👂🎴 🕸️
<
div
>
Next
talk
:
 
Make
Your
Own
Not
-
so
-
large
-
language
-
model
 
@
State
Of
The
Art
(
s
)_
GenAI
Applied
_<
br
/>
July
5th
from
17
:
00
-
18
:
45
at
Gallerie
at
Medienhaus
(
Grunewaldstrasse
2
)<
br
/><
br
/>
Keywords
:
Large
Language
Models
''
Low
Rank
Adaptation
''
Retrieval
Augmented
Generation
''
Direct
Preference
Optimization
''
Embeddings
div
>
In
the
second
Hromada
&
Kim
(
2023
)
article
''
we
describe
first
''
syllable
-
oriented
exercise
by
means
of
which
the
Primer
aimed
to
assist
one
5
-
year
-
old
pre
-
schooler
in
increase
of
her
reading
competence
.
The
pupil
went
through
sequence
of
exercises
composed
of
evaluation
and
learning
tasks
.
Consistently
with
previous
HMPL
study
''
we
observe
increase
of
both
child
'
s
reading
skill
as
well
as
of
machine
'
s
ability
to
accurately
process
child
'
s
speech
.<
br
>
Front
.
Educ
.''
2023
<
br
>
Sec
.
Digital
Education
<
br
>
Volume
8
-
2023
|
<
a
href
=
https
://
doi
.
org
/
10
.
3389
/
feduc
.
2023
.
1063337
>
https
://
doi
.
org
/
10
.
3389
/
feduc
.
2023
.
1063337
a
><
br
><
div
>
Proof
-
of
-
concept
of
feasibility
of
human
machine
peer
learning
for
German
noun
vocabulary
learning
<
br
>
div
>
Human
Machine
Peer
Learning
(
HMPL
)
is
a
proposal
that
is
positioned
at
the
very
frontier
between
educational
''
cognitive
''
and
computer
sciences
.
HMPL
'
s
core
precepts
which
I
introduced
in
my
2022
and
2023
papers
are
simple
:
<
br
/><
br
/><
div
style
=
text
-
align
:
center
;
>
Humans
and
machines
can
learn
together
.
div
><
div
style
=
text
-
align
:
center
;
>
Humans
and
machines
can
learn
from
each
other
.
div
>
Human
learning
is
a
complex
''
multifaceted
process
that
encompasses
the
acquisition
''
understanding
''
and
application
of
knowledge
and
skills
.
It
involves
various
cognitive
''
emotional
''
and
environmental
interactions
that
lead
to
changes
in
an
individual
'
s
knowledge
''
behaviors
''
and
attitudes
.
Machine
learning
is
a
subset
of
artificial
intelligence
focused
on
developing
algorithms
that
enable
computers
to
learn
and
make
decisions
from
data
without
being
explicitly
programmed
.
It
involves
training
models
on
large
datasets
''
allowing
them
to
discover
patterns
and
relationships
.
These
models
can
then
make
inferences
''
decisions
or
predictions
or
generate
outputs
consistent
with
and
contingent
on
the
provided
input
even
in
cases
where
no
such
inputs
were
present
during
the
learning
process
.<
br
>
On
the
material
level
''
human
biochemistry
-
based
learning
is
fundamentally
different
from
machine
learning
taking
place
on
silicon
-
based
transistors
.
A
human
-
machine
peer
learning
curriculum
(
i
.
e
.''
a
HMPL
-
C
)
is
a
planned
sequence
of
educational
instructions
i
.
e
.''
a
curriculum
which
involves
:<
br
>
1
.
At
least
one
human
learner
G
''
H
''
I
''
...
which
gradually
develops
her
/
his
/
their
skill
Γ
.<
br
>
2
.
At
least
one
artificial
learner
a
''
b
''
c
''
...
which
gradually
develops
its
/
her
/
his
/
their
skill
σ
.<
br
>
3
.
Activities
by
means
of
which
G
(
resp
.
H
''
I
''
etc
.)
develops
her
/
his
/
their
skill
Γ
''
which
directly
involve
knowledge
and
competence
exhibited
by
a
(
resp
.
b
''
c
''
etc
.).<
br
>
4
.
Activities
by
means
of
which
a
(
resp
.
b
''
c
''
etc
.)
develops
her
/
his
/
their
skill
σ
''
which
directly
involve
knowledge
and
competence
exhibited
by
G
(
resp
.
H
''
I
''
etc
.).<
br
>
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