Collaborative edge intelligence-mihir punjabi and sunil kumar jang bahadur_cwin18_india

CW
IN
CAPGEMINI
WEEK OF
INNOVATION
NETWORKS
Collaborative Edge Intelligence
Mihir Punjabi
Sunil Kumar Jang Bahadur
Mumbai, 26th Sep 2018

2Collaborative Edge Intelligence | Mihir Punjabi, Sunil Kumar | 26th Sept 2018 © 2018 Capgemini. All rights reserved.
• Principal Solutions Architect @ Capgemini Engineering Services
• Capgemini AI expert (https://www.capgemini.com/experts/product-and-engineering-services/mihir-punjabi/)
• SogetiLabs Fellow (http://labs.sogeti.com/experts/mihir-punjabi/)
• IBM Certified Watson Application Developer
• Machine Learning evangelist at Capgemini focusing around Digital Assistants, Cognitive ML, Edge intelligence,
Hybrid ML, Swarm Intelligence, etc.
Email: mihir.punjabi@capgemini.com
LinkedIn: punjabimihir (www.linkedin.com/in/punjabimihir)
Twitter: @punjabimihir
• Technical Architect @ Capgemini Engineering Services.
• Capgemini AI expert (https://www.capgemini.com/experts/product-and-engineering-services/sunil-kumar-jang-
bahadur/)
• Machine Learning and Data Science enthusiast
• Microsoft Certified Azure Architect and IBM Certified Watson Application Developer
• Currently focusing on Machine learning, Deep learning, MS IoT Edge, Swarm Intelligence, Reinforcement etc.
Email: sunil-Kumar.jang-bahadur@capgemini.com
LinkedIn: sunilkumar88 (www.linkedin.com/in/sunilkumar88)
Twitter: @SunilKumarJB
Mihir Punjabi
Sunil Kumar Jang Bahadur
Speakers

Swarm Intelligence
Reinforcement Learning
Collaborative AI + Blockchain
Autonomous Transparency: PoC Architecture
Future Vision Around Collaborative Reinforcement
Q&A
Agenda

Multi-agent, distributed systems where each agent, human or machine, is uniquely positioned, with autonomy to
contribute to a problem-solving network.
Swarm Intelligence

• Multi-agent and distributed system
• Non centralized communication or control
• Global/world utility function that graded and
calculate the result based on the possible
histories of the full system
• Reinforcement learning algorithm running on
each agents.
Collective Intelligence (COIN) Framework

Swarm Intelligence

Swarm Intelligence
Ant Colony Optimization Particle Swarm Optimization Bees Algorithm

Reinforcement Learning: Overview
Environment
Agent
Interpreter
Action
Episodic tasks Continuing tasks
Type of tasks

Reinforcement Learning: Challenges and Techniques
RL TechniquesFundamental Challenges
Representation
Generalization
Exploration/Exploitation
Temporal Credit Assignment
• Various Bandit Algorithms
• Q learning
• DQN (Deep Q Network)
• Double DQN
• AC3 (Asynchronous Advantage Actor
Critic)

Reinforcement Learning: Q Learning
0 1 2 3 4 5 6 7 8 9 10
0 -1 -1 -1 -1 0 0 -1 -1 -1 -1 -1
1 -1 -1 0 0 -1 0 0 -1 -1 -1 -1
2 -1 0 -1 -1 -1 0 -1 -1 0 0 -1
3 -1 0 -1 -1 -1 -1 0 -1 -1 0 -1
4 0 -1 -1 -1 -1 0 -1 0 -1 -1 -1
5 0 0 0 -1 0 -1 -1 -1 -1 -1 -1
6 -1 0 -1 0 -1 -1 -1 -1 -1 0 -1
7 -1 -1 -1 -1 0 -1 -1 -1 0 -1 -1
8 -1 -1 0 -1 -1 -1 -1 0 -1 0 -1
9 -1 -1 0 0 -1 -1 0 -1 0 -1 100
10 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 100
Q(state, action) = R(state, action) + Gamma * Max[Q(next state, all actions)]
Reward Matrix Learning Rate Max Q value

Reinforcement Learning: Q Learning
0 1 2 3 4 5 6 7 8 9 10
0 0 0 0 0 40 51 0 0 0 0 0
1 0 0 64 64 0 51 64 0 0 0 0
2 0 51 0 0 0 51 0 0 64 80 0
3 0 51 0 0 0 0 64 0 0 80 0
4 41 0 0 0 0 51 0 51 0 0 0
5 41 51 64 0 41 0 0 0 0 0 0
6 0 51 0 64 0 0 0 0 0 80 0
7 0 0 0 0 41 0 0 0 64 0 0
8 0 0 64 0 0 0 0 51 0 80 0
9 0 0 64 64 0 0 64 0 64 0 100
10 0 0 0 0 0 0 0 0 0 80 100
Q(state, action) = R(state, action) + Gamma * Max[Q(next state, all actions)]
Reward Matrix Learning Rate Max Q value

Collaborative AI + Blockchain = Autonomous Transparency

Natural Digital Experience Leveraging Ambient Intelligence | Mihir Punjabi, Sunil Kumar | 19TH June 2018 © 2018 Capgemini. All rights reserved.
Autonomous Transparency

Autonomous Transparency Overview
Manual Entry Autonomous System

PoC Architecture

Architecture Deep Dive
Warehouse
Shipping port/ Retail
IoT
Edge ML
Camera 1
Camera 2
Camera 3
Exit Camera

Packaging and Palletizing
Search and Rescue
Construction and Maintenance
Nano bots
Traffic Management

20© 2018 Capgemini. All rights reserved.Collaborative Edge Intelligence | Mihir Punjabi, Sunil Kumar | 26th Sept 2018
Q&A
Email:
▪ mihir.punjabi@capgemini.com
▪ sunil-Kumar.jang-bahadur@capgemini.com
LinkedIn:
• punjabimihir (www.linkedin.com/in/punjabimihir)
• sunilkumar88 (www.linkedin.com/in/sunilkumar88)
Twitter:
• @punjabimihir
• @SunilKumarJB
#ThinkEdgeML

With more than 190,000 people, Capgemini is present in over 40 countries and
celebrates its 50th Anniversary year in 2018. A global leader in consulting, technology
and outsourcing services, the Group reported 2016 global revenues of EUR 12.5 billion.
Together with its clients, Capgemini creates and delivers business, technology and
digital solutions that fit their needs, enabling them to achieve innovation and
competitiveness. A deeply multicultural organization, Capgemini has developed its own
way of working, the Collaborative Business Experience™, and draws on Rightshore®, its
worldwide delivery model.
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