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Artificial intelligence (AI) - Definition, Classification, Development, & Concepts

Overview of artificial intelligence, its definition and classification, its history and historical development, as well as several theories and concepts.

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Artificial intelligence (AI) - Definition, Classification, Development, & Concepts

  1. 1. Artificial Intelligence Definition, Classification, Development, & Concepts Andreas Kaplan www.linkedin.com/in/andreaskaplan
  2. 2. Definition
  3. 3. Defining AI Kaplan Andreas and Michael Haenlein (2019) Siri, Siri in my hand, who’s the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence, Business Horizons, 62(1). Three reasons due to which it is difficult to define AI: 1. Difficulty to clearly define human intelligence 2. AI effect: AI considered as moving target which “occurs when onlookers discount the behavior of an AI program by arguing that it is not real intelligence” (Haenlein and Kaplan 2019) 3. Mixing-up of various different AI stages and types Artificial intelligence is “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” (Kaplan and Haenlein 2019b, p. 17)
  4. 4. Distinctions of AI - Big Data - IoT Kaplan Andreas and Michael Haenlein (2019) Siri, Siri in my hand, who’s the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence, Business Horizons, 62(1). Artificial Intelligence (AI) Big Data “are datasets characterized by huge amounts (volume) of frequently updated data (velocity) in various formats, such as numeric, textual or images/ videos (variety)” (Kaplan and Haenlein, 2019b, p. 17) Input Internet of Things (IoT) “is the idea that devices around us are equipped with sensors &softwaretocollect&exchange data” (Kaplan and Haenlein, 2019b, p.17) Data from, e.g., (Mobile) Social Media Data from, e.g., Firm’s Internal Databases InputInput Data Data DigitalTransformation:“theintegrationof digitaltechnologyintoallareasofsociety,and thechangesthatresultfromthisintegration.” (KaplanandHaenlein2019a)
  5. 5. (Un)Supervised,& Reinforcement learning Kaplan Andreas and Michael Haenlein (2019) Siri, Siri in my hand, who’s the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence, Business Horizons, 62(1). Supervised Learning: Mapping a given set of inputs to a given set of (labeled) outputs (Linear regression, Classification trees, Neural networks) Unsupervised Learning: Only labeled inputs but unlabeled outputs; Algorithm needs to infer the underlying structure from the data itself (black box) (Cluster analysis) Reinforcement Learning: System receives an output variable to be maximized and a series of decisions that can be taken and which impact the output
  6. 6. Classification
  7. 7. Classification 1: Evolutionary stages of AI Kaplan Andreas and Michael Haenlein (2019) Siri, Siri in my hand, who’s the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence, Business Horizons, 62(1). Artificial Narrow Intelligence ANI (Weak, Below human-level AI) • Applies AI only to specific areas • Unable to autonomously solve problems in other areas • Outperforms/ equals humans in the specific area Artificial General Intelligence AGI (Strong, Human-level AI) • Applies AI to several areas • Able to autonomously solve problems in other areas • Outperforms/equals humans in several areas Artificial Super Intelligence ASI (Conscious/ Self-aware, Above human-level AI) • Applies AI to any area • Able to solve problems in other areas instantaneously • Outperforms humans in all areas Siri can recognize your voice but cannot perform other tasks like driving a car Siri evolves into a humanoid robot with wide capabilities including voice recognition, coffee preparation and writing skills Siri develops super-human capabilities such as solving complex mathematical problems instantaneously or writing a best seller in a heart (or clock) beat
  8. 8. Classification 2: Competency types of AI Kaplan Andreas and Michael Haenlein (2019) Siri, Siri in my hand, who’s the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence, Business Horizons, 62(1). Expert Systems Analytical AI Human- Inspired AI Humanized AI Human Beings Cognitive Intelligence      Emotional Intelligence      Social Intelligence      Artistic Creativity      Supervised learning, Unsupervised learning, Reinforcement learning Artificial Intelligence (AI)
  9. 9. Sector-specific examples of AI applications Kaplan Andreas and Michael Haenlein (2019) Siri, Siri in my hand, who’s the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence, Business Horizons, 62(1). Analytical AI Human-Inspired AI Humanized AI Universities Virtual teaching assistants able to answer student questions and tailor reactions to individual data AI-based career services able to identify emotions to improve interview techniques of students Robo-teachers animating a student group by acting as moderator and sparring partners Corporations Robo-advisors leveraging automation and AI algorithms to manage client portfolios Stores identifying unhappy shoppers via facial recognition at checkouts to trigger remedial actions Virtual agents dealing with customer complaints and addressing concerns of unhappy customers Governments Automation systems to set the brightness of streetlights based on traffic and pedestrian movements Virtual army recruiters interviewing and selecting candidates based on emotional cues AI systems able to psychologically train soldiers before entering a war zone
  10. 10. Development
  11. 11. Development and History: AI’s 4 seasons Haenlein Michael and Andreas Kaplan (2019) A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence, California Management Review, 61(4). Spring: AI‘s Birth Beginnings of AI in the ~1940s: • Fiction: Isaac Asimov‘s Runaround • Non-fiction: Alan Turing‘s Computing Machinery and Intelligence • Minsky and McCarthy’s DSRPAI (~1942-1956) Winter: AI‘s Downs AI pessimism and scarce funding: • U.S. Congress’s criticism of high spending on AI research • James Lighthill’s pessimistic report on AI progress (1974-80 and 1987-93) Summer: AI‘s Ups AI euphory and extensive funding: • Joseph Weizenbaum‘s ELIZA • Herbert Simon, Cliff Shaw and Allen Newell‘s General Problem Solver program (1957-73, 1981-86, 1994-~2014) Fall: AI‘s Harvest Harvest of the fruits of: • past statistical advances • increased computer processing power led to the period of AI Fall, in which we find ourselves in today (from ~2015 onwards) AI seasons
  12. 12. 1956: Term AI is coined at Dartmouth Haenlein Michael and Andreas Kaplan (2019) A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence, California Management Review, 61(4).
  13. 13. 2015: AlphaGo defeats human player Haenlein Michael and Andreas Kaplan (2019) A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence, California Management Review, 61(4).
  14. 14. Concepts
  15. 15. Six Ws of Artificial Intelligence WHY Misconceptions exist because media tend to emphasize the dangers and threats of AI more than its potential and opportunities WHAT AI is “a system’s ability to interpret external data correctly, to learn from such data and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein, 2019, p. 17) WHO As any tool, AI-driven robots can be used for good or evil. It is unclear if robots can turn evil by themselves, but likely that they do not and will not think like humans WHEN It is impossible to say when artificial superintelligence will arrive – maybe tomorrow, in our lifetime, or never WHERE The current epicenters of AI development are China and the US, but other world regions (especially Europe) will play important (niche) roles in future HOW The extent of AI risk is hard to predict but likely to be substantial. Even if the chance of occurrence is small, the discounted risk is sufficiently large to warrant preventive measures already today Kaplan Andreas and Michael Haenlein (2020) Rulers of the World, Unite! The Challenges and Opportunities of Artificial Intelligence, Business Horizons, 63(1).
  16. 16. PESTEL analysis of Artificial Intelligence Kaplan Andreas and Michael Haenlein (2020) Rulers of the World, Unite! The Challenges and Opportunities of Artificial Intelligence, Business Horizons, 63(1). Environment Pollution or Renewable? Law Deadlock or Innovation? Economics Layoffs or Growth? Society Hell or Heaven? PESTEL Analysis of AI Politics War or Peace? Technology Collapse or Control? All six PESTEL dimensions can have positive as well as negative aspects
  17. 17. Six dirEctions for Artificial Intelligence Kaplan Andreas and Michael Haenlein (2020) Rulers of the World, Unite! The Challenges and Opportunities of Artificial Intelligence, Business Horizons, 63(1). Education Link science and humanities strongly to guarantee human-friendly AI progress Entente Foster cooperation regarding AI on an international level to ensure world stability Evolution Adapt to pace in AI advances and changes in human IQ Ethics Commit to equip AI systems with an understanding of human values and instincts Employment Create frameworks where humans and machines can work and live together Enforcement Develop smart regulations to guarantee law and order
  18. 18. Three Cs of AI’s organizational impacts Kaplan Andreas and Michael Haenlein (2019) Siri, Siri in my hand, who’s the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence, Business Horizons, 62(1). Internal External Confidence Managers need to exude confidence with respect to their employees in a fast- evolving work environment Consumers need to put confidence in the abilities and recommendations of an organization’s AI systems Change Employees need to constantly change and adapt their functions and skills through lifelong learning Competitors need to be monitored and outperformed permanently by use of better hardware or data Control Machines need to be controlled to avoid autonomous decisions and implicit biases States need to control the ecosystem of managers, employees, machines, consumers and competitors Three common traits that are of organizational relevance both internally and externally: Confidence, Change, and Control – The 3 C’s of Organizational Implications of AI
  19. 19. Bibliography
  20. 20. Bibliography • Haenlein Michael and Andreas Kaplan (2019) A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence, California Management Review, 61(4). • Kaplan Andreas and Michael Haenlein (2019a) Digital transformation and disruption. On Big Data, Blockchain, Artificial Intelligence, and Other Things, Business Horizons, 62(6). • Kaplan Andreas and Michael Haenlein (2019b) Siri, Siri in my hand, who’s the fairest in the land? On the interpretations, illustrations and implications of artificial intelligence, Business Horizons, 62(1). • Kaplan Andreas and Michael Haenlein (2020) Rulers of the World, Unite! The Challenges and Opportunities of Artificial Intelligence, Business Horizons, 63(1). www.linkedin.com/in/andreaskaplan

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