This document discusses whether machines can be creative. It summarizes interviews with experts in artificial intelligence and machine learning. They acknowledge that while current systems can perform specific tasks, true creativity requires an understanding of what it means to be creative that remains elusive. However, some believe that as neural networks become more complex and are given general goals like "produce something new and beautiful," they may develop novel solutions and even artistic styles unseen before. Most agree that continued research using neural networks will provide insights into human thinking and creativity.

1. Can Machines Be Creative?
A Look into Machine Intelligence
By: Cameron Aaron

2. Who am I ?
Artificial General Intelligence Researcher
Software Engineer
Founder of Sybil Systems
Creator of Learning Disability Simulation
Bridges Academy Student
I have ADD

3. Talk Agenda
1. Background on Machine Intelligence
1. Conversations that I had with experts and industry leaders
1. My own work

4. What is Machine Learning?
Supervised Learning- A form of machine learning that uses labeled training data to
solve a task.
Unsupervised Learning- Finds hidden patterns or intrinsic structures in data
Reinforcement Learning - Learning based on cumulative reward
Deep Q naturepaper
Hortonworks

5. Neural Networks
This is a model of Machine
Learning based on the human
brain and human neuron
structures that asks millions of
virtual neurons to work together to
solve a task.
- Works in both auditory and
visual simulations https://www.mql5.com/en/code/9002

6. What is Artificial Intelligence?
Artificial Machine Intelligence - An AI algorithm that is able to perform a specific task
that requires human intelligence.
Artificial General Intelligence - An AI algorithm that is able to perform many different
tasks using the same algorithm, usually using the Reinforcement Learning Model of
machine learning.
http://www.nature.com/nature/journal/v

7. What does it mean for a machine to be intelligent?
Multiple Intelligences
“A big issue is that while it is simple to build a model to learn a specific task, such as
translation, by providing examples of human translations and minimizing an error
metric in those examples, there is no clear quantification of what it means to be
creative.”
- Wang Ling, Scientist at Google's DeepMind
Can ask questions
Must understand what it is doing

8. Interview with Lucas Baker
Software Engineer who worked on Google DeepMind’s AlphaGo project
Stanford Graduate
I asked Lucas, “Do you believe that sometimes glitches in a neural network can
produce unique results?”

9. Interview with Lucas Baker
“The entire human brain is a large neural network, though more complex than
anything we can currently build, and our "glitches" and mistakes often inspire new
approaches. In the same way as the random mutations of evolution usually harm but
sometimes help, the imperfections in a neural network usually produce a lesser reward
but sometimes yield a greater one. As neural networks become more powerful and
intricate, however, they will almost certainly give us further insight into the workings
of our own minds.”
- Lucas Baker, Google DeepMind

10. Interview with Jason Freidenfelds
Senior Manager, Global Communications & Public Affairs at Google
Harvard University AB, Psychology / Neuroscience
Front-end web development
I asked Jason, “What does creativity mean to you? What does it mean for a machine to
be creative?”

11. Interview with Jason Freidenfelds
“We hope someday to build much more generalizable machine intelligence, where we
give a general goal -- "help me optimize this health care system" -- and the machine
figures out novel solutions we hadn't thought of. That could extend to art as well -- we
could tell the machine, "Come up with something new and beautiful," and it might be
able to produce a style we've never seen before that is indeed beautiful.”
- Jason Freidenfelds, Senior Communications Manager, Google

12. Interview with Dr.Vivienne Ming, Ph.D
Named one of Inc. Magazine's 10 Women to Watch in Tech.
Retired Chief Scientist at Guild
Founder of Socos
Notorious for her work on theoretic generative models offering “insight into how
information in the environment is processed by sensory systems.”
I asked Dr. Ming, “How closely do neural networks simulate the human brain? Will
machines help us better understand how humans think?”

13. Interview with Dr.Vivienne Ming Ph.D
“Many of the techniques and theories used in machine learning offer huge
insights into how brains work, this includes neural networks... when you look
at deep neural networks in particular, they engage in a sort of undifferentiated
processing; learning any function you give them in their own way. But their
solutions are often very limited and pathological. When humans and animals
think it seems much more like we entertain models of models. When we
engage with the world, we implicitly test many, many types of models to see
which ones explain falling apples and spurned friends. In that sense, neural
networks seem like a better representation of those models that we test.”
Dr. Vivienne Ming Ph.D

14. Interview with Dr. Preethi Raghavan, Ph.D
Member of the DeepQA research team at IBM
PhD thesis titled "Temporal Reasoning and Information Fusion in Longitudinal
Clinical Narratives"
Ph.D in Computer Science
I asked Dr. Raghavan, “How closely do neural networks simulate the human brain?
Will machines help us understand how humans think better?

15. Interview with Dr. Preethi Raghavan Ph.D
“It is possible to create neural networks that try and mimic
biological neural networks i.e. the human brain, thus allowing
scientists to understand brain connectivity. They may also be
trained to assist in expert human tasks like disease diagnosis and
identifying students with learning disabilities.”
- Dr. Preethi Raghavan Ph.D

16. Sybil System (My company)
I am striving to see if we can use AI to assist humans and better understand ourselves.
By being able to create models of the way brain works, perhaps we can better invent
ways to help and understand the effects and causes of sensory/motor pathway defects
"What I cannot create, I do not understand" -Richard Feynman
HealthPop/CBS

17. Neural Network Experiment
(Phase 1- Auditory Impairment)

18. Experiment
(Phase 2- Visual Impairment)

19. Conclusion: Can a machine be creative?
Not exactly, at least not yet, this is mostly due to the fact unlike knowledge creativity
cannot be quantified. However, there is evidence that we will see creative machines in
the future once we are able to figure out a clear way to quantify it.

20. Special Thanks!
Special thanks to:
Susan Baum
Dr. Vivienne Ming
Lucas Baker
Miriam Singer
Wang Ling
Jason Freidenfelds
Dr. Preethi Raghavan

21. Citation
Works Cited
"Deep Blue." IBM100 -. N.p., n.d. Web. 28 Apr. 2016.
"The Era of Citizen Doctors." RSS. N.p., n.d. Web. 28 Apr. 2016.
"IBM Research: Computational Creativity." IBM Research: Computational Creativity. N.p., n.d. Web. 28 Apr. 2016.
Ling, Wang, Lucas Baker, Dr.Vivienne Ming, Ph.D, Jason Freidenfelds Freidenfelds, and Dr. Preethi Raghavan, Ph.D. "Machine
Creativity." E-mail interview. Apr.-May 2016.
"Result Filters." National Center for Biotechnology Information. U.S. National Library of Medicine, n.d. Web. 28 Apr. 2016.