👂🎴 🕸️ ⊞
 <
br
>
Each
student
randomly
picks
up
one
sticker
.
To
each
sticker
type
''
point
value
is
associated
.
You
have
15
minutes
to
do
whatever
You
want
(
e
.
g
.
deception
''
corruption
''
seduction
etc
.
*).
After
15
minutes
''
the
person
(
or
student
group
)
which
managed
to
collect
stickers
with
highest
sum
of
points
will
be
allowed
to
establish
one
rule
which
the
whole
class
will
follow
during
rest
of
semester
.<
div
><
br
>
div
><
div
>*
the
only
thing
prohibited
is
violence
div
>
All
those
who
have
a
UdK
account
''
log
in
here
*:<
br
/><
br
/><
a
href
=
„
https
://
medienhaus
.
udk
-
berlin
.
de
/
login
“
target
=
„
blank
“
rel
=
„
noopener
“
>
https
://
medienhaus
.
udk
-
berlin
.
de
/
login
a
><
br
/> <
br
/>
and
subsequently
join
the
course
(#
edu
-
intelligence
)
room
:<
br
/> <
br
/><
a
href
=
„
https
://
medienhaus
.
udk
-
berlin
.
de
/
classroom
/#/
room
/#
edu
-
art
-
cognition
:
medienhaus
.
udk
-
berlin
.
de
“
target
=
„
blank
“
rel
=
„
noopener
“
>
https
://
medienhaus
.
udk
-
berlin
.
de
/
classroom
/#/
room
/#
edu
-
art
-
cognition
:
medienhaus
.
udk
-
berlin
.
de
a
><
br
/><
br
/>(
or
install
matrix
client
apps
like
Element
or
Fluffychat
and
put
„
medienhaus
.
udk
-
berlin
.
de
“
as
homeserver
)
<
p
class
=
„
fragment
“
>
who
am
I
p
><
p
class
=
„
fragment
“
>
who
are
You
p
><
p
class
=
„
fragment
“
>
is
this
a
course
for
You
?
p
><
p
class
=
„
fragment
“
>
credits
(
2
ECTS
for
>
75
%
attendance
''
+
1
for
referat
/
Congress
contribution
''
+
2
Hausarbeit
)
p
><
p
class
=
„
fragment
“
>
Leistungsnachweis
p
><
p
class
=
„
fragment
“
>
signature
-
related
issues
p
><
p
class
=
„
fragment
“
>
Feedback
box
p
><
p
class
=
„
fragment
“
>
Congress
p
>
Please
answer
(
anonymously
)
on
the
piece
of
paper
at
least
one
among
following
questions
:<
p
class
=
„
fragment
“
>
1
.
What
did
You
learn
?
p
><
p
class
=
„
fragment
“
>
2
.
What
did
You
like
?
p
><
p
class
=
„
fragment
“
>
3
.
What
did
disturb
You
?
p
><
p
class
=
„
fragment
“
>
4
.
What
did
You
not
like
?
p
>
and
throw
it
into
Feedbackbox
.
„
Man
is
a
'
homo
discens
,'
a
learning
being
.
People
learn
as
long
as
they
live
.
Life
is
inseparably
connected
with
learning
.
“
Horst
Siebert
<
br
>
<
p
class
=
„
fragment
“
>
TAKEN
Chapter
3
(
Babies
'
Invisible
Knowledge
)
and
4
(
The
birth
of
a
brain
)
from
Dehaene
'
s
„
How
we
Learn
“
p
><
p
class
=
„
fragment
“
>
TAKEN
Chapter
5
(
Nurture
'
s
Share
)
and
6
(
Recycle
Your
Brain
)
from
Dehaene
'
s
„
How
we
Learn
“
p
><
p
class
=
„
fragment
“
>
Chapter
7
(
Attention
)
and
8
(
Active
Engagement
)
from
Dehaene
'
s
„
How
we
Learn
“
p
><
p
class
=
„
fragment
“
>
Chapter
9
(
Error
Feedback
)
and
10
(
Consolidation
)
from
Dehaene
'
s
„
How
we
Learn
“
p
><
p
class
=
„
fragment
“
>
AI
unplugged
activity
-
Classification
with
Decision
Trees
p
><
p
class
=
„
fragment
“
>
AI
unplugged
activity
-
#
deeplearning
p
><
p
class
=
„
fragment
“
>
AI
unplugged
activity
-
Reinforcement
learning
p
><
p
class
=
„
fragment
“
>
Non
-
human
learning
(
plants
''
animals
etc
.)
p
><
p
class
=
„
fragment
“
>
Un
-
learning
&
„
altered
“
learning
.
p
>
Implicit
learning
is
the
process
of
acquiring
knowledge
or
skills
unconsciously
''
without
intentional
effort
or
explicit
awareness
of
what
is
being
learned
.
It
typically
occurs
through
repeated
exposure
to
patterns
''
stimuli
''
or
behaviors
''
allowing
individuals
to
internalize
rules
or
structures
without
being
able
to
articulate
them
directly
.
Experiential
learning
is
a
process
of
learning
through
direct
experience
''
where
individuals
engage
in
activities
''
reflect
on
their
actions
''
and
apply
what
they
’
ve
learned
to
new
situations
.
Rather
than
solely
reading
or
listening
''
learners
actively
participate
''
often
experimenting
''
making
mistakes
''
and
adapting
.<
br
>
Supervised
learning
is
a
type
of
machine
learning
where
a
model
is
trained
on
labeled
data
to
learn
the
mapping
between
input
features
and
corresponding
outputs
.
The
goal
is
to
enable
the
model
to
make
accurate
predictions
or
classifications
on
unseen
data
by
minimizing
the
error
between
its
predictions
and
the
true
labels
.
Common
tasks
include
regression
(
predicting
continuous
values
)
and
classification
(
assigning
categories
).
Supervised
learning
relies
on
a
training
dataset
with
known
inputs
and
outputs
and
evaluates
performance
using
a
separate
test
dataset
.
Examples
include
spam
email
detection
''
image
recognition
''
and
speech
-
to
-
text
systems
.
Reinforcement
learning
(
RL
)
is
a
machine
learning
paradigm
where
an
agent
learns
to
make
decisions
by
interacting
with
an
environment
.
Instead
of
being
told
what
to
do
''
the
agent
takes
actions
and
receives
feedback
in
the
form
of
rewards
or
penalties
.
The
goal
is
to
maximize
cumulative
rewards
over
time
by
discovering
an
optimal
strategy
''
known
as
a
policy
.
RL
is
inspired
by
trial
-
and
-
error
learning
in
humans
and
animals
''
where
behavior
improves
through
experience
.
It
’
s
particularly
useful
for
tasks
with
sequential
decision
-
making
''
such
as
robotics
''
game
playing
''
and
autonomous
systems
''
where
actions
impact
not
only
immediate
rewards
but
also
future
outcomes
.
<
br
>
Social
learning
Peer
learning
Machine
Learning
Human
-
Machine
Peer
Learning
Teaching
''
Pedagogy
''
Didactics
Artificial
Teacher
Avatars
Educational
Systems
Extended
Educational
Environments
The
Congress
What
is
„
intelligence
“
and
how
is
it
defined
by
different
people
and
different
cultures
?
Is
there
only
one
„
general
“
intelligence
thanks
to
which
humans
and
machines
solve
problems
or
is
it
more
appropriate
to
speak
about
combinations
of
„
multiple
intelligences
“
-
emotional
''
intepersonal
''
intrapersonal
''
spatial
''
visual
''
logical
''
mathematical
''
bodily
''
moral
''
narrative
''
etc
.
?
Can
we
speak
about
intelligence
independent
of
cultural
and
socio
-
economical
context
within
which
its
acts
and
is
embedded
?
Do
organic
(
OI
)
and
artificial
intelligence
(
AI
)
have
something
in
common
or
are
they
fundamentally
and
unreconcilably
different
?
In
order
to
explore
potential
answers
to
these
questions
''
we
will
look
into
history
of
cognitive
(
psychology
''
anthropology
)
and
computer
 
(
informatics
''
cybernetics
) 
sciences
''
we
will
read
stories
about
„
idiot
savants
“
and
children
raised
in
wilderness
and
briliant
minds
of
the
past
in
order
to
ultimately
ask
our
own
AI
systems
to
tell
them
something
about
themselves
.<
br
>
 <
br
>
The
seminar
will
start
with
a
question
„
What
is
language
and
how
can
You
define
it
?
“
.
Subsequently
''
we
will
see
how
men
and
women
of
past
and
present
answered
that
question
-
from
grammarians
of
ancient
India
all
the
way
to
most
modern
theories
of
phonetics
''
phonology
''
morphosyntax
''
semantics
and
pragmatics
.
Special
focus
will
be
put
on
theories
of
language
acquisition
''
that
is
''
on
discussion
the
process
by
means
of
which
maternal
language
is
acquired
by
human
children
.
All
this
to
be
able
to
end
the
seminar
with
an
answer
to
the
question
:
„
Could
artificial
intelligences
like
GPT
-
X
be
ever
able
to
understand
the
meaning
of
the
word
meaning
?
“
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