Artificial intelligence and Expert systems by dr. protik.pptx

Artificial intelligence
&
Expert systems
Dr. Protik Kumar Banik
MBBS, MPH (Epidemiology)
Lecturer
Department of Epidemiology
National Institute of Preventive & Social Medicine
(NIPSOM)
Dr. Protik/Epidemiology/NIPSOM/2022 1

Artificial Intelligence Definition
Artificial intelligence is a generic term for a machine
(or process) that responds to environmental stimuli
(or new data) and then modifies its operation to
maximize a performance index.
In practice, the learning process is implemented
using mathematics, statistics, logic, and computer
programming. The learning process enables the AI
model to be trained on data in an iterative procedure
with parameter adjustment by trial and error using
reinforcement rules.

Machine learning and Deep learning
Machine learning is a specific type of AI, whereby
algorithms use data to train neural network models
so that when such models take in real-time data,
they are able to make decisions and use such data
to further enhance their learning phase.
Deep learning is a more advanced form of machine
learning that uses layers of neural networks and
massive data sets to achieve a similar objective.

Artificial Intelligence vs Human Intelligence
Following are the fundamental differences between
artificial intelligence and human intelligence;
1. If we can compare it nature wise then, human
intelligence intends to revise to modern environments
by using a mixture of distinct cognitive procedures,
whereas artificial intelligence intends to create
devices that can mock human behaviour and conduct
human-like actions. Thus, we can say that the human
brain is analogous, but machines are digital.
2. The simple difference is that human beings use their
brain, ability to think, memory, while AI machines
depend on the data given to them.

3. As we all know that humans learn from past mistakes
and intelligent ideas and intelligent attitudes lie at the
basis of human intelligence. Hence, this point is
simply because machines cannot think and learn
from the past. They can learn from information and
through regular training, but they can never attain the
thinking procedure unique to humans.
4. Artificial intelligence takes much more time to adjust
to the new changes whereas human beings can
adapt to changes easily and this makes people able
to learn and ace several abilities.

5. Machines can handle more data at a speedier rate as
compared to humans. As of now, humans cannot
beat the speed of computers.
6. Artificial Intelligence has not aced the ability to
choose up on related social and excited codes.
People are many ways better at social interaction
since they can develop academic data, have self-
awareness, and are elegant to others’ emotions.

Advantages of Artificial Intelligence
1) Reduction in Human Error
2) Takes risks instead of Humans
3) Available 24x7
4) Helping in Repetitive Jobs
5) Digital Assistance
6) Faster Decisions
7) Daily Applications
8) New Inventions

Disadvantages of Artificial Intelligence
1) High Costs of Creation
2) Making Humans Lazy
3) Unemployment
4) No Emotions
5) Lacking Out of Box Thinking

What are the applications of AI?
1. AI in Healthcare
AI is helping in making diagnoses faster and better than
humans do and also helps in reducing human mistakes.
AI is used in numerous ways in the healthcare sector
such as AI-assisted robotic surgery, helps in workflow
and administrative tasks, managing big data and several
more.
2. AI in Social Media
In social media like FB, Snapchat, Twitter there are
billions of user-profiles and we need to maintain the data
and AI is the one that manages this.

Applications of AI
3. AI in Robotics
AI has a great role in Robotics. Some robots can do
repetitive tasks given to them but with the help of AI,
they can create robots that can think and do tasks.
4. AI in Astronomy
AI can be used in solving complex issues related to the
universe. It will help in understanding how it works, its
origin, and so on.
5. AI in Finance
The finance sector is carrying out automation, chatbot
(ALEXA, SIRI), and adaptive intelligence

Applications of AI
6. AI in Travel
AI is very essential in the travel industry. Travel sectors
are using AI-powered chatbots which can create human-
like interaction with customers for better answers.
7. AI in Data Security
Data is very important for any company and nowadays
fraud cases are very high in this digital world. So, AI
helps in making your data more safe and secure.
8. AI in the Automotive Industry
For better performance, some automotive industries are
using Artificial Intelligence to give real assistants to their
users.

Applications of AI
9. AI in Agriculture
AI is making the agriculture field digital and AI can be very
beneficial for farmers.
10. AI in Education
AI chatbots can communicate with students in the form of
educating assistants. In the future, AI will show much more
development in the field of education.
11. AI in E-Commerce
Artificial Intelligence is assisting buyers to find out related
commodities with recommended color, size, or brand. AI is
coming to be more demanding in the e-commerce industry.
12. AI in Gaming
AI is present in almost every field. So, while playing
chess, the opponent player is controlled by AI.

AI and Public health
Public health is the science of protecting and
improving the health of people and their
communities. This work is achieved by promoting
healthy lifestyles, researching disease and injury
prevention, and detecting, preventing, and
responding to infectious diseases.
At its core, it is concerned with the well-being of a
population. The American Public Health Association
(APHA) lists 33 targeted topics and issues that they
consider within the rubric of public health (American
Public Health Association 2019).

The Centers for Disease Control and Prevention (CDC)
Organizational Chart partitions public health into one institute
(Occupational Health and Safety) and four directorates
(Public Health Service and Implementation Science, Public
Health Science and Surveillance, Non-infectious Diseases,
Infectious Diseases), providing a broader classification that
includes the APHA topics and issues.
The CDC Strategic Priorities are the following:
1. improve health security at home and around the world;
2. prevent the leading causes of illness, injury, disability,
and death; and
3. strengthen public health and healthcare collaboration.

To meet these priorities, the CDC activities include
detecting, responding to, and stopping new and
emerging health threats; preventing injuries,
illnesses, and premature deaths; and discovering
new ways to protect and improve the public’s health
through science and advanced technology like AI.
These activities involve tracking the causes and
rates of death in a population, as well as
surveillance of population health data. AI plays a
vital role here.

AI tools can also be used to inform public health
policy. For example, predictive analytics can be
used to identify risk factors for disease; and
optimization frameworks (whether single stage or
repeated) can be used to identify when to screen or
treat disease, or which risk groups to target given
limited resources.

Current projects
1. HIV prevention among homeless youth- Influence
maximization for social good using social
networks to spread health based information
2. Improving maternal and child health outcomes in
partnership with ARMMAN- AI for assisting NGOs
in improving maternal and child health outcomes
3. Using machine learning & multi-agent planning
to fight tuberculosis
4. Using social networks for prevention
interventions

Combatting COVID-19 by AI
Mathematical modeling and multi-agent based
analysis of the pandemic allows
better understanding of the disease spread and may
help inform policy at the national and regional level.
Using tools and modeling techniques from AI to help
understand the situation better and design aids that
may help policymakers design better solutions in the
fight against this pandemic.

How AI is being used in Public Health?
Component / Subfield Uses of AI in Public Health
Machine Learning Understanding complex connections
between genetics, environment and
disease by Data Science and Machine
Learning in Public Health
Natural Language Processing
(NLP)
Behavior analysis through the social media
and consumer generated data
Natural Language
Understanding (NLU)
Prediction of Loneliness in Older Adults
Natural Language Generation
(NLG)
Removing identifiers from electronic health
records data
Cognitive Search Search Engine to Evaluate and Analyze
Information About COVID-19
Digital decisioning platforms Decision-making through modeling and
understanding of multiple variables and
complex systems
Dr. Protik/Epidemiology/NIPSOM/2022
20

How AI is being used in Public Health?
Component / Subfield Uses of AI in Public Health
Robotic Disinfection of areas, Delivery of medications &
food, Measuring of vital signs in the COVID-19
environment
Virtual Agents (Chatbots) Healthy Lifestyle/Wellness, Mental Health,
Reproductive Health, Weight Control and
Smoking Cessation
Computer Vision Medical imaging and predictive modelling for
pulmonary medicine
Deep Learning Deep Learning Algorithm for Detection of
Diabetic Retinopathy in Retinal Fundus
Photographs
Speech Analytics Analysis of Human Behavior and States

The principles for the use of Artificial Intelligence for Public
Health interventions
The use of Artificial Intelligence (AI) in public health must be
guided by superior technical and ethical considerations aimed
to mitigate ethical risk in public health and related policy
interventions, reflected in the following eight guiding
principles:
1. People-centered
Actions and solutions must be people centered and not be
used solely by itself. As one of many technologies to aid
public health AI should respect the rights of the individual.
2. Ethically grounded
Discussions, developments, and implementation must be
grounded in the globally-agreed ethical principles of human
dignity, beneficence, nonmaleficence and justice.

The principles for the use of Artificial Intelligence for
Public Health interventions
3. Transparent
Transparent approaches must always be used and
communicated when developing AI algorithms.
4. Data protected
Privacy, confidentiality, and security of data use must be
foundational to every AI development.
5. Demonstrates scientific integrity
AI interventions should follow scientific best practice
including being reliable, reproducible, fair, honest, and
accountable.

The principles for the use of Artificial Intelligence for
Public Health interventions
6. Open and sharable
Everything must be as open and sharable as possible.
Tools and underlying concept of Openness must be a
feature and a critical success factor of any AI development.
7. Non-discriminatory
Fairness, equality and inclusiveness in impact and design
should always form the foundation of any AI initiative for
Public Health.
8. Human-controlled technology
Formal processes for human control and review of
automated decisions are mandatory.

What is Expert System ?
An expert system, is an interactive computer-based
decision tool that uses both facts and heuristics to
solve difficult decision making problems, based on
knowledge acquired from an expert.
First expert system, called DENDRAL, was
developed in the early 70's at Stanford University.

What is Expert System ?
An expert system is a model and associated
procedure that exhibits, within a specific domain, a
degree of expertise in problem solving that is
comparable to that of a human expert.
• An expert system compared with traditional
computer :
Inference engine + Knowledge = Expert system
(Algorithm + Data structures = Program in
traditional computer)

Component of Expert system
1. Knowledge base : A declarative representation
of the expertise; often in IF THEN rules ;
2. Working storage : The data which is specific to
a problem being solved;
3. Inference engine : The code at the core of the
system which derives recommendations from the
knowledge base and problem specific data in
working storage;
4. User interface : The code that controls the
dialog between the user and the system.

Roles of Individuals who interact with the
system
• Domain expert : The individuals who currently are
experts in solving the problems; here the system is
intended to solve;
• Knowledge engineer : The individual who
encodes the expert's knowledge in a declarative
form that can be used by the expert system;
• User : The individual who will be consulting with
the system to get advice which would have been
provided by the expert.

Expert System Characteristics
Expert system operates as an interactive system that
responds to questions, asks for clarifications, makes
recommendations and generally aids the decision-
making process.
Expert systems have many Characteristics :
1. Operates as an interactive system
This means an expert system :
 Responds to questions
 Asks for clarifications
 Makes recommendations
 Aids the decision-making process.

2. Tools have ability to filter knowledge
• Storage and retrieval of knowledge
• Mechanisms to expand and update knowledge base
on a continuing basis.
3. Make logical inferences based on knowledge
stored
• Simple reasoning mechanisms is used
• Knowledge base must have means of exploiting the
knowledge stored, else it is useless; e.g., learning
all the words in a language, without knowing how to
combine those words to form a meaningful
sentence.

4. Ability to Explain Reasoning
• Remembers logical chain of reasoning; therefore user may ask
◊ for explanation of a recommendation
◊ factors considered in recommendation
• Enhances user confidence in recommendation and acceptance
of expert system
5. Domain-Specific
• A particular system caters a narrow area of specialization; e.g.,
a medical expert system cannot be used to find faults in an
electrical circuit.
• Quality of advice offered by an expert system is dependent on
the amount of knowledge stored.

6. Capability to assign Confidence Values
• Can deliver quantitative information
• Can interpret qualitatively derived values
• Can address imprecise and incomplete data
through assignment of confidence values.

The development of full artificial intelligence could spell
the end of the human race….It would take off on its
own, and re-design itself at an ever-increasing rate.
Humans, who are limited by slow biological evolution,
couldn’t compete, and would be superseded.”- Stephen
Hawking, BBC Dr. Protik/Epidemiology/NIPSOM/2022 34

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