AI Symposium Keynote Manila, 2017
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Technical Introduction to AI and Data with context of education and business models.
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AI Symposium Keynote Manila, 2017
- 1. Ikhlaq Sidhu, content author Ikhlaq Sidhu Founding Faculty Director Sutardja Center for Entrepreneurship & Technology Department of Industrial Engineering & Operations Research IEOR Emerging Area Professor Award Innovation & Entrepreneurship and AI
- 4. Ikhlaq Sidhu, content author Answer# 2: Many News Stories about our Curriculum 5/27/2017 Meat substitutes are on the curriculum at UC Berkeley - San Francisco Chronicle http://www.sfchronicle.com/business/article/Meat-substitutes-are-on-the-curriculum-at-UC-10881462.php 1/5 By Jonathan Kauffman | January 24, 2017 | Updated: January 25, 2017 2:44pm 0 Meat substitutes are on the curriculum at UC Berkeley BizĀ &Ā Tech IMAGE 1 OF 4 Ricardo San Martin, a chemical engineering professor, leads the Challenge Lab researching meat substitutes. Photo: Scott Strazzante, The Chronicle 5/27/2017 Meat substitutes are on the curriculum at UC Berkeley - San Francisco Ch Banking Un Course - Stu Singapore Most UC Berkeley students will tell you that theyāre shooting for an A. But the 45 young men and women packing a Barrows Hall classroom this Monday were pursuing more ambitious goals: saving the world, and perhaps winning $5,000 in the process. The students are enrolled in a four-credit Challenge Lab at the Sutardja Center for Entrepreneurship & Technology, a practicum that pits teams against one another to develop the most innovative plant-based meat. At the labās second gathering, there were no Tofurky samples on hand. Instead, Christie Lagally, a senior scientist with the Go the students a PowerPoint crash course on the reasons the world needs m āFactory farming allows us to have an afļ¬uence of meat,ā she told them, several dozen charts illustrating the downside of our omnivorous appetit ļ¬gures, animal agriculture produces up to 24 percent of greenhouse gase About 7 Stories about our plant based meat focus area including Vice Magazine and SF Chronicle
- 9. Ikhlaq Sidhu, content author ā¢ Wide Comfort Zone ā¢ Generate Trust ā¢ Good Connectors ā¢ Inductive Learning: Experiments and Reflection ā¢ Self awareness and Emotional Intelligence ā¢ And a few more .. Innovation Behaviors and Mindset Skill in a Core Area Too Narrow Street Smart, but lacking depth High Potential What are the Behaviors and Mindsets? Taught in situation, during the journey
- 11. Ikhlaq Sidhu, content author Sample Project from the First Data-X Course ā¢ Detection of fake news ā¢ Prediction of long-term energy prices to solve am Wall Street problem ā¢ Prediction applications stock market, sports betting, and more ā¢ AI for Crime detection, traffic guidance, medical diagnostics, .. etc ā¢ A version of Zillow that is recalculated with the effects of AirBnB income ā¢ and many moreā¦
- 12. Ikhlaq Sidhu, content author Propose Low Tech Solution (1) Brainstorm Challenge and Validate (4) Demo or Die (1) Execute * Iterate BMoE Reflections Agile Sprint (8) Insightful Story Solution How the Data-X Course Works: Team: typically 5 students, with available advisor network
- 14. Ikhlaq Sidhu, content author Our Newest Course contributed to IEORās core Area: IEOR 135 Applied Data Science (Data-X) 5/27/2017 Data-X: An Experimental Course Model that is Working - UC Berkeley Sutardja Center Search 8 MAY 2017Data-X: An Experimental Course Model that is Working by Ikhlaq | posted in: ariti cial intelligence, big data, Sutardja Center News, undergraduate classes | Iād like to start by congratulating all the teams that participated in our rst Data-X course this spring. We just watched the nal presentations, and it has been a great experience. Three months ago, we were just introducing the basic frameworks. And now, by the end of the semester, the projects have included running code and insightful approaches to topics such as: Detection of fake news Prediction of long-term energy prices to solve a Wall Street problem Prediction applications for the stock market and sports betting AI for Crime detection, traf c guidance, and medical diagnostics A version of Zillow that is recalculated with the effects of AirBnB income and many moreā¦ Students presenting at Data-X nals Ā These are technically dif cult projects, not to mention creative and inspiring. Everyone has come up a very large learning curve. I want to thank Kevin Bozhe Li and Alexander Fred Ojala for being part of the teaching team. And our guests, such as Rob von Behren from Google who spoke on TensorFlow and entrepreneurs like Antonio Vitti who brought real life problems and context to the course. Today, the world is literally reinventing itself with Data and AI. However, neither leading companies nor the worldās top students have the complete knowledge set or access to the full networks they need to participate in this newly developing world. Data-X is a UC Berkeley course and a global project designed to x this problem. Undergraduate Courses and Certi cate Graduate Program The Berkeley Method of Entrepreneurship BMoE Bootcamp Engineering Leadership Professional Program Startup Semester at Berkeley Innovation Collider 2017 Spring Newton Lecture Series About the Center Login Recent Posts Free Ventures Demo Day: From Seed to Startup Why You Should Learn Data-X Engineered In uence: Weak Data, Machine Learning & Behavioral Economics Students serve up next generation plant-based seafood Data-X: An Experimental Course Model that is Working Home About Courses People Insight News Explore Contact Q: What Are you getting from this class? A: I feel like I'm really learning how powerful data science tools can be. When we were brainstorming project ideas, I didn't think any of the ideas were feasible. However, with each week, I'm learning how pre existing libraries and tools can be easily used and combined to create really powerful products.
- 15. Ikhlaq Sidhu, content author We are developing a large-scale, holistic, data-related skill base ā¢ The Data-X Project is program and open course model ā¢ Offers deep skills, the powerful open source CS tools, and the real-life applications ā¢ Ready to scale and include more stakeholders
- 16. Ikhlaq Sidhu, content author Data, AI, and Business Models
- 18. Ikhlaq Sidhu, content author Real-life Example: ZestCash ā¢ āAll data is credit dataā Online Loan Application Name: JOE SMITH Online Loan Application Name: Joe Smith The data says: greater credit risk! The data says: lesser credit risk! Reference: Shomit Ghose Example: Data and information is a competitive advantage
- 20. Ikhlaq Sidhu, content author 1. Knowing your customer, better targeting and relationship. E.g. Target, Disney, Netflix 2. Improving physical product or servicer with complimentary information: E.g. UPS, FedEx 3. Data-driven reliability or security E.g. GE, BMW, Siemens 4. Information Brokers, Arbitrage, and Trading Opportunities: E.g. Investment funds. 5. Improving the customer journey/experience.. E.g. Harrahās 6. Functional Applications: HR/Hiring, Operations etc.. Eg Walmart, Baseball, Sports 7. Efficiency or better performance per dollar cost. E.G. General IT, SAP, etc 8. Risk Management, regulation, and compliance Eg. Compliance 360 Top 8 Business Models Using Data
- 21. Ikhlaq Sidhu, content author Top Business Models for Using Data 1. Knowing your customer, leading to better targeting and relationship. E.g. Target, Disney 2. Information based better services. E.g. UPS, FedEx 3. Data driven reliability. E.g. GE and Siemens 4. Information Brokers, Arbitrage, and Trading Opportunities: Investment funds. 5. Improving the customer journey/experience.. E.g. Harrahs 6. Functional Applications: HR/Hiring, Operations etc.. 7. Efficiency or Better Performance per dollar cost 8. Risk Management, regulation, and compliance Usage Models ā¢ Efficiency (save money) ā¢ Wallet Share (top customers spend more time and money with you) ā¢ Brand alignment (It reinforces how people think positively about the company) Value to Business Customers More Value
- 26. Ikhlaq Sidhu, content author Data and AI Fundamentals
- 28. Ikhlaq Sidhu, content author Human Interpretation of Data Human Machines Large Sets of Data Insight
- 31. Ikhlaq Sidhu, content author A Fundamental Idea: From Table to Score Element F1 F2 F3 A 4 2 2 B 4.5 1.5 3 C 3 3 5 D 1 2 2 E 3 1.5 5 F 3.5 3.5 1 .. .. .. .. F(X) Element Credit Score A 552 B 381 C 760 D 330 E 452 F 678 .. .. X Y
- 32. Ikhlaq Sidhu, content author A Fundamental Idea: From Table to N- Dimensional Space A B CD E F G H 1 2 3 4 5 5 4 3 2 1 Element F1 F2 F3 A 4 2 2 B 4.5 1.5 3 C 3 3 5 D 1 2 2 E 3 1.5 5 F 3.5 3.5 1 .. .. .. .. X =
- 34. Ikhlaq Sidhu, content author Machine Learning: Learning from Data Input Data = Matrix X Customer 1: [Name, income, x, y, .. Features ..z] Customer 2: [Name, income, x, y, .. Features ..z] Customer N: [Name, income, x, y, .. Features ..z] Output Data = Column Vector Y Customer 1: [20] Customer 2: [60] Customer N: [05] Purchases/year, repaid loan, ā¦ Target: What is F(X) = Y a formula that we donāt know Sample data (training): (x1,y1) (x2,y2) ā¦ (xm,ym) we have this Algorithm A from H H: Hypothesis Set: All possible algorithms or formulas Find G(x) which is approx. F(x) a) Supervised ML ā as shown b) Unsupervised - no training data c) Reinforced learning ā done by simulation
- 35. Ikhlaq Sidhu, content author KNN / K Means Illustration 12/19/2016 How to choose machine learning algorithms | Microsoft Docs https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice 17/19 A data set is grouped into 5 clusters using K-means There is also an ensemble one-v-all multiclass classifier, which breaks the N-class classification problem into N-1 two-class classification problems. The accuracy, training time, and linearity properties are determined by the two-class classifiers used. + Options https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice Tip Flavors of machine learning Supervised Letter recognition. To download and print a diagram that gives an overview of the capabilities Studio, see Overview diagram of Azure Machine Learning Studio capabilitie Supervised learning algorithms make predictions based on a set of e historical stock prices can be used to hazard guesses at future prices training is labeled with the value of interestāin this case the stock p learning algorithm looks for patterns in those value labels. It can use might be relevantāthe day of the week, the season, the company's f industry, the presence of disruptive geopolicitical eventsāand each Illustration Source: KNN Method: Find the k nearest images and have them vote on the label (i.e. take the mode)
- 36. Ikhlaq Sidhu, content author K Means / KNN Illustration 12/19/2016 How to choose machine learning algorithms | Microsoft Docs https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice 17/19 A data set is grouped into 5 clusters using K-means There is also an ensemble one-v-all multiclass classifier, which breaks the N-class classification problem into N-1 two-class classification problems. The accuracy, training time, and linearity properties are determined by the two-class classifiers used. + Options https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice Tip Flavors of machine learning Supervised Letter recognition. To download and print a diagram that gives an overview of the capabilities Studio, see Overview diagram of Azure Machine Learning Studio capabilitie Supervised learning algorithms make predictions based on a set of e historical stock prices can be used to hazard guesses at future prices training is labeled with the value of interestāin this case the stock p learning algorithm looks for patterns in those value labels. It can use might be relevantāthe day of the week, the season, the company's f industry, the presence of disruptive geopolicitical eventsāand each Illustration Source: KNN Method: Find the k nearest images and have them vote on the label (i.e. take the mode) from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors = 3) knn.fit(X_train, Y_train) Y_pred = knn.predict(X_test) acc_knn = round(knn.score(X_train, Y_train) * 100, 2) acc_knn # or compare Y_pred with Y_test
- 37. Ikhlaq Sidhu, content author Our experiment with the Titanic Data Set Model Score Random Forest 86.76 Decision Tree 86.76 KNN 84.74 Support Vector Machines 83.84 Logistic Regression 80.36 Linear SVC 79.01 Perceptron 78.00 Naive Bayes 72.28 Stochastic Gradient Decent 72.28 More Accuracy Generally more training time More risk of overfitting Less Accuracy Generally less computation
- 42. Ikhlaq Sidhu, content author All our course materials: ā¢ Slides ā¢ Code samples ā¢ References are available at data-x.blog Free to use. Course Material
- 43. Ikhlaq Sidhu, content author Anticipating the Next Industrial Revolution Industrial Revolution 1.0 Industrial Revolution 2.0 ā¢ Winner was whoever made something most cheaply ā¢ Leveraged scale ā¢ Winner will be whoever makes best sense of the data ā¢ Leveraging scale Shomit Ghose
- 44. Ikhlaq Sidhu, content author Data and AI Effects Everything We Know Every Business: Will deconstructed by Data, AI, and Automated Decisions Society: Danger of even larger gap between the highly skilled and the lessor skilled Government: Must adapt to a new level of transparency and efficiency or face trouble from their people People: Will change their behaviors (like cell phone to the power 10). Work like balance, social structure, and hybrid human machine.