1) The document discusses the possibility of creating an artificially creative rapping robot named Flowboto. 2) It outlines 5 steps to develop Flowboto's abilities, including replicating speech patterns, adding rhyming, learning about popular music styles from databases, combining novel sounds, and having human producers select the best song renditions. 3) With learning algorithms, Flowboto could improve its own music creation over time similar to how human artists learn through collaboration.

1. D O M O A R I G AT O , M R . F L O W B O T O
Produced by
Wes Berry
Imagine a rap battle between Jay-Z and a robot…
A robot whose mic skills are indistinguishable from those of a human (i.e. one that passes the Turing Test)?
Requires creativity
We can look at machine creativity by looking at necessary processes for a creative, rapping robot—Flowboto

2. W H AT I S C R E AT I V I T Y ?
Creating novel concepts and pieces?
Is it something that springs randomly and suddenly or pours from séance-like concentration?

3. Humans have it…

4. …right?

5. What about animals?
Some evidence of creative selection and processes in chimps, Vogelkop Gardener bowerbirds, and dogs

6. Robots?
Can robots be creative?
Take potential rapping robot, Flowboto
Let’s examine a recipe for Flowboto…

7. Replicating Speech Patterns, Grammar, and Stressed
Syllables, while realizing connections between words
• Siri, Watson, GPS navigational devices
Step 1
• Strategy a la NEIL
• Mix in some Google NGrams
Robust programs exist that do many of these things
The Never Ending Image Learner (NEIL) + Google Ngrams
Lovechild of Siri and Watson
Still boring…

8. But Flowboto…
…needs to Flow!

9. Speech re-constrained by complex,
poetic structures
Step 2: we need…
Rhyming
+

10. Key Breakthroughs!
• Google Translate can handle poetry and produce
rhymes in appropriate places using brute force
search
• 2001: study concludes that speaking robots can
mimic emotion well enough to be perceived by
humans
• 2002: study suggests humans can recognize
general communicative intent in robot-directed
speech
What does it mean?
This means Flowboto can ‘emote’ in ways understandable to humans, critical to being a believable musician

11. Step 3:
Tempo, pitch, key all digitally accessible (pitch-shifters, beat-matchers, digital tuners)
But, there are also complex, underlying elements to songs that human artists understand—part of creativity
Understanding of these is critical to Flowboto as an artist

12. Human-Driven Approaches
• Pandora’s Musical Genome Project
• Crowd-Sourced Social Tagging Data
Two solutions:
Both human-based
Similar drawbacks to both, but existences demonstrate potential and precedence for converting musical intricacies into data for a computer

13. Step 4:
Subject Matter
Flowboto needs subject matter—parse the web!
Social trends, pop culture, and events
Can create a web of the world

15. Step 5:
Unique Sound Combinations and
Appealing New Music
• Might use understandings of poetic forms,
musicality, and databases to recognize popular
musical styles (what gets the people going?)
• Wide, data-parsing algorithms to produce various
probable renditions of novel songs or sections,
graded on likability scale
Finally…
It can do the tasks, but it still needs to create!
By…

16. FlowBot produces probable renditions of novel music
Unique Sound Combinations and
Appealing New Music
Human producers select, sample, and mix the
renditions that sound best
Release the hit!
(And credit Flowbot to avoid a lawsuit)
Human producers as filters, differentiating good and bad song/section renditions

17. If Flowboto also incorporates learning algorithms, it can
make better musical decisions going forward, creating
and self-selecting more appropriate renditions in the
future based on past human-choices
Interesting Implication…
Then…
Learning algorithms would allow Flowboto to self-filter more effectively in the future…

18. Flowboto is almost
a real boy!
(er…artist)
• Improving as an artist
• Similar to human artist, whose
work is critiqued and improved
through collaborative efforts
with other artists
And we begin to see similarities to a human artist learning through collaboration

19. Speech Patterns
RhymingMusicality
Databases
Novel Sounds
FLOWBOTO
Existent, but dispersed technologies, themselves precedented or conceivable from past projects, allow for innovation
Laying the building blocks for an artificially-creative musical agent like Flowboto

20. What does this mean
for machine creativity?
Then…
Details different depending on the tasks, but the process translates well across creative forms
Mixing, reimagining, and improving the available tools, while using human and animal creative processes as models, may unlock machine creativity sooner rather than
later

21. References
Then…
• Breazeal, C. (2001). Emotive qualities in robot speech. Proceedings from the International
Conference on Intelligent Robots and Systems. Maui, HI: IEEE.
• Breazeal, C., & Aryananda, L. (2002). Recognition of Affective Communicative Intent in Robot-
Directed Speech. Autonomous Robots, 12(1), 83-104.
• NPR Staff. (2011). Google’s artificial intelligence translates poetry. NPR. Retrieved from http://
www.npr.org/2011/01/16/132959095/googles-artificial-intelligence-translates-poetry.
• Pandora. (n.d.). About the Music Genome Project. Retrieved from http://www.pandora.com/
about/mgp.
• Saunders, R. (2002, February). Curious design agents and artificial creativity (Doctoral
dissertation). Department of Architectural and Design Science, Faculty of Architecture, University
of Sydney.
• Schmidhuber, J. (2006). Developmental robotics, optimal artificial curiosity, creativity, music, and
the fine arts. Connection Science, 18(2), 173-187.
• West, K. (2008, September). Novel techniques for audio music classification and search (Doctoral
dissertation). School of Computing Sciences, University of East Anglia.
• Goldman, J. G. (2014). Creativity: The weird and wonderful art of animals. BBC. Retrieved from
http://www.bbc.com/future/story/20140723-are-we-the-only-creative-species.