👂 🎴 🕸️
 <
br
>
„
Man
is
a
'
homo
discens
,'
a
learning
being
.
People
learn
as
long
as
they
live
.
Life
is
inseparably
connected
with
learning
.
“
Horst
Siebert
<
br
>
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
.
Machine
Learning
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
is
„
a
process
in
which
individuals
learn
by
observing
the
behaviors
of
others
''
imitating
them
''
and
experiencing
the
consequences
of
these
actions
.
“
(
Bandura
''
1977
)
Peer
learning
Active
engagement
:::
Attention
:::
Error
Feedback
:::
Consolidation
Human
-
Machine
Peer
Learning
Teaching
''
Pedagogy
''
Didactics
Artificial
Teacher
Avatars
Educational
Systems
Extended
Educational
Environments
The
Congress
Initiation
into
Optimization
&
Problem
-
Solving
<
div
>
The
term
„
heuristics
“
comes
from
the
Greek
word
„
heuriskein
''
“
which
means
„
to
find
“
or
„
to
discover
.
“
This
term
reflects
the
idea
of
finding
or
discovering
solutions
through
intuitive
or
trial
-
and
-
error
methods
. <
br
>
div
>
<
div
>
In
engineering
''
a
problem
P
is
defined
by
an
objective
function
that
needs
to
be
optimized
''
a
vector
of
parameters
that
can
be
adjusted
''
and
constraints
that
must
be
satisfied
.
div
><
div
><
br
>
div
><
div
>
The
solution
S
is
the
optimal
set
of
parameter
values
that
achieve
the
desired
optimization
while
staying
within
the
bounds
of
the
constraints
.
div
>
Parameters
&
Constraints
<
br
>
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
>
 <
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>
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