2. 2
Topics
• Introduction to AI
• AI Techniques
• Problem - solving with AI
• AI Models
• Data acquisition and learning aspects in AI
• Problem solving-Problem solving process
• Formulating problems
• Problem types and characteristics
• Problem space and search
• Toy Problems–Tic-tac-toe problems
• Missionaries and Cannibals Problem
• Real World Problem–Travelling Salesman Problem
Department of Data Science and Business Systems
Dr.T.Veeramakali
3. 3
AI Techniques
What are artificial intelligence techniques?
Artificial Intelligence techniques refer to a set of methods and algorithms used to
develop intelligent systems that can perform tasks requiring human-like
intelligence.
These techniques encompass various approaches like machine learning, NLP,
computer vision, and deep learning.
Dr.T.Veeramakali Department of Data Science and Business Systems
4. 4
AI Techniques
What are the 4 types of AI?
The four types of AI are as follows:
• Narrow AI (Weak AI) : AI designed for a specific task or domain, limited in scope.
• General AI (Strong AI) : AI with human-level intelligence and abilities across various tasks.
• Artificial Super intelligence: AI surpassing human intelligence in all aspects.
• Reactive Machines : AI capable of reacting to specific situations but lacking memory or
learning capabilities.
Dr.T.Veeramakali Department of Data Science and Business Systems
5. 5
AI Techniques
• AI deals with a large spectrum of problems
• The spectrum of AI applications is spread across the domains, and even
across the complexities of problems. This includes the following:
Various day-to-day practical problems
Different identification and authentication problems with their applications in
security
Various classification problems resulting in decision-making
Interdependent and cross-domain problems
Dr.T.Veeramakali Department of Data Science and Business Systems
6. 6
AI Techniques
Dr.T.Veeramakali Department of Data Science and Business Systems
Scenarios for data handlings:
Need for analysis of voluminous and large amount of data.
The analysis should be followed by the characterisation of miscellaneous data
Dealing with the constantly changing scenarios and situations, and the dynamic
nature of data.
The way in which data appears, the way it is used, the way it is organised and the way it
should be used are different.
Identification of relevant data, irrelevant data and outliers.
The main objective of AI techniques is to capture knowledge based on the data
and information.
7. 7
AI Techniques
Dr.T.Veeramakali Department of Data Science and Business Systems
The broad categorization of these problems can be as follows:
1. Structured problems : These problems yield a right answer or right inference when an
appropriate algorithm is applied.
2. Unstructured problems: These are the problems which do not yield a particular answer. In
this case, there is possibility of more than one answer, and
even a particular situation decides the correctness of the answer.
3. Linear problems : It is the problem which can be solved or where decision can be
obtained by linear solution.
4. Non-linear problems : It is the problem which cannot be solved or separated by linear
equations.
8. 8
AI Techniques
Dr.T.Veeramakali Department of Data Science and Business Systems
Top Artificial Intelligence Techniques:
1. Machine Learning:
Machine Learning (ML) is one of AI's foundational pillars. This technique empowers computers to learn
from data and improve their performance with time without explicit programming. ML models can make
accurate predictions and decisions through supervised and unsupervised learning, impacting everything
from personalized recommendations to fraud detection.
2. Natural Language Processing
Natural Language Processing (NLP) allows machines to comprehend, interpret, and generate human
language. This AI technique has paved the way for virtual assistants, chatbots, and language translation tools,
making communication between humans and machines more seamless than ever.
9. 9
AI Techniques
Dr.T.Veeramakali Department of Data Science and Business Systems
Top Artificial Intelligence Techniques:
3. Computer Vision
Computer Vision equips machines with the ability to interpret visual information from the world. This
technique has revolutionized industries like healthcare, automotive, and robotics, enabling tasks such as
facial recognition, object detection, and autonomous driving.
4. Deep Learning
Deep Learning takes ML to a higher level by employing neural networks with multiple layers to process
complex data representations. It has propelled AI achievements, such as beating human champions in
games like chess and Go and enhancing image and speech recognition systems.
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AI Techniques
Application of AI
Dr.T.Veeramakali Department of Data Science and Business Systems
The following are the examples of AI-Artificial Intelligence:
1. Google Maps and Ride-Hailing Applications
2. Face Detection and recognition
3. Text Editors and Autocorrect
4. Chatbots
5. E-Payments
6. Search and Recommendation algorithms
7. Digital Assistant
8. Social media
12. 12
Conclusion
Everything related to artificial intelligence technologies being at the
core of a new endeavor to create computer models of intelligence has been
covered.
Dr.T.Veeramakali Department of Data Science and Business Systems
