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Artificial Intelligence – a Driving Force in Industrial 4.0 Shaibal Barua, Phd Researcher, Artificial Intelligence and Intelligent Systems [email protected]

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Artificial Intelligence – a Driving Force in Industrial 4.0 Shaibal Barua, Phd Researcher, Artificial Intelligence and Intelligent Systems Shaibal.Barua@Mdh.Se Artificial Intelligence – A Driving Force in Industrial 4.0 Shaibal Barua, PhD Researcher, Artificial Intelligence and Intelligent Systems [email protected] 29 May, 2020 Outline ● Artificial Intelligence – what’s the deal? ● Industrial Artificial Intelligence ● The applied AI workflow ● Data cleaning and preparation ● Data representation ● AI problems and methods ● Validation ● Use cases ● What’s next? 2 Part 1: Artificial Intelligence 3 Poll 1 Go to: www.menti.com Use the code: 16 54 26 4 "Mainstream Science on Intelligence experiences, adaptability to the environment, plan, problem solving ….. ….. solving problem plan, environment, the to adaptability experiences, from learning reasoning, to ability ideas, complex understand to Capability Intelligence”, 1994, the Wall Street Journal “The ability to learn, understand and think in a logical way about things; the the things; about way logical a in think and understand learn, to ability “The ability to do thiswell”ability todo ”Intelligence: Knowns and Unknowns”,1995, the Board of Scientific Affairs of the American Psychological Association - Oxford dictionary 5 Poll 2 Go to: www.menti.com Use the code: 49 25 59 6 Artificial Intelligence Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. AI is the study of programmed systems that can simulate, to some extent, human activities such as perceiving, thinking, learning and acting. Behavior by a machine that, if performed by a human being, would be called intelligent Fig: Turing test 7 Artificial Intelligence Natural Social Reasoning language Intelligence Artificial Knowledge Learning Intelligence representation Planning Perception Robotics 8 Three types of AI Narrow AI General AI Superintelligence • Singular task • Machine intelligence • Hypothetical agent • Successfully realized • Carry out any • Machines become to date cognitive function self-aware • Operate under a that a human can • Surpass the capacity narrow set of • Knowledge transfer of human constraints and between domains intelligence limitations • Fujitsu’s ”K” supercomputer 9 Poll 3 Go to: www.menti.com Use the code: 60 72 51 10 How Old is the idea of AI? ● Sixth-fifth century BC ● Aristotle layout the epistemological basis; introduces syllogistic logic ● The Iliad – assorted automata from the workshops of Greek god Hephaestus ● Late first century ● Fable automata built by Heron of Alexander ● Fifteenth-sixteenth century ● Mechanic clocks, Paracelus introduces a recipe for a humanculus, an intelligent “little man” ● Eighteen century ● Philosophers try to formulate the laws of thought Hoffman’s The Sandman ● Nineteenth century Goethe’s Faust ● Literary artificial intelligences proliferation Mary Shelley’s Frankenstein ● Twentieth century ● Alan Turin proposes an abstract of universal computing machine 11 AI: Past, Present and Future The Turing test Deep learning, big "I propose to consider data and general the question, 'Can 2 nd AI: 2011-present machines think?’” (A. 1987AI Winter: Goals fulfilled: Access to large 1993-2011 amounts of data Turing, 1950) -1993 Faster computers An interrogator asks Deep Blue (1997) Deep learning drives questions to an progress in image and Victory of the “neats” (unseen) person A. If 1 st Expert systems video processing, text AI Winter: boom: 1980-1987 (2003) A is replaced by an AI, 1974 analysis, speech DARPA Grand recognition can the interrogator -1980 Rule-based, logical systems Challenge (2005) detect this or not? Google DeepMind Selection of components AI untold successes in defeats world based on customer data mining, robotics, champion in Go logistics, speech (2016) requirements recognition, search Golden years: 5th gen project (Japan) Widespread 1957-1974 engines discussions around Neural networks, Strong AI: Symbolic AI, search backprop. algorithms, neural superhuman nets, industrial intelligence robots, etc. 2017 AlphaGo: Google’s AI beats world champion Ke Jie. Notable for vast number of 2170 of possible positions 12 Poll 4 Go to: www.menti.com Use the code: 88 11 55 13 Responsible AI ● Ethical Reasoning ● Accountability, Responsibility, Transparency Figure: Trolley problem dilemma ● Responsible AI concerned with the fact that decisions and actions taken by intelligent autonomous systems have consequences that can be seen as being of Figure: Interrelationship of the seven an ethical nature. requirements: all are of equal importance, support each other, and should be implemented and evaluated throughout the AI system’s lifecycle Source: EU Ethics Guidelines for Trustworthy AI, https://ec.europa.eu/futurium/en/ai-alliance-consultation/guidelines/1 14 Poll 5 Go to: www.menti.com Use the code: 71 62 93 15 Part 2: Industrial Artificial Intelligence 16 The four industrial revolutions •Connected machines 4th Industrial Revolution •Complex human-machine interaction 2011 •Artificial intelligence •Nuclear energy 3rd Industrial Revolution •Electronics, 1969 telecommunication, computers •Automation - PLCs, control theory, PID regulators, etc. •Industrial robots •Electricity, gas and oil 2nd Industrial Revolution •Combustion engine, steel industry, chemical 1870 industry •Telegraph, telephone •Division of Labour (Taylorism), Mass •Mechanical production production (Ford) 1st Industrial Revolution •Industry instead of agriculture 1765 as basis of economy •Water power •Steam engine 17 Industrial Artificial Intelligence A systematic discipline, which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Jay Lee, Hossein Davari, Jaskaran Singh, Vibhor Pandhare, Industrial Artificial Intelligence for industry 4.0-based manufacturing systems, Manufacturing Letters, Volume 18, 2018, Pages 20-23, 18 AI and Industry 4.0 Decision Product Company Manufacturer Supplier making and applications Deep insights Knowledge AI/ML Enabled Advanced Analytics Descriptive Diagnostics Predictive Prescriptive (What happened) (Why it happened) (What will happen) (What action to take) Capture Products’ Examine the Predict quality and Identify measures to Condition, causes of reduced patterns that signal improve outcomes or environment and product impending events correct problems Pattern operation performance or detect failure Data Processing Data Aggregation Enterprise External Smart, connected products Data (Service histories, warranty (Price, weather, supplier (Location, condition, use, etc.) status, etc.) inventory, etc.) Smart Connected Process 19 Adapted from: Jinjiang Wang, Yulin Ma, Laibin Zhang, Robert X. Gao, Dazhong Wu, Deep learning for smart manufacturing: Methods and applications, Journal of Manufacturing Systems, Volume 48, Part C, 2018, Pages 144-156, Key elements in Industrial AI ● Analytics technology (A), ● Big data technology (B), ● Cloud or Cyber technology (C), ● Domain knowhow (D) and ● Evidence (E) 20 Industrial AI Figure: Comparison of Industrial AI with other learning systems Jay Lee, Hossein Davari, Jaskaran Singh, Vibhor Pandhare, Industrial Artificial Intelligence for industry 4.0-based manufacturing systems, Manufacturing Letters, Volume 18, 2018, Pages 20-23, 21 Challenges of Industrial AI ● Machine-to-machine interactions ● Machine-to-human interactions ● Data quality ● Cyber security 22 Poll 6 Go to: www.menti.com Use the code: 92 63 4 23 Part 3: The applied AI workflow 24 The Industrial AI stack Business Understanding Data collection and processing Representation “Solving the problem” Validation Deployment, maintenance and support 25 Planning, scheduling, etc. Common in industrial problems everywhere: ● How should we schedule a workforce? ● How to order manufacturing steps in a product variant? ● How to order individual manufacturing orders/items? ● On what units should which maintenance be performed and when? … etc. Typically, a deep understanding of the business is needed. 26 The Industrial AI stack Business Understanding Data collection and processing Representation “Solving the problem” Validation Deployment, maintenance and support 27 Data cleaning and preparation Data from real applications is dirty: ● Duplicates and missing data ● Values with special meaning (ID 9999 means ”missing”) ● Invalid data ● Logically inconsistent data ● Mystery data (railway cars which are 600 meters long) ● Spiking data (temperature is 10e+10 for 1 millisecond) ● Sensor drift, ”almost” values (0.6% really means 0.0%; 100.6% means 100%) ● Multiple data files which are not in sync ● Misspellings ● Different wordings Data preparation and cleaning takes a long time! Validity threat: data cleaning removes realistic details 28 Example 29 Poll 7 Go to: www.menti.com Use the code: 70 24 27 30 The Industrial AI stack Business Understanding Data collection and processing Representation “Solving the problem” Validation Deployment, maintenance and support 31 Representation ● Before a method is chosen, the representation should be considered ● For machine learning – what should be the input? ● E.g. vibration/noise analysis – representation in time/space or frequency domain? ● For planning, scheduling, simulation – what model abstraction should be used? ● E.g. microscopic model of robot movements, mesoscopic model of discrete manufacturing steps, or macroscopic model of completion time distribution for product variants. ● In both cases, the representation of the problem can impact performance substantially. ● Finding the right representation requires in-depth understanding of the application!
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