In these four simulations of a simple Monte Carlo tree search,
the program, playing as black, evaluates the winning potential
of possible moves. Starting from a position to be evaluated
(the leaf node), the program plays a random sequence of legal
moves, playing for both black and white. It plays to the end of
the game and then determines if the result is a win (1) or a loss
(0). Then it discards all the information about that move
sequence except for the result, which it uses to update the
winning ratio for the leaf node and the nodes that came before
it, back to the root of the game tree.
Algorithm
1. Tree Descent: From the existing board
position (the root node of the search tree), select a
candidate move (a leaf node) for evaluation. At the
very beginning of the search, the leaf node is directly
connected to the root. Later on, as the search
deepens, the program follows a long path of branches
to reach the leaf node to be evaluated.
2. Simulation: From the selected leaf node,
choose a random sequence of alternating moves until
the end of the game is reached.
14

3. Evaluation and Back Propagation:
Determine whether the simulation ends in a win or
loss. Use that result to update the statistics for each
node on the path from the leaf back to the root.
Discard the simulation sequence from memory—only
the result matters.
4. Tree Expansion: Grow the game tree by
adding an extra leaf node to it.
To learn more about MCTS --> http://mcts.ai/index.htm
To learn more about how to play go:
http://www.youtube.com/watch?v=nuWuXj2V6Rk Beginner
http://www.youtube.com/watch?v=3O-lwNzN0G0 Advanced
15

16
If they can make an
app that beats me at 9x9 go,
give’em an
A+
deal!

(Abridged from the book by
Henry Brighton and Howard
Selina, Introducing AI)
AI Goals
1. Understand the man as a Machine
2. Understand anything (not only humans ) that performs
actions. We refer to this as agents. Therefore agents can
be human or non-human or a combination
3.Weak vs. Strong AI Weak AI: Machines can be made to behave as if they were
intelligent Strong AI: Machines can have consciousness
4.Alien AI
AI that works and is not similar to human AI
17
1 | Introduction to AI - Intro to AI
In one sur vey AI researchers
were aske d what discipline they
feel the clo se st to
(Philosophy was the most common
answer)

5. Trans Humanism & Immortality
PBS Documentary http://www.youtube.com/watch?v=53K1dMyslJg
6. AI and Psychology
(see chapter 3.1)
7. Intelligence
the computational part of achieving goals in the world.
8.Cognitive Psychology
Studying psychology by means of a computational theory of
mind. To explain the human cognitive function in terms of
information processing terms. “internal mental processes”,
“students as primary agents of their own learning”
18

9. Is Elsie intelligent? (are students roaming around a
campus and going to the cafeteria when they are hungry
intelligent? :) )
19

10. Chomsky
The capacity of language seems to be due that we have a part
in the brain dedicated to it (like the organ of the heart).
Otherwise how can we explain that every kid learns to speak
just by listening to its parents speaking? That is not plausible.
Therefore, the organ of language must exist int he brain.
http://inside-the-brain.com/tag/noam-chomsky/
“According to one view the human mind is a’ general-purpose
problem-solver’.” A rival view argues that the
human mind contains a number of subsystems or modules –
each of which is designed to perform a very limited number of
tasks and cannot do anything else. This is known as the
modularity of mind hypothesis. So for example it is widely
believed that there is special module for learning a language –
a view deriving from the linguist Noam Chomsky. Chomsky
insisted that a child does not learn to speak by overhearing
adult conversation and then using ‘general intelligence’ to
figure out the rules of the language being spoken; rather there
is a distinct neuronal circuit – a module – which specialises in
language acquisition in every human child which operates
automatically and whose sole function is to enable that child
to learn a language, given appropriate prompting. The fact
that even those with very low ‘general intelligence’ can often
learn to speak perfectly well strengthens this view. Who is
right Andrew or Chomsky?
Aloha! I am Noam
Chomsky
“if you have a hammer, does
everything looks like a nail?”
----
20

11. The Touring Machine
Model for all machines by way of states and inputs.
I am Alan, Alan Touring.
12. Functionalism
The separation of mind from brain (Software from hardware)
21

13. Physical Symbol Systems Hypothesis
Physical Symbol Systems Hypothesis 1976 Cognition requires
the manipulation of symbolic representations.
14. Touring Test
Can machines think? is an ill-defined (not very intelligent
question) question. Noam Chomsky says: Thats like asking
if submarines can swim. So Touring replaced it by... Can u
fool a human in to believing you are as smart as a human?The
Loebner prize is $100,000 for the first place. The prize was
taken in June 2014 according to: https://www.youtube.com/watch?v=njmAUhUwKys
22

15. Searle’s Chinese Room
A man inside a room with all the Chinese books in the world.
Someone can slide Chinese messages through an orifice. The
man then might learn what to reply even though he does not
know any Chinese symbols at all. “He can pass the touring
test”. (mark this words for later)
Would that be regarded as intelligence? Searle’s Chinese
room passes the PSSH regarding manipulation of symbols!
What about,
Searle himself
+
the books (!)
---------------------------
Understand Chinese?
Which means: Can the whole be more that sum of its parts?
23

16. Complexity Theory
Self-organization occurs when high-level properties emerge
from interaction of simple components. ACO is an example.
http://en.wikipedia.org/wiki/Computational_complexity_theory
“So you are saying that Intelligence is just spontaneous self
organization of neural activity?”
The Brain process Experiment and the mental only realm.
Replace each neuron by an artificial one. What would
happen? Penrose conjectures that consciousness requires of
quantum effects, that are not present in silicon based chips.
ie. non computable processes (today). Microtubes.
24

17. Understanding,
Consciousness and Thought
Intentionality and aboutness.
Example: Mental states have aboutness on beliefs and desires
and that requires a conscious mind. Conscious is ALWAYS
about something.
18. Cognitive Modeling
Modeling is not understanding
19. Module based cognition could explain optical illusions
20. Game playing AI represent the game internally with
trees
21. Common Sense
25

Machines do not have common sense? Is this because of
lack of background info?
22. Sense model plan act
23. Conectionism
Humans have the ability to pursue abstract intellectual feats
such as science, mathematics, philosophy, and law. This is
surprising, given that opportunities to exercise these talents
did not exist in the hunter-gatherer societies where humans
evolved.
https://blogs.commons.georgetown.edu/cctp-903-summer2013/2013/06/29/trends-in-cognitive-science/
24. Symbol grounding and meaning
25. New AI Rodney Brooks. A machine does not think.
What thinks is the machine + his environment as a system.
Examples: Frogs do not have planing modules. It's
directed by the eye perception. Reflex.
26. Dreyfus says that AI is misguided if it thinks
disembodied intelligence is possible. If Dreyfus is
correct then agents must be used as engaged in everyday
world not as disengaged.
26

27. Principles of Embodiment
1. The constraints of having a body are important for the
AI function. Elsie recharging station.
2. “The world is the best model ;) ” - Rodney Brooks
3. Bottom-to-top construction
4. Brooks on conventional robotics, they are based on
Sense.Plan.Do
5. Brooks new AI, based on:
Behaviors by design
27
Hi I am Genhis!
Each of my leg has a
“programmed” behavior -
just like roaches are
After Genhis, we
started iRobot! And we
made some money (not
alot) out of it
When I first
started selling
iRoombas people said
iRoomba was a robot.
But now they say it is a
vacuum cleaner
------
MSNBC 2012

By the way, iRoomba
was poor investment.
Each vacuming averages at $3,
Because the batteries die after 1
year
28
It was only after Colin
Angle became a vacuum
salesman that iRobot took
off
Ok,
so by now the
smart ones must have
realized that there are (at
least )two people making lots
of money with vacuum
automation
in 2011 70% of IRBT
profit came from the robots
they sold to the US army to
fight in Afghanistan

Exercise. Ask the students to plan an optimal path for an iRoomba (remember iRoomba does not have navigation or a map of
the room)
Student A (random walk)
29
under cleans wall
si des, can get
“trappe d”

Student B ( the orderly)
30
This is unfeasible
fo r i Ro o m ba
(to le ra n c e s )

iRoomba strategy is to combine wall following behavior with random bouncing behavior to avoid trapping
50% of the dirt is on the wall sides
50% of the dirt is not on the wall sides
31
Filipino mai d
monthly salar y in Al
Ain: 250 to 330
USD$

A cautionary tale.
Samsung uses a camera to do orderly
cleaning. It has very few stars on Amazon
product reviews.
http://www.youtube.com/watch?v=PA_huJQOPD0
32
The efficiency of the
algorithm affects the
valu e fo r t h e c u sto m e r

28.Mirror Neurons
http://www.ercim.eu/publication/Ercim_News/enw55/
wiedermann.html
29. Evolution without biology
30. Only when interaction between humans and
their environment are more well
understood will AI begin to solve the right problem
Ants + pheromone + food to
search for = Intelligence. Now I
get it. Before I was looking at the
ant + pheromone only. It is the
whole system we have to
consider.
------
33

1 | Introduction to AI - iRoomba Workshop 34
What is the way to
settle a discussion about
what behavior is more
efficient fo r iRo omba?

iRoomba behavior simulation CODE
#!/usr/bin/python
# Copyright Jose Berengueres - @medialabae - 2011.11.29
# iRoomba Vacuum cleaning algorithm (Random VS iRobot
Strategy)
# Ai Strategy What is best and why?
import random, math
class iRoomba:
x = -1
y = -1
o = -1
d = [-1,-1]
step = 0
moveseq = ["N"]
def __init__(self,x,y,o,d):
self.x = x
self.y = y
self.o = o
self.d = d
self.step = 0
def pos(self):
return [self.x,self.y]
def orientation(self):
return self.o
def setOrientation(self,o):
self.o = o
def setDirection(self,d):
self.d = d
def checkWall(self):
#IF BUMP TO WALL CHANGE DIRECTION RND
if (self.o == "N" and self.y == 7) or (self.o == "S"
and self.y == 0) or (self.o == "E" and self.x == 7) or (self.o
== "W" and self.x == 0):
return True
else:
return False
def changeDirection(self):
seq =[-3,-2,-2,-1,-1,1,1,2,4,5]
self.d = [random.sample(seq,1)[0],random.sample(seq,1)
[0] ]
if (self.d[0] == 0 or self.d[1] == 0):
self.changeDirection()
return
else:
#make moveseq
self.moveseq = []
self.step = 0
for x in range(0,int(math.fabs(self.d[0]))):
if self.d[0] > 0:
self.moveseq.append("E")
else:
self.moveseq.append("W")
for y in range(0,int(math.fabs(self.d[1]))):
if self.d[1] > 0:
self.moveseq.append("N")
else:
self.moveseq.append("S")
random.shuffle(self.moveseq)
print "new direction is : " , self.moveseq , " -
", self.d, " - " , self.o , " --> ", self.moveseq[self.step]
# imitate iRoomba movement by change direction if bumped
to wall
def moveLikeRoomba(self):
if self.checkWall():
self.changeDirection()
self.o = self.moveseq[self.step]
self.step = (self.step + 1 ) % len(self.moveseq)
return self.move()
# Simple random wall proof movement
def move(self):
if (self.o == "N" and self.y == 7):
self.o = "E"
return "R"
if (self.o == "S" and self.y == 0):
self.o = "W"
return "R"
if (self.o == "E" and self.x == 7):
self.o = "S"
return "R"
if (self.o == "W" and self.x == 0):
self.o = "N"
35

return "R"
#MOVE ONE STEP FWD ACCORDING TO ORIENTATION
if self.o == "N":
self.y = self.y + 1
if self.o == "S":
self.y = self.y - 1
if self.o == "E":
self.x = self.x + 1
if self.o == "W":
self.x = self.x - 1
return "fwd"
def markvisited(visited,celltag):
try:
visited[celltag] = visited[celltag] + 1
except:
visited[celltag] = 1
def main():
visited ={}
dirt = [[1,1],[4,6],[7,7], [3,6]]
items = ["N", "S", "W", "E"]
print dirt
print "Zooooommmmmm startin cleaning..."
vc = iRoomba(4,4,"N",[1,1])
k = 0
while (len(dirt) > 0 and k < 3500 ):
#RANDOM cleanning strategy
#m = vc.move()
#vc.setOrientation( str(random.sample(items,1)[0]) )
m = vc.moveLikeRoomba()
celltag = vc.pos()[0] + 8*vc.pos()[1]
markvisited(visited,celltag)
print k, len(dirt),vc.pos(), vc.orientation(), m
if vc.pos() in dirt:
dirt.remove(vc.pos())
print "*** found dirt *** remaining dirt ",
len(dirt)
k = k + 1
print "Cleaned the room in ", k
print visited
mean = 0.0
var = 0.0
for k in visited:
#print k, visited[k]
mean = mean + visited[k]
print "each cell is visited an avg = ", mean/len(visited),
" times"
for k in visited:
var = var + (visited[k]-mean)*(visited[k]-mean)
print "measure of dispersion of visit std = ",
math.sqrt(var)/len(visited)
print "Number of cells visited ", len(visited)
return k
if __name__ == "__main__":
main()
CODE for Statistics compilation on different cleaning
strategies
#!/usr/bin/python
# Copyright Jose Berengueres - @medialabae - 2011.11.29
# iRoomba Vacuum cleaning algorithm (Random VS iRobot
Strategy)
# Ai Strategy What is best and why?
import iroomba2
def main():
sum = 0
L = 1000
for k in range(0,L):
sum = sum + iroomba2.main()
print "the average number of steps to clean all dirt is =
",sum, " ", sum/L
if __name__ == "__main__":
main()
36

ipad:~ ber$ python roomba.py
[[1, 1], [4, 6], [7, 7], [3, 6]]
Zooooommmmmm startin cleaning...
0 4 [4, 5] N fwd
1 4 [4, 6] N fwd
*** found dirt *** remaining dirt 3
2 3 [4, 7] N fwd
new direction is : ['N', 'N', 'W', 'N', 'N'] - [-1, 4] - N
--> N
3 3 [4, 7] E R
4 3 [4, 7] E R
5 3 [3, 7] W fwd
6 3 [3, 7] E R
7 3 [3, 7] E R
8 3 [3, 7] E R
9 3 [3, 7] E R
...
*** found dirt *** remaining dirt 0
Cleaned the room in 143
{0: 2, 6: 1, 7: 3, 8: 3, 9: 1, 14: 3, 15: 8, 16: 2, 17: 1, 18: 1,
21: 1, 22: 3, 23: 8, 24: 2, 26: 1, 28: 1, 29: 2, 30: 2, 31: 7, 32:
2, 34: 1, 35: 1, 36: 1, 37: 1, 38: 1, 39: 10, 40: 2, 42: 2, 43: 2,
44: 2, 45: 2, 46: 3, 47: 8, 48: 2, 49: 2, 50: 1, 51: 1, 52: 4, 53:
2, 54: 3, 55: 8, 56: 1, 57: 7, 58: 6, 59: 6, 60: 5, 62: 1, 63: 4}
each cell is visited an avg = 2.97916666667 times
measure of dispersion of visit std = 20.2133048009
Number of cells visited 48
ipad:~ ber$
37

2 | Robotics
Maja Rudinac. ENxJ. Gymnastics &
Robotics. CEO of Lerovis. TUDelft
...Blip
Blip
----
Photo courtesy of Elsevier http://issuu.com/beleggersbelangen/docs/previewjuist11/12
Baby inspired Artificial
Intelligence (Saliency)
Key Point
“Intelligence is the capacity to
focus on the important things, to
filter - babies are good at this since
the fourth month”
“I studied how
babies make sense of the
world to build a low cost
robot”
------
Maja Rudinac
Recorded at Building 34 TU Delft on
August 21st, 2014
Chapter Flight Plan
Fo r d -T T h i n k i ng
Saliency
Fa m o u s R o b o t s

2 | Robotics - Home Robotics 39
The arm motors
are on the body, to
allow for low weight, low
backlash movement.
Coupling
2DoF
---- Intuitive Control
----

Section 1 Robotics 40
Maja, like Andrew found
inspiration in humans
This is the Ford-T
of Robotics

Baby Development
Developmental Stages for Baby: 8-10 months
http://www.youtube.com/watch?v=KzEI8z7Q0RU
Milestones
Sitting
Crawling
Object disappearing detection
hand eye coordination
Baby: 6-8 months
http://www.youtube.com/watch?v=uQmqRIR2YxA
Milestones
Moving
Rolling
Gross grasping (See video of Lea robot)
41
2 | Robotics - Saliency in Babies

4-6 months
http://www.youtube.com/watch?v=iIOPROa0BoI
Milestones
Reflexes
Visual Tracking
Use of hearing to track
Also: http://www.youtube.com/watch?v=xbyBXKiL0LU
Sensor y depr ivation
decreases the IQ.
A baby who is deprive d
of to uch dies in a few
days
42
2 | Robotics - Saliency in Babies

Hit a Wall
When Maja hit the wall of unpractical computer vision, she
turned to developmental psychologists for strategies to
cope with large amounts of image pixels. This is what she
found (so you don’t have to):
Visual Developmental Psychology
Basics
(Abridged from Maja Rudinac PhD thesis, TUDelft)
Babies at the 4th month can already tell if a character is
bad or good because we can see who they hug longer.
Infants look longer at new faces or new objects
Independent of where are born, all babies know
boundaries of objects.
Can predict collisions
Basic additive and subtractive cognition
Can identify members of own group
versus non-own group
Spontaneous motor movement is not goal directed at the
onset. The baby explores the degrees of freedom
Goal directed arm-grasp appears at the 4th month
The ability to engage and disengage attention on targets
appears from day 1 in babies.
Smooth visual tracking is present at birth
How baby cognition “works”
Development of actions of babies is goal directed by two
motives. Actions are either,
1. To discover novelty
2. To discover regularity
3. To discover the potential of their own body
Development of Perception
Perception in babies is driven by two processes:
1. Detection of structure or patterns
43
2 | Robotics - Developmental Psychology

2. Discarding of irrelevant info and keeping relevant info
Cognitive Robot Shopping List
So if we want to make a minimum viable product (MVP)
that can understand the world at least (as well or as
poorly) as a baby does, this are the functions that
according to Mrs. Maja (pronounced Maya) we will need:
A WebCam
Object Rracking
Object Discrimination
Attraction to peoples faces
Face Recognition
Use the hand to move objects to scan them form
various angles
Shades and 3D
Turns out that shades have a disproportionate influence in
helping us figure out 3D info from 2D retina pixels. When
researchers at Univ. of Texas used fake shades in a virtual
reality world, participants got head aches (because the
faking of the shades was not precise enough to fool the
brain. The brain got confused by the imperceptible
mismatches... that’s why smart people get head aches in
3D cinemas)
44
2 | Robotics - Developmental Psychology

http://www.youtube.com/watch?v=S5AnWzjHtWA
Asimo - Pet Man - Curiosity - Kokoro’s Actroid
Honda Asimo evolution and Michio Kaku on AI
http://www.youtube.com/watch?v=97iZY9DySws
45
2 | Robotics - Review of State-of-the-art
One goal of AI is
to build robots that
are better than humans
----

46
Uncanny valley helps the
human race to prevent the
spread of infectious diseases
(such as Ebola) by heavy un-liking
the sick members of the
tribe. It’s hard wired in every
one of us. See also #zoombie
http://spectrum.ieee.org/
automaton/robotics/
humanoids/the-uncanny-valley-
revisited-a-tribute-to-masahiro-
mori

47

A humanoid robot at Citec, Germany
48

The Fog of Robotics Today
They are trying to build this They should be trying to build this:
49

2 | Robotics - Review of State-of-the-art This is a pre
50
Ford-T Robot lab
----
CITEC

3 | UX - The heart of AI
2014 Flobi robot head. Bielefeld
University, Germany.
In Japan where engineers grew up
with Doraemon (a robot), Arale-chan
(a robot), Gatchaman,
Pathlabor (two robots), Ghost in
the Shell (a robot), Mazinger-Z (a
robot)... (Do you see where I am
going?) - Robots are not seen as
antagonistic characters. In the
West, we grew up with Terminator
and only recently we got Wall-e to
s our primitive cavern men fears Hello
Photo courtesy of Elsevier http://issuu.com/beleggersbelangen/docs/previewjuist11/12
I am Flobi
Sociologists like
Selma Savanovic and
Friederike Eyssel use me
to do gender studies

3 | UX - The heart of AI - Behaviourism 52
People u nder
estimate so much the
influence of
environment upon
their behavior
In previous section
you made a spell
checker... but did it
make the user happy?
Check
ch.1 of Seven
Women that changed
the UX industry
for a compelling case

How design that makes you happy is easier to use simply
because the fact that a happy brain has a higher IQ.
Don’t believe it? I don’t blame you. Lets do a little experiment
Which video is more serious about safety...
http://www.youtube.com/watch?v=DtyfiPIHsIg&feature=kp
or
or this one... http://www.youtube.com/watch?v=fSGaRVOLPfg
What is the purpose of the video. Which one’s safety
tips do you remember most.
53
3 | UX - The heart of AI - Why Happy is Better
The po int of the exercise is
not to find a w inner.
Add itionally, Each Airline
ser ves different cultures and
socioeconomics. Maybe I
should compare it to United
Airlines.
More Happy,
more IQ?

3 | UX - The heart of AI - Not every one is the same - Isabel Myers
Isabel Myers-Briggs
Knowing psychology should be a prerequisite before doing
any AI at all. Anyhow, one of the best books to go up to speed
on how humans “work”, is 50 Psychology Classics, a book I
recommend 100% because is compact. The audio book is
great. If you want to leverage human knowledge of the self my
second rec. is a book by Hellen Fisher that classifies people
into Builders, Explorers, Directors and Negotiators. There
is a lot confusion about the personality types. The most
famous is the Myers Brigs that classifies people into 16 types.
I trained myself to classify people I meet in types. I am good
at it. ^.^; It helps me to work with them better and to
understand them better. (seek to understand, then to be
understood - 7HoHEP). However, because it is popular, there
is a lot of confusion on he Myers-Briggs system.
http://www.youtube.com/watch?v=aQ2QbS-EgrM
OK, I know that you are thinking. What is my personality type?
http://www.humanmetrics.com/cgi-win/jtypes2.asp
You are welcome.
54
You want to do
“ai” with a captial
letter? you’d better start
discerning personality
types yourself...
Because
if your AI can’t
distinguish between an
ISFJ vs. ENFP he will be
perceived as inept at
social interactions...

55

via www.furthered.com
56

4 | Advanced Topics
Big Data. Exploded circa 2007.
Big Data
“A new fashionable name for Data Mining”
Deep Learning
Deep Learning is a new area of Machine
Learning research, which has been introduced
with the objective of moving Machine Learning
closer to one of its original goals: Artificial
Intelligence.
Renaissance Technologies is an American
hedge fund management company that operates
three funds.[3] It operates in East Setauket, Long
Island, New York, near Stony Brook University
with administrative functions handled in
Manhattan.
If you
want to learn Big
Data join the fight club
at kaggle.com
------
I made billions by
using AI to especulate in
Stocks. We are the Google of
trading.
-----
James Simons
Medallion Fund
23 Billion (2011)
Cloudera!
------
J. Hammerbacher
(Check Wienner vs. Wall Street 1947)

A Case
This case will help you understand a typical big data project
at the three levels:
Corporate or How to deal with conflicts of interest inside a
organization
Business logic or How to pose the right questions that
make sense from a money point of view and add value
Mathematical or How to actually do the calculations
(if you can do well the above three, you can be a successful
data scientist that everybody loves*)
* http://hbr.org/product/big-data-the-management-revolution/an/R1210C-PDF-ENG
http://www.journalofbigdata.com/content/pdf/2196-1115-1-3.pdf
58
4 | Advanced Topics - A Real Case
Berengueres and Efimov Journal of Big Data 2014, 1:3
http://www.journalofbigdata.com/content/1/1/3
C A SE S T U D Y Open Access
Airline new customer tier level forecasting for
real-time resource allocation of a miles program
Jose Berengueres1* and Dmitry Efimov2
* Correspondence: jose@uaeu.ac.ae
1UAE University, 17551 Al Ain, Abu
Dhabi, UAE
Full list of author information is
available at the end of the article
Abstract
This is a case study on an airline’s miles program resource optimization. The airline
had a large miles loyalty program but was not taking advantage of recent data mining
techniques. As an example, to predict whether in the coming month(s), a new passenger
would become a privileged frequent flyer or not, a linear extrapolation of the
miles earned during the past months was used. This information was then used
in CRM interactions between the airline and the passenger. The correlation of
extrapolation with whether a new user would attain a privileged miles status
was 39% when one month of data was used to make a prediction. In contrast,
when GBM and other blending techniques were used, a correlation of 70% was
achieved. This corresponded to a prediction accuracy of 87% with less than 3%
false positives. The accuracy reached 97% if three months of data instead of
one were used. An application that ranks users according to their probability to become
part of privileged miles-tier was proposed. Theapplicationperformsrealtimeallocation
of limited resources such as available upgrades on a given flight. Moreover, the airline
can assign now those resources to the passengers with the highest revenue potential
thus increasing the perceived value of the program at no extra cost.
Keywords: Airline; Customer equity; Customer profitability; Life time value; Loyalty program;
Frequent flyer; FFP; Miles program; Churn; Data mining
Background
Previously, several works have addressed the issue of optimizing operations of the air-line
industry. In 2003, a model that predicts no-show ratio with the purpose of opti-mizing
overbooking practice claimed to increase revenues by 0.4% to 3.2% [1]. More
recently, in 2013, Alaska Airlines in cooperation with G.E and Kaggle Inc., launched a
$250,000 prize competition with the aim of optimizing costs by modifying flight plans
[2]. However, there are very little works on miles programs or frequent flier programs
that focus on enhancing their value. One example is [3] from Taiwan, but they focused
on segmenting customers by extracting decision-rules from questionnaires. Using a
Rough Set Approach they report classification accuracies of 95%. [4] focused on seg-menting
users according to return flight and length of stays using a k-means algorithm.
A few other works such as [5], focused on predicting the future value of passengers
(Customer Equity). They attempt to predict the top 20% most valuable customers.
Using SAS software they report false positive and false negative rate of 13% and
55% respectively. None of them have applied this knowledge to increase the value-
© 2014 Berengueres and Efimov; licensee Springer. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited.

4 | Advanced Topics - Reverse Engineering the Neuro Cortex
The NeuroCortex
Jeff Hawkins (the inventor of Palm Pilot) explains how he
reverse engineers the brain. Did you know that Jeff he started
all these companies just to have enough money to study the
brain?
memory-prediction framework
http://www.youtube.com/watch?v=IOkiFOIbTkE
59
I
invented
this, to have money for
this
------
J.Hawkins

Deep Learning is a new area of Machine Learning research,
which has been introduced with the objective of moving
Machine Learning closer to one of its original goals: Artificial
Intelligence. See these course notes for a brief introduction to
Machine Learning for AI and an introduction toDeep Learning
algorithms. www.deeplearning.net/tutorial/
Deep Learning explained
(Abridged from the original from Pete Warden | @petewarden)
http://radar.oreilly.com/2014/07/what-is-deep-learning-and-why-should-you-care.html
Inside an ANN
The functions that are run inside an ANN are controlled by the
memory of the neural network, arrays of numbers known as
weights that define how the inputs are combined and
recombined to produce the results. Dealing with real-world
problems like cat-detection requires very complex functions,
which mean these arrays are very large, containing around
60 million (60MBytes) numbers in the case of one of
the recent computer vision networks. The biggest obstacle to
using neural networks has been figuring out how to set all
these massive arrays to values that will do a good job
transforming the input signals into output predictions.
Renaissance
It has always been difficult to train an ANN. But in 2012, a
breakthrough, a paper sparks a renaissance in ANN. Alex
Krizhevsky, Ilya Sutskever, and Geoff Hinton bring together a
whole bunch of different ways of accelerating the
learning process, including convolutional networks, clever
use of GPUs, and some novel mathematical tricks like ReLU
and dropout, and showed that in a few weeks they could
train a very complex network to a level that outperformed
conventional approaches to computer vision.
60
4 | Advanced Topics - Deep Learning

GPU photo by Pete Warden slides (Jetpack)
Listen to the Webcast at Strata 2013
http://www.oreilly.com/pub/e/3121
http://www.iro.umontreal.ca/~pift6266/H10/intro_diapos.pdf
Deep NN failed unitl 2006....
61

Automatic speech recognition
The results shown in the table below are for automatic speech
recognition on the popular TIMIT data set. This is a common
data set used for initial evaluations of deep learning
architectures. The entire set contains 630 speakers from eight
major dialects of American English, with each speaker reading
10 different sentences.[48] Its small size allows many different
configurations to be tried effectively with it. The error rates
presented are phone error rates (PER).
http://en.wikipedia.org/wiki/Deep_learning#Fundamental_concepts
62

Andrew Ng on Deep Learning
where AI will learn from untagged data
https://www.youtube.com/watch?v=W15K9PegQt0#t=221
To learn more about Andrew Ng on Deep Learning and the
future of #AI
- http://new.livestream.com/gigaom/FutureofAI (~1:20:00)
- https://www.youtube.com/watch?v=W15K9PegQt0#t=221
-http://deeplearning.stanford.edu
A good book to learn neural networks is...
http://neuralnetworksanddeeplearning.com/chap1.html
63

4 | Advanced Topics - The Singularity and Singularity U.
Ray thinks that in 2029 machines will have enough processors
to display consciousness. He along with Mr. Diamantis started
a summer university near Palo Alto (http://singularityu.org/) for
entrepreneurs who want to leverage his predictions. This
mo v i e d e p i c t s Ra y ’s y o u t h i n fl u e n c e s . h t t p : / /
www.rottentomatoes.com/m/transcendent-man/
64
I
invented the
Singularity, a
univeristy and one
synthesizer brand.
-----

Type to enter text
65
http://www.singularity.com/
charts/page48.html

66

1. Among all the traits of an intelligent system lack of
speed was never one of them
1. True
2. False
2. Saliency is the ability to discriminate important from
not important in formation
1. T
2. F
3. Pattern recognition is useful if you want to react to
something
1. T
2. F
4. ACO is an example of swarm intelligence
1. T
2. F
5. ACO is an example of an emerging property of a
complex system
1. yes
2. no
3. it is not complex
4. it is not emerging
5. none of above
6. ACO is suited to optimize travel time
1. yes
2. no
3. sometimes
4. none
7. Norvig’s spell checker is based on
1. Bayes
2. Probability
3. Likelihood
4. none
5. all of above
8. A way to improve Norvig spell checker is to:
1. look at distance 2 character words
2. look at the context
3. use triplets of words
4. user more precise probability
5. none
6. all
9. Norvig's spell checker is fast because
1. Python is faster than Java
2. the probabilities are pre calculated
67
4 | Advanced Topics - Sample Questions

3. the dictionary lookup time is really fast
4. none
5. all
10. The key concept of Norvig’s spell checker is to:
1. minimize spell checking mistakes
2. maximize spell checking mistakes
3. none
4. all
11. Machine Translation has greatly benefited from
Linguistics because
1. knowing the rules that govern language makes it
easy to translate
2. because most translation mistakes are syntax error
mistakes
3. because knowing the grammar is key to translate
4. all
5. none
12. The Monte Carlo Tree Simulation algorithm for Go
game is based on:
1. select a leaf node, run a random sequence of
alternating moves, run sequence to the end of game, update
outcome of game for that leaf node, add a leaf to tree.
2. select two leaf nodes, selecting a random sequence
of alternating moves, run sequence to the end of game,
update outcome of game for that leaf node, keep the best
leaf.
3. select some leaf nodes, run a random sequence of
alternating moves, run sequence to the end of game, update
outcome of game for that leaf node, keep the best leaves and
prune the worse
4. select a leaf, run some random simulations, prune
the longest branches and add some water so it does not dry
5. none
6. all are true
13. The reason computers are better at chess than at Go
is that…
1. Chess has less combinations
2. Chess rules are more complex so naturally
computers are better at it
3. none
4. all are true
14. The ACO can be used to solve the Traveling Salesman
Problem
1. True
2. False
68

15. One of the Goals of AI is to understand the man as a
machine
1. True
2. False
16. What is an agent in AI?
1. 007
2. anything that performs actions
3. anything that performs actions and is not human
4. all are true
5. none
17. The difference between the so called strong vs weak
AI is that:
1. weak postulates about ‘emulating’ intelligent
behavior
2. strong AI postulates about ‘machines’ that have
consciousness
3. all above are true
4. none is correct
18. Alien AI refers to AI that
1. is not based on neurons like humans
2. that is based on silicon
3. that is based on carbon nanotubes
4. none except 3 and 5 is correct
5. all is correct
6. It is the AI of the extra terrestrial life
19. Intelligence can be defined as
1. the computations needed to achieve goals
2. anything that looks like intelligent is intelligent
3. anything that looks like intelligent is not necessarily
intelligent
4. all are true
5. none is true
20. The field of cognitive psychology was born out of
1. AI
2. Psychology and AI
3. Cognitive science
4. It was born from the three of them
5. none is true
21. Cognitive Psychology tries to explain
1. the cognitive function in terms of information
processing
2. How we relate as social species
3. why the baby is so smart
4. how the computer can behave like a human
69

5. how the human behaves as a computer
22. Is Elsie Intelligent?
1. Elsie’s intelligence depends on the environment so
no it is not intelligent
2. According to weak AI yes
3. According to weak AI no
4. According to strong AI yes
5. According to strong AI no
6. if she could pass the Touring test it will be regarded
as intelligent according to Strong AI
23. An Alien is watching the university campus from 5 km
over the air. She observes the behavior of the students,
that from that distance appear as tiny as little ants.
According to this observation what can the alien
conclude:
1. The students as a colony exhibit some interesting
behaviors so yes they are
2. On student as an individual is not intelligent but as a
group they are
3. Students are humans so yes
4. Students are do not seem to have consciousness
because they just move from building to building and go to
feed to a special building when they are hungry, so no they are
not intelligent
5. all are true
6. none
24. Noam Chomsky’s view on AI is influenced by his
views on linguistics
1. yes
2. no
3. he would never do that
4. all are true
5. none
25. Noam Chomsky view on AI,
1. is based on the poverty of stimulus hypothesis
2. is based on the fact that kids have a “natural” ability
for language
3. is on the fact that the brain can learn from few
examples
4. none is true
5. all are true
26. Touring Machine
1. is a virtual machine
2. touring machines exist in the World
3. it is a class of machines
4. it is not a machine, it is a concept to describe how
the brain works
70

5. the brain is a touring machine of type I-IV
6. all touring machines are based on computers
7. all computers are touring machines
8. the iPhone 6 is a touring machine
27. Functionalism in Ai means that
1. it does not matter what kind of brain or computer u
use, what matters is the “software”
2. it refers to the fact that AI functions like your brain
3. it refers to the fact that form follow function
4. Excel sheet is an example of functionalism AI
5. all are true
6. none
28. The “Physical Symbol Systems Hypothesis” states
that Cognition requires the manipulation of symbolic
representations.
1. True, but only for neural networks
2. False
3. It was never proved
4. Elsie is an example of AI that disproves PSSH
5. Searle’s Chinese Room is an example that disproves
PSSH because The man inside the room does not
understand Chinese
1. T
2. F
29. The Loebner prize is
1. a robotics prize
2. a touring test prize
3. a price for linguists
4. none
5. all
30. Searle’s Chinese rooms tells something about AI
which is…
1. the whole is more than its parts
2. emerging properties come from the system
3. AI should consider the agent and the environment
as a whole
4. none
5. all are true
31. If Searle’s spoke Chinese then he would pass the
PSSH
1. true
2. false
3. depends on what is written in the books
32. If Searle’s spoke Chinese then according to PSSH.
1. he would be regarded as intelligent
71

2. No, he still would not be regarded as intelligent
3. if he spoke Chinese then it does not disprove the
hypothesis
4. The hypothesis is disproved if he speaks Chinese
5. all
6. none
33. Complexity theory states that self-organization occurs
when high level properties emerge from simple
components, therefore
1. we can say that brain cells self-organize
2. Human Intelligence is a by product of self-organization
of neuron in human brain
3. all are true
4. none are true
34. If micro-tubes are subject to quantum effects then
the brain is a touring machine
1. yes
2. no
3. yes, it is a quantum touring machine
4. all are true
5. none
35. “Mental states have aboutness on beliefs and
desires and that requires a conscious mind.”
1. True
2. False
36. Connectionism is inspired by the human brain
1. True
2. False
37. Model of the world vs. Ground Model. Model of the
world is more accurate than Ground models.
1. T
2. F
38. Gross Grasping appears in babies since the second
month
1. T
2. F
39. The uncanny valley is a legacy of a survival habit to
avoid contagion by disease
1. T
2. F
40. The uncanny valley is deeper if the robot doll moves
1. T
2. F
41. A barbie doll is at the the right side of the uncanny
valley
72

1. T
2. F
42. Pokemon is to the left of the valley:
1. T
2. F
43. Draw a flow Chart diagram of Norvig’s spell checker
44. Draw a diagram of Searle’s Chinese room
45. Draw a diagram of a touring machine
46. Code in pseudocde Norvig’s spell checker
47. Code in pseudocode a spell checker of numbers. that
correct any number which is not in the following list:
list_of_prime_numbers_from_1_to_7433.txt
48. Code in pseudocode the ACO. Given:
1. Space: matrix of L x L cells
2. N ants
3. pheromone evaporation rate 0.95 per turn
4. food location: (23,44) infinite
5. nest location: (122,133)
6. Pheromone: ants leave trail of pheromone
7. movement: ants move randomly to any of the
surrounding 8 squares, with equal probability but if
one square contains pheromone then the
probability to move to that square doubles if the
pheromone there is more than 0.05
8. Ants take food from source to nest
49. iRoomba performance is best programmed and
explained in terms of:
1. behaviors
2. plan action goal
3. a combination of both
4. none
5. all is true
50. Packbot perfromarnce is best explained in terms of
1. behaviors
2. plan action goal
3. a combination of both
4. none
51. According to Andrew Ng, Deep Learning is a buzz word
for
1. Layered neural networks
2. Is superior to support vector machines
3. It is not only referring to layered neural networks but
a whole more things
73

52. Big Data is phenomenon in which people model
large amounts of data by using machine learning
algorithms and the likes
1. True
2. False
53. The Singularity is
1. a point in time where the Earth ends
2. a point in time where machines become conscious
3. a point in time where AI surpasses the capability of
human AI
4. all
5. none
6. it is a resort at the ESA in Luxembourg
7. it is a spa popular with AI
54. The Singularity is based in the exponential growth
such as the sequence 1 2 4 8 16
1. False and 1 2 4 8 is not exponential growth
2. True
3. False
4. all
5. none
55. Nao robot can be considered as intelligent because
1. if it passes the Touring test yes
2. according to weak AI, if it passes the Touring test but
has no consciousness then no
3. Nao can never be intelligent in it’s present form
because the Intel Atom chip is very limited in
processing power
4. All are true
74

Dr. Jose Berengueres joined UAE University
as Assistant Professor in 2011. He received
MEE from Polytechnic University of
Catalonia in 1999 and a PhD in bio-inspired
robotics from Tokyo Institute of Technology
in 2007.
He has authored books on:
The Toyota Production System
Design Thinking
Human Computer Interaction
UX women designers
Business Models Innovation
He has given talks and workshops on Design
Thinking & Business Models in Germany,
Mexico, Dubai, and California.

Honda Development Center. Asimo project initial sketch
here!
Can Follow You
at different speeds
Stairs and floors
Carry On
Not taller
than human
Not heavier
than human
Can follow a human
one step behind
Two leg walking
Can ask for help in case of
problem
a bit slower
please

Back cover | Back cover ⤏ Back cover lxxvii

Artificial Intelligence for Undergrads

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