Machine creativity TED Talk 2.0
Transcript of Machine creativity TED Talk 2.0
Can Machines Be Creative?
A Look into Machine Intelligence
By: Cameron Aaron
Who am I ?Artificial General Intelligence Researcher
Founder of Sybil Systems
Creator of Learning Disability Simulation
Bridges Academy Student
I have ADD
Talk Agenda1.Background on Machine Intelligence
2.Conversations that I had with experts and industry leaders
3.My own work
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
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
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.
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
Interview with Lucas Baker Software Engineer who worked on Google DeepMind’s AlphaGo project
I asked Lucas, “Do you believe that sometimes glitches in a neural network can produce unique results?”
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
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?”
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
Interview with Dr.Vivienne Ming, Ph.DNamed 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?”
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
Interview with Dr. Preethi Raghavan, Ph.DMember 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?
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
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
Neural Network Experiment (Phase 1- Auditory Impairment)
Experiment (Phase 2- Visual Impairment)
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.
Special Thanks!Special thanks to:Susan Baum
Dr. Vivienne Ming
Miriam Singer Wang Ling
Dr. Preethi Raghavan
"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.