1.
Artificial Intelligence
Hans-Dieter Wehle, hdw@idhorb.de

2.
AGENDA
1. Introduction
2. Areas &
Samples
ARTIFICIAL INTELLIGENCE

3.
introduction
ARTIFICIAL INTELLIGENCE

4.
Any organization that is not a math house now
or is unable to become one soon is already
a legacy company.
Ram Charan
(author)
ARTIFICIAL INTELLIGENCE

5.
INTRODUCTION
Definition
Artificial intelligence (AI) is a branch of
computer science that describes the research
and development of simulated human
intelligent behavior in machines.
“This involves researching methods that
enable a computer to develop intelligent
behavior and work independently on
problems.”
ARTIFICIAL INTELLIGENCE

6.
INTRODUCTION
Artificial Intelligence
ARTIFICIAL INTELLIGENCE
Learns from
experience
Uses what is
learned to draw
conclusions
Identifies
images
Solves difficult
problems
Understands
different
languages
Creates new
perspectives
AI SYSTEMS

7.
INTRODUCTION
Timeline
1950
Turing test:
measures machine
intelligence
1957-1965
First attempts to
simulate human
problem solving
(General Problem
Solver)
Ende 1960er
First chatbot
1988
German Research
Center for Artificial
Intelligence (DFKI) is
established
Ab 1997
Annual Robocup
1956
The term artificial
intelligence is
introduced by John
McCarthy
First functioning AI
program
1965-1975
Little progress is made,
cutbacks are made to
AI financing
1975-1985
Public awareness of AI
research is created
through expert system
technologies (e.g.,
MYCIN)
1997
AI chess computer
becomes world chess
champion
2011
IBM develops Watson
2016
Google develops
AlphaGo
1936
Turing machine: first
machine to simulate
any computer
algorithm
Further Internet 4.0 and
Internet of Things
innovations
ARTIFICIAL INTELLIGENCE

8.
INTRODUCTION
AI Opportunities and Challenges
Opportunities
− faster decision making
− better forecasting
− increased efficiency
− eliminate human error
− help humans perform better
− reduce costs/labor force
ARTIFICIAL INTELLIGENCE

9.
INTRODUCTION
AI Opportunities and Challenges
Challenges
− resistance and cultural change
− poses threat to labor intensive and management
positions
− lacks empathy
− lacks moral compass
− increased competition
− rapid technological development
ARTIFICIAL INTELLIGENCE

10.
Cognitive Technologies
simulate the perceptive and cognitive abilities of humans.
Knowledge
Representation &
Reasoning
Speech
recognition
Robotics
Smart Advisors
Machine Learning
Image
Recognition
Natural Language
Processing

11.
Areas
ARTIFICIAL INTELLIGENCE

12.
AREAS
Areas Using Artificial Intelligence
Intelligent Data
Management
Intelligent Robots
Smart Homes Autonomous Cars
Adaptive Learning
Software
Smart Warehouses
Smart Meters Smart Control Systems
ARTIFICIAL INTELLIGENCE

13.
AREAS
Overview of Areas Within AI
ARTIFICIAL INTELLIGENCE
Q&A
Systems
Machine
Translation
Social
Network
Analysis
Roboti
cs
Graph
Analysis
Machine
Learning
Visualization
Internet
of
Things
Speech
Analysis
Image
Analysis
Recommenda
tion Systems
Natural
Language
Generation
Natural
Language
Processing
Virtual
Personal
Assistants
Knowledge
Re-
presentatio
n

14.
AREAS
Definitions
Semantic Data
Analytics
Enables data that is not causally related
to be linked.
Operational
Intelligence
Improves operational processes and
economic decisions through data
analysis.
Cognitive
Computing
Enables independent data discovery and
processing by linking AI to enterprise IT.
Bots
Computer programs that work
independently and perform repeated
tasks either automatically or with minimal
human intervention.
Social Analytics
Explores and analyzes data from blogs
and social media websites and derives
business decisions from them.
Data Lakes
Store raw data that can be accessed
when needed and used in big data
analytics to create a competitive
advantage.
ARTIFICIAL INTELLIGENCE

15.
AREAS
Deep Learning
Deep learning is the most widespread machine learning method. It is used by IT companies such as Google, Facebook, and Apple.
Computers can autonomously learn from data, such as images.
ARTIFICIAL INTELLIGENCE
classic Neural Network Neural Network: Deep Learning
Input Layer
Hidden Layer
Output Layer

16.
AREAS
Machine Learning
An artificial system learns
from examples, recognizes
patterns and regularities,
and generalizes them after
the learning phase.
ARTIFICIAL INTELLIGENCE
Traditional Programming
Machine Learning
DATA
PROGRAM
DATA
OUTPUT
PROGRAM
OUTPUT
In machine learning,
a computer learns from
experience.