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SkillsFuture Festival at NUS 2019- Artificial Intelligence for Everyone - A Practical Experience
- 2. What is Artificial Intelligence? • The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.” – Google
- 3. AI is the new electricity “About 100 years ago, electricity transformed every major industry. AI has advanced to the point where it has the power to transform” every major sector in coming years.– Andrew Ng Ref: AI Singapore
- 5. What drives AI? Cutting Edge Results in a Variety of Fields Bigger datasets Faster Computers Neural Nets Faster hardware is one of the key areas driving the modern era of AI.
- 6. Bigger Datasets In 2020, it is expected that: • The average internet user will generate ~1.5 GB of traffic per day. • A smart hospital will generate 3,000 GB/day. • Self-driving cars are each generating over 4,000 GB/day. • Connected planes will generate 40,000 gigabytes per day. • A connected factory will generate 1 million gigabytes per day.
- 7. The MIC represents a continuum from simple, scripted automation to superhuman intelligence and highlights the functional capabilities of different levels of machine intelligence. https://www.topbots.com/topbots-ai-machine-intelligence-continuum/?utm_medium=article&utm_source=medium&utm_campaign=AI-Continuum
- 16. Artificial Narrow Intelligence vs Artificial General Intelligence Artificial General Intelligence Understand Abstract Concepts Explain Why Be Creative Like Children Tell Right From Wrong Have Emotions Beat Go World Champions Read Facial Expressions Write Music Diagnose Mental Disorders Comfort Earthquake Survivors Artificial Narrow Intelligence
- 17. Mapping Human Perceptions to AI-enabled Capabilities Source: Frost & Sullivan
- 18. Use cases in our daily life • Smart Phone – Voice Assistants, image enhancement, App store Recommendations, Face unlock • Transportation – Dynamic Pricing in Travel, hospitality, logistic… • Web Services – Email Filtering, search, translation, Facebook and LinkedIn recommendations. • Sales – Netflix, Spotify, Amazon Recommendation Engines, Customer Support Queries (and Chatbots) • Security – Video Surveillance, Cyber Security • Financial – Catching Fraud
- 19. Definitions Artificial Intelligence Machine Learning Deep Learning Deep Learning A subset of machine learning in which multilayered neural networks learn from vast amount of data. Machine Learning Subset of Al techniques which use statistical methods to enable machines to improve with experiences. Artificial Intelligence Any technique which enables computers to sense, reason, act and adapt
- 20. AI vs ML vs DS
- 21. Is this weird? Anomaly detection Is this pressure gauge reading normal? Is this message from the internet typical? How many? How Much? Regression What will the temperature be next Tuesday? What will my fourth quarter sales be? How is this organized? Clustering Which viewers like the same types of movies? Which printer models fail the same way? What should I do? Reinforce Learning If I'm a self- driving car: At a yellow light, brake or accelerate? For a robot vacuum: Keep vacuuming, or go back to the charging station? Is this A or B? (Classification) Will this tire fail in the next 1,000 miles: Yes or no? Which brings in more customers: a $5 coupon or a 25% discount? 5 questions data science answers
- 22. Types of Machine Learning
- 25. Machine Learning Example •Suppose you wanted to identify fraudulent credit card transactions. •You could define features to be: •Transaction time •Transaction amount •Transaction location •Category of purchase •The algorithm could learn what feature combinations suggest unusual activity.
- 26. Machine Learning Limitations • Suppose you wanted to determine if an image is of a cat or a dog. • What features would you use? • This is where Deep Learning can come in. Dog and cat recognition
- 27. What is deep learning? Deep Learning “Machine learning that involves using very complicated models called “deep neural networks”." (Intel) Models determine best representation of original data; in classic machine learning, humans must do this.
- 28. Deep Learning Example Classic Machine Learning Step 1: Determine features. Step 2: Feed them through model. “Arjun" Neural Network “Arjun" Deep Learning Steps 1 and 2 are combined into 1 step. Machine Learning Classifier Algorithm Feature Detection
- 29. Reinforcement Learning Source: Practical Reinforcement Learning
- 31. What are the populate tools? https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html
- 33. AI Services • Google’s AI Services for Companies • https://experiments.withgoogle.com/collection/ai • Google’s cloud-based AI Tools • https://ai.google • Microsoft AI Language Translator • Amazon’s AI Platform • Alibaba Machine Learning Platform for AI
- 34. AI changes Job Market • Jobs that Don’t Involve Large Quantities of Data • Jobs Based on Human Interaction • Jobs that Have Minimal Repetition or Routine • Jobs that are Difficult to Learn Through Simple Observation https://www.futuristspeaker.com/business/20-common-jobs-in-2040/
- 35. Can a robot do your job? http://bit.ly/2C3igDx http://bit.ly/2C3igDx
- 36. Current Manpower and Projected Demand for ICT Professional 2018 - 2020 87800 33600 15500 100800 35900 20200 IT Development Network and Infrastructure Critical Emerging Tech 2017 2020 13,100 IT Development and 4700 Critical Emerging Tech Projected Demand IMDA 2018 Survey Critical Emerging Tech Specialists a. Includes Data analysts/Data scientists, Machine Learning/Artificial Intelligence Engineer, IT Security specialists, IT Security Operations Analysts/Engineers, Infocomm R&D, Internet of Things (IoT) Engineer, Embedded Systems/Fireware Developers, IoT Solution Architect b. Projected demand to grow by another 4,700 headcounts in the next three years (2018 – 2020)
- 37. Estimate of Technology Displaced Jobs by Country 8.1% 9.5 m 7.4% 1.2 m 10.1% 4.5 m 20.6% 0.5 m 11.9% 4.9 m 13.8% 7.5 m Indonesia Malaysia Philippines Singapore Thailand Vietnam Displacement Percentage https://www.oxfordeconomics.com/recent-releases/dd577680-7297-4677-aa8f-450da197e132 Technology and the Future of Asean Jobs : The Impact of AI on Workers in ASEAN’s 6 Largest Economies
- 38. Projected Proportion of Technology Displaced Jobs in Singapore Technology and the Future of Asean Jobs : The Impact of AI on Workers in ASEAN’s 6 Largest Economies https://www.oxfordeconomics.com/recent-releases/dd577680-7297-4677-aa8f-450da197e132
- 39. Job Roles in AI Role AI Roles Composition Summary of Key Functions Role 1 AI Researcher / AI Scientist / Data Scientist (often PhD / Master) ~10 - 20% Research & develop algorithms; Develop data strategy, models and insights Role 2 AI Engineers / AI Developers ~25 - 35% Develop AI software and products; Experiment, configure and test algorithms Role 3 Data Engineer ~25 - 35% Design and implement data systems and solutions Role 4 AI Application Developers | System Integrators | Infra Engineers ~25 - 35% Develop / integrate AI applications; Deploy infra for AI services
- 40. DO YOU WANT TO LEARN AI? Get it I still want you to learn something. Y N Use pre- trained ML APIs in your apps Take AI/ML short/ online courses DO YOU DEVELOP APP? DO YOU WANT TO CODE YOUR OWN ML? ARE YOU ALREADY AN SOFTWARE ENGINEER? DO YOU WANT TO BE AN AI ENGINEER? ARE YOU FOCUSED ON VERTICAL INDUSTRY? (e.g. healthcare, finance, retail) CAN YOU STUDY AI FULL-TIME? ARE YOU LEARNING FOR PROFESSIONAL GROWTH OR PERSONAL CURIOSITY? Read an entire AI report Watch videos from AI experts Use code- free ML tools Enroll in part-time courses e.g. SDAAI Enroll in immersive bootcamp e.g. TIPP AI Set daily Google alerts Y N N Y N Y Y N NY Y N Pro Per Est 12 wksEst 1 yr Est 20 days Est 5 days Est 2 days Inspiration from Allie K. Miller
- 41. Specialist Diploma in Applied Artificial Intelligence Total Hours = 270 hours Time to Complete = 12 months PDC in Fundamentals of Artificial Intelligence (120 hours) PDC in Applications of Artificial Intelligence 150 hours) Math for Machine Learning (30 hours) Pattern Recognition and Anomaly Detection (30 hours) Introduction to Programming (30 hours) Recommender Systems (30 hours) Introduction to Data Management for Machine Learning (30 hours) Virtual Assistants (30 hours) Machine Learning Fundamentals (30 hours) Capstone Project* (60 hours)
- 44. Create Azure account Create Custom Vision resources Setup Allow the classifier to know what constitutes a given class. Train the classifier Use unseen data to test your classifier. Test your model Gather training and validation images from internet or other sources Prepare images Check for accuracy, recall and probability threshold. Evaluate the classifier Use various techniques to improve your classifier. Improve your classifier 01 02 03 04 05 06 Hotdog/Not-Hotdog - HBO’s Silicon Valley Training an image classifier Hands-on Training an image classifier
- 45. Hands-on NLP • Use Google Cloud Platform, we will: • Classify Text • Named Entity Recognition • Sentiment Analysis • Syntax Analysis https://cloud.google.com/natural-language/docs/apis
- 46. Hands on – Object Recognition • Workflow 1. Load Data. 2. Define Model. 3. Compile Model. 4. Fit/Train Model. 5. Evaluate Model. 6. Predict with new data.
- 47. Conclusion Embrace technology, but don’t become it. - Gerd Leonhard