👂🎴 🕸️
The
objective
of
„
0th
Berlin
Symposium
on
Artificial
Teacher
Avatars
“
is
the
presentation
''
discussion
and
development
of
tools
''
models
and
methodologies
leading
to
emergence
of
generative
AI
avatars
usable
and
useful
in
domain
of
education
.
<
p
class
=
„
fragment
“
>
Generative
AI
is
reality
and
it
'
s
better
to
be
its
co
-
creator
then
its
consumer
.
p
><
p
class
=
„
fragment
“
>
Learning
by
doing
&
experimenting
&
creating
critical
stance
.
p
><
p
class
=
„
fragment
“
>
Personalized
AI
tutors
are
important
components
of
vaste
majority
of
future
educational
systems
.
p
><
p
class
=
„
fragment
“
>
Property
„
avatarized
“
is
the
property
23
on
the
roadmap
to
an
ideal
Bildungsinstrument
I
'
ve
been
working
on
since
some
times
.
p
>
It
'
s
about
:
<
br
/><
p
class
=
„
fragment
“
>
Artificial
Intelligence
in
Education
(
AIED
):
Education
about
AI
&
Education
with
AI
p
><
p
class
=
„
fragment
“
>
generative
artificial
intelligence
(
notably
large
language
models
''
text
-
to
-
speech
/
voice
cloning
''
talking
head
generation
)
p
><
p
class
=
„
fragment
“
>
multi
-
modal
&
collective
avatars
p
><
p
class
=
„
fragment
“
>
open
-
source
tools
(
text
-
webui
-...''
comfy
ui
)''
programming
languages
(
python
''
mojo
)''
models
(
Stable
Diffusion
''
Mistral
)
&
concrete
methods
(
LORA
''
Direct
Preferenc
Optimization
)
p
><
p
class
=
„
fragment
“
>
tweaking
knobs
&
parameters
p
><
p
class
=
„
fragment
“
>&
maybe
little
bit
of
„
prompt
engineering
“
p
>
This
symposium
is
NOT
about
:
<
br
/><
p
class
=
„
fragment
“
>
dysinformation
p
><
p
class
=
„
fragment
“
>
deep
-
fakes
of
living
persons
p
><
p
class
=
„
fragment
“
>
corporate
products
p
><
p
class
=
„
fragment
“
>
entertainment
(
e
.
g
.
humor
/
horror
/
porn
/
cognitive
spam
)
p
><
p
class
=
„
fragment
“
>
so
-
called
discriminative
AI
(
e
.
g
.
speech
/
face
/
object
recognition
etc
.)
p
><
p
class
=
„
fragment
“
>
I
-
avatarizaion
p
>
Anything
else
?
During
today
'
s
workshop
''
I
will
introduce
You
to
two
methods
how
You
can
fine
-
tune
Your
model
to
allow
it
to
provide
better
inferences
for
the
task
You
want
to
solve
.<
br
><
p
class
=
„
fragment
“
>
Lora
training
::
here
You
just
have
a
bunch
of
data
p
><
p
class
=
„
fragment
“
>
Direct
Preference
Optimization
::
here
You
have
data
which
contains
list
of
tasks
(
e
.
g
.
questions
)
and
associated
correct
and
incorrect
answers
p
>
In
the
context
of
Artificial
Intelligence
in
Education
(
AIED
)''
teacher
avatarization
is
the
process
of
transforming
the
dataset
produced
by
one
concrete
human
teacher
(
or
a
precisely
-
defined
group
of
human
teachers
)
into
a
multi
-
modal
generative
artificial
intelligence
system
with
which
future
generations
of
learners
can
seemlessly
interact
.
<
div
>
Generative
künstliche
Intelligenz
(
KI
)
widmet
sich
der
Erstellung
neuer
''
oft
unvorhergesehener
Daten
oder
Inhalte
''
die
das
Ergebnis
des
Lernens
aus
bestehenden
Daten
sind
.
Diese
Modelle
'
verstehen
'
irgendwie
die
Struktur
und
Verteilung
der
Daten
''
auf
denen
sie
trainiert
wurden
''
und
versuchen
''
neue
Muster
zu
erstellen
''
die
mit
diesen
erlernten
Mustern
übereinstimmen
.
Generative
Modelle
können
für
verschiedene
Zwecke
verwendet
werden
''
wie
zum
Beispiel
die
Erstellung
von
Bildern
''
Texten
''
Tönen
oder
Videos
und
werden
oft
in
Bereichen
wie
der
künstlichen
Inhaltsproduktion
oder
Sprachsynthese
eingesetzt
.
div
>
Neuronale
Netze
im
Kontext
der
KI
sind
<
strong
>
keine
echten
Nervenzellen
strong
>''
sondern
softwarebasierte
Modelle
''
deren
Architektur
von
der
Art
und
Weise
inspiriert
ist
''
wie
das
menschliche
Gehirn
Informationen
verarbeitet
.
Diese
Modelle
bestehen
aus
„
Schichten
“
von
Datenstrukturen
''
die
„
Neuronen
“
genannt
werden
.
Die
Neuronen
sind
miteinander
verbunden
und
ihre
Verbindungen
haben
ein
bestimmtes
„
Gewicht
“
.
Im
Lernprozess
passt
das
System
diese
Gewichte
-
auch
„
Parameter
“
genannt
-
allmählich
an
''
um
den
Fehler
zwischen
seiner
Vorhersage
und
dem
''
was
vorhergesagt
werden
soll
''
zu
verringern
.
A
trained
neural
network
is
stored
in
a
file
.
This
file
is
called
„
a
model
“
.
When
You
want
to
use
it
-
either
for
inferencing
or
training
or
both
-
You
need
to
load
the
model
from
the
disk
into
memory
.<
div
><
br
>
div
><
div
>
Based
on
the
amount
of
„
parameters
“
(
e
.
g
.
numbers
which
represent
the
synaptic
weight
)
the
model
contains
''
the
process
of
loading
into
memory
shall
be
or
shall
not
be
succesful
;)
div
>
In
machine
learning
and
AI
''
we
speak
about
<
br
><
p
class
=
„
fragment
“
>
„
training
“
when
the
AI
is
learning
from
the
data
we
provide
it
p
><
p
class
=
„
fragment
“
>
„
inferencing
“
when
the
AI
is
using
its
current
„
knowledge
“
to
solve
new
problems
(
e
.
g
.
problems
which
maybe
weren
'
t
in
the
training
data
at
all
)
p
>
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