Module 1_Artificial Intelligence_1234.pptx

BMEE407L - Artificial Intelligence
Dr. R. Mohan, Ph.D.,
Professor
SMEC, VIT Chennai

BMEE407L
Artificial Intelligence
Pre-requisite:
BMAT202L & BMAT202P
Probability and Statistics
Pre-requisite:
BMAT101L & BMAT101P
Calculus

1.To provide basic understanding on Artificial Intelligence
with its sub-sets.
2.To impart knowledge of search algorithm, logics,
reasoning and uncertainty.
3.To introduce the basic concepts of machine learning
and its application in mechanical engineering.
Course Objectives

At the end of the course, the student will be able to
1.Translate the characteristics of artificial intelligence and its sub-sets.
2.Implement appropriate algorithm for problem solving by searching.
3.Construct the logical agents and familiar in the application of fuzzy in AI.
4.Design the decision making algorithm with the reasoning of uncertainties.
5.Develop machine learning programs based on supervised, unsupervised and
reinforcement learning.
6.Experiment the benefit of neural network in deep learning.
7.Apply machine learning approach to solve problems related to mechanical
engineering.
Course Outcome

Text Books
1. Russell S, Norvig P, Artificial Intelligence - A Modern Approach, 2021, 4th edition,
Prentice Hall. Agenda Item 65/46 - Annexure - 42
2. Ivan Vasilev, Advanced Deep Learning with Python: Design and implement advanced
next-generation AI solutions using TensorFlow and PyTorch, 2019, 1st edition, Packt
Publishing Ltd.
Reference Books
1. Bishop C. M, Pattern Recognition and Machine Learning, 2011, 2nd edition, Springer.
2. Nilsson N.J, Artificial Intelligence: A New Synthesis, 1998, 1st edition, Morgan Kaufmann.

Course Plan
Module 1 & 2 CAT-1 (as per schedule)
Module 5 & 6 CAT-2 (As per schedule)
Module 3, 4, 7 & Contemporary Discussion  FAT Exam (As per schedule)
Digital Assignment1: Before CAT1
2 IBM certificates or equivalent (AWS / Python courses and so on). Based on
the platform on which the mini project (Assignment3) to be carried out.
Digital Assignment2: Before CAT2
2 IBM certificates or equivalent (AWS / Python courses and so on). Based on
the platform on which the mini project (Assignment3) to be carried out.
Digital Assignment3: Before FAT
Mini AI Project based on Assignment1 & Assignemnt2 learning as well as
class room learning and self learning

Question Paper Pattern
CAT-I & CAT-II (50 Marks)
Part A (5 x 10 = 50 Marks)
FAT Exam (100 Marks)
Part A (7 x 10 = 70 Marks)
Part B (2 x 15 = 30 Marks)
Note: Any change in question pattern will be communicated in advance

Faculty Details
Dr. R. Mohan, Ph.D.,
Professor
SMEC
VIT Chennai
Cabin Location: AB1 – 406A, 3D Printing Lab
Contact#: 9884216335
Email ID: mohan.r@vit.ac.in

Module:1 Foundation of AI (4 Hours)
Introduction – Foundations of AI – Evolution of AI –
Intelligent Agents: Agents and environments, Concept
of rationality, structure of agents – Structure of
Knowledge based system - Risks and Benefits of AI.

Introduction
Artificial intelligence is a field of science concerned
with building computers and machines that can
reason, learn, and act in such a way that would
normally require human intelligence or that
involves data whose scale exceeds what humans
can analyze.
With the help of AI, machines are programmed to
understand, think, learn, behave like humans and
mimic human behavior.
AI allows computers to process large amounts of
data and identify patterns in them, gradually learn
from their own experience, adapt to the set
parameters, and perform human-like tasks, such as
learning, decision-making, problem-solving,
speech, and language perception.

Four Approaches in Defining AI
• Systems that act like humans
• Systems that think like humans
• Systems that think rationally
• Systems that act rationally

1. Acting humanly: The Turing test approach
The art of creating machines that perform functions that require intelligence when
performed by people i.e., making computers to act like humans.
Example : Turing Test
• Provides satisfactory operational definition for intelligence.
• Provided by conducting Turing Test
• The Turing Test is a method of inquiry in artificial intelligence (AI) for determining
whether or not a computer is capable of thinking like a human being. The test is
named after Alan Turing, the founder of the Turing Test

• Woman, Machine & Judge.
• Pass the test ?
• if the interrogator cannot tell
• if there is a computer or
• a human at the other end.
Which one’s
the computer?
A
B
The system requires these abilities
to pass the test
• Natural language processing
• for communication with human
• Knowledge representation
• to store information effectively
& efficiently
• Automated reasoning
• to retrieve & answer questions
using the stored information
• Machine learning
• to adapt to new circumstances

Turing viewed the physical simulation of a person as unnecessary
to demonstrate intelligence.
However, other researchers have proposed a total Turing test, which
requires interaction with objects and people in the real world.
To pass the total Turing test, a robot will need
• computer vision and speech recognition to perceive the world
• robotics to manipulate objects and move about.

2. Thinking humanly: The cognitive modeling approach
• Making computers to think like humans
• Goal is to build systems that function internally in some way similar to human mind
• Also called cognitive modeling approach
We can learn about human thought in three ways
• introspection—trying to catch our own thoughts as they go by;
• psychological experiments—observing a person in action;
• brain imaging—observing the brain in action
Precise theory of the mind =>becomes possible to express the
theory as a computer program.
If the program’s input–output behavior matches corresponding human behavior, that is
evidence that some of the program’s mechanisms could also be operating in humans
Example GPS (General
Problem Solver)
comparing the reasoning
steps of the program and
human solving the same
problems.

3. Thinking rationally: The “laws of thought” approach
• Thinking “Right Thing” : Making computers to think the “Right Thing”
• Relies on logic (to make inferences) rather than human to measure correctness.
• Logic: provide a precise notation for statements about all kinds of things in the world
and the relations between them.
• Syllogism: Provide patterns for argument structures
• Always give correct conclusion given correct premises.
• For example,
• Premise: John is a human and all humans are mortal
• Conclusion: John is mortal
Can be done using logics. Example Propositional and predicate logic.

3. Thinking rationally: The “laws of thought” approach
Two obstacles: Laws of Thought of Approach
• It’s not easy to take informal knowledge and state it in the formal terms
required by logical notation, particularly when the knowledge is less than
100% certain.
• Being able to solve a problem “in principle” and doing so “in practice” are
very different.
• i.e., 1. Informal Knowledge is not precise.
2. Difficult to model uncertainty
3. Theory and practice cannot be put together.

4. Acting rationally: The rational agent approach
Doing “Right Thing”
A rational agent is one that acts so as to achieve the best outcome
or, when there is uncertainty, the best expected outcome
• Design of rational agent
• Advantages
• More general than laws of thought approach
• Concentrates on scientific development
Limited Rationality
• acting appropriately when there is not enough time to do all computation

AI has focused on the study and construction of
agents that do the right thing.
What counts as the right thing is defined by the
objective that we provide to the agent.

AI Careers &
Job Outlook
Examples of artificial intelligence include:
• Smart assistants like Siri and Alexa
• Pandora and Netflix, which provide personalized song
and entertainment recommendations
• Chatbots
• Robotic vacuum cleaners
• Self-driving vehicles
• Facial recognition software
The need for skilled AI professionals
spans nearly every industry, including:
• Financial services
• Healthcare
• Technology
• Media
• Marketing
• Government and military
• National security
• IoT-enabled systems
• Agriculture
• Gaming
• Retail
Career Path Description
Big Data Analyst Find meaningful patterns in data by looking at the past to help
make predictions about the future.
User Experience
(UX)
Designer/Developer
Work with products to help customers understand their
function and can use them easily. Understand how people use
equipment and how computer scientists can apply that
understanding to produce more advanced software.
Natural Language
Processing Engineer
Explore the connection between human language and
computational systems; this includes working on projects like
chatbots and virtual assistants.
Researcher Work with computer science and AI research Discover ways to
advance AI technology
Research Scientist
Expert in applied math, machine learning, deep learning, and
computational stats. Expected to have an advanced degree in
computer science or an advanced degree in a related field
supported by experience.
Software Engineer
Develop programs in which AI tools function. The role may
also be referred to as a Programmer or Artificial Intelligence
Developer.
AI Engineer Build AI models from scratch and help product managers and
stakeholders understand results.
Data Mining and
Analysis
Finding anomalies, patterns, etc. within large data sets to
predict outcomes.
Machine Learning
Engineer
Using data to design, build and manage ML software
applications.
Data Scientist Collect, analyze and interpret data sets.
Business
Intelligence (BI)
Developer
Analyze complex data sets to identify business and market
trends
Big Data
Engineer/Architect
Develop systems that allow businesses to communicate and
collect data
Robotics Engineer Design, build and test robots or robotic systems.
Computer Vision
Engineer
Develop and work on projects and systems involving visual
data.
programming
languages
Python
C/C++
MATLAB
additional skills for AI professionals:
Solid knowledge of applied mathematics
and algorithms
Problem-solving skills
Industry knowledge
Management and leadership skills
Machine learning
Top 5 skills required for AI jobs:
Communication skills
Knowledge and experience with
Python specifically (in general,
proficiency in programming language)
Digital marketing goals and strategies
Collaborating effectively with others
Analytical skills

AI Careers & Job Outlook [2024]
Companies Currently Hiring AI Positions
A recent search for “artificial intelligence” job openings on
LinkedIn revealed thousands and thousands of results at a
wide variety of companies. Here is a sample of some of the
positions we found.
Wells Fargo — Sr. Conversational AI Content Strategists
Nike — Data Scientist, Experience Research & Analytics
Amazon Web Services — Machine Learning Engineer
Apple — AI/ML Software Engineer
Spotify — Research Scientist – Language Technologies
Microsoft — Senior Researcher
As you can see from the list above, there are many different
types of positions within artificial intelligence. Some of the
most common AI-related job titles, courtesy of Glassdoor,
include:
Software engineer
Data scientist
Software development engineer
Research scientist
In general, tech companies (both software and
hardware) dominate the list of companies that are
hiring AI professionals. But a quick search on any
reputable job listing site will give you a list of positions
that span a variety of industries. Here is a sample of
some of the top companies that are hiring for these
types of AI roles:
Deloitte
Amazon
Accenture
H&R Block
IBM
PwC
Fidelity Investments
PayPal
Major League Baseball
Harvard Business School
IKEA
Salesforce

Introduction
History
of
the
various
AI
areas.

The Foundations of Artificial Intelligence
The foundations of Artificial Intelligence (AI) encompass various interdisciplinary principles
and methodologies that enable the creation and functioning of intelligent systems.
Ethics & Philosophy: Ethical Considerations, Philosophical Questions
Mathematics: Linear Algebra, Probability and Statistics, Calculus, Discrete Mathematics
Cognitive Science: Psychology, Neuroscience
Computer engineering: Algorithms and Data Structures, Complexity Theory, Programming Languages
Control theory and cybernetics: Behavioural Control, Optimization and Adaptation, System Stability, Controller
Design, Self-Regulating Systems, Complex Systems Modelling
Computer Vision: Image Processing, Object Recognition, Segmentation
Robotics: Perception, Planning, Control
Linguistics: syntax, semantics, phonetics, phonology, morphology, pragmatics, and discourse analysis
Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Deep Learning
Natural Language Processing (NLP): Syntax and Semantics, Language Models, Speech Recognition and Synthesis
Economics: Resource Allocation and Investment, Impact on Industries and Sectors, Regulation and Policy Development

The Foundations of Artificial Intelligence
Key Concepts and Techniques
Knowledge Representation: Methods for encoding information about the world in
a form that AI systems can utilize.
Search Algorithms: Techniques for navigating through problem spaces to find
solutions (e.g., A*, Dijkstra's algorithm).
Logic and Reasoning: Using formal logic to derive conclusions from known facts
(e.g., propositional logic, first-order logic).
Bayesian Networks: Probabilistic graphical models for representing and reasoning
about uncertainty.
Markov Decision Processes (MDPs): Models for decision-making in situations
where outcomes are partly random and partly under the control of a decision
maker.

Applications of Artificial Intelligence

Intelligent Agent
An agent is anything that can be viewed as perceiving its environment through
sensors and acting upon that environment through actuators.

The Structure of Agents
agent = architecture + program
The job of AI is to design an agent program that
implements the agent function - the mapping from
percepts to actions
We assume this program will run on some sort of
computing device with physical sensors and actuator -
we call this the agent architecture

Intelligent Agent
• Simple reflex agent
• Model-based reflex agents
• Goal-based agents
• Utility-based agents
• Learning agents

Model Based Reflex Agent
smart home heating, ventilation, and air conditioning (HVAC) system.
https://www.fibaro.com/en/smart-home-in-use/smart-hvac/
Environmental Model: Includes the
layout of the house, thermal
properties of rooms, and typical
occupancy patterns.
Weather Model: Considers current
and forecasted weather conditions.
User Preferences: Includes
preferred temperature and
humidity settings.

Model Based Reflex Agent
https://www.youtube.com/watch?v=WWINyVj2MIo
Smart Irrigation in Agriculture
https://intellias.com/smart-irrigation-in-agriculture/
Environmental Model: Includes
data on soil types, crop water
requirements, and weather
patterns.
State Model: Maintains current
soil moisture levels, weather
conditions, and the status of
irrigation equipment.

Goal-based agents
autonomous delivery drone
Environmental Model: Includes maps of
the delivery area, no-fly zones, and known
obstacles.
State Model: Maintains the drone's
current position, battery level, and
package status.
Goal: Deliver the package to the specified
address efficiently and safely.
https://www.youtube.com/watch?v=Ih3oN7J-v5w https://www.youtube.com/watch?v=qs2VeyzRMzQ

Utility-based agents
Utility Function:
Comfort Level: Weighted utility for maintaining a comfortable temperature and humidity.
Energy Efficiency: Weighted utility for minimizing energy usage.
Cost: Weighted utility for minimizing the cost based on real-time electricity rates.
smart home heating, ventilation, and air conditioning (HVAC) system.
An autonomous drone is tasked with delivering packages to various locations.
•Utility Function:
•Delivery Time: Ensuring the package reaches its destination as quickly as possible.
•Safety: Avoiding obstacles and adverse weather conditions to minimize the risk of crashes.
•Battery Life: Managing its flight path to conserve battery and ensure it has enough power to return to
the base if needed.
•Customer Preferences: Delivering the package within a specific time window if required by the
customer.

Learning agents
Personalized Recommendation System
https://research.netflix.com/research-area/machine-learning
Netflix's AI recommendation engine analyzes
massive amounts of data, including viewing habits,
ratings, searches, and time spent on the platform, to
curate personalized content recommendations for
each viewer.

Concept of rationality and Performance
Concept of rationality
• A rational agent is an agent whose acts try to maximize some performance
measure.
• An agent should strive to "do the right thing", based on what it can perceive
and the actions it can perform.
• The right action is the one that will cause the agent to be most successful
Performance measures
• An objective criterion for success of an agent's behavior
• E.g., performance measure of a vacuum-cleaner agent could be amount of dirt
cleaned up, amount of time taken, amount of electricity consumed, amount of
noise generated, etc.

The rationality of an agent depends on four things
• the performance measure defining the agent's degree of
success
• the percept sequence, the sequence of all the things perceived
by the agent
• the agent's prior knowledge of the environment
• the actions that the agent can perform

Definition of Rational agents
•Rational Agent: For each possible percept sequence,
a rational agent should select an action that is
expected to maximize its performance measure,
given the evidence provided by the percept
sequence and whatever built-in knowledge the
agent has.A rational agent should be autonomous

Task Environment
• To design a rational agent, we need to specify a task
environment
• a problem specification for which the agent is a solution
• PEAS: to specify a task environment
• Performance measure
• Environment
• Actuators
• Sensors

Task Environment
PEAS: Specifying an automated taxi driver
Performance measure:
• ?
Environment:
• ?
Actuators:
• ?
Sensors:
• ?

Task Environment
PEAS: Specifying an automated taxi driver
Performance measure:
• safe, fast, legal, comfortable, maximize profits
Environment:
• roads, other traffic, pedestrians, customers
Actuators:
• steering, accelerator, brake, signal, horn
Sensors:
• cameras, sonar, speedometer, GPS

Task Environment
Agent Type
Performance
Measure Environment Actuators Sensors
Automated Taxi
Driver
Safe, fast, legal,
comfortable trip,
maximize profits
Roads,
traffic,
pedestrian
customers
Steering
accelerator,
brake, signal,
horn, display
Cameras, sonar,
speedometer,
GPS, odometer,
accelerometer
engine sensors,
keyboard
Medical
diagnosis system
Healthy patient,
minimize costs,
lawsuits
Patient, hospital,
staff
Screen display
(question tests,
diagnoses
treatment
referrals)
Keyboard (entry of
symptoms, findings,
patient's answers)
Part-Picking
Robot
Percentage of parts in
correct bin
Conveyor belt with
parts, bins
Jointed arm
and hand
Camera, joint angle
sensors
Interactive
English tutor
Maximize student’s
score on test
Set of students Screen display
(exercises)
Keyboard

Task Environment
Agent Type
Performance
Measure Environment Actuators Sensors
robot soccer
player
amount of goals
scored
soccer match field legs
cameras, sonar or
infrared
Satellite Image
Analysis
Correct Image
Categorization
Downlink from
satellite
Display
categorization
of scene
Color pixel arrays
Refinery
controller
Maximum purity,
safety
Refinery operators
Valves, pumps,
heaters,
displays
Temperature, pressure,
chemical sensors
Vacuum Agent
minimize energy
consumption,
maximize dirt pick up
two squares Left, Right,
Suck, NoOp
Sensors to identify the
dirt

Properties of task environments
1.Fully observable vs. Partially observable
Fully observable
– If an agent’s sensors give it access to the complete state of the environment at each point in
time then the environment is effectively and fully observable. There will be no portion of the
environment that is hidden for the agent.
Real-life Example 1: While running a car on the road ( Environment ), The driver ( Agent ) is
able to see road conditions, signboard and pedestrians on the road at a given time and drive
accordingly. So Road is a fully observable environment for a driver while driving the car.
Example 2 : A chess playing system is an example of a system that operates in a fully
observable environment.

Partially observable
The agent is not familiar with the complete environment at a
given time.
Real-life Example: Playing card games is a perfect example of
a partially-observable environment where a player is not aware
of the card in the opponent’s hand

2.Deterministic vs. stochastic
Deterministic
• next state of the environment is completely determined by the
current state and the actions executed by the agent, then the
environment is deterministic, otherwise, it is Stochastic.
– Real-life Example: The traffic signal is a deterministic environment
where the next signal is known for a pedestrian (Agent)

stochastic
The Stochastic environment is the opposite of a deterministic
environment. The next state is totally unpredictable for the
agent.
Real-life Example 1: The radio station is a stochastic
environment where the listener is not aware about the next
song.
Example 2:Taxi driving is clearly stochastic in this sense, because one can never
predict the behavior of traffic exactly;

3.Episodic vs. sequential
Episodic
• An episodic environment means that subsequent episodes do not depend
on what actions occurred in previous episodes.
• Real-life Example 1: A support bot (agent) answer to a question and then
answer to another question and so on. So each question-answer is a single
episode.
• Example 2: Pick and Place robot, which is used to detect defective parts
from the conveyor belts.

Sequential
• In a sequential environment, the agent engages in a series of connected
episodes.
• In sequential environments, on the other hand, the current decision could
affect all future decisions.
• Real-life Example: Checkers- Where the previous move can affect all the
following moves.
• Medical Diagnosis –Diagnosis diseases is wrong, giving medicine, taking all
test is going to be wrong.

•4.Static vs. dynamic
Dynamics
•A dynamic environment is always changing over time
Example: the number of people in the street
Static
• Environment is not changed over time
Example: Crossword Puzzle

5.Discrete vs. continuous
Discrete
It consists of a finite number of states and agents have
a finite number of actions.
Example: A chess game comes under discrete
environment as there is a finite number of moves that
can be performed.

Continuous
It consists of a infinite number of states and agents
have a infinite number of actions.
Example:
Taxi driving is a continuous state and continuous-
time problem.

• 6.Single agent VS. multiagent
Single agent:
An environment is explored by a single agent. All actions are performed
by a single agent in the environment.
Real-life Example: Brushing a teeth
Multiagent:
If two or more agents are taking actions in the environment, it is known as a
multi-agent environment.
Real-life Example :Playing Cards

7.Known vs. unknown
The agent’s knows about the complete environment
and the outcomes for all actions .
example: solitaire card games
- If the environment is unknown, the agent will have
to learn how it works in order to make good
decisions.( example: new video game).

Task Environment Observable Deterministic Episodic Static Discrete Agent
Crossword puzzle Fully Deterministic Sequential Static Discrete Single
Chess with a clock Fully Stochastic Sequential Semi Discrete Multi
Poker Partially Stochastic Sequential Static Discrete Multi
Backgammon Fully Stochastic Sequential Static Discrete Multi
Taxi driving Partially Stochastic Sequential Dynamic Continuous Multi
Medical diagnosis Partially Stochastic Sequential Dynamic Continuous Single
Image-analysis Fully Deterministic Episodic Semi Continuous Single
Part-picking robot Partially Stochastic Episodic Dynamic Continuous Single
Refinery controller Partially Stochastic Sequential Dynamic Continuous Single
Interactive English tutor Partially Stochastic Sequential Dynamic Discrete Multi

Structure of Knowledge Based System
One of the most famous KBSs was MYCIN, a program for medical diagnosis. The real-world
facts were represented as a simple assertion stored in a flat database

Acquisition and maintenance: There is no need for a programmer to maintain the facts.
The domain experts can define and maintain the rules themselves.
Function: Facilitates the process of gathering knowledge from experts, documents,
databases, or other sources.
Tools and Methods: May include interviews, questionnaires, data mining techniques, or
machine learning algorithms to capture and update knowledge.
Explaining: Existing facts can be used to infer a new conclusion, and results can be
explained for usage purposes. A simple example can be to follow a series of inferences
towards a diagnosis, and then use these facts to clarify the diagnosis.
Importance: Provides users with insights into the reasoning process of the system,
explaining how decisions or conclusions were reached.
Functionality: Helps in building user trust and understanding of the system’s logic.

Reasoning: Decoupling knowledge from the processing of that knowledge enabled general-
purpose inference engines to be developed. That system can bring new solutions never
seen before by developers, which follow from a data set stored in the knowledgebase.
Deductive Reasoning: Derives conclusions from general rules.
Inductive Reasoning: Infers general rules from specific cases.
Abductive Reasoning: Infers the best explanation for a given set of observations.
Knowledgebase
A KBS uses its knowledgebase to store all the knowledge data. The gathered knowledge from
experts, either human or artificial, is first symbolized and then stored
Content: Contains domain-specific facts, rules, and relationships.
Representation: Knowledge can be represented using various methods such as:
Rules: If-then statements.
Frames: Data structures for dividing knowledge into substructures by representing
stereotyped situations.
Semantic Networks: Graph structures for representing knowledge in patterns of
interconnected nodes and arcs.

Inference Engine
•Role: The brain of the KBS, It applies logical rules to the facts stored in the knowledgebase to deduce new
knowledge.
•Techniques: Common methods include:
• Forward Chaining: Starts with known facts and applies inference rules to extract more data until a
goal is reached.
• Backward Chaining: Starts with goals and works backward to determine what facts must be asserted
to achieve those goals.
An IE operates in three states: i.e. matching rules, selection rules and execution rules.
The IE filters all the content of the data that are satisfied by the rules. The content items are considered execution
candidates.
In the selection rules, some selection strategies are applied to further filter the rules for execution.
In the broader AI context, the term heuristics is used for these selection strategies.
Finally, the data are executed in the third state of the IE, where the right-hand sides of the stored rules are changed, or
further processed, by any input from outside the IE, either from the user through user interface (UI) or by a function or
program call.

Risks and Benefits of AI
As AI plays an increasingly important role in the economic, social,
scientific, medical, financial, and military spheres. The risks and
benefits—that it can bring
The potential for AI and robotics to free humanity from menial repetitive work and to
dramatically increase the production of goods and services could presage an era of
peace and plenty.
The capacity to accelerate scientific research could result in cures for disease and
solutions for climate change and resource shortages.
Customized Experiences: AI algorithms can provide personalized recommendations in
areas like e-commerce, streaming services, and social media.
Healthcare: AI can tailor medical treatments to individual patients based on their genetic
information and health data.

Risks and Benefits of AI
As AI plays an increasingly important role in the economic, social,
scientific, medical, financial, and military spheres. The risks and
benefits—that it can bring
Lethal autonomous weapons:
Surveillance and persuasion
Biased decision making:
Impact on employment:
Safety-critical applications:
Cyber security:

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