Rapid-X

Transcript

1. Ikhlaq Sidhu Chief Scientist & Founding Director, Sutardja Center for

   Entrepreneurship & Technology IEOR Emerging Area Professor Award, UC Berkeley About Me: Rapid AI/Data A Framework for Digital Transformation Rapid-X

2. Ikhlaq Sidhu Chief Scientist & Founding Director Sutardja Center for

   Entrepreneurship & Technology Industrial Engineering & Operations Research IEOR Emerging Area Professor Award UC Berkeley Ikhlaq Sidhu, UC Berkeley q Chief Scientist and Founder Sutardja Center q Professor in IEOR at UC Berkeley q Created many Berkeley programs q Developed Data-X q Advisor to many firms and executives q Granted over 60 US Patents q Invented technologies used at Skype, HP, US Robotics, IBM, and licensed to many others … q Awarded 3Com’s “Inventor of the Year” q HP – Laser Printer Design q Venture Advisor at Onset Ventures, X-Fund q Numerous Advisory Boards and non-profits All degrees: Electrical Engineering and Computer Science (EECS), BS to Ph.D.

3. One of my newest courses at Berkeley: IEOR 135 Applied

   Data Science with Venture Applications Based on the Rapid-X Project Framework

4. • Detection of fake news • Prediction of long-term energy

   prices to solve Wall Street problem • Prediction applications stock market, sports betting, and more • AI for crime detection, traffic guidance, medical diagnostics, etc. • A version of Zillow that is recalculated with the effects of AirBnB income and many more… IEOR 135 Applied Data Science with Venture Applications Sample Rapid AI/Data Projects

5. We are in a new phase of evolution due to

   data, AI, crypto- systems, blockchain, algorithms Drivers of Rapid-X

6. It is a significant problem to our national agenda if

   students can’t participate, build, and harness these types of technologies New technologies on the horizon * World is changing * Next Industrial Revolution National and Global Security National Competitiveness The result of skill and behavior mismatch:

7. Our model has adapted: Business training is not the only

   key element I’ve seen many technical projects with smart people go off track Why we can’t deliver: • Theoretical understanding without a practical understanding of implementation • Narrow focus: silos of disconnected expertise not leading to any useful work product or innovation • Over-design: way too complex • Not even sure what to create. Wanting implementation specs that no one has. • Expensive cost over-runs on development, sometimes even trying to create something that already exists • Disconnected from technical reality • People not on the same page (misaligned), cannot work with each other, team breakdown.

8. Rapid-X Framework Innovation Leadership Culture of Innovation: Behaviors and Mindsets

   Story Adaptation Ecosystem, Stakeholders Operational & Financial System Architecture Open Source Tools Components Minimal Implementation Working Model Innovation in Algorithms At Berkeley, we have results: People in our programs can build amazing, working projects in 3 months with a relatively little background in ML, AI, and other data technologies. Applicable to all categories of digital transformation Students/ technical staff Leaders/ Entrepreneur A Solution for Rapid Implementation Innovation Leadership Rapid-X

9. RAPID-X DATA PROJECT EXAMPLES Deep Dave David Lin Sharon Ng

   Vanessa Salas Alexandre Vincent Airfare Data Scraper 14 Final Product Safest Path Suggestion • GREEN: SAFEST PATH • RED: SHORTEST PATH Downtown Berkeley to Cal Memorial Stadium Watch live demo here: https://stayfe.herokuapp.com/ CartilageX: Automated anomaly detection in knee MRIs Iriondo C, Jain D, Muhamedrahimov R, Papanikolaou V, Trotskovsky K, Sun L Commercialization of RecycleAI 1 Image taken of waste object and input into model 2 Model classifies waste object Our Project 3 Object sorted to its appropriate destination - Bin Sorter - Robots - Conveyor Belts Prediction of Bitcoin Prices Aashray Yadav Nicolas Sarquis Bhavya Vashisht Sai Kannan Sampath Mubarak Abdul Kader UC Berkeley | Data-X Berkeley Innovation Index My Dinh Jessica Gu Aaron Lu Dayou Wang Yan Zeng Yujun Zou

10. What happens is we don’t teach courses in this manner?

    1. Deep technical students learn many disconnected theories and skills, but they cannot deliver implementations 2. And they work in teams which cannot deliver innovation within companies, government, and research instiutions

11. Open Source CS Tools Math Quantitative Data-X Project: Real Applications

    with Code Samples What is in this course Holistic Perspective: Industry, Social Applications, Customer Driven

12. What is in this class? Common Open Source CS Tools:

    • Numpy, SciPy • Pandas • TensorFlow, Sklearn • SQL to Pandas • NLP / NLTK • Matplotlib Quantitative • Prediction: Regression • ML Classification: Logistic, SVM.. Trees, Forests, Bagging, Boosting,.. • Entropy / Information Topics • Deep Learning examples, including CCNs • Correlations • Markov Processes • LTI Systems: Fourier, Filters where applicable • Control Models where applicable Building Block Code Samples • Webscraping • Stock market live download, simple trading • Convolutional Neural Networks • Next Word Predictor, Spell Checking • Recommendation • Web Crawler • Chatbot, E-mail • Social net interfaces including twitter This class will help you combine math and data concepts The course updates with new tools to stay current. You may learn and use tools not presented in the class project. Often: Working Code First Fill In Theory After

13. What is actually in this class? Common Open Source CS

    Tools: • Numpy, SciPy • Pandas • TensorFlow, Sklearn • SQL to Pandas • NLP / NLTK • Matplotlib Quantitative • Prediction: Regression • ML Classification: Logistic, SVM.. Trees, Forests, Bagging, Boosting,.. • Entropy / Information Topics • Deep Learning examples, including CCNs • Correlations • Markov Processes • LTI Systems: Fourier, Filters where applicable • Control Models where applicable Building Block Code Samples • Webscraping • Stock market live download, simple trading • Convolutional Neural Networks • Next Word Predictor, Spell Checking • Recommendation • Web Crawler • Chatbot, E-mail • Social net interfaces including twitter Often: Working Code First Fill In Theory After • The ML stack use most commonly used in creating ML/AI/Data applications • Application and systems viewpoint of data and ML • Implementation, architecture, and relevant process to build anything • Statistical, rule based, and hybrid decision systems • Connection with relevant mathematical foundations (entropy, correlation, spectral, LTI, basic prediction, classification) • Practical insight into advanced techniques and tools: (eg. CNNs, NLP, scraping, recurrent networks, etc.) • System modeling for data applications

14. For Rapid AI/Data: Many Resources Are Already Available at data-x.blog

    For those who want to help students or technical experts learn these skills We can help in other ways as well

15. OUR APPROACH

16. Make the Tools Use the Tools (Optimally) Architect the System

    Why and how you build Most CS Sutardja Center This Course Where we focus:

17. 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 Rapid AI/Data Course Works: Team: typically 5 students, with available advisor network

18. The Rapid-AI/Data System View Web Scrape Possible Input Code Blocks

    Download Crawl … Stream or Poll Social Net / IoT Application with Automated Decisions Algorithm Options w/ Tables/Matrix Prediction / Classification Test, train, split Keep state Pandas: Short Term Storage Long Term Storage: SQL and File Formats (JSON, CSV, Excel) Web Possible Output Code Blocks Email Control Decision … Chatbot Feedback from External System (World) Pre- process Natural Language, State Features Blockchain (public ledger or cryptolock) APIs, Services APIs, Services

19. Our model has adapted: Business training is not the only

    key element Observation: student projects and professional projects that do well require a different understanding. We created a model and framework to provide these: Rapid-X Model Layers 1. Tools: using vs making. Learn to use and understand state of the art tools and technical approaches 2. Theory: Understanding the theory and frameworks behind the tools using first principals 3. Projects: Story first, second is development agility and stakeholders acquisition 4. Project Viewpoints: 5 Viewpoints integrated into the teaching model 5. Behaviors and Mindsets: 6 Behaviors and mindsets tuned for innovation

20. Our model has adapted: Business training is not the only

    key element Notes: # * combinations of on-line active systems, API economy, powerful open source tools, live systems that must run and be current all the time, cloud infrastructure compute and storage blocks, .. Rapid-X Model Layers 5 Project Viewpoints: a) Customer touchpoints b) Systems and architecture c) Risk mitigation, d) Agile increments, e) Swim-lanes and team dynamics 6 Necessary Behaviors and Mindsets: a) The target is moving, b) Tools are powerful - use them c) The system is the whole world * d) There is no greenfield - connect to the existing structure, means know the existing structure e) You can’t know it all before you start f) Develop insight, use technical/theoretical analogies, first principals, but don’t just plug and play

21. Rapid-AI/Data Project Types Business or Consumer Use Case Social Impact

    Its Just Cool (or improve part of a data pipeline or work towards a research result)

22. End of Section