Abstract

Creativity is a fundamental feature of human intelligence. However, achieving creativity is often considered a challenging task, particularly in design. In recent years, using computational machines to support people in creative activities in design, such as idea generation and evaluation, has become a popular research topic. Although there exist many creativity support tools, few of them could produce creative solutions in a direct manner, but produce stimuli instead. DALL·E is currently the most advanced computational model that could generate creative ideas in pictorial formats based on textual descriptions. This study conducts a Turing test, a computational test, and an expert test to evaluate DALL·E’s capability in achieving combinational creativity comparing with human designers. The results reveal that DALL·E could achieve combinational creativity at a similar level to novice designers and indicate the differences between computer and human creativity.

References

1.
Childs
,
P.
,
Han
,
J.
,
Chen
,
L.
,
Jiang
,
P.
,
Wang
,
P.
,
Park
,
D.
,
Yin
,
Y.
,
Dieckmann
,
E.
, and
Vilanova
,
I.
,
2022
, “
The Creativity Diamond—A Framework to Aid Creativity
,”
J. Intell.
,
10
(
4
), p.
73
.
2.
Amabile
,
T. M.
,
1983
,
The Social Psychology of Creativity
,
Springer
,
New York
.
3.
Shute
,
V. J.
, and
Rahimi
,
S.
,
2021
, “
Stealth Assessment of Creativity in a Physics Video Game
,”
Comput. Hum. Behav.
,
116
, p.
106647
.
4.
De Bono
,
E.
,
1985
,
Six Thinking Hats
,
Little, Brown and Company
,
Boston, MA
.
5.
Eberle
,
B.
,
1996
,
Scamper: Games for Imagination Development
,
Prufrock Press
,
Waco, TX
.
6.
Zwicky
,
F.
,
1969
,
Discovery, Invention, Research Through the Morphological Approach
,
Macmillan
,
New York
.
7.
Altshuller
,
G. S.
,
1984
,
Creativity as an Exact Science: The Theory of the Solution of Inventive Problems
,
Gordon and Breach Publishers
,
Amsterdam, Netherlands
.
8.
Linsey
,
J. S.
,
Markman
,
A. B.
, and
Wood
,
K. L.
,
2012
, “
Design by Analogy: A Study of the WordTree Method for Problem Re-Representation
,”
ASME J. Mech. Des.
,
134
(
4
), p.
041009
.
9.
Yilmaz
,
S.
,
Daly
,
S. R.
,
Seifert
,
C. M.
, and
Gonzalez
,
R.
,
2016
, “
Evidence-Based Design Heuristics for Idea Generation
,”
Des. Stud.
,
46
, pp.
95
124
.
10.
Helms
,
M.
,
Vattam
,
S. S.
, and
Goel
,
A. K.
,
2009
, “
Biologically Inspired Design: Process and Products
,”
Des. Stud.
,
30
(
5
), pp.
606
622
.
11.
Chakrabarti
,
A.
, and
Shu
,
L. H.
,
2010
, “
Biologically Inspired Design
,”
Artif. Intell. Eng. Des. Anal. Manuf.
,
24
(
4
), pp.
453
454
.
12.
Oman
,
S. K.
,
Tumer
,
I. Y.
,
Wood
,
K.
, and
Seepersad
,
C.
,
2013
, “
A Comparison of Creativity and Innovation Metrics and Sample Validation Through in-Class Design Projects
,”
Res. Eng. Des.
,
24
(
1
), pp.
65
92
.
13.
Han
,
J.
,
Shi
,
F.
,
Chen
,
L.
, and
Childs
,
P. R. N.
,
2018
, “
A Computational Tool for Creative Idea Generation Based on Analogical Reasoning and Ontology
,”
Artif. Intell. Eng. Des. Anal. Manuf.
,
32
(
4
), pp.
462
477
.
14.
Sarica
,
S.
,
Luo
,
J.
, and
Wood
,
K. L.
,
2020
, “
TechNet: Technology Semantic Network Based on Patent Data
,”
Expert Syst. Appl.
,
142
, p.
112995
.
15.
Siddharth
,
L.
,
Blessing
,
L. T. M.
,
Wood
,
K. L.
, and
Luo
,
J.
,
2022
, “
Engineering Knowledge Graph From Patent Database
,”
ASME J. Comput. Inf. Sci. Eng.
,
22
(
2
), p.
021008
.
16.
Obieke
,
C. C.
,
Milisavljevic-Syed
,
J.
,
Silva
,
A.
, and
Han
,
J.
,
2023
, “
A Computational Approach to Identifying Engineering Design Problems
,”
ASME J. Mech. Des.
,
145
(
4
), p.
041406
.
17.
Boden
,
M. A.
,
2004
,
The Creative Mind: Myths and Mechanisms
, 2nd ed.,
Routledge
,
London
.
18.
Simonton
,
D. K.
,
2017
, “Domain-General Creativity: On Generating Original, Useful, and Surprising Combinations,”
The Cambridge Handbook of Creativity Across Domains
,
J. C.
Kaufman
,
V. P.
Glăveanu
, and
B.
John
, eds.,
The Cambridge University Press
,
Cambridge, UK
, pp.
18
40
.
19.
Han
,
J.
,
Shi
,
F.
,
Chen
,
L.
, and
Childs
,
P. R. N.
,
2018
, “
The Combinator—A Computer-Based Tool for Creative Idea Generation Based on a Simulation Approach
,”
Des. Sci.
,
4
, p.
e11
.
20.
Garvey
,
B.
,
Chen
,
L.
,
Shi
,
F.
,
Han
,
J.
, and
Childs
,
P. R.
,
2019
, “
New Directions in Computational, Combinational and Structural Creativity
,”
Proc. Inst. Mech. Eng. C: J. Mech. Eng. Sci.
,
233
(
2
), pp.
425
431
.
21.
Beaty
,
R. E.
, and
Johnson
,
D. R.
,
2021
, “
Automating Creativity Assessment With SemDis: An Open Platform for Computing Semantic Distance
,”
Behav. Res. Methods
,
53
(
2
), pp.
757
780
.
22.
Ramesh
,
A.
,
Pavlov
,
M.
,
Goh
,
G.
,
Gray
,
S.
,
Voss
,
C.
,
Radford
,
A.
,
Chen
,
M.
, and
Sutskever
,
I.
,
2021
, “
Zero-Shot Text-to-Image Generation
,”
The 38th International Conference on Machine Learning, PMLR
,
Virtual
,
July 18–24
.
23.
Besemer
,
S. P.
, and
O'quin
,
K.
,
1986
, “
Analyzing Creative Products: Refinement and Test of a Judging Instrument
,”
J. Creat. Behav.
,
20
(
2
), pp.
115
126
.
24.
Horn
,
D.
, and
Salvendy
,
G.
,
2006
, “
Product Creativity: Conceptual Model, Measurement and Characteristics
,”
Theor. Issues Ergon. Sci.
,
7
(
4
), pp.
395
412
.
25.
Cropley
,
D.
, and
Cropley
,
A.
,
2005
, “Engineering Creativity: A Systems Concept of Functional Creativity,”
Creativity Across Domains: Faces of the Muse
,
J. C.
Kaufman
, and
J.
Baer
, eds.,
Lawrence Erlbaum Associates Publishers
,
Mahwah, NJ
, pp.
169
185
.
26.
Shah
,
J. J.
,
Smith
,
S. M.
, and
Vargas-Hernandez
,
N.
,
2003
, “
Metrics for Measuring Ideation Effectiveness
,”
Des. Stud.
,
24
(
2
), pp.
111
134
.
27.
Han
,
J.
,
Forbes
,
H.
, and
Schaefer
,
D.
,
2021
, “
An Exploration of How Creativity, Functionality, and Aesthetics Are Related in Design
,”
Res. Eng. Des.
,
32
(
3
), pp.
289
307
.
28.
Gulrajani
,
I.
,
Ahmed
,
F.
,
Arjovsky
,
M.
,
Dumoulin
,
V.
, and
Courville
,
A.
,
2017
, “
Improved Training of Wasserstein Gans
,”
Adv. Neural Inf. Process. Syst.
,
30
, pp.
5769
5779
.
29.
Heusel
,
M.
,
Ramsauer
,
H.
,
Unterthiner
,
T.
,
Nessler
,
B.
, and
Hochreiter
,
S.
,
2017
, “
Gans Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
,”
Adv. Neural Inf. Process. Syst.
,
30
, pp.
6629
6640
.
30.
Ward
,
T. B.
, and
Kolomyts
,
Y.
,
2010
, “Cognition and Creativity,”
The Cambridge Handbook of Creativity
,
J. C.
Kaufman
, and
R. J.
Sternberg
, eds.,
The Cambridge University Press
,
Cambridge, UK
, pp.
93
112
.
31.
Yang
,
H.
, and
Zhang
,
L.
,
2016
, “
Promoting Creative Computing: Origin, Scope, Research and Applications
,”
Digit. Commun. Netw.
,
2
(
2
), pp.
84
91
.
32.
Nagai
,
Y.
,
Taura
,
T.
, and
Mukai
,
F.
,
2009
, “
Concept Blending and Dissimilarity: Factors for Creative Concept Generation Process
,”
Des. Stud.
,
30
(
6
), pp.
648
675
.
33.
Han
,
J.
,
Shi
,
F.
,
Park
,
D.
,
Chen
,
L.
, and
Childs
,
P.
,
2018
, “
The Conceptual Distances Between Ideas in Combinational Creativity
,”
DS92: Proceedings of the DESIGN 2018 15th International Design Conference
,
Dubrovnik, Croatia
,
May 21–24
.
34.
Han
,
J.
,
Park
,
D.
,
Shi
,
F.
,
Chen
,
L.
,
Hua
,
M.
, and
Childs
,
P. R.
,
2019
, “
Three Driven Approaches to Combinational Creativity: Problem-, Similarity- and Inspiration-Driven
,”
Proc. Inst. Mech. Eng. C: J. Mech. Eng. Sci.
,
233
(
2
), pp.
373
384
.
35.
Chen
,
L.
,
Wang
,
P.
,
Dong
,
H.
,
Shi
,
F.
,
Han
,
J.
,
Guo
,
Y.
,
Childs
,
P. R. N.
,
Xiao
,
J.
, and
Wu
,
C.
,
2019
, “
An Artificial Intelligence Based Data-Driven Approach for Design Ideation
,”
J. Vis. Commun. Image Represent.
,
61
, pp.
10
22
.
36.
Chen
,
L.
,
Wang
,
P.
,
Shi
,
F.
,
Han
,
J.
, and
Childs
,
P.
,
2018
, “
A Computational Approach for Combinational Creativity in Design
,”
DS 92: Proceedings of the DESIGN 2018 15th International Design Conference
,
Dubrovnik, Croatia
,
May 21–24
.
37.
Qiao
,
T.
,
Zhang
,
J.
,
Xu
,
D.
, and
Tao
,
D.
,
2019
, “
Learn, Imagine and Create: Text-to-Image Generation From Prior Knowledge
,”
Adv. Neural Inf. Process. Syst.
,
32
, pp.
887
897
.
38.
Liao
,
W.
,
Hu
,
K.
,
Yang
,
M. Y.
, and
Rosenhahn
,
B.
,
2021
, “
Text to Image Generation with Semantic-Spatial Aware GAN
,”
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
,
New Orleans, LA
,
June 18–24
.
39.
Brown
,
T. B.
,
Mann
,
B.
,
Ryder
,
N.
,
Subbiah
,
M.
,
Kaplan
,
J.
,
Dhariwal
,
P.
,
Neelakantan
,
A.
,
Shyam
,
P.
,
Sastry
,
G.
, and
Askell
,
A.
,
2020
, “
Language Models are Few-Shot Learners
,”
Adv. Neural Inf. Process. Syst.
,
33
, pp.
1877
1901
.
40.
Ramesh
,
A.
,
Pavlov
,
M.
,
Goh
,
G.
, and
Gray
,
S.
,
2021
, DALL·E: Creating Images From Text, https://openai.com/research/dall-e.
41.
Turing
,
A. M.
,
2009
, “Computing Machinery and Intelligence,”
Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer
,
R.
Epstein
,
G.
Roberts
, and
G.
Beber
, eds.,
Springer Netherlands
,
Dordrecht
, pp.
23
65
.
42.
Boden
,
M. A.
,
2010
, “
The Turing Test and Artistic Creativity
,”
Kybernetes
,
39
(
3
), pp.
409
413
.
43.
Pease
,
A.
, and
Colton
,
S.
,
2011
, “
On Impact and Evaluation in Computational Creativity: A Discussion of the Turing Test and an Alternative Proposal
,”
Proceedings of the AISB Symposium on AI and Philosophy
,
York, UK
,
Apr. 4–7
.
44.
Peter Berrar
,
D.
, and
Schuster
,
A.
,
2014
, “
Computing Machinery and Creativity: Lessons Learned From the Turing Test
,”
Kybernetes
,
43
(
1
), pp.
82
91
.
45.
Doersch
,
C.
,
2016
, “
Tutorial on Variational Autoencoders
,”
arXiv preprint
arXiv:1606.05908. https://arxiv.org/abs/1606.05908
46.
Goodfellow
,
I.
,
Pouget-Abadie
,
J.
,
Mirza
,
M.
,
Xu
,
B.
,
Warde-Farley
,
D.
,
Ozair
,
S.
,
Courville
,
A.
, and
Bengio
,
Y.
,
2014
, “
Generative Adversarial Nets
,”
Adv. Neural Inf. Process. Syst.
,
27
, pp.
2672
2680
.
47.
Amabile
,
T. M.
,
1982
, “
Social Psychology of Creativity: A Consensual Assessment Technique
,”
J. Pers. Soc. Psychol.
,
43
(
5
), pp.
997
1013
.
48.
Zhu
,
M.
,
Pan
,
P.
,
Chen
,
W.
, and
Yang
,
Y.
,
2019
, “
Dm-gan: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis
,”
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
,
Long Beach, CA
,
June 16–20
.
49.
Lin
,
T.-Y.
,
Maire
,
M.
,
Belongie
,
S.
,
Hays
,
J.
,
Perona
,
P.
,
Ramanan
,
D.
,
Dollár
,
P.
, and
Zitnick
,
C. L.
,
2014
, “
Microsoft Coco: Common Objects in Context
,”
European Conference on Computer Vision
,
Zurich, Switzerland
,
Sept. 6–12
.
50.
Sarica
,
S.
,
Han
,
J.
, and
Luo
,
J.
,
2023
, “
Design Representation as Semantic Networks
,”
Comput. Ind.
,
144
, p.
103791
.
51.
Zhu
,
Q.
,
Zhang
,
X.
, and
Luo
,
J.
,
2023
, “
Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers
,”
ASME J. Mech. Des.
,
145
(
4
), p.
041409
.
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