3. Agenda
• Motivation
• Overview of the AI and ML
• Cloud and the Intelligent APIs
• Demo 1. Cognitive Race AWS vs. Azure
• Demo 2. AWS Bot with Lex (optional)
• Demo 3. Azure ML Studio
• Demo 4. Alexa Playground
• Mind-Factories Event
• Conclusion
• Q & A
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4. AI – why we should care?
• According to McKinzey “Automation of knowledge
work” – AI, ML, Natural User Interfaces and BigData
– could have economic impact of $5 - $7 trillion or
110-140 Mio. full-time workers in the next decade.
• According to IDC Big Data will generate about $187
Mio. By 2019 (or +50% vs. 2015). Without ML/AI
most of the Data especially unstructured and short-
living would be lost.
• By 2018 about 50% of developers will embed ML/AI-
Features in their application.
• With democratized Cloud AI-APIs the lean Start-ups
will compete with established companies on the
emerging AI-Markets.
• AI already transforms IT, Communication, Energy,
Financial and Healthcare and soon will transform or
impact almost every industry
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5. AI and 4. Industrial Revolution
Artifical Intelligence is the “electricity”
of the 4. Industrial Revolution
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Source: Alan Murray. Fortune.com
6. AI History
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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
8. Objective reasons
for the AI-Revolution
• Exponential data growth – the companies
recognized the value of the gathered Big Data
and don’t want to delete or “forget” it (just like
human brain it does).
• Lots of unstructured data – many sensors, IoT
etc. gather tons of unstructured data like audio,
video, environment measurements etc. This
“dark matter” data has to be processed
(visualized) by AI in a meaningful way.
• Lots of short-time living data – i.e. sensor data
used to exchange-prediction of a technical part
becomes useless, when this part is broken.
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9. AI “take-off” essential exponents
Besides of profound academic AI theory since mid
50th and objective reasons in field there are 4
essential exponent factors, that make rise of AI
possible:
1. Moor’s Law (CPU / GPU / HPC / Cloud )
2. Big Data (Training-Input & Subject-Goal)
3. Sinking Error-Rate (i.e. IMAGE-Net)
4. AI Investments / Revenues
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10. 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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11. 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
12. 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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13. 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.
14. 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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19. Training of the Neural Networks
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Source: https://www.embedded-vision.com
20. Convolutional neural network
(aka CNN)
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Neurons of a
convolutional layer
(blue), connected to their
receptive field (red)
Max pooling with a 2x2
filter and stride = 2
Source: https://en.wikipedia.org/wiki/Convolutional_neural_network
The convolution of f and g is
written f∗g. It is defined as the
integral of the product of the two
functions after one is reversed and
shifted. As such, it is a particular
kind of integral transform
21. 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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22. ML to AI - Success-Factors
• 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
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23. 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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24. AI - products, services and research
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System Provider Type
Microsoft Cognitive Services Microsoft Cloud-Service, AI-API
Google Cloud Machine Learning Plattform Google Cloud-Service, AI-API
Google Assistant Google digital AI-Assistant
Deep Mind DeepMind (Google) AI-Research
Brain Team Google AI-Research
Amazon AI Amazon Cloud-Service, AI-API
Echo / Alexa Amazon digital AI-Assistant
IBM Watson IBM Cloud-Service, AI-API
Facebook AI Research Facebook AI-Research
Open AI Open AI AI-Research (non-profit)
api.ai Google / API AI Cloud-Service, AI-API
25. Few Useful Links
• Session-Materials: https://bizzdozer.com/ai
• Azure Cognitive Services: https://www.microsoft.com/cognitive-services
• Amazon Rekognition: https://console.aws.amazon.com/rekognition
• Deep Learning Online-Book: http://www.deeplearningbook.org
• Deep Mind Home: https://deepmind.com/
• Open-source AI Library: https://www.tensorflow.org
• Software Factories Home: http://www.soft-fact.de
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29. 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 in
Sep. 2016, as AlphaGo-program won)
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30. Thank you! Questions?
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Mykola Dobrochynskyy is Managing Director of Software
Factories. His focus and interests are Model-driven Software
Development, Code Generation, Artificial Intelligence (AI) and
Machine Learning, as well as Cloud and Service-oriented
Software Architectures.
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