18CSC305J - Artificial Intelligence UNIT 4 & 5
I have only updated some answers...The ppt is not clear and many contents are missing, so you can say we have to refer online only for the answers. So please make do with these.
4M:
Supervised vs Unsupervised
What is adaptive network?
Adaptive learning brings human analysts into the process at every step. This is in contrast to rule-based, simple machine learning and deep learning approaches, where the humans only create rules and label data at the start of the process.
Adaptive learning systems require the least human effort because they only require human input when it matters most and continually expand their knowledge when new information is encountered.
They are also the most accurate. They combine the three other types of machine intelligence, adding new types of ‘unsupervised machine learning’ and methods for optimizing the input from multiple, possibly disagreeing, humans.
Define Machine Translation
Machine translation is the process of using artificial intelligence to automatically translate text from one language to another without human involvement
Modern machine translation goes beyond simple word to word translation to communicate the full meaning of the original language text in the target language
It analyzes all text elements and recognizes how the words influence one another
Benefits:
Automated translation assistance
Speed and volume
Cost effective
Large language selction
NLP applications
Chatbot
Voice assistants
Autocomplete in search engines
Sentiment analysis
Machine translation
Syntax and semantic analysis
Pragmatics and discourse analysis
12M:
Goal Stack planning
Push the original goal to the stack
Repeat until stack is empty
If stack top is a
Compound goal, push its unsatisfied subgoals to the stack
Single unsatisfied goal, replace with operator and push operator’s precondition to the stack
Operator, pop it, execute it and change the database by its effects
Satisfied goal, pop it
Goal Stack problem - idea:
Place goal in goal stack
Considering top as goal 1, place it’s subgoals on top of it
Then try to solve subgoals goal S1-2 and continue…
Goal Stack Problem - algorithm:
Find an operator that satisfies subgoal G1 and replace G1 with operator
If more than one operator satisfies G1, apply heuristics and choose one
To execute the top most statement, its preconditions are added onto the stack
Once preconditions are satisfied, then operator can be applied to a new state
New state is obtained by using ADD and DELETE lists of an operator
Problem solver keeps track of operators applied
This process is repeated until stack empty and problem solver returns the plan of the problem
Goal stack problem:
Initial state:
Ontable(A)
ontable(B)
on(D,A)
on(C,D)
clear(C)
clear(B)
Handempty
Goal state:
Ontable(B)
ontable(C)
on(D,B)
on(A,D)
clear(C)
clear(B)
Handempty
Complete solution:
Mycin, Dendral, XCON
Mycin:
MYCIN was an early expert system that used artificial intelligence to identify bacteria causing severe infections
It was also used for the diagnosis of blood clotting diseases
Also recommended antibiotics, with dosage adjusted based on patient’s weight
Developed at the Stanford University in the early 1970s
It was written in Lisp
It was a standalone system that required the user to enter all relevant information about the patient that MYCIN asks
It would ask the doctor some simple yes/no or textual questions
Used a simple inference engine and a knowledge base of ~600 rules
XCON:
The R1 was a production rule based expert system written in OPS5 by John McDermott in 1978
It performed ordering of DEC’s VAX computer systems by automatically selecting the computer system components based on the customer’s requirements
It helped by reducing the need to give customers free components when technicians made errors, by speeding the assembly process and by increasing customer satisfaction
It had about 2500 rules
XCON’s success made DEC rewrite XCON as XSEL for use by DEC’s Salesforce
DENDRAL:
Dendral was an influential pioneer expert system
The name is a short form of ‘Dendritic algorithm’
It was written in Lisp
Developed at Stanford University
The main aim was to help organic chemists in identifying unknown organic molecules, by analyzing their mass spectra and using knowledge of chemistry
Parts of a dendral:
Heuristic dendral (graph theory algorithm)
Meta dendral (machine learning)
CNN:
A Convolutional Neural Network is a type of deep learning neural network architecture commonly used in computer vision
CNN consists of multiple layers like the input layer, convolutional layer and fully connected layer
CNN is mainly used for
Image recognition
Image classification
Object detection
Types of layers in CNN:
Input layer:
The layer in which we give input to our model
Generally the input will be an image or a sequence of images
Convolution layer:
An image matrix of dimension h x w x d
A filter fh x fw x fd
Outputs a volume dimension
Layer which is used to extract the feature from the input dataset
It applies a set of filters to the input images
Example: applying filters
The result matrix is
6 -9 -8
-3 -2 -3
-3 0 -2
Activation layer:
Activation layers add nonlinearity to the network
Apply an element wise activation function to the output of the convolution layer
Pooling layer:
Layer is periodically inserted in the CNN and main function is to reduce the size of volume
This makes computation fast, reduces memory and prevents overfitting
Two common types:
Max pooling
Average pooling
Advantages:
Good at detecting patterns and features in images, videos and audio signals
No need for manual feature extraction
Can handle large amount of data and give high accuracy
Robust to translation, rotation and scaling invariance
Disadvantages:
Computationally expensive
Requires large amount of labeled data
Can be prone to overfitting
Interpretability is limited
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