👂 🎴 🕸️
<
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informatics
*''
artificial
intelligence
''
machine
learning
''
cybernetics
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psychology
''
linguistics
''
pedagogy
''
didactics
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philology
''
germanistics
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philosophy
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art
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note
:
in
informatics
''
we
count
from
zero
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<
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slightly
different
than
an
ordinary
„
Tagung
“
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limited
number
of
participants
with
different
perspectives
and
skills
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international
''
cross
-
generational
and
multi
-
lingual
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>
gradual
shift
from
frontal
lecture
to
interactive
sessions
and
roundtable
discussions
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>
<
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inform
''
network
''
brainstorm
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exchange
best
practices
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answer
the
question
:
„
Should
we
use
AI
in
Leseförderung
?
“
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>
create
tradition
of
„
AI
&
Reading
“
Symposia
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have
fun
&
enjoy
the
moment
!
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>
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>
learning
&
teaching
how
to
read
is
a
very
complex
process
where
cognitive
''
social
''
cultural
''
economical
''
technological
and
even
ideological
factors
intertwine
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>
reading
competence
falls
down
drastically
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not
only
because
of
CoVid
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>
new
repositories
(
Internet
)''
media
(
smartphones
)
and
semiotic
systems
(
emojis
)
emerge
which
undermine
authority
of
traditional
means
of
transferring
knowledge
(
books
''
libraries
''
human
teachers
...)
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>
<
div
>
The
advent
of
AI
already
obliges
us
to
rethink
many
educational
practices
(
e
.
g
.
exams
).<
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>
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><
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><
br
>
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><
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>
Two
types
of
AI
will
be
addressed
during
this
symposium
:
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discriminative
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generative
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>
<
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>
Discriminative
artificial
intelligence
<
strong
>
recognizes
and
classifies
strong
>
objects
into
categories
. 
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><
div
> 
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><
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>
In
context
of
reading
&
writing
acquisition
''
these
forms
of
dAI
are
of
particular
relevance
:
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voice
identification
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handwriting
recognition
/
identification
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facial
expressions
....
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><
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>
speech
signal
classification
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><
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>
speech
-
to
-
text
(
STT
)''
automatic
speech
recognition
(
ASR
):
both
disciminative
and
generative
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>
<
div
>
Generative
artificial
intelligence
<
strong
>
produces
and
creates
strong
>.
<
br
>
div
><
div
><
br
>
div
>
In
context
of
this
symposium
''
following
technologies
will
be
addressed
:<
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><
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>
text
-
to
-
speech
and
voice
cloning
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><
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=
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fragment
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>
text
-
to
-
image
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><
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>
„
talking
head
“
generation
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><
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class
=
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fragment
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>
large
language
models
p
>
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