Basic of Artificial intelligence

1. Elon Musk
CEO ofTesla & SpaceX
AI is a fundamental risk to the existence of human civilization.

2. I, Robot
2004 ‧ Sci-fi/Action

3. ?
?
?
What is AI?

4. Recognition Letter
A
Procedure to recognition the image is Letter “A”
 Make the logic
 Read the image
pixel
 Return result

5. Take a Deep
Look
Letter “A” make by three lines

6. Different Front
Style Letter A
Baby Learn pattern to detect Object.
 So is your programming login is
capable to detect these type of letter?
 If No, then what is the lacking for
your logic?
 What should be add to detect
object dynamically?

7. Programming
Logic
Dynamically
Recognition able,
Self-learning Model.
Design Dynamic Model

8. What is exactly
Artificial Intelligence?
Artificial Intelligence is a
model/procedure/tool who has
capability for self learning,
dynamically detect the pattern/object
and take decision by own knowledge
just like human brain.
“So according to the definition, is it proved that AI is
really threat for human existence?”

9. Human Intelligence Curve

10. Human Brain Learning Process
Input Image Feature Extraction Learning
Human Brain Neuron
Recognition

11. Artificial Neural Network vs human brain Neural

12. Learning Vs Recognition
Learning
Learning is a search
through the space of
possible hypotheses for
one that will perform
well, even on new
examples beyond the
training set. To
measure the accuracy
of a hypothesis we give
it a test set of examples
that are distinct from
the training set.
Recognition
According to the
training dataset
learning process is
performed and engine
is updated. By pass
through the input
sample over the engine
and it will return an
output according to the
learning accuracy.

13. AI Hierarchy
AI
Symbolic
Learning
Machine
Learning
Computer
Vision
Robotics
Statistical
Learning
Deep
Learning
Speech
Recognition
NLP CNN RNN
Object
Recognition
Humans can
speak & listen to
communicate
through language.
Much of speech
recognition is
statistically based
Human can
write & read text
in a language
Humans can
see with their
eyes & process
what they see.
Computer Vision falls
under the symbolic
way for computers to
process information.
Image
Processin
g
Humans recognize the
scene around them through
their eyes which create
images of that world.
Humans can
understand their
environment and
move around fluidly.
Pattern
Recognition
Humans have the
ability to see patterns
such as grouping of
like objects.
Machines are even
better at pattern
recognition because it
can use more data
and dimensions of
data.
ANN
The human brain is a network of
neurons and we use these to learn
things if we replicate the structure
and function of the human brain
we might be able to get cognitive
capabilities in machines.
ANN’s are more
complex & deeper,
we use those to
learn complex thing
To replicate the human
brain if we get the
network to scan images
from left-right, top-
bottom.
With accomplished
by CNN &
computer vision.
Neural Network to
remember a limited
past

14. Artificial Neural Network
(ANN)
ANN, is a group of multiple perceptrons/ neurons at each
layer. ANN can be used to solve problems related to:
 Tabular data
 Text Data
 Image Data
ANN Application:
 Image Recognition
 Natural Language Processing
 Pattern Recognition
 Text to Speech

15. Recurrent Neural Network (RNN)
RNN is a class of artificial neural networks where
connections between nodes form a directed graph along a
temporal sequence.
 Audio data
 Text Data
 Time Series Data
ANN Application:-
 Speech Recognition
ANN can be used to solve problems related to:-
A looping constraint on the hidden layer of
ANN turns to RNN.
 Text Processing(Chatbot)
 Face detection, OCR Applications as Image Recognition
 Music composition

16. Convolution Neural Network (CNN)
A Convolutional Neural Network (ConvNet/CNN) is a
Deep Learning algorithm which can take in an input
image, assign importance (learnable weights and biases)
to various aspects/objects in the image and be able to
differentiate one from the other.
CNN can be used to solve problems related to:
 Image Data
CNN Application:
 Image Recognition
 Image Classification
 Face Recognition

17. Reinforcement Learning
Reinforcement Learning(RL) is a type of machine learning
technique that enables an agent to learn in an interactive
environment by trial and error using feedback from its
own actions and experiences.
Application:
 Robot deciding its path
 Next move in a chess game

18. Supervised Learning
Supervised Learning use of labeled datasets to train
algorithms that to classify data or predict outcomes
accurately. As input data is fed into the model, it adjusts
its weights through a reinforcement learning process,
which ensures that the model has been fitted
appropriately.
The model first learns from the given training data. The
training data contains different patterns, which the model
will learn.
Application:
 classifying spam in a separate folder from your inbox
 Image- and object-recognition
 Predictive analytics

19. Unsupervised Learning
Unsupervised learning has no training phase; instead, the
algorithm is simply handed a dataset and uses the
variables within the data to identify and separate out
natural clusters.
Application:
 Finding customer segments
 Feature selection