UNDERSTANDING THE WORLD, BY LEARNING HOW TO MODEL IT - DEEP LEARNING @ HDM 2018 - CCC STUTTGART
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UNDERSTANDING THE WORLD, BY LEARNING HOW TO MODEL IT
Deep Learning @ HdM 2018
www.hdm-stuttgart.de
About me
› Johannes Theodoridis
› Audiovisuelle Medien @ HdM
› Computer Science and Media @ HdM
› Exchange @ KTH Stockholm deepart.io
› Currently working with Johannes Maucher on AI and ML @ HdM
› Email: theodoridis@hdm-stuttgart.de
(Image first slide: https://i.redd.it/2ag4n25oq02y.jpg)
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 2
What do you do?
IRGEN
DWAS
MITM
EDIEN
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 3
What do you do?
IRGEN
DWAS
MITM
EDIEN
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 4
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 5
What today is not about
But don’t be fooled!
Details matter in Deep Learning.
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 6
2017 in AI: Poker
› Brains Vs. AI - January 2017 @ Rivers Casino Pittsburgh
› AI wins 20-day Heads-up, No-Limit Texas Holdém
tournament against 4 top-class human poker players.
› ~ 10ˆ161 different decision points in Texas hold’em. Libratus: The Superhuman AI for
› Infeasible to pre-compute a strategy for each of the No-Limit Poker
[Brown, Sandholm – IJCAI 2017]
moves.
Name Rank Results (in chips)
Dong Kim 1 -$85,649
"I didn’t realize how good it was until today. I felt like I
was playing against someone who was cheating, like it Daniel MacAulay 2 -$277,657
could see my cards. I’m not accusing it of cheating. It Jimmy Chou 3 -$522,857
was just that good.” – Dong Kim
Jason Les 4 -$880,087
(Source: https://www.wired.com/2017/01/ai-conquer-poker-not-without-human-help/)
Total: -$1,766,250
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 7
2017 in AI: Board Games
› 2016 AlphaGO
Mastering the game of Go with deep
neural networks and tree search
[Silver et al. – Nature 2016]
› learned from expert games + selfplay
› defeats Lee Sedol (world champion) 4:1
› 2017 AlphaGo Zero
Mastering the game of Go without
human knowledge
[Silver et al. – Nature 2017]
› learned entirely on ist own
› defeats AlphaGo 5:0
(Credit: Photo courtesy of Google)
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 8
2017 in AI: Video Games
› 2015
Human-level control through
deep reinforcement learning
[Mnih et al. – Nature 2015]
› 2017
OpenAI bot wins 1vs1 against Dendi
in a best-of-three match.
https://blog.openai.com/dota-2/
https://blog.openai.com/more-on-dota-2/
https://openai.com/the-international/
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 9
2018 in AI: Video Games
› August 5, 2018
OpenAI Five wins 2 out of 3
games against a Semi-Pro Team
https://blog.openai.com/openai-five/
› Long time horizons: ~ 20000 Moves (Chess ~ 40, Go ~ 150)
› Action Space: ~1000 valid actions each tick (Chess ~35, Go ~250)
› Observation Space: 20,000 numbers representing all game information (Chess 70, Go 400)
› Learned via self play: “OpenAI Five plays 180 years worth of games against itself every day.“
› Hardware: Training is running on 256 GPUs and 128,000 CPU cores. Images: blog.openai.com/
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 102017 in AI: Healthcare
› Dermatologist-level classification of skin
cancer with deep neural networks
[Esteva et al. – Nature 2017]
› Trained on 129,450 clinical images
› Performance on par when tested against
21 board-certified dermatologists
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 112018 in AI: Healthcare
› April 11, 2018 - FDA Permits Marketing of
First AI-based Medical Device: IDx – DR.
› Diagnostic system that autonomously
analyzes images of the retina for signs of
diabetic retinopathy.
› “Machines can help the doctor make a
better diagnosis, but they are not good at
making medical decisions afterward.”
[EyeNet: Artificial Intelligence: The Next Step in Diagnostics - American
Academy of Ophthalmology (AAO), Nov 2017]
Source: https://www.eyediagnosis.net
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 122017 in AI: Systems
› The Case for Learned Index Structures
[Kraska et al. – arxiv 1712.01208]
› Replace B-Trees-Index or Hash-Index with
a Neural Network
› + 70% in speed
› + saving an order-of-magnitude in
memory (over several real-world data sets)
› Authors argue that “replacing core
components of a data management
system through learned models has far
reaching implications for future systems
designs”
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 13Wait what?
› “I have a terrible confession to make. AI systems today suck“
Yann LeCun at Brown University 2017
› “All of these AI systems we see, none of them is ‘real‘ AI“
Josh Tennenbaum at CCN 2017
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 14A rough distinction
› Strong AI (or Artificial General Intelligence AGI) - can solve every task.
This is what everyone is worried about in the media, Singularity etc. but, we are not even close!
› Weak AI (or narrow AI) – can solve a specific task.
This is everything you have seen so far. Works really well for some tasks like image and speech recognition.
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 15Why are we “not even close“ to AGI? › The brain learns with an efficiency that none of our machine learning methods can match. › Our supervised learning systems require large numbers of examples. › Our reinforcement learning systems require millions of trials. › That is why we don‘t have robots that are as agile as a cat or a rat. › That is why we don‘t have dialog systems that have common sense. › What is missing? › Learning paradigms that build (predictive) models of the world through observation and action. Slide copied from: Dr. Yann LeCun, "How Could Machines Learn as Efficiently as Animals and Humans?" https://www.youtube.com/watch?v=uYwH4TSdVYs Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 16
What is Machine Learning?
› Machine Learning is the subfield of artificial intelligence
concerned with programs that learn from experience.
[Russell and Norvig - Artificial intelligence: a modern approach]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 17What is Machine Learning?
› Task: Tell if there is an apple in the image
Approach 1: write code Approach2: learn from data
def contains_apple(image)
red_pixels = count(image.RED)
if red_pixels > 300:
return True
else
return False Machine Learning
YES NO YES NO
Does not scale Does scale: With enough compute power and training samples
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 18What is Deep Learning?
› Traditional Pattern Recognition: Fixed/Handcrafted Feature Extractor
Feature Trainable
Extractor Classifier
› Deep Learning: Representations are hierarchical and trained
Low-Level Mid-Level High-Level Trainable
Features Features Features Classifier
Understanding Neural Networks
Through Deep Visualization
[Yosinski et al. – ICML 2015]
Slide Credit: Yann LeCun
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 19How do we train these things?
Training Data – Labeled by category
Label: Fruits
Select a random Calculate the error by comparing
mini-batch of data predicted and true labels
Predict Labels P
P T
Label: Vehicles
Error
Update the pipeline towards less error
› Because of the labels we call this SUPERVISED LEARNING.
› These labels need to be generated somehow (by humans mostly).
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 20What is in the boxes?
Convolutional Neural Network – CNN
(note: no pooling layers in this architecture)
Input: Output:
Current game screen Best action to choose
› CNN architecture that was used by [Mnih et al. – Nature 2015]
to play Atari Games (Deep Q-Networks - DQN)
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 21A bit of CNN history: Thank you cats :)
› RECEPTIVE FIELDS, BINOCULAR
INTERACTION AND FUNCTIONAL LeNet-5
ARCHITECTURE IN THE CAT'S VISUAL [LeCun, Bengio, Haffner 1998]
CORTEX [Hubel & Wiesel 1962] Deep Learning
Neocognitron
[Fukushima 1980]
AlexNet
(Photo by Bertil Videt CC BY-SA 3.0) [Krizhevsky, Sutskever, Hinton 2012]
Large Scale Visual Recognition
Challenge (ILSVRC)
› ½ Nobel Prize in Physiology or Medicine 1981: David H. Hubel and Torsten
N. Wiesel "for their discoveries concerning information processing in the
visual system".
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 22What does “deep“ mean?
VGG
conv 128
conv 128
conv 256
conv 256
conv 512
conv 512
conv 512
conv 512
conv 512
conv 512
conv 512
conv 512
maxpool
maxpool
maxpool
maxpool
maxpool
softmax
FC 4096
FC 4096
FC 1000
conv 64
conv 64
Input
[Simonyan, Zisserman 2014]
GoogLeNet
[Szegedy et al. 2014]
ResNet
[He et al. 2015]
DenseNet
[Huang et al. 2017]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 Slide Credit: Yann LeCun 23Supervised Learning
› Image Classification Image Retrieval
ImageNet Classification with Deep
Convolutional Neural Networks
[Krizhevsky, Sutskever, Hinton 2012]
› Machine Translation
Convolutional Sequence to Sequence
English: ”They agree” German: ”Sie stimmen zu” Learning
[Gehring et al. 2017]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 24Supervised Learning
› Image Caption Generation
Show, Attend and Tell: Neural
Image Caption Generation with
Visual Attention
[Xu et al. 2015]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 25Supervised Learning
› Instance Segmentation
Mask R-CNN
[He et al. 2017]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 26Supervised Learning
› Instance Segmentation in traffic
Mask R-CNN
[He et al. 2017]
(Source: 4K Mask RCNN COCO Object detection and segmentation #2
https://www.youtube.com/watch?v=OOT3UIXZztE )
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 27Supervised Learning
› Pose Estimation
Mask R-CNN
[He et al. 2017]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 28Reinforcement Learning
› Play SNES games (Bachelor Thesis @ HdM ) Learn Locomotion Behaviours @ DeepMind
Emergence of Locomotion Behaviours in Rich Environments
[Heess et al. 2017] (Video: https://www.youtube.com/watch?v=hx_bgoTF7bs)
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 29What are we missing?
› Obstacles to AI
› Learning models of the world
› Learning to reason and plan
Yann LeCun at CCN 2017
(but he made this point in many talks)
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 30Common Sense Knowledge
› Image Caption Fails.
› The teddy doesn't fit into the brown suitcase because it's too
[small/large]. What is too [small/large]?
Answers:The suitcase/the teddy. (Winograd Schemas)
› ”Tom picked up his bag and left the room”.
› These questions are easy for us because we have a model of the
world.
(Sources: https://techcrunch.com/2016/11/08/shining-light-on-facebooks-ai-strategy/ , 31
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018
http://www.reactiongifs.com/wp-content/uploads/2013/02/nwld.gif , http://images.memes.com/meme/999039 )Common Sense Knowledge
› Common Sense is the ability to fill in the blanks
› Filling in the visual field at the retinal blind spot.
› Filling in occluded images, missing segments in speech.
› Intuitive Physics + Intuitive Psychology
› track objects over time
› discount physically implausible trajectories
› distinguish animate agents from inanimate objects
› understand that other people have mental states like goals and beliefs
› Where can this come from? -> Unsupervised Learning
› Most of the learning performed by animals and humans is unsupervised. (no teacher)
› We learn how the world works by observing it.
› We learn that the world is 3-dimensional.
› We learn object permanence.
› We build a model of the world through predictive unsupervised learning.
(This predictive model gives us “common sense“)
(Slide is composition from: Yann LeCun, "How Could Machines Learn as Efficiently as Animals and Humans?" https://www.youtube.com/watch?v=uYwH4TSdVYs
, Sources: Baby http://www.mommyshorts.com/wp-content/uploads/2014/09/6a0133f30ae399970b0192aa1b4c77970d-800wi.jpg , Retina by Jerry Crimson 32
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018
Mann CC-BY-SA 3.0)Learning Predictive Forward Models of the world.
› Task: Predict in which direction the Mikado sticks will fall
Y1
observation 1 observation 2 …
Y2
› Problem: Invariant prediction: The training samples are merely representatives
of a whole set of possible outputs (e.g. a manifold of outputs)
› We need to represent a distribution. But how do you represent a distribution
in high dimensional space?
› Solution (one): Energy-Based Unsupervised Learning
Slide Credit: Yann LeCun
› Idea: Take low value on data manifold, higher values everywhere else Thx: Raphy for playing Mikado with me
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 33Generative Adversarial Networks (GAN) [Goodfellow et al. 2014]
Real world Real
images
Discriminator
(Neural Network)
”Noise”
Generator Fake
(Neural Network)
› The Generator network will try to generate fake images that fool the discriminator.
› The Discriminator network will try to distinguish between a real and a generated image.
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 34Welcome to the GAN Zoo
› Generate bedrooms - 2016
Unsupervised Representation
Learning with Deep Convolutional
Generative Adversarial Networks
[Radford et al. ICLR 2016]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 35GAN Zoo
› Generate bedrooms, buildings, cats - 2017
StackGAN++: Realistic Image
Synthesis with Stacked Generative
Adversarial Networks
[Zhang et al. 2017]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 36GAN Zoo
› Generate celebrities 2018
High resolution: 1024 x 1024 pixel
Progressive Growing of GANs for
Improved Quality, Stability, and
Variation
[Karras et al. 2018]
IntroVAE: Introspective Variational
Autoencoders for Photographic
Image Synthesis
[Huang et al. 2018]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 37GAN Zoo
› Face arithmetic
StarGAN: Unified Generative
Adversarial Networks for Multi-Domain
Image-to-Image Translation
[Choi et al. 2017]
Unsupervised Representation
Learning with Deep Convolutional
Generative Adversarial Networks
[Radford et al. ICLR 2016]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 38GAN Zoo
› Next Frame Prediction
Deep multi-scale video prediction
beyond mean square error
[Mathieu et al. 2017]
Predicting Deeper into the Future
of Semantic Segmentation
[Luc and Neverova et al. 2017]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 (Sources: https://cs.nyu.edu/~mathieu/iclr2016.html, https://github.com/facebookresearch/SegmPred ) 39GAN Zoo
› Image-to-Image translation
Image-to-Image Translation
with Conditional Adversarial
Networks
[Isola et al. 2017]
Image-to-Image Demo
https://affinelayer.com/pixsrv/
Unpaired Image-to-Image
Translation using Cycle-Consistent
Adversarial Networks
[Zhu and Park et al. 2017]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 40GAN Zoo
› Text-to-Image translation
StackGAN++: Realistic Image
Synthesis with Stacked Generative
Adversarial Networks
[Zhang et al. 2017]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 41GAN Zoo
› Image Colorization
Colorful Image Colorization
[Zhang, Isola, Efros 2016]
Style2Paints 2.1
https://github.com/lllyasviel/style2paints
Scribbler: Controlling Deep Image Synthesis
with Sketch and Color
[Sangkloy et al. 2017]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 42GAN Zoo
› Interactive drawing
Generative Visual Manipulation on the Natural
Image Manifold
[Zhu et al. 2016]
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 43Whats next? (my prediction) › We will see a lot more real world applications of Supervised Learning in many (new) domains. › We will see more efficient Reinforcement Learning. (good for robotics) › Research in Unsupervised Learning “just started“. › Key to “stronger“ AI: Prediction + Planning = Reasoning. Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 44
› We do AI and ML since 2006 / 2007 (Medieninformatik / Mobile Medien)
› Applied approach: How can we bring AI into production?
› Lectures are split ~50/50 between theory and programming
› Constantly growing number of students in AI lectures (last ML course was 60+)
› NEW: ML specialization within the Computer Science and Media Master program.
› Many AI related projects in: Gaming, Apps, Websites, Embedded Systems
› 10 - 15 degree theses per semester (inhouse and with industry: Daimler, Bosch, Porsche etc.)
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 45AI @ HdM Stuttgart
› We go to Hackathons J
› Visit us:
www.hdm-stuttgart.de/~maucher
› or come to the HdM Media Night!
(next one is end of Winter Term 18/19 ~ end of January)
› Thank you! Daimler TSS Artificial Intelligence Garage – November 2017
Deep Learning @ HdM Stuttgart | Johannes Theodoridis | 09.08.2018 46You can also read
