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Intro Artificial Intelligence

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A short introduction to the topic AI explained with some examples

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Intro Artificial Intelligence

  1. 1. Artificial Intelligence Hans-Dieter Wehle, hdw@idhorb.de
  2. 2. AGENDA 1. Introduction 2. Areas & Samples ARTIFICIAL INTELLIGENCE
  3. 3. introduction ARTIFICIAL INTELLIGENCE
  4. 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. 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. 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. 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. 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. 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. 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
  12. 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. 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. 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. 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. 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.