Intro to ai for grid & ethical AI
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Session for Emerson on role of AI in the Grid
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Intro to ai for grid & ethical AI
- 1. INTRO TO ETHICAL AI & ENTERPRISE LUIS F GONZALEZ
- 2. Confused Yet? It’s ok, we all are
- 4. Data Driven -Intelligence Theory Key Question
- 5. 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. Why did it happened? What will happen? Descriptive Statistics Inferential Statistics 2 Processing Focus on getting Information out of data. Terms like Data Analytics, Data Mining & Analytic Reporting.
- 6. 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 ComputerVision Natural Language Understanding & Generation Forecasting and Operations Research KnowledgeGraphs 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
- 7. Examples of AI Disciplines applied in Enterprise
- 8. Power Industry – Energy Transition with AI
- 9. Digital Enablement Vision– Disruption Resilience and Optionality Avoid unplanned outages in Thermal Sites, reduce emissions and optimize base load pricing in the market Produce Revenue from Energy Services and know our customers better than any other retailer Easiest utility to do business with and facilitate customer centricity (e.g. P2P, Prosumers) Optimize Plant Design and Market MW$ Decisions as well improve stability of renewable generation as partial peak load Manage increasing disruptions from EV, Prosumers, Distributed Power while maintaining grid stability and reliability Maximize dispatch opportunity and spot price opportunities while better predicting weather impact and minimizing impact to plant and cost (Automation). “ We need to help Deliver Power Safely to everyone, Reliably, Affordably to customers and Responsibly for the environment” Maintain Reliability, avoid outages and minimize impact from nature, market and changes in the system
- 10. 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 ComputerVision Natural Language Understanding & Generation Forecasting and Operations Research KnowledgeGraphs 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?
- 11. Enterprise AI Governance & Ethics Source: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/getting-to-know-and-manage-your-biggest-ai-risks
- 12. Transitioning to Contextual AI
- 13. INTRO TO AI & ENTERPRISE USECASES Appendix LUIS F GONZALEZ
Editor's Notes
- AL Lets use the example: A 13 Year Old boy wants to find out if the girl he likes likes him back. The trigger of an Intelligence REQUIREMENT is the Question “Does she Like me?” The first step is to look for DATA sources, Her Friends,.- This is Called Human Intelligence Source (The most valuable but the most unreliable) Her Instagram “Signal Intelligence, which Includes Photos on who she hangs out with and what boy. She has posted” Having some Surveillance (A friend of your follows her) Looking at Historical events (Other Boys she may have liked in the past All these are COLLECTION Then each of his friend process the information for validity, bias, relevance looking for SIGNALS, good predictors point “singlaing” that she could like him. This is called PROCESSING Then lets say all the friend meet back on show their individual work. They will collate the information and ANALYZE the signals for trends. They will then REPORT to his friend that based on the data gather and the trends observed she may or may not like him. And lets say they where not entirely sure but then they will get close to probabilistic approaches and report an 80% chance that she does like him. So in the process of answering question A, we could discover that there are better questions to ask like “Does she have a Boyfriend already?” Cycling through the Intelligence cycle is the art of achieving KNOWLDGE through learning how to ask the right questions and how to derive INTELLIGENCE so to obtain predictors on who she may like in the future. If we can conceptualize the future through restructuring how we think of the questions we ask and how to answer them then we have build intelligence.
- AL So if we look into our path towards intelligence, we will iterate questions until we arrive at the ability to predict and prescribed. Enough questions will get us there. Here is the progression of the kinds of quewtions we will need the answers to in order to build our intelligence capabilities.