A task-oriented framework for generative AI in design
Journal of Creativity,
Год журнала:
2024,
Номер
34(2), С. 100086 - 100086
Опубликована: Апрель 2, 2024
The
intersection
of
Artificial
Intelligence
and
Design
disciplines
such
as
Architecture,
Urban
Planning,
Engineering
Product
has
been
a
longstanding
pursuit,
with
Generative
AI
(GAI)
ushering
in
new
era
possibilities.
research
presented
herein
explores
how
GAI
can
enhance
creativity
assist
practitioners
tasks
needed
to
create
products
as,
but
not
limited
to,
renderings,
concepts,
construction
techniques,
materials,
data
analytics
or
maps.
We
apply
framework
combinational,
exploratory
transformational
organize
recent
advancements
support
each
creative
category.
propose
conceptual
towards
creativity,
identify
real-world
examples
demonstrate
GAI's
impact,
transforming
sketches
into
detailed
renders,
facilitating
real-time
3D
model
generation,
predicting
trends
through
creating
images
reports
via
text
prompts.
Our
work
envisions
future
where
becomes
collaborator
complete
certain
automated
while
liberating
Designers
focus
on
innovation.
Язык: Английский
Student Perspectives on Generative Ai: Perceived Functionality, Impact on Technocreative and Entrepreneurial Skills, and its Utility in Business Management
Опубликована: Янв. 1, 2025
Язык: Английский
The discovery and innovation of AI does not qualify as creativity
Journal of Cognitive Psychology,
Год журнала:
2024,
Номер
unknown, С. 1 - 10
Опубликована: Дек. 9, 2024
This
paper
explores
the
possibility
that
AI
can
be
creative.
The
output
of
sometimes
appears
to
creative,
and
has
been
attributed
with
creativity,
but
processes
used
by
are
indicative
artificial
creativity
rather
than
authentic
creativity.
There
important
implications.
If
is
a
creative
capacity,
for
example,
meaning
"creativity"
would
diluted
such
it
relegates
some
what
humans
do
when
they
in
turn
misinform
could
mislead
efforts
support
human
focus
on
outcomes
does
allow
objectivity,
most
useful
explanations
require
description
underlying
processes.
present
effort
examines
involved
(a)
memory
(b)
perception,
(c)
machine
learning.
It
suggests
generative
potentially
innovative,
not
truly
Further,
suggest
discovery
Язык: Английский
How technoscientific knowledge advances: A Bell-Labs-inspired architecture
Research Policy,
Год журнала:
2024,
Номер
53(4), С. 104983 - 104983
Опубликована: Фев. 21, 2024
Understanding
how
science
and
technology
advance
has
long
been
of
interest
to
diverse
scholarly
communities.
Thus
far,
however,
such
understanding
not
easy
map
to,
thus
improve,
the
operational
practice
research
development.
Indeed,
one
might
argue
that
development,
particularly
its
exploratory
half,
become
less
effective
in
recent
decades.
In
this
paper,
we
describe
a
rethinking
advance,
is
consistent
with
many
(though
all)
perspectives
communities
just
mentioned,
helps
bridge
divide
between
theory
practice.
The
result
an
architecture
call
"Bell's
Dodecants,"
reflect
six
mechanisms
two
flavors,
their
balanced
nurturing
at
Bell
Labs,
iconic
20th
century
industrial
development
laboratory.
Язык: Английский
Collaborative Natural and Artificial Intelligence: a Multilayer Network Interpretation
Опубликована: Июль 10, 2024
The
revolutionary
idea
of
complementary
intelligence
involves
a
unique
strategy
in
which
artificial
(AI)
works
with
human
cognitive
abilities
embodied
by
natural
(NI),
creating
harmonious
partnership
instead
engaging
competitive
relationship.
This
innovative
concept
can
potentially
revolutionize
how
we
approach
scientific
research
and
discovery.
cutting-edge
process
capitalizes
on
pooling
the
vast
information
from
various
distinct
autonomous
sources
to
generate
forecasts
that
frequently
outshine
perspectives
significant
margins,
leading
groundbreaking
advancements
development
based
hybrid
intelligence.
Multilayer
networks
extend
traditional
network
theory
adjacent
layers
linked
through
copula
nodes.
Artificial
intelligence,
its
retrieval
capacity
collect
interpret
big
data,
sharply
increases
number
nodes
their
linking
interpretation.
process,
amplified
multilayer
networks,
dramatically
improves
scalability
networked
ecosystem.
represent
NI
AI
hyperplanes,
connecting
them
edge
properties.
Thus,
it
is
possible
measure
interaction
AI,
assessing
scalable
value
co-creation
study
examines
an
multidisciplinary
approach.
A
theoretical
framework
these
related
concepts
precedes
practical
insights
for
applications
future
research.
Язык: Английский