1.
Artificial intelligence
Why we should care, how it works and
what benefits we can get from it ?
Mykola Dobrochynskyy
Software Factories, 2018
ceo@soft-fact.de
1

2.
What is this Session about
2
Agenda
This Talk like thin rope pulling your big ship
(AI, ML & Deep Learning knowledge).

3.
3 “gold” Circles –
start with Why!
3
Agenda
What?
(Agenda 6-7)
How?
(Agenda 2-5)
Why?
(Agenda 1)
Wie von Simon Sinek geprägt

4.
Agenda
1. Motivation
2. History, present and the future
3. AI - Artificial Intelligence
4. ML - Machine Learning
5. DL - Deep Learning
6. Sandbox playing
7. Chances and Risks
8. Start now! (Resources and references)
9. Questions and Answers. *
AWS TTS service "Polly": https://console.aws.amazon.com/polly
* see demo/Joanna_Intro.txt
4
Agenda

5.
Entropy of a (Software-)System
5
𝑺 = 𝒌 𝑩 ∗ 𝒍𝒏 Ω 1 Entropy (physical)
N (Ω) – Number of states
Time / H (S)
𝑯 ~ 𝒍𝒏 N 1 Entropy (software)
Software entropy (H) grows over time. That's why the complexity
and information loss probability of an IT system increases.
To counteract, we must reduce software entropy!
1. Motivation

6.
How to combat software and
data erosion?
6
Optimizing
IT-Infrastructure
Test-Driven
Development
Software-
Refactoring
Optimizing
Software-
Architecture
Model-Driven
Development
AI, Machine &
Deep Learning
Optimizing
Prozesses
i.E. Agile
ALM – Application
Lifecycle
Management
Continuous
Integration &
Delivery
1. Motivation

7.
7
1. Motivation
Objective reasons for AI revolution
Exponential data
growth
Large amounts of
unstructured data
Short-lived live
data
Exponential data growth - companies
have recognized the value of big data and
want it not to delete or "forget" (just like
the human brain does) - data is the gold of
the 21en century!
Many unstructured data - many areas
of IoT, weather, physics, chemistry,
organic, transport (autonomous driving),
etc. collect lots of unstructured data such
i.E. measurements. This "dark matter" of
data must be represented by AI in a
meaningful way and/or classified.
Many short-lived live data - such as
sensor data from Exchange forecast a
technical part are useless, if this part is
broken "earlier".

8.
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1. Motivation
AI - Economy Forecasts

9.
A dream of 'Thinking figure'
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2. History & future
Pygmalion und Galatea
Pandora and her box

10.
AI story had begun with a negative
statement
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Augusta Ada (Byron) King, Countess of Lovelace.
English mathematician and writer.
“The Analytical Engine* has no pretensions
whatever to originate anything. It can do
whatever we know how to order it to
perform. It can follow analysis, but it has no
power of anticipating any analytical
revelations or truths. Its province is to assist
us in making available what we are already
acquainted with" (Ada Lovelace 1843)
* - the “Analytical Engine” was a proposed mechanical
general-purpose computer designed by English
mathematician and computer pioneer Charles Babbage.
2. History & future

11.
… and has been continued with
double negation a century later
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Alan Mathison Turing
„The Analytical Engine was a universal
digital computer, so that, if its storage
capacity and speed were adequate, it could
by suitable programming be made to mimic
the machine in question**. Probably this
argument did not occur to the Countess
(Ada Lovelace) or to Babbage”*
* § 6. / (6) in A. Turing. Computing Machinery and
Intelligence. Mind-Journal, 1950:
https://www.csee.umbc.edu/courses/471/papers/turing.pdf
** as a „machine in question“ a digital „participant“ of
the Imitation Game as well known as Turing-Test had
been meant (see the participant “A” in the next slide).
2. History & future

12.
Can machines "think"?
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„The new form of the problem can be described in terms of a game
which we call the 'imitation game." It is played with three people, a
man (A), a woman (B), and an interrogator (C) who may be of either
sex. The interrogator stays in a room apart front the other two. The
object of the game for the interrogator is to determine which of the
other two is the man and which is the woman.
…
We now ask the question, "What will happen when a machine takes
the part of A in this game?" Will the interrogator decide wrongly as
often when the game is played like this as he does when the game is
played between a man and a woman? These questions replace our
original, "Can machines think?"
“ *
* A. Turing. Computing Machinery and Intelligence. Mind-Journal, 1950:
https://www.csee.umbc.edu/courses/471/papers/turing.pdf

13.
AI History
13
Artifical Intelligence
On September 2, 1955, the project was formally proposed by McCarthy, Marvin Minsky, Nathaniel
Rochester and Claude Shannon.
“We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer
of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the
conjecture that every aspect of learning or any other feature of intelligence can in principle be so
precisely described that a machine can be made to simulate it. An attempt will be made to find how to
make machines use language, form abstractions and concepts, solve kinds of problems now reserved
for humans, and improve themselves. We think that a significant advance can be made in one or more
of these problems if a carefully selected group of scientists work on it together for a summer.”
* Timeline-Source: K.E. Park

14.
AI and 4. Industrial Revolution
Artifical Intelligence is the “electricity”
of the 4. Industrial Revolution
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Source: Alan Murray. Fortune.com
2. History & future

15.
Key AI success factors
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2. History & future
1. Moor's Law (CPU / GPU / TPU / HPC / Cloud)
2. Big Data (Training Input & Subject Goal)
3. Falling Error Rate (i.e. IMAGE-Net)
4. Rising investments / sales
In addition to the well-founded academic AI, Machine
And Deep Learning theory since mid 50th and
objective reasons in the field,
there are 4 key exponents that drives AI revolution:

16.
AI success factors – Moor’s Law
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Source: https://humanswlord.files.wordpress.com
2. History & future

17.
AI success factors – Big Data
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2. History & future

18.
AI success factors – Moor’s Law
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Source: https://www.quora.com
2. History & future

19.
AI success factors – special hardware
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2. History & future

20.
CPU vs. GPU
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2. History & future

21.
CPU vs. GPU vs. TPU
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Source: GOOGLE CLOUD BIG DATA AND MACHINE LEARNING BLOG
2. History & future

22.
AI Definition
According to John McCarthy, Artificial Intelligence (AI) is an
information and engineering science dedicated to the
production of "intelligent" machines and especially
"intelligent" computer programs.
The research area wants to use computer intelligence to
understand human intelligence, but does not have to limit
itself to the methods that are observed biologically in
human intelligence. In humans, many animals, and in some
machines, different types and degrees of intelligence occur.
According to McCarthy, the computational part of the
intelligence is the ability to achieve the goals in the world. In
other words, a computer is built and / or programmed
(trained) in such a way that it can independently solve
problems, learn from the mistakes, make decisions, perceive
its surroundings, and communicate with people in a natural
way (for example, linguistically).
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3. Artificial Intelligence

23.
Ontology of the Human Intelligence
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Creati-
vity
Facts/Solutions
Predict
Judge
Abstract/Compose
Action
Re-usesolutions
Decide
Experiment
Manipulate
Speak/gesticulate/emotions
Under-
standing
Analyze
Compare/recognize
Search
Translate
Link
Knowledge
Learn
Remember
Discover
Observe
Associate
Sen-
ses
Feel
Hear
See
3. Artificial Intelligence

24.
AWI - Artificial weak Intelligence
Artifical weak (or narrow) Intelligence does not solve all, but
only a given narrow range of the human intelligence
ontology. In the case of a narrow AI, the simulation of a
certain range of intelligent behavior with the aid of
mathematics and computer science is concerned.
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3. Artificial Intelligence

25.
AHI - Artificial hybrid Intelligence
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Hybrid artificial intelligence does not solve all but several of
the AI domains in parallel that are crucial for the problem
domain and can be combined with human intelligence and
interaction. This is a combination of several simulations of
intelligent behavior with one another and (in some cases)
with human intelligence.
3. Artificial Intelligence

26.
ASI - Artificial strong Intelligence
Artificial strong intelligence aka AI-Singularity has as its goal
to create an artificial intelligence that "mechanizes" human
thinking, consciousness and emotions. Even after decades
of research, the questions of the strong AI are not fully
understood philosophically and the objectives remain
largely visionary.
According to some predictions however AI-Singularity could
be reached in a few decades or even sooner.
As a powerful technology ASI could be very good or very
bad thing for human beings.
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3. Artificial Intelligence

27.
AI, ML & Deep Learning Ontology
27 Source: www.deeplearningbook.org
3. Artificial Intelligence

28.
Machine Learning - Definition
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4. Machine Learning
Machine Learning (ML) is general term for the artificial
generation of knowledge from experience.
An artificial system learns from examples and can
generalize after completion of the learning phase. That is, it
Do not just memorize the examples, but recognize them
in the learning data regularities.
For software that means, according to Thomas Mitchell: "A
computer program is said to learn from experience E with
respect to some class of tasks T and performance measure P if
its performance at tasks in T, as measured by P, improves with
experience E".

29.
Machine Learning - Ontology
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4. Machine Learning
Machine Learning
Supervised
Learning
Classification
Regression
Ranking
Unsupervised
Learning
Clustering
Segmentation
DimensionReduction
Reinforcement
Learning
Decisionprocess
Rewardsystem
Recommendation
system

30.
Machine Learning Tasks
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4. Machine Learning

31.
ML – Reinforcement Learning
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4. Machine Learning

32.
Machine Learning Algorithms
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4. Machine Learning
By Girisch Khanzode

33.
Deep Learning Advances in Timeline
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Source - Keynote: Deep Learning Frameworks - Yoshua Bengio #reworkDL
5. Deep Learning

34.
The new AI Paradigm –
replace Programming with Training
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5. Deep Learning

35.
Biological neuron
35
Source: https://www.embedded-vision.com
5. Deep Learning

36.
Neuron Mathematical Model
36
Source: https://www.embedded-vision.com
5. Deep Learning

37.
Artifical Neural Network
37
Source: https://www.embedded-vision.com
5. Deep Learning
Important Deep Learning Architectures see here

38.
Backpropagation – adjust weights
through gradient descent
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5. Deep Learning
Source - Geoffrey Hinton:
The Foundations of Deep Learning

39.
Training of a Neural Network
39
Source: https://www.embedded-vision.com
5. Deep Learning

40.
How a Deep Learning Model
is trained
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5. Deep Learning

41.
The breakthrough in Computer Vision
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5. Deep Learning
In 2012, Alex Krizhevskiy, Ilya Sutzkever and Geoffrey
Hinton won the ImageNet Competition by far.
Dropout regularization and ReLU activation were
introduced and GPUs used for model training.
Deep Convolutional Neural Network (CNN) - AlexNet
Source: A. Krizhevsky, I. Sutskever, G. Hinton. ImageNet Classification with Deep Convolutional Neural Networks.

42.
Testbilder mit passenden Labels
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5. Deep Learning
Source: A. Krizhevsky, I. Sutskever, G. Hinton. ImageNet Classification with Deep
Convolutional Neural Networks.

43.
Deep Learning Libs
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5. Deep Learning
• Keras is able to run seamlessly on both CPUs and GPUs
• TensorFlow, Theano backend, and the Microsoft Cognitive
Toolkit (CNTK) backend
• CUDA (Compute Unified Device Architecture) – Nvidia GPU-Lib
• cuDNN (CUDA Deep Learning Network) – Nvidia GPU-LiB
• TensorFlow is itself wrapping a low-level library for tensor
operations called Eigen
• BLAS (Basic Linear Algebra Subprograms) – Linear Algebra Libs

44.
Progress in Deep Learning
• Speech recognition
• Computer vision
• Machine translation
• Reasoning, attention and memory
• Reinforcement learning (Games, Go etc.)
• Robotics & control
• Long-term dependencies, very deep nets
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5. Deep Learning

45.
Deep Learning Success drivers
• Lots and lots of data
• Very flexible ML models
• Enough computing power
• Computationally efficient inference
• Powerful predecessors that can beat
dimensionality problem through
compositions (like human abstractions)
• Deep ML Architectures with multiple
levels
45
5. Deep Learning

46.
Demo. Alexa Playground
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6. Sandbox playing
AWS Cloud
Architecural Diagramm
of the Alexa Powerpoint-Skill
Alexa, TRIGGER
presentation start
Pull Next Alexa-command
from the Message-Queue

47.
Demo Azure ML-Studio.
Income-Prediction
with Two-Class Decision-Jungle.
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6. Sandbox playing

48.
Demo Azure ML-Studio.
Income-Prediction
with Two-Class Neural Network.
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6. Sandbox playing

49.
Demo. AI stars recognition.
AWS – Round 1.
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6. Sandbox playing

50.
Demo. AI stars recognition.
Azure Cognitive Services – Round 2.
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6. Sandbox playing

51.
Classification-Demo
playground.tensorflow.org
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6. Sandbox playing

52.
Regression-Demo
playground.tensorflow.org
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6. Sandbox playing

53.
Demo. AI just for Fun!
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• AI Experiments-Collection
• Music MixLab
• Quick-draw - guess what I've drawn!
• X-Degrees Separation
6. Sandbox playing

54.
AI Applications
• Computer vision (Security, healthcare, IoT,
science …)
• Machine translation
• Natural Language Processing & Speech (i.e.
Alexa, Siri etc.)
• Search / Suggestions / Analytics (Google,
Amazon, financials …)
• Robotics & control (industry, aero-space,
public sector…)
• Autonomous vehicles (Mars-Rover, Self-
driving cars …)
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7. Chances and Risks

55.
From AI to AGI / ASI
• Exponential data growth: big data, weather, science,
entertainment, unstructured and short-living data
• Complexity: climate, energy, resources, economics,
physics etc.
• Solving Al as Artificial General Intelligence (AGI) is
potentially the meta-solution to all these problems
• The goal is to make Al science and/or Al-assisted
science come true
• Artificial Strong Intelligence (ASI) aka AI-Singularity
with human-level and beyond could be a big Meta-
AI-Network of the AI-/AGI-Domains.
• ASI could come faster as we could think! It could be
very powerful and useful (and scary!). So it should be
used ethically and responsibly.
• Philosophical problems of the ASI
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7. Chances and Risks

56.
ML Adoption Matrix –
where your see yourself?
56
8. Start now!
ML-
Provider
ML-Driver
ML-
Ignorer
ML-
Adopter
ML-Adoption
ML-Development

57.
Recommended Links
57
• Materials of this session:
https://bizzdozer.com/dwx2018
• ML Online Course: http://course.fast.ai/
• Artificial Intelligence. MIT Open Coursware.
MIT, 2015:
https://ocw.mit.edu/courses/electrical-
engineering-and-computer-science/6-034-
artificial-intelligence-fall-2010/
• Kaggle - place to data science projects:
https://www.kaggle.com
8. Start now!

58.
Recommended Publications
58
1. Ian Goodfellow, Yoshua Bengio, Aaron Courville.
Deep Learning (Adaptive Computation and
Machine Learning). MIT Press, 2016:
http://www.deeplearningbook.org
2. Francois Chollet. Deep Learning with Python.
Manning Publications Company, 2017
3. Santanu Pattanayak. Pro Deep Learning with
TensorFlow: A Mathematical Approach to
Advanced Artificial Intelligence in Python. Apress,
2017.
4. Mykola Dobrochynskyy. „Deep Learning“ articles
in dotnetpro-Magazin (ab 2018/09)
8. Start now!

59.
Conclusion
• You need concrete AI-Plan / Strategy (like for
Mobile in the past decade “Mobile first” goes to “AI
First”) in order to keep pace with competitors.
• AI converts Information into Knowledge and
programmers into data scientists.
• AI learns differently as a human – AI with training on
the Big-Data an the human with small chunks of
data, learned experiences and abstractions as well as
from genome derived information.
• Most of the value (by now) is generated by
supervised learning models (i.e. cognitive services)
• AI-Singularity is not expected in the near feature, but
things could change quickly (i.e. winning machine-
algorithm for the Go-game was expected at least in
10-15 years, but the big sensation was happened
March. 2016, as AlphaGo-program* won Lee Sedol –
winner of 18 world titles)
59
Artifical Intelligence
* - There are an astonishing 10 to the power of 170 possible board configurations - more than the
number of atoms in the known universe!

60.
Thank you!
60
Mykola Dobrochynskyy is Managing Director of Software
Factories. His focus and interests are Model-driven Software
Development, Code Generation, Artificial Intelligence, Machine
and Deep Learning, as well as Cloud and Service-oriented
Software Architectures.
Artifical Intelligence
ceo@soft-fact.de
@my_dobro