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Artificial Intelligence in Project Management by Dr. Khaled A. Hamdy

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Video recording of the Dr. Khaled's session can be found at https://youtu.be/TFNhvAXNU5E.

The presentation explores how Artificial Intelligence (AI) can be used in the Project Management field. The origins and history of AI are discussed followed by a brief simplified explanation of the theories behind its application. The actual utilization of AI tools in the Project Management domain is discussed covering diverse areas such as Engineering Design, Cost Estimating and Bidding, Planning and Scheduling, Risk Management, Performance Prediction as well as Project Monitoring and Control. The presentation concludes by a brief discussion about Data Management and Knowledge Engineering and how they are used today to simplify (or complicate) our lives.

Published in: Technology

Artificial Intelligence in Project Management by Dr. Khaled A. Hamdy

  1. 1. Artificial Intelligence in Project Management KHALED HAMDY, PH.D., PMP
  2. 2. Introduction of Khaled Hamdy • Civil Engineer • Ph.D. in Engineering • Professional Project Manager, PMP© • Projects Advisor in RTA Why am I here talking about Artificial Intelligence?
  3. 3. Contents • What is Artificial Intelligence? • Artificial Intelligence in Project Management • Data Mining and Knowledge Engineering
  4. 4. Contents • What is Artificial Intelligence? • Artificial Intelligence in Project Management • Data Mining and Knowledge Engineering
  5. 5. What is AI? Is it Science? Is itTechnology? Is it Magic?
  6. 6. IS AI MAGIC? Artificial Intelligence is a Science that uses Technology to Create Magic
  7. 7. Contents • What is Artificial Intelligence? • Definition • DifferentTypes of Artificial Intelligence • History of Artificial Intelligence • Theories behind Artificial Intelligence • Artificial Intelligence in Project Management • Engineering Design • Cost Estimation and Bidding • Planning and Scheduling • Risk Management • Prediction of Project Performance • Data Mining and Knowledge Engineering
  8. 8. Definition of AI • AI is a machine’s ability to: • Perceive • Reason • Learn • Act Sensors (Perceive) Environment Perceptions Actions Effectors (Act) Reason & Learn
  9. 9. Another Definition of AI • AI is the ability of a computer (or a machine) to perform certain tasks thought to require intelligence including: • Logical deduction and inference • Learning and adaptation • Ability to make decisions based on: • Past Experience • Insufficient Information • Conflicting Information • Ability to understand spoken/natural language
  10. 10. Different Types of AI AI Cognitive Science Robotics Natural Interface • Expert Systems • Learning Systems • Fuzzy Logic • Genetic Algorithms • Neural Networks • IntelligentAgents • Visual Perception • Tactility • Dexterity • Locomotion • Navigation • Natural Languages • Speech Recognition • Virtual Reality • Multisensory Interfaces
  11. 11. History of AI • A branch of Computer Science • Around for Decades • Term first created in 1956 • One of the definitive references on AI (still used today) was LAST published in 1992 • Still used as a text book at MIT
  12. 12. ALVINN learns to Drive in 1989 Computer 100 Million FLOPS (Floating-Point Operations per second) 1/10 Computing Power of the AppleWatch
  13. 13. 20161989 Reasons for AI Prominence 1. Huge Advancements inTechnology
  14. 14. Reasons for AI Prominence 2. Exceptional (Exponential) Growth in Computing Power 1980’s – 3 CRAY Supercomputers 2017 – iPhone 7 More Powerful than 80’s CRAYS
  15. 15. Advances in Computing • Deep Blue defeatedGarry Kasparov – World Chess Champion in 1997
  16. 16. Advances in Technology & Computing
  17. 17. AI is already Changing our Lives
  18. 18. Artificial Intelligence - The Science behind the Magic • Algorithms that tell computers and other machines how to think and act intelligently • Many tools and techniques including: • Knowledge Based Expert System • Artificial Neural Network • GeneticAlgorithm • Fuzzy Logic
  19. 19. Knowledge Based Expert System (KBES) • Consist of a Knowledge Base and Inference Engine • The knowledge base is created by collecting the facts and opinions from Subject Matter Experts • The Inference Engine utilizes IF-THEN statements to provide expertise
  20. 20. Artificial Neural Network (ANN) • A machine learning approach that models the human brain and consists of a number of artificial neurons linked by weighted connections • ANNs demonstrate the ability to learn, recall, and generalize from training patterns or data (numeric, non-numeric or both).
  21. 21. Artificial Neural Network (ANN) Input Layer Hidden Layer (s) Output Layer Inputs Numeric or Non-Numeric Outputs Numeric or Non-Numeric Neuron Neuron Connections Connections
  22. 22. Artificial Neural Network (ANN) Neuron
  23. 23. Genetic Algorithms (GA) • Iterative search methods that mimic the natural biological evolution process and/or the social behavior of species. • GAs utilize the natural evolution processes of selection, mutation and cross-over to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical search techniques may fail.
  24. 24. Genetic Algorithms (GA)
  25. 25. Fuzzy Logic • Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based • A form of many-valued logic in which the truth values of variables may be any real number between 0 and 1
  26. 26. Fuzzy vs Boolean Logic Boolean Logic Not Tall (0) 150 cm Tall (1) 180 cm Fuzzy Logic Not very Tall (0.2) 150 cm Definitely Tall (0.9) 180 cm
  27. 27. Contents • What is Artificial Intelligence? • Artificial Intelligence in Project Management • Data Mining and Knowledge Engineering
  28. 28. AI in Engineering Design • ANN to select the most suitable structural system for High Rise Buildings subjected to wind and seismic loads • GA to optimize the LCC of Buildings in hot climates using different combinations of location, orientation and construction materials
  29. 29. AI in Project Planning • KBES to provide estimates of the duration and resource requirements for project activities based on expert knowledge • ANN to automate the sequencing of project activities based on functional requirements. • GA to optimize the schedule of construction project activities in order to minimize the total cost with resource constraints • Fuzzy Logic to determine project priorities in the Portfolio Management Process
  30. 30. AI in Cost Estimation • ANN to estimate the suitable mark-up to increase the possibility of winning tenders • ANN to predict the possible cost over-runs based on the selected contractor, the competence of the project manager, the project size and the type of contract used • Fuzzy Logic to optimize the cost-time trade-offs in construction projects
  31. 31. AI in Risk Management • ANN to estimate the probability of occurrence for project risks allowing a more accurate quantitative approach to risk analysis. • ANN combined with Monte Carlo Simulation to mimic the human procedure of risk evaluation and adaptation • Fuzzy Logic to assess risks in Construction projects to model probability distributions
  32. 32. AI in Performance Management • ANN to predict the performance of future projects based on the project parameters such as the Project Manager’sCompetence, the Contractor’s ability and the contracting method used. • KBES to assess claims and provide expert decisions based on the claim conditions • Fuzzy Logic to improve Project Management Efficiency in Construction Projects
  33. 33. AI in PM AITools Design Planning Cost Estimation Risk Management Performance Management KBES ANN GA Fuzzy Logic
  34. 34. AI in PM • AI can be and has been used in several applications in Project Management enabling better project performance • AI can make the life of project managers less (or maybe more) miserable
  35. 35. Contents • What is Artificial Intelligence? • Artificial Intelligence in Project Management • Data Mining and Knowledge Engineering
  36. 36. Data Mining & Knowledge Engineering • Sources of Data: • Machine Data • Organizations Data • People Data • Data-Information-Knowledge-Wisdom
  37. 37. Sources of Data • Machines • Organizations • People
  38. 38. Machine Data • Sources: • Medical Equipment • Smart Devices • Sensors
  39. 39. Machine Data • Characteristics: • Extensive • Tabular • Detailed • Can be connected
  40. 40. Organizations Data • Sources: • Banks & Financial Institutions • Hospitals • HR Departments • Retails Stores
  41. 41. Organizations Data • Characteristics: • Structured • In Silos • Variability
  42. 42. People Data • Sources: • Facebook • Twitter • Instagram • YouTube 20,000 Years The time people spend on Facebook everyday 342,239 Years Of Video is watched every month on YouTube
  43. 43. People Data • Characteristics: • Un-Structured (80% ofWorld Data) • Fragmented • Variability • HUGE How Huge is HUGE? 30+ PB vs 2 PB
  44. 44. Data, Information, Knowledge and Wisdom
  45. 45. Data, Information, Knowledge and Wisdom
  46. 46. Data, Information, Knowledge and Wisdom
  47. 47. Data Mining in Project Management • Knowledge Discovery in Data (KDD) used to extract useful knowledge from large sets of resources data to improve resource management practices • Data Mining used to improve Project Cost Estimation and Scheduling for Software Projects
  48. 48. Remember Artificial Intelligence is a Science that uses Technology to create Magic
  49. 49. Also Remember DATA is the Currency of the Future
  50. 50. GOLD SPONSORS BRONZE SPONSOR SUPPORTER MEDIA PARTNERS SPONSORS & PARTNERS
  51. 51. Questions Ask the Computer It is Intelligent
  52. 52. What should a Self-Driving Car Do?
  53. 53. Myths about AI MaxTegmark, President of the Future of Life Institute
  54. 54. Myths about AI MaxTegmark, President of the Future of Life Institute
  55. 55. Myths about AI MaxTegmark, President of the Future of Life Institute
  56. 56. Myths about AI MaxTegmark, President of the Future of Life Institute
  57. 57. Myths about AI MaxTegmark, President of the Future of Life Institute
  58. 58. Myths about AI MaxTegmark, President of the Future of Life Institute
  59. 59. Myths about AI MaxTegmark, President of the Future of Life Institute

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