machine learning and art hack day
TRANSCRIPT
THE PRESENTATION
show some of my experiments, both testing the limits of other people’s models and training my own models
overview of how the models work
for artists and machine learners in the audience, i tried to make it so you will all learn a little
hopefully inspire some of you to go out and build something awesome
MY BACKGROUND - JASON TOY
my main passion is general artificial intelligence
studied math and computer science
generalists, program a little of everything, master of nothing
founded a couple of companies: rubynow, socmetrics - using ML for mining social media
CEO of filepicker,sold in beginning of 2016
exploring the intersection of machine learning,art, entrepreneurship
WHAT IS GENERATIVE MODELING
generative vs discriminative
architect of models
GM around for a long time - used in architect, design,games,etc
miniature systems that mimic something in real life, “artist in a box”
more fun; I'm not as interested to increase ad clickthrough rates
DISCRIMINATIVE VS GENERATIVE
generative:
naive bayes
LDA
deep learning
discriminative:
SVM
random forest
linear/logistic regression
CHAR-RNN
“I'm not going anywhere. I will bring the poorly educated back bigger and better. It's an incredible movement. ”
“We're losing companies, the economy. We are going to save it. We're going to bring the party. Let's Make America Great Again”
“I want to thank the volunteers. They've been unbelievable, they work like endlessly, you know, they don't want to die. My leadership is good”
CHAR-RNN
1vanilla (image classification) 2 sequence output (image -> text) 3 sequence input ( sentiment analysis)
4 seq2seq (machine translation) 5 synced seq2seq (video classification)
CHAR-RNN
RNN - recurrent because they perform the same task for every element of a sequence; typically 2-3 layers
LSTM - long short term memory
similar, state is calculated differently
MY CHAR-RNN EXPERIMENTS
what does Hellen Keller think?
seeing is like or inspirents of a kiss licks, in child, for the last decting of accomplish with me for the mistakes in silence is to keep the moments filled whiter, the chaps of the house language was sends a humanise.
i wish i could presepred its repepenting and the days like the poor discuss of language of the poem in the letters, dotiment in the endless good and eager and over the charicality of the hall of rubbings that I hapmende the comprehend, the birds like your mind to perhaps the not wind I should do?
MY CHAR-RNN EXPERIMENTS
“i love you. Now her before it just numberse idevening with the press over. I was probably ever need to ever admit? Right” - Trump
“life is an economy. I was in the LGBT communities can to the worst of the gun not only the fight are of us safe and I start up these are not grow…” - Hillary
FUTURE CHAR-RNN EXPERIMENTS
train a model to talk like a person with little data? transfer learning?
could we train a model off of a standard “human” model ?
could we train a model to talk in different emotions/styles?
LAYER AMPLIFICATION
objective function: activate as many neurons in a layer
key trick: push back to image
feedback loop
choose different layers for different effects: conv2/3x3,inception_3a,etc
INCEPTION FUTURE EXPERIMENTS
train with different image sets - sea life, reptiles?
different objective function - activate only 1 group of neurons?
selective regions of hallucinating?
testing different network architects
A NEURAL ALGORITHM OF ARTISTIC STYLE
paper: http://arxiv.org/abs/1508.06576
The key finding of this paper is that the representations of content and style in the CNNs are separable.
CNNs - convolutional Neural Network
high layers in the network act as the content of the image
style computed from multiple layers’ filter responses
NEURALSTYLE FUTURE EXPERIMENTS
can we automatically find the “good” images from a combination?
can we know beforehand if a combo style/content will look good?
currently trained on vggnet data, what happens if we train it on a different data set, will the art look different?
will a different architect make better art?
I ACCIDENTALLY GAVE THE ANIMAL BACK OF MY HEAD , BREATHING DEEPLY . THERE WAS NO DOUBT IN HER EYES , AND I COULD TELL BY THE LOOK ON HIS FACE THAT HE DID N'T APPROVE OF WHAT WAS HAPPENING TO ME . IN FACT , IT MUST HAVE BEEN ONE OF THOSE RARE OCCASIONS , AS WELL AS A PET ANIMAL . HER SCENT FILLED THE AIR . THAT 'S WHAT SHE WAS LOOKING FOR , AND NOW SHE HAD TO STAY AWAKE LONG ENOUGH TO DIG UP THE LEASH
FUTURE NEURAL STORY EXPERIMENTS
train with different text
a “seeing” Hellen Keller version
train on different visual features
DATA IS ESSENTIAL
many of these models are built on public datasets
always has been a problem; bigger problem for DL and general models
very hard to get data; how can this be solved?
constantly on my mind ; lets connect me if interested
DL IS NOT ALL FUN AND UNICORNS
data issue
specialized software/hardware pipelines; GPUs
be prepared to wait; think weeks, not hours
model tuning
architect tuning
techniques and architects changing everyday
WHY?
I dream of building larger models
AGI and multi modal models
larger experiments
want to collaborate with cool artists and coders
fun? lets talk!
STUDY LINKS
what is deep learning: http://www.jtoy.net/2016/02/14/opening-up-deep-learning-for-everyone.html
generative models: https://en.wikipedia.org/wiki/Generative_model
discriminative models: https://en.wikipedia.org/wiki/Discriminative_model
TEST LIVE MODEL LINKS
trump char-rnn model: http://somatic.io/models/WZmmBjZ9
neural style model: http://www.somatic.io/models/5BkaqkMR
neural talk model: http://somatic.io/models/qoEGanRe
romance story telling: http://somatic.io/models/2n6g7RZQ
LINKS
VGG net data used: http://www.robots.ox.ac.uk/~vgg/research/very_deep/
tensorflow version: https://github.com/anishathalye/neural-style
neural style paper: http://arxiv.org/abs/1508.06576
char-rnn code: https://github.com/somaticio/char-rnn-tensorflow
mscoco: http://mscoco.org
imagenet: http://image-net.org/
LINKS
char-rnn: https://github.com/somaticio/char-rnn-tensorflow
tensorflow char-rnn tutorial: https://www.tensorflow.org/versions/r0.9/tutorials/seq2seq/index.html#recurrent-neural-networks
neuralstyle: https://github.com/anishathalye/neural-style
–John Dewey
“Every great advance in science has issued from a new audacity of imagination.”
Jason [email protected]
I write here:http://jtoy.net http://somatic.io/bogmy models here: http://somatic.io
@jtoy
QUESTIONS?