Why AI matters
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AI will augment our humanity—it will give each of us “superpowers” to meet challenges of all kinds—including some of our biggest ones.. All around the world, the Artificial Intelligence and Research team is leading the way, working to fulfill our mission—to empower every person and every organization on the planet to achieve more—with AI.
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Why AI matters
- 1. Artificial Intelligence Why it matters Ashish Jaiman @ashishjaiman linkedin.com/in/ashishjaiman
- 3. What is AI
- 7. Video
- 8. Cloud
- 9. What is learning Machine learning is a way to try to make machines intelligent by allowing computers to learn from examples about the world around us or about some specific aspect of it. Deep learning is an approach to machine learning, particular among all the machine learning methods in that it is inspired by some of the things we know about the brain. It’s trying to make computers learn multiple levels of abstraction and representation, which is presumably what makes these systems so successful Reinforcement learning is a type of machine learning where the learner doesn’t get to know what a human would do in this context. The learner only gets to see if the actions were good or bad after a long set of actions. A lot of the recent progress in this area is in things like playing games, but reinforcement learning probably is going to be very important for things like self-driving cars. Transfer Learning (Inductive transfer), is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
- 11. Machine Learning Is the crux Machine learning algorithm Model Application Data Contains patterns Finds patterns Recognizes patterns Provides new data to see if it matches known patterns
- 12. Which means, two types of learning Training data The prepared data used to create a model Creating a model is called training a model Supervised learning The value you want to predict is in training data The data is labeled You use classification to get to prediction Unsupervised learning The value you want to predict is not in the training data The data is unlabeled You are trying to find groupings in your data The most common approach
- 13. How do you do “learning” in your own mind when you want to pick a flight to Seattle? Flight How close is arrival to when I need to get there (hours) How much does it cost I ‘m in their frequent flyer program I’ll take it AC360 4 $288 N N LV666 1 $650 Y N AJ555 2 $289 Y Y AH444 5 $299 Y N I chose AJ555 What was my thought process?
- 14. My mental model Cost Arrival Time Frequent Flyer YESNO NO NO >$500 Yes >4 hrs<=4 hrs No Yep Model: Decision Tree Splitting Attributes Flight How close to when I need to get there (hours) How much does it cost I ‘m in their frequent flyer program I’ll take it AC360 4 $288 N N LV666 1 $650 Y N AJ555 2 $289 Y Y AH444 5 $299 Y N
- 15. What Happens in ML? The same! Flight How close to when I need to get there (hours) How much does it cost I ‘m in their frequent flyer program I’ll take it AC360 4 $288 N ? LV666 1 $650 Y ? AJ555 2 $289 Y ? AH444 5 $299 Y ? Flight How close to when I need to get there (hours) How much does it cost I ‘m in their frequent flyer program I’ll take it AA245 4 $300 N N ALA132 1 $538 Y N SW585 2 $129 Y Y ELA44 5 $659 Y N BS222 6 $125 N N … … … … … Learning Set (training) Testing Set (training) Learn Model Learning Algorithm Apply Model My Fabulous Models
- 16. Deploy chosen model Chosen Model Apply learning algorithm to data Candidate Model The Machine Learning Process Prepared Data Apply pre- processing to data Iterate to find the best model Data Preprocessing Modules Iterate until data is ready Preprocessing Modules Machine Learning Algorithms Applications The goal: Smarter applications Raw Data Raw Data Choose data
- 17. switch case case case // Need to list every dog type :( animaltype = "dog”; break // Managing different poses, ouch // What about dogs that I missed? // How about other animals? // This is tedious default break
- 18. Styles of Machine Learning Algorithms Examples Decision tree Neural network Bayesian K-means P(A) P(B|A) P(B) P(A|B) = Deep learning uses this
- 20. The Microsoft AI platform Services Infrastructure Tools
- 24. AI must maximize efficiencies without destroying the dignity of people1 AI must guard against bias2 AI needs accountability so humans can undo unintended harm3 AI must be transparent4 AI must be designed for intelligent privacy5 AI must be designed to assist humanity6
- 25. Thank You AI for ALL https://learn.microsoft.com/activity/S1443002/launch#/ AI School https://aischool.microsoft.com/learning-paths