Engineering Intelligent Systems using Machine Learning

What is Next in MLTechnology? Use Cases & Demo 1 2 3 4 5 4. Machine Learning "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E" – T. Michell (1997) Example: A program for soccer tactics • Task: Win the game • Performance: Goals • Experience: (x) Players' movements (y) Evaluation 6. A few thousand years ago: Manual Plowing Today:Automated Plowing Path of Machine Evolution… 7. Automation Evolution System that Do • Replicate repetitive human actions System that Think • Cognitive capabilities handle judgment-oriented tasks System that Learn/Adapt • Learn to understand context and adapt to users and systemsRobotic Automation CognitiveAutomation IntelligentAutomation Natural Language Processing Big Data Analytics Artificial Intelligence Machine Learning Large Scale Processing Adaptive Alteration Rule Engine Screen Scraping Workflow Unstructured Data Processing (Extraction) Knowledge Modelling (Ontologies) Implementation: • Macro-based applets • Screen Scraping data collection • Workflow Implementation • Process Mapping • Business Process Management Implementation: • Built-in Knowledge repository • Learning capabilities • Ability to work with unstructured data • Pattern recognition • Reading source data manuals Implementation: • Artificial Intelligence Systems • Natural Language Understanding and Generation • Self Optimizing / Self Learning • Predictive Analytics / hypothesis generation • Evidence based learning Capabilities Capabilities Capabilities 8. Evolution of Machine Intelligence • Raw computing power can automate complex tasks!Great Algorithms Fast Computers • Automating automobiles into autonomous automata!More Data Real- Time Processing • Automating question answering and information retrieval!Big Data In- Memory Clusters • Deep Learning Smart Algorithms Master Gamer Deep Learning • New algorithm learns handwriting of unseen symbols from very few training examples (unlike typical Deep Learning) ImproveTraining Efficiency IBM Deep Blue Google Self Driven Cars Watson Jeopardy Deepmind Atari Game One Shot Learning 9. Why Machine Learning? Human Behavior & their Life are not logical like Code, not linear like a Formulas and not consistent like Rules, so it is hard for Machines to understand & respond to humans, that is the challenge for todays Digital world. Unless, Machine starts to Learn this ever changing human behavior, it can neither understand effectively nor respond intelligently & personally with its human counterpart.