Intro to ai application emeritus uob-final
0 likes112 views
This document provides an overview of an introductory webinar on AI concepts and terminologies. The webinar agenda includes discussing concepts of AI learning algorithms, understanding the 5 spokes framework of AI, and a team exercise. Key concepts covered are the 7 stages of building intelligence from data, examples of common learning algorithms like neural networks, and the 5 components of the 5 spokes AI framework: sensing, perception, communication, decision making, and interaction. Real-world applications of each component are also examined, such as computer vision, natural language processing, time series analysis, and reinforcement learning.
Intro to ai application emeritus uob-final
- 1. INTRO TO AI CONCEPTS & TERMINOLOGIES Webinar 1 LUIS F GONZALEZ / ALVIN NG
- 2. Agenda Concepts of AI (Learning Algorithms) 30mins Understanding AI (5 Spokes Framework) 60 mins • Reasoning • Communication • Decision Making • Interaction Team Exercise 30 mins
- 3. Confused Yet? It’s ok, we all are
- 4. CONCEPTS & TERMINOLOGY OF AI
- 5. Data Driven -Intelligence Theory Key Question
- 6. The path to AI (the search for “Learnings”) 5 Learning Focus on retaining correlations in intelligent repositories and reapplication of inferences across the company. Terms Like Deep Learning, Representations and Autonomous Entity 3 Root Cause Analysis Focus on correlation and inference building with a specific context to the business. Terms like Intelligent Reports and Smart Machines, . 1 Collection The focus on Data acquisition Sensoring and Governance to lay the foundation for Data. Terms like Big Data and Data Lakes DashBoards and Control reporting. 4 Simulation Focus on feedback loops to forecasting results and accuracy on predictions models. Terms life Digital Twin, Lifeing Modelling. What happened? What is happening? Why did it happened? What will happen? What should happen? Descriptive Statistics Inferential Statistics 2 Processing Focus on getting Information out of data. Terms like Data Analytics, Data Mining & Analytic Reporting.
- 7. 7 Building Intelligence - Data Learning
- 8. 8 Learn user’s behaviour based on voice commands and can adjust settings automatically in subsequent interactions Target user with personalized products and services ads based on their demographic profile, search history, visited sites, liked social media posts, etc. Improve efficiency/quality in servicing customers by using AI assistants (e.g., chatbots, robo-greeters in bank branches and cardless ATM machines via facial recognition) Detect suspicious/fraudulent activities in network and/or transactions using predictive analytics Recommend music or videos based on user’s historical consumption and preferences Provide best driving routes, ETA, and/or match drivers with riders based on historical and real- time data Smart Home Devices Media & Entertainment Navigation and Transportation E-commerce & Targeted Ads Customer Service Assistants Security and Fraud Detection What we see every day….
- 9. Machine Assisted Intelligence “Learning Algorithms” data, software, hardware, and research Larger and more sophisticated datasets Faster hardware and better software Larger and more sophisticated models Research breakthroughs for training models Increasing interconnectivity in the research community (TensorFlow, Theano, Caffe, Torch) Increasing Availability, Use and Expertise of Data Value, Governance and Engineering Increasing Maturity of Cloud Computing and Processing Power Increased Development and Experience of Learning Algorithms (AlexNet, BatchNorm, DeepLearning) https://spectrum-ieee-org.cdn.ampproject.org/c/s/spectrum.ieee.org/amp/stop- calling-everything-ai-machinelearning-pioneer-says-2652904044
- 10. What are Learning Algorithms? Artificial Intelligence is somewhat inaccurate as systems are not intelligent alas, yet they learn. It is a set of algorithms that learn from data to make predictions .
- 11. What can Neural Networks (NN’s) or perceptron do? 11 MACHINE LEARNING Dislike Like Provide training examples Distinguish likes from dislikes Learn useful features <Citrus-ness> <Shape> < T e x t u r e > Define fields to describe fruit <Sweetness= ? > <Shape= ? > <Colour= ? > Pre-program rules If <round> & <sweet> Or if <red> & not <sour> Or if <green> & <sour> Provide input and get fixed output or error <Apple> = Like <Kiwi> = Like <Banana> = ??? RULES-BASED SYSTEM <Citrus-ness> <Shape> < T e x t u r e >
- 13. Let’s simplify the progression Input Input Input Hand-designed rules Output Learned simple features Learned complex features Mapping from features Output Hand-designed features Mapping from features Output Rules-based systems 1960’s Classic machine learning 2000’s Deep learning 2020’s Input Random Probability Distribution Output Stochastic systems 1980’s
- 14. Hierarchical Abstractions and their power 14
- 15. Types of Learning Possible
- 16. Examples of AI Disciplines applied in Enterprise
- 17. 5 SPOKES AI FRAMEWORK
- 18. What are Learning Technologies Components? Sensing the world Perception Learning from every interaction Communication Optimizing to specific outcomes Decision making Understanding concepts & relations Reasoning Taking actions in the world to achieve goals Interaction Computer Vision Natural Language Understanding & Generation Forecasting and Operations Research Knowledge Graphs and Representations Reinforcement Learning Answer questions about a scene Determine if a growth is cancerous or not Infer what happened to characters in a story Drive on city streets and highways Identify objects in a scene
- 19. Sensing the world Perception Can I verify the face of the customer? COMPUTER VISION
- 20. Perception: Vision and its applications https://aidemos.microsoft.com/computer-vision Try it your self
- 21. Vision Leading Practice Watch an intro class to Computer Vision @ https://www.youtube.com/watch?v=CLOAswsxudo
- 22. KNOWLEDGE GRAPHS Understanding concepts & relations Reasoning Are our customer segments mapped to market trends?
- 24. Knowledge Graph Basic Architecture https://medium.com/octavian-ai/deep-learning-with-knowledge-graphs-3df0b469a61a
- 25. Reasoning : Use case Covid 19 treatment Knowledge Graphs Leading Practice
- 26. NATURAL LANGUAGE Z Learning from every interaction Communication Is the customer happy with our service?
- 27. Communication: Natural Language U/G/P https://corenlp.run/ Try it your self
- 29. Other Applications today For NLP/G/U Abstract Summarization helps us understand your Context:
- 30. FORECASTING & OPERATIONS RESEARCH Optimizing to specific outcomes Decision making Determine future price of a stock given volatility
- 31. https://towardsdatascience.com/deep-learning-for-time-series-classification-inceptiontime-245703f422db letting the model learn how to process time series data on its own is a more promising solution when dealing with unstructured noisy data. Autoregressive CNN for Asynchronous Time Series Decision: Time Series and its applications https://youtu.be/U9yZIaVMa2c Try it your self
- 32. Decision: Time Series and its applications Time Series Best Practice
- 33. Time Series Best Practice Decision: Time Series and its applications
- 34. Time Series Best Practice Decision: Time Series and its applications
- 35. REINFORCEMENT LEARNING Taking actions in the world to achieve goals Interaction Autonomous Investment for ROI guarantees
- 36. Interaction: Reinforcement Learning Reward Functions https://youtu.be/n2gE7n11h1Y Try it your self
- 37. Interaction: Reinforcement Learning and its Applications Reinforcement Learning: Dynamic Treatment Regimes https://arxiv.org/pdf/1908.08796.pdf Best approach to optimal decision making to Game against a disease https://opendatascience.com/deep-learning-research-review-week-2-reinforcement-learning/
- 38. Reinforcement Learning: Dynamic Treatment Regimes Interaction: Reinforcement Learning and its Applications
- 39. INTRO TO AI CONCEPTS & TERMINOLOGIES Webinar 2 LUIS F GONZALEZ / ALVIN NG
- 40. Agenda • Use case Workshop • “Decision Needs Analysis” • AI Solution Template • Team Presentations • Intro to Ethical AI • Fairness. Ethics. Accountability. Transparency. • Data vs Model • AI Global Standards
- 42. Innovative thoughts – Retail Banking & AI
- 43. Innovative thoughts – Commercial Banking & AI
- 45. Data Driven -Intelligence Theory 1 Learning Focus on retaining correlations in intelligent repositories and reapplication of inferences across the company. Terms Like Deep Learning, Representations and Autonomous Entity 3 Root Cause Analysis Focus on correlation and inference building with a specific context to the business. Terms like Intelligent Reports and Smart Machines, . 5 Collection The focus on Data acquisition Sensoring and Governance to lay the foundation for Data. Terms like Big Data and Data Lakes DashBoards and Control reporting. 2 Simulation Focus on feedback loops to forecasting results and accuracy on predictions models. Terms life Digital Twin, Lifeing Modelling. What happened? What is happening? Why did it happened? What will happen? What should happen? Descriptive Statistics Inferential Statistics
- 46. Set 1- Decision Needs Analysis What critical business QUESTIONS do you need to answer within your department? Why is it important to know? What DECISION will you make with this information? Who will use information? How often will you use this information? 1 What are the key predictors of loan defaults in 25 to 30 year Old Single Professionals? This indicates the highest risk yet the highest reward lending profile so we are looking to increase this sector of our loans while running more predictions to better underwrite the risk of defaults. Loan Risk Assessor Weekly Loan Application Revie What data is necessary to make this Decision Today? (Signals) What potential Models would we use? (Prediction, Knowledge, Classification, Clustering, Forecast, Vision, NL) 1 What are the key predictors of loan defaults in 25 to 30 year Old Single Professionals? This indicates the highest risk yet the highest reward lending profile so we are looking to increase this sector of our loans while running more predictions to better underwrite the risk of defaults.
- 47. Ste 2- Use case Template se Case ID: F.21 Use Case Title: Loan Risk Predictor & Underwriting Intelligence Data Maturity Level How Mature are we to deliver use case? Value Objective (Objective Key Result): What is the estimated % Increase in Revenue, or Reduction of Risk % per loan category if you had the answer? Driving Business Unit: Contributing Business Unit: NA Use Case Description Describe what the use case would do and how would the insight be consumed? Potential AI Technology / Learning Algorithm Techniques Describe what kind of Learning Algorithm would you use for this use case and why? Use Case Complexity Gage Complexity Feasibility Window Gage Feasibility Window Data Implications What kind of Data would we need for this use case? Comment on Cleansing, Modelling, Annotation, etc. Architectural / Infrastructure / Sensing Considerations / UX CX Any new Sensing or existing architectures that will need to be considered? Use Case Examples Link use cases, research papers or offerings in the market that cover your idea. Notes Mention other considerations
- 48. Ethical AI
- 49. Sensing the world Perception Learning from every interaction Communication Optimizing to specific outcomes Decision making Understanding concepts & relations Reasoning Taking actions in the world to achieve goals Interaction Computer Vision Natural Language Understanding & Generation Forecasting and Operations Research Knowledge Graphs and Representations Reinforcement Learning Answer questions about a scene Determine if a growth is cancerous or not Infer what happened to characters in a story Drive on city streets and highways Identify objects in a scene Mis-identification of Threat Dis-advantaging Groups Promoting Hate Speech Incrementing Market Volatility Pedestrian Fatality -Autonomous Vehicles What are the implications to humans?
- 50. Managing Enterprise AI Source: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/getting-to-know-and-manage-your-biggest-ai-risks
- 51. Fairness Ethics Accountability Transparency FEAT principles were created to guide better deployment of AI Justifiability Accuracy & Bias Internal & External Outcome Explainability Interpretability Align to our Ethos
- 52. A I A S I A P A C I F I C I N S T I T U T E AI & Fairness Is the data used a fair representation of reality. Is our model having Unintended Consequences, Systemic Issues?
- 53. Fairness Example
- 54. AI & Transparency Strong evidence on the accuracy of the output for high-stake decisions. Interpretation- why model output is counter- intuitive & do I trust it?
- 55. Going beyond model debugging What kind of action can we drive? Actionable XAI Explanations that drive actions “I need to understand why a model made a decision so I can complete a regulatory audit report” Human-AI Interaction for XAI AI can interact and also learn from human insight “I want to easily see the reasoning so I can correct errors and give better feedback on what I want the output to be” End to End XAI Moving away explaining a model to explaining a business process. “If I am assembling a car, understanding every piece separately is not sufficient. I must be able to audit the assembly process.” 1 2 3 55
- 56. AI for Compliance Right to an explanation if receiving an adverse decision Why did we decide an unpopular decision?
- 57. Element AI | Copyright © 2020 | Strictly Confidential Government Systems Designer Research Data Vendor AI Model Service Vendor Platform User Environment And it becomes more complicated... Who is accountable now?
- 58. AI for Bias Ensure that people are not being unfairly or unknowingly excluded
- 60. AI for Generalization Ensure that the right model is used for our business objective (Concept Drift)
- 61. Data & Concept Governance Source: https://twitter.com/AporiaAi/status/1406999597575254018/photo/1
- 62. IEEE P7003TM Standard for Algorithmic Bias Considerations •IEEE P7000: Model Process for Addressing Ethical Concerns During System Design •IEEE P7001: Transparency of Autonomous Systems •IEEE P7002: Data Privacy Process •IEEE P7003: Algorithmic Bias Considerations •IEEE P7004: Standard on Child and Student Data Governance •IEEE P7005: Standard on Employer Data Governance •IEEE P7006: Standard on Personal Data AI Agent Working Group •IEEE P7007: Ontological Standard for Ethically Driven Robotics and Automation Systems •IEEE P7008: Standard for Ethically Driven Nudging for Robotic, Intelligent and Autonomous Systems •IEEE P7009: Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems •IEEE P7010: Wellbeing Metrics Standard for Ethical Artificial Intelligence and Autonomous Systems Source: https://doi.org/10.1145/3194770.3194773
- 63. INTRO TO AI CONCEPTS & TERMINOLOGIES Appendix LUIS F GONZALEZ / ALVIN NG
- 64. Competencies needed in an Enterprise for AI 64 5 Object Detection & Monitoring Robust detection, counting and tracking of objects and people in a wide variety of environments, enabling valuable workflows in many real-world situations. Image Classification Video Alteration Object Tracking and Counting 6 Optimization AI-powered optimization boosts the efficiency of business processes and tasks, maximizing lift and ROI compared to traditional optimization methods. Routing Disruption management Re-optimization 7 Explainability Making your AI explainable to users helps drive adoption and lowers the barrier of entry for users. Technical Explanations Bias Evaluation and Tracking Sample-based Explanations And more including... ● Recommender systems ● Assignment with constraints ● Association rule learning ● Human-AI interaction ● Image segmentation ● Image clustering ● Routing with constraints ● etc.. 4 Time-Series Forecasting Best-in-class AI-powered forecasting provides better accuracy and can deliver lift for a wide range of forecasting scenarios. Hybrid Forecasting Models Statistical Forecasting Deep-learning Forecasting Models 3 Anomaly Detection Detect anomalies on objects in natural environments and in various types of data, allowing for near real-time reaction. Visual Anomaly Detection Event-based Anomaly Detection Anomalies in Forecasting Data 1 Text Extraction & Analysis Accelerate the extraction of insights from multiple forms of text, catching signals that are easily overlooked by humans. Text Summarization Sentiment Analysis Text Classification 2 Optical Character Recognition Instantly transcribe text from natural environments or digital documents, reducing manual work and enabling automation. Documents Handwritten Notes Live Scenes and Video