The document discusses a MuleSoft meetup on artificial intelligence. It begins with an agenda and introductions. It then provides definitions of intelligence and AI, discussing how humans and machines differ in perception, memory, and other areas. It outlines key contributors to AI like computer science and different task classifications. The document explains how AI works using sensors, actuators, and other components. It discusses search algorithms, fuzzy logic, natural language processing, and machine learning technologies. It concludes by discussing how MuleSoft can help build connected application networks that are important for developing scalable and deployable intelligent systems.

1. [08/31/2019]: [MuleSoft And Artificial Intelligence]
[Pune Meetup]

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Agenda
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• Introductions
• What is Artificial Intelligence
• Intelligent Application with Mulesoft and AI
• [MuleSoft and AI Application]
• What’s next
• Networking time

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Your Organizer
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Niranjan
Joshi
Santosh
Ojha
Seemant
Jain
Vijay
Pande
Mukesh Thakur
A SHOW OF HANDS:
Who is new to this Meetup?
Manish Yadav

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INTRODUCTION TO AI

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Lets First Understand How We Think (Human
Intelligence)
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• What is Intelligence ?
• How you play chess?
• How you play cards?
• How you drive a car?
• How you recognize voices of your relatives and
friends?

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Intelligence
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• Intelligence has been defined in many ways, including: the capacity for logic,
understanding, self-awareness, learning, emotional knowledge, reasoning,
planning, creativity, critical thinking, and problem solving. More generally, it can be
described as the ability to perceive or infer information, and to retain it as
knowledge to be applied towards adaptive behaviors within an environment or
context.
• Intelligence is most often studied in humans but has also been observed in both
non-human animals and in plants. Intelligence in machines is called artificial
intelligence, which is commonly implemented in computer systems using programs
and, sometimes, appropriate hardware.

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What is Artificial Intelligence?
“The art of creating machines that perform functions that
require intelligence when performed by people.” (Kurzweil)
“The study of how to make computers do things at which, at
the moment, people are better.” (Rich and Knight)

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Humans perceive by patterns whereas the machines perceive by set of
rules and data.
Humans store and recall information by patterns, machines do it by
searching algorithms. For example, the number 40404040 is easy to
remember, store, and recall as its pattern is simple.
Humans can figure out the complete object even if some part of it is
missing or distorted; whereas the machines cannot do it correctly.
Difference between Human and Machine
Intelligence

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• What Contributes to AI?
• Artificial intelligence is a science and technology based on disciplines
such as Computer Science, Biology, Psychology, Linguistics,
Mathematics, and Engineering. A major thrust of AI is in the
development of computer functions associated with human
intelligence, such as reasoning, learning, and problem solving.

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• The domain of artificial intelligence is huge in breadth and width.
While proceeding, we consider the broadly common and prospering
research areas in the domain of AI −

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Real Life Applications of Research Areas
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There is a large array of applications where AI is serving common people in their day-to-day
lives

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Task Classification of AI
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The domain of AI is classified into Formal tasks, Mundane
tasks, and Expert tasks.

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How AI works
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• In our IoT and Mulesoft Meet up ,we discussed about sensors and
Actuators,So sensors take data from External enviroment and feed to
AI engine and Actuators performs the action after reacving signal
from AI engine
• Mechatronics- mechanics, electronics and computing which,
combined, make possible the generation of simpler, more
economical, reliable and versatile systems.
• Cybernetics- the study of communication and control, typically
involving regulatory feedback, in living organisms, in machines, and
in combinations of the two.

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Agents and Environment
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• An AI system is composed of an agent and its environment. The agents act in their
environment. The environment may contain other agents.
• What are Agent and Environment?
• An agent is anything that can perceive its environment through sensors and acts upon
that environment through effectors.
• A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to
the sensors, and other organs such as hands, legs, mouth, for effectors.
• A robotic agent replaces cameras and infrared range finders for the sensors, and various
motors and actuators for effectors.
• A software agent has encoded bit strings as its programs and actions.

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Programming Algorithm, Fuzzy logic in AI
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• Searching 
• Searching is the universal technique of problem solving in AI. There are some single-player
games such as tile games, Sudoku, crossword, etc. The search algorithms help you to
search for a particular position in such games.

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Example of search Algorithms
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• Breadth-First Search
• It starts from the root node, explores the neighboring nodes first and moves towards the
next level neighbors. It generates one tree at a time until the solution is found. It can be
implemented using FIFO queue data structure.
• Depth-First Search
• It is implemented in recursion with LIFO stack data structure. It creates the same set of
nodes as Breadth-First method, only in the different order.
• Bidirectional Search
• Uniform Cost Search
• Iterative Deepening Depth-First Search
• Informed (Heuristic) Search Strategies
• To solve large problems with large number of possible states, problem-specific knowledge
needs to be added to increase the efficiency of search algorithms.

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Fuzzy Logic Systems
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• Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates
the way of decision making in humans that involves all intermediate possibilities between digital values
YES and NO.
• The conventional logic block that a computer can understand takes precise input and produces a definite
output as TRUE or FALSE, which is equivalent to human’s YES or NO.
• The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making
includes a range of possibilities between YES and NO, such as −
• fuzzy logic works on the levels of possibilities of input to achieve the definite output.
• Fuzzy Logic Systems Architecture
• It has four main parts as shown −
• Fuzzification Module − It transforms the system inputs, which are crisp numbers, into fuzzy sets. It
splits the input signal into five steps such as −

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Fuzzy Logic Systems four main parts
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• Fuzzification Module − It transforms the system inputs, which are crisp numbers, into
fuzzy sets. It splits the input signal into five steps such as −
•Knowledge Base − It stores IF-THEN rules provided by experts.
•Inference Engine − It simulates the human reasoning process by making fuzzy inference on the inputs
and IF-THEN rules.
•Defuzzification Module − It transforms the fuzzy set obtained by the inference engine into a crisp value.

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Natural Language Processing
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• Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems
using a natural language such as English.
• Processing of Natural Language is required when you want an intelligent system like robot to perform as
per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc.
• The field of NLP involves making computers to perform useful tasks with the natural languages humans
use. The input and output of an NLP system can be −
• Speech
• Written Text
• Components of NLP
• There are two components of NLP as given −
• Natural Language Understanding (NLU)
• Understanding involves the following tasks −
• Mapping the given input in natural language into useful representations.
• Analyzing different aspects of the language.

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Machine learning and AI Technologies
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• Machine learning (ML) is the scientific study of algorithms and statistical models that
computer systems use to perform a specific task without using explicit instructions, relying
on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine
learning algorithms build a mathematical model based on sample data, known as "training
data", in order to make predictions or decisions without being explicitly programmed to
perform the task.Machine learning algorithms are used in a wide variety of applications,
such as email filtering and computer vision, where it is difficult or infeasible to develop a
conventional algorithm for effectively performing the task.
There are Many AI tools available in Market for doveloping AI solution,some of
them are
• Google ML Kit Google ML Kit, Google's machine learning beta SDK for mobile developers.
• Infosys Niaknowledge-based AI platform, to collect and aggregate organisational data
from people, processes and legacy systems into a self-learning knowledge base
• Tesnsor FlowFor dataflow and differentiable programming across a range of tasks. It is a
symbolic math library, and is also used for machine learning applications
• IBM watsonQuestion-answering computer system capable of answering questions posed in
natural language

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Intelligent Application with MuleSoft and AI
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Importance of MuleSoft(connected application
network) in AI
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• It is a way to connect applications, data, and devices through APIs that exposes
some or all of their assets and data on the network. That network allows other
consumers from other parts of the business to come in and discover and use those
assets.
.Without a connected application network, AI is not going anywhere their is need of
strong Application network for development ,easy to use, Monitoring, scalable,
deployable AI system.
• API strategies enable organizations to easily connect together any application, data
source or device into a central nervous system where data can freely flow. This
application network is how the AI ‘brain’ can plug into a business’ digital ecosystem
to consume its data, provide valuable insights, and ultimately take action.

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Orchestrating the ML pipeline with Mule flows
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• Now we have Intelligent mule

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Sample Project ->MuleSoft and AI
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IBM Watson Tone Analyzer
Integration Using Mule

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Let's begin with what we need before we start:
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• In this competitive world, it is very important to understand the linguistic tone of a
customer so that we can decide whether we need to respond to the customer
immediately.
• These days, customer satisfaction is more important to a sales manager or
representative. A sales manager or representative may want to understand the
linguistic tone from his customer's sent mail. For example, when a customer sends
a mail to a sales manager or representative, the sales manager or representative
may want to understand the tone of the mail so that the sales manager or
representative can decide on further actions. This powerful feature can be achieved
by leveraging IBM Watson Tone Analyzer service.

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• Here we want to show how a Salesforce user (sales manager or representative) can
understand the tone of his customer's sent mail by integrating Salesforce and IBM
Watson Tone Analyzer service using Mule Soft ESB.
• We have written a Mule flow that makes the integration of Salesforce and IBM
Watson Tone Analyzerservice very easy.
• Prerequisites
• However, below are the prerequisites to test the flow shown below:
• An account at IBM Blumix.
• A deployed Tone Analyzer service to IBM Blumix.
• An account at Salesforce.
• A custom contact field CutomerTone__c on Salesforce. See here to create a custom
field on any
• Salesforce object.

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Flow Steps
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• Receive mail from the configured mailbox using Mule IMAP connector. Once mail is received, the
current Mule Message contains
• email body as payload and the customer mail address as an inbound header fromAddress.
• Enrich the current Mule Message with the Salesforce contact ID corresponding to
the fromAddressretrieved above. The enriched Mule Message now contains a flow
variable contactId that holds the Salesforce contact ID corresponding to the customer mailaddress.
• Post the mail body held in the current payload to the IBM Tone Analyzer service. The Tone Analyzer
service gives a JSON response representingthe tone of the mail in the case of success.
• Calculate the tone based on the rule described here. As of now, we are interested only in Emotion
Tone. The Emotion Tone has threechildren tones named Cheerfulness, Negative, and Anger. Each of
these tones has an attribute called normalized score representing the value of the tone.If the sum
of normalized scores of Negative and Anger tones is greater than Cheerfulness tone, then we
consider the tone of the customer to be Angry otherwise Normal.
• Update the Salesforce Contact corresponding to the contact ID held in the flow
variable contactId with the tone calculated above.

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Tone-Analyzer Flow Design
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• The main flow that receives an incoming mail from a customer.

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Get-Sf-Contact-Id Flow
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• Sub-flow retrieves the Salesforce contact ID corresponding to the customer mail
address.
• This sub-flow finally returns a contact ID corresponding to the customer mail
address.
• In the tone analyzer flow, we use an enricher to enrich the message with the
contact ID
• retrieved from Salesforce and this contact id is stored in a flow variable
#[flowVars.contactId]

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Get-Tone Flow
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• This sub-flow gets the tone of the mail from the customer. It posts the mail body
received from the customer to IBM Watson Tone Analyzer and retrieves the
linguistic tone of the mail.

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Update-Sf-Contact-With-Tone Flow
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• This sub-flow updates the tone of the customer mail to the
custome field CustomerTone__c of Salesforce contact ID.
• This flow applies some logic on the tone response retrieved from
the get-tone flow. This logic is encapsulated in the Mule expression
component.
• This logic finally gives a JSON payload, represents the tone either
Angry or Normal, and is updated to Salesforce contact's custom field
CustomerTone__c.
• So finally, this flow updates tone, either Angry or Normal,
to the Salesforce contact.

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What’s next
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• Share:
– Tweet your pictures with the hashtag #MuleMeetup
– Invite your network to join: <https://meetups.mulesoft.com/pune/>
• Feedback:
– Contact your organizer <Santosh Ojha> to suggest topics
– Contact MuleSoft at meetup@mulesoft.com for ways to improve the program
• Our next meetup:
– Date: <___>
– Location: <___>
– Topic: <___>

37. Networking time
Introduce yourself to your neighbors!

38. See you next time
Please send topic suggestions to the organizer

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Thank you !