This document discusses applied innovations in machine learning and soft computing in the United States. It begins with introducing machine learning and soft computing techniques, including artificial neural networks, clustering, decision trees, fuzzy logic and Bayesian networks. Then, it discusses several applications: energy management using meta-heuristics controllers and cloud/IoT; human-machine interaction like speech recognition; and infrastructure/transport like navigation, self-driving cars and drones. Finally, it outlines research areas like efficiency optimization of dynamic systems, humanizing machine interactions, and hyper-personalization of automated systems.

1. APPLIED
INNOVATIONS IN
MACHINE LEARNING
AND SOFT
COMPUTINGTHE USA
ABHISHEK SYAL
February, 2016
NITTTR, CHANDIGARH
ABHISHEK SYAL

2. About me
■ MBA from MIT Sloan, B.E. (Hons.) BITS, Pilani
■ Market Intelligence at EMC Corp., Research at BHEL
Corp. R&D using Predictive Analytics
■ Co-inventor to 4 patent pending tech
■ Founded and led my own social venture, ARISE
– Differentiated offering: self-learning for disabled
for over 300+ children; proprietary methodology

3. Agenda
■ Part – I: Brief Introduction to Machine Learning and Soft
Computing
■ Part – II: Applied Innovations in the USA
■ Part – III: Research Areas

4. BRIEF INTRODUCTIONTO
MACHINE LEARNING AND
SOFT COMPUTING
PART - I

5. Machine Learning
■ Machine learning is the science of getting computers to act
and respond without being explicitly programmed
■ Type of AI where computers learn and grow better with time
to solve for the objective function
■ Data analysis and actions using automatic model building,
many of them using mathematical optimization techniques

6. Selected Machine LearningTechniques
■ ANN: Artificial Neural Network – group of artificial neurons
■ Clustering: putting a set of object into clusters, building
compactness
■ DecisionTree Learning: Using decision trees as predictive
models

7. Soft Computing
■ Use of inexact solutions to computationally hard tasks,
difficult to solve for in the time objective
■ Tolerant for imprecision, uncertainty, partial truth and
approximation
■ Role model for soft computing is human mind

8. Selected Soft ComputingTechniques
■ Artificial Neural Networks
■ Fuzzy logic: many valued logic between 0 and 1, usage of
linguistic variables
■ Bayesian Network: probabilistic graphical model with set of
random variables and their conditional dependencies in a
Directed acyclic graph (directed graph with no directed
cycles)

9. APPLIED INNOVATIONS
INTHE USA
PART - II

10. 1. Energy Management and Efficiency
■ My experience is in designing a meta-heuristics controller
application using model heuristics for Operational Efficiency
■ Cloud computing and IIoT (Industrial Internet ofThings) to aid
in real-time grid optimization in terms of connectivity, load
balancing and remote operation
■ Preventive Maintenance of energy sources and distribution
infrastructure, saving CapEx and OpEx
■ Home Automation and Building Energy Management

15. GridPoint is an innovator in comprehensive, data-driven energy management solutions (EMS) that leverage the power of real-time data collection, big data analytics
and cloud computing to maximize energy savings, operational efficiency, capital utilization and sustainability benefits.

16. 2. Human Machine Interaction
■ Speech recognition (e.g. Alexa)
■ Web search
■ Face and gesture recognition
■ Image Identification and classification
■ Applications in mobile retail, social media, security, fraud
detection and surveillance

21. 3. Infrastructure andTransport
■ Navigation
■ On-demand match of supply and demand for uber, olacabs, etc.
– GPS as the main sensor, supply agents (cabs), inputs (ask requests)
■ Self-driving cars
■ Drone flights
■ Construction & Farms
– Inspection
– Survey

26. Other Applications
■ Understanding human genome
■ Fraud detection
– Credit card
– Internet
■ Brain-machine interfaces
■ Computational finance
■ Sentiment analysis
■ Online Advertising
■ Robotics
■ Augmented Reality

27. RESEARCH AREAS
PART - III

28. ResearchAreas
■ Efficiency management, preventive maintenance and
optimization of dynamic multi-agent systems
■ Humanization of machine-human interactions for more life-like
natural experiences as well as to prevent fraud
■ Hyper-personalization, localization and contextualization of
automation systems with added learning

29. THANKS
abhishek.syal@sloan.mit.edu