In this talk Gerbert will give an overview of Artificial Intelligence, outline the current state of the art in research and explain what it takes to actually do an AI project. Using practical cases and tools he will give you insight in the phases of an AI project and explain some of the problems you might encounter along the way and how you might be able to solve them.

2. Gerbert Kaandorp

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5. ● Intro to AI
● Neural Networks
● State of the art
● Data
● Autoencoders
● Hardware
● Software
● Cases
Today

6. AI is in the air...

7. In perspective..

9. AI History

10. What is Artificial Intelligence?
Jacques de Vaucanson (1739)

11. 1936
Alan Turing

12. Turing Test

13. 1968
Apollo Mission
IBM System/360 Model 75s
3.5 M$ (~size of car)
100 operations / sec

14. 1980s
Artificial Strategy
“It’s you against the computer”

15. "AI is whatever hasn't been done yet"
Larry Tesler
the guy who invented copy &
paste while working at Xerox
Research (1973-1976)

16. What is AI ?
Deep learning
Neural Networks
Machine
learning
Robotics
Big data
Self-learning
Cognitive
modeling
Artificial Intelligence
Prediction
Recognition
Data Analytics
Classification
Semantic reasoning
Regression
Natural Language Processing

17. AI and Machine Learning

18. Biological Neuron
Neural Networks

19. Artificial Neuron
Neural Networks

20. 1
neuron
1960s
Neural Networks

21. 1
neuron
2012
Deep Neural Networks

22. LeNet
28×28
(1998)
http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/
AlexNet
224×224
(2012)
GoogLeNet
224×224
(9/2014)
Resnet (n=9, 56 Layers)
28×28
(12/2015)

23. http://www.asimovinstitute.org/neural-network-zoo/
Lots of architectures

30. The source of most of these projects is
freely available..
..but usually the data is not!

32. Unreasonable effectiveness of data
Source: Scaling to Very Very Large Corpora for Natural Language Disambiguation (2001 Microsoft)
Data beats
algorithms!

33. Unreasonable effectiveness of data
If the product is free, you are the product
● - free email, free image storage, free maps, free video storage, free search,
free mobile phone OS, free video calls, free translations
● - free social media channel, free messages, free image storage, free video
storage
● - shopping search data, voice data (Alexa), music & video taste data (Prime
Music / Video), fashion (Echo Look)
● - free search, Office 365 usage, professional network data (LinkedIn)

34. Unreasonable effectiveness of data
Source: The Internet of Things: Getting Ready to Embrace Its Impact on the Digital Economy, IDC 2016

35. So what if you are not
Source: https://techcrunch.com/2017/09/30/ai-hype-has-peaked-so-whats-next/
or

36. menu

37. Data
Scraping Data
Data Augmentation Data Simulation
Proprietary Data Public Data

38. Proprietary Data
Scraping Data
Data Augmentation Data Simulation
Proprietary Data Public Data

39. Proprietary data
Unstructured Structured Labeled

40. Proprietary data
~ 1M Articles
~ 178M words
~ 258K unique words
Automatic discovery of categories

41. Public Data
Scraping Data
Data Augmentation Data Simulation
Proprietary Data Public Data

42. ● Starting point for any project
● Essential to know what is available for
commercial use and what not
● Good sources (list would be endless):
○ http://publicdata.eu/dataset.html (~48K
datasets)
○ https://catalog.data.gov/dataset (~197K
datasets)
○ https://datahub.io/dataset (~12K datasets)
○ http://lod-cloud.net/ (~1K datasets)
○ https://open.nasa.gov/open-data/
Publicly available Data
http://lod-cloud.net/versions/2017-02-20/lod.svg

43. Public data

44. Scraping Data
Scraping Data
Data Augmentation Data Simulation
Proprietary Data Public Data

45. Scraping Data
● Lots of relevant data can be found on
specific websites
● Structure of data available on target
sites allows for some level of automatic
tagging

46. Data gathered from 100s of scraped webshops

47. Millions of products gathered

48. Data Augmentation
Scraping Data
Data Augmentation Data Simulation
Proprietary Data Public Data

49. Data Augmentation
● Automatically identify
company sending an
envelope given a single
scan

50. Generated training data for each logo
● Domain knowledge
● Constrained domain
● Realistic modifications
easy to generate

51. Learning cycles
1. Input logos 2. Augmented dataset
3. CNN training
4. Performance
evaluation
5. Iterative refinement

52. Data Simulation
Scraping Data
Data Augmentation Data Simulation
Proprietary Data Public Data

53. Microsoft AirSim Drone Simulator DeepDrive Universe (GTA)
Data simulation - drones / automotive

55. menu

57. Comparing products by weight and
size is trivial to most of us
Autoencoders

58. How would you compare them quantitatively by how they look?
Autoencoders

59. Color?
Shape?
A sense of similarity to
other products?
Autoencoders

60. Read the pixels into a network that gets smaller at
each step, compressing the representation
Autoencoders
Encoder

61. Then decompress the image
to predict itself as output
Autoencoders
Decoder

62. Repeat for every image in the set and repeat the process thousands of times
Image 1
Image 2
Image 3
Autoencoders

63. We can then take the embedding (‘code’) representation and use
it as a measure of the images similarity to other images
Autoencoders

64. Learn a compact representations of the data
Autoencoders

65. Arithmetics on word embeddings space
vec("king") - vec("man") + vec("woman") = vec("queen")
Autoencoders

66. Exploiting embeddings for labeling
Clustering Sorting

67. BrainMatter© platform

68. BrainMatter© platform

69. BrainMatter© platform

70. BrainMatter© platform

71. BrainMatter© platform

72. BrainMatter© platform

74. 1011 neurons
104 synapses per neuron
1016 “operations” per second
100 peta flops?
Cortex: 2.500 cm 2, 2 mm thick
1.4 kg, 1.7 liters
250 million neurons per mm3 .
180,000 km of “wires”
25 Watts
Hardware: The Human Brain

75. 0.2 tera-FLOPS
6 cores
95 Watt
Hardware: CPU
Core i7 8700k

76. 10.6 tera-FLOPS
3584 cores
250 Watts
Hardware: GPU
NVIDIA 1080-TI GPU

77. 500 tera-FLOPS
4x5120 cores
1500 Watts
Hardware: Dedicated Solution
NVIDIA DGX-
STATION
(4x V100)

78. NVIDIA Partner :)

79. Hardware: CLOUD
93.014 tera-FLOPS

80. Hardware: AI chips
Startup valuated at ~900M
Google TPU
Tensor Processing Unit
Intel Nervana
Neural Network Processor
Graphcore: IPU
Intelligent Processing Unit
~110M funding
Microsoft
Project Brainwave

82. Containerization
IT needs control
● Portability (on-premise, cloud)
● Data Security / Network
Isolation
● Agility and elasticity
● Standardized environments
(dev, test, production)
● Higher resource utilization
Data Scientists needs flexibility
● Faster development lifecycles
● Different set of tools
● Different versions
● Default packaging
● Repeatable builds

83. ML Frameworks

84. Google Tensorflow
Pros:
● Computational graph abstraction.
● TensorBoard for visualization.
Cons:
● Computational graph abstraction.
● Lack of pre-trained models.
● Not completely open-source.

85. Microsoft Cognitive Toolkit
Pros:
● It is very flexible.
● Allows for distributed training.
● Supports C++, C#, Java, and Python.
● Significant Recurrent Neural Network
modelling capabilities
Cons:
● It is implemented in a new language,
Network Description Language (NDL).
● Lack of visualizations.

86. Berkeley Caffe
Pros:
● Supports Python and MATLAB
● Great performance.
● Allows for the training of models
without writing code.
Cons:
● Bad for recurrent networks.
● Not great with new architectures.

87. PyTorch
Pros:
● Great development and debugging
experience
● Love all things Pythonic
Cons:
● Lack of visualizations.
● Deployment
facebook, twitter, nvidia, salesforce

88. “We decided to marry PyTorch and Caffe2 which gives
the production-level readiness for PyTorch”

89. Things to consider
● Ecosystem and code availability
code examples, latest research available
● Research versus production
hardened for serving at scale
● Mobile support
fast kernels for ARM, Metal, etc.
● Language bindings
support for R, Scala, Java
● Programming style
imperative versus declarative
● Compute and memory footprint
platform scalability
● Scalability and performance
efficient multi-GPU and multi-instance support

90. Logistics: delivery optimization
Industry: steel sheet quality control
Medical: stroke diagnostics
Cases

91. ● Manual identification of address data for 15% of total volume
● 4% delivered to wrong address
● Geographical location of delivery points imprecise
● Delivery window too coarse
Before application of AI
Case: Logistics

92. ● Fuzzy logic address matching
● GPS delivery point prediction
● Time window estimation & optimisation
● Automated location mapping (inc. po-boxes)
● Trained on historic data and self learning
AI under the hood
Case: Logistics

93. ● Manual correction reduced to <2% of total volume
● Delivery failures reduced by 50%
● 2000 man hours saved per month
● Improved customer service through better time windows
Results
Case: Logistics

94. ● A major European steel producer
● Total of 7.1 million tonnes of steel
products in 2016
● High quality sheet and strip steel
● Automotive, packaging, and construction
sectors
General
Case: Steel sheet quality control

95. ● Kilometers of steel sheet each day
● Accurate quality assessment enables
more profitable trading
● Defects need to be detected to prevent
machine breaks
● Manual inspection supported by
automatic camera system
Initial Situation
Case: Steel sheet quality control

96. ● Infrared cameras inspect moving steel
sheet on conveyor belts
● Basic image processing detects regions
of interest
● Manual inspection often needed
● Accuracy can still be improved
Camera system
Case: Steel sheet quality control

97. ● Up to 50 different defect types
● 5 million (!) new images each day
● Currently only 25 thousand annotated
images available in total
● Severely imbalanced data sets
● Manual annotation is costly
Data sets
Case: Steel sheet quality control

98. ● Deep Learning for robust image
classification
● Ai & Active Learning approach for
efficient image annotation
● Integration in existing systems
● Knowledge transfer to customer’s own
tech team
● Already more than 90% accurate
Solution
Case: Steel sheet quality control

99. ● 46.000 patients affected by a stroke /
year in the NL
● Limited time to decide which hospital
to send patient to
● ~6 minutes for a skilled radiologist to
identify stroke location
● Small hospitals do not always have
trained radiologists on staff
Case: Stroke diagnostics
Initial situation

100. Case: Stroke diagnostics
Initial situation
● Imitate the process of expert
radiologists
● Train a deep neural network on 3D
volumes from left/right hemispheres
● Compare intensities and local brain
structures to discern affected from
healthy regions

101. Case: Stroke diagnostics
Result
● 95% classification accuracy in
detecting “blocks” with an occlusion
present
● Complete process in under 30 secs
● Visualization tools to localize area of
interest

102. http://www.braincreators.com

103. BrainCreators
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