In this presentation, we have tried to explore the basics of AI, key concepts, and real-world applications.

1. Artificial Intelligence
What you need to know

2. ➔ What is AI
➔ Key Components of AI
➔ Uses of AI
➔ Pros and Cons of AI
➔ Approaches to AI
➔ Future Applications of AI
Contents

3. What is AI

4. Learning from
data and past
experience
Decision and
conclusion
Complex
problem
solving
Languages/
images/
videos
Integration
with robot
for physical
tasks
Adaptation/
Creativity/
New
Perspective
How AI works
Creates an expert system that can
learn, demonstrate, explain, and
advise users of technology
Artificial intelligence is the ability of a
machine to learn, reason, and solve
problems the same way humans do

5. What are the key
components of AI

6. Machine
Learning
Cognitive
Computing
Computer
Vision
Neural
Network
Natural Language
Processing
Deep
Learning
Enables deep
learning
Improves interaction
between human and
machines by
understanding human
language and meaning of
images
Interpretes
image content
in all format
Recognize, interpret
and produce human
language and
speech
Learns by
processing data
with the help of
neural networks
Learns from previous set
of data w/o requiring
explicit programming

7. What is AI?
Definitions of key areas within AI
Machine Learning:
ML algorithms are trained on large datasets to learn
patterns and make predictions; ML algorithms are a
subfield of AI focused on the development of
algorithms and statistical models.
Deep Learning:
This subfield of machine learning uses deep neural
networks for learning representations of data.
Applications include speech recognition, image and
video processing, and natural language processing.

8. Natural Language Processing (NLP):
Using NLP techniques, application such as chatbots,
virtual assistants, and language translators can
understand, interpret, and generate human language.
Definitions of key areas within AI
Computer Vision (CV):
Using these techniques, computers are able to
interpret and analyze visual information from the
world around them. These techniques are used in
self-driving cars, face recognition, and image
recognition.

9. Reinforcement Learning:
Type of ML technique that involves a system
learning to make decisions based on trial and error.
This technique is used in games, robotics, and
autonomous vehicles.
Cognitive Computing:
It is a subfield of AI that focuses on building
machines that can mimic human thought processes,
such as reasoning, learning, and decision-making.
Definitions of key areas within AI

10. Robotics:
In robotics, robots are designed, built, and operated.
Artificial intelligence (AI) techniques are used to
enhance robot capabilities, such as perception,
decision making, and motion control.
Expert Systems:
It is a type of AI that simulates the decision-making
abilities of an expert in a particular field, such as
finance, medicine, and engineering.
Definitions of key areas within AI

11. Knowledge
representation
Natural
Language
Processing
Natural
Language
Generation
Visual
Perception
Computer
Vision
Machine
Learning
Virtual
Assistant
Robotics
Visualization Search
Internet
of
Things
Automatic
Reasoning

12. Uses of AI

13. Artificial
Intelligence
Replicate human intelligence
Address knowledge-intensive problems
Clever link between perception and action
Automating task that needs human intelligence
Show, explain, and advise users in a self-directed manner

14. Pros and Cons

15. Higher accuracy, speed,
precision
Reduced labor cost
Handle risky situations
Digital Assistant
Public utility*
High cost
Resistance to culture change
Lacks empathy and moral
compass
Poses threat to human
resources
Redundant due to rapid
technology changes
Advantage Disadvantage
VS
*Self-driving cars, facial recognition, natural language processing etc

16. Approaches to
AI

17. Four
main
approaches to
AI
Thinking Rationally:
The “Laws of Thought”
Approach
Acting Rationally:
The “Rational Agent”
Approach
Acting Humanly :
Turing Test
Approach
Thinking Humanly:
Cognitive Modelling
Approach

18. AI application in
real world

19. Prevention/
Wellness
Chronic care
management
Diagnostics
Care
Delivery
Clinical
Decision
Support
AI in Healthcare

20. Adaptive
Learning
Automated
Grading
Chatbot
Campus
Data
Accumulation
Proctoring
AI in Education

21. Infotainment
Navigation
system
Traffic
prediction
Remote
diagnostic
Roadside
assistance
AI in Automotive

22. Agriculture
robotics
Precision
farming
Weather
prediction
Crop
monitoring
Soil and
crop
monitoring
AI in Agriculture

23. Observation
Artificial data
Simulation
Anomaly
detection
Planet
discovery
AI in Astronomy

24. Social Media
Robotics
Data security Gaming
E-commerce
AI in other major sectors

25. Brainware University
Department of Computer Science and Engineering
www.brainwareuniversity.ac.in/
Thank You