Topic-2-AI CONCEPTS AND APPLICATIONS
AI CONCEPTS AND APPLICATIONS
Let us start with the basic example. Yesterday we discussed basic
data intelligence that the functions which should be done through human
intelligence are now being done or trying to do by AI Machine learning. For
which some basic components are reused.
For example Large Language Modal which is a language model in which
a text input is given and it returns a text message as output.
This modal has established an industry. Everyone is making of its
own language modal with the help of his own algorithm. Few of which are good
language modals and few a bad ones. Some are more popular and some are less
popular. Point is that every one is working in his own niche so the basic
building block is similar.
There are few things in AI that are actually being utilized in many
areas. For example in Object detection a vehicle is recognized by system and
this recognition is being utilized at many places.
Another example is that a weapon movement and detection systems has
been developed that detects any unusual movement in the shopping mall. This system
is being utilized by other areas such as traffic system and in space and lot of
other places.
For facilitating the blinds such sticks have been developed which
detect the objects before stick strikes with it. There are such cameras which
tell the name of the object when these are pointed towards them.
These devices are ultimately linked with the IOT (Internet of
Things). So it is for those who are
thinking about coding of AI. You have not to code the AI , but have to do some
thing remarkable by using these small building blocks.
Classification helps us to sort the input data. For example in the
example given above. It would sort the Spam and Non-Spam emails.
For example we want to know how many students in a class want to
learn AI and how many are not interested in learning AI.
The system in this case would generate few question that would be
answered by the students and on the basis of their answers the systems will tell
about the students interested or not interested in learning AI.
AI has detected both human and the horse. Same is the case with the
human. The watching sense of human among other senses which help in making the
decision is the most important sense (although others are as well). Do you
think that AI has learnt to detect things through watching ability like human.
So AI is developing thinking ability.
AI is trained like human baby. Human as a child is told that this
is horse so data is feed in child brain. Similarly, AI is also trained by
feeding data about the horse in it.
It was told that in Generative Models we give data to system in
parts and system goes beyond the given data to some extent . It does not mean
that system goes much far above the given information.
It produces synthetic data that resembles with the original data.
Chat GPT works on the Generative Model.
Have you ever noticed that Chat GPT does not provide the same
answer in response to one question when asked multiple times. It changes the
words and arrangement of sentences every time.
In the image given above, Model will generate the image by using
two sources A & B. However keep in mind that it is impossible to generate
every image for example picture of Monkey on Moon.
We will discuss it at the end
AI is the branch of Computer Science in which we apply such methods
by which computers learn without explicit programming.
Advancement so far achieved is in the field of ANI and we are far
away from General AI. Although some people say that Large Language Programming
and other generative modals are examples of Baby Generalized AI, about which we
are not going to discuss here. However, its food for thought you people to
explore further.
We also
discussed in our last lecture that AI value is going to be in trillions as
referred in McKinsey’s report.
There are many ways to learn AI, but most popular way
is learning it through Machine Learning which is called sub part of AI.
In supervised Learning, there misconception that
modal is trained as we use it.
A modal operates in two modes:
·
In
first mode which is called Training Mode. In this mode we train modal on the
basis of previous data. In preview of the Supervised Learning, input is given
to the modal, modal predicts the out put and this predicted out put is compared
with the original out put and feed back is given to the Modal about accuracy of
the predicted output. On the basis of this feedback modal will update itself.
·
Prediction
/ Entrance Mode. On the basis of training in above mode, when unknown data is
given to the modal, the modal converts this input to output. If the modal is
well trained there are more chances that its output would be better.
It was asked that when a modal generates 100%
accurate results, should it be acceptable or not. In other words, 100% out put
is good thing or bad thing?
It is not acceptable as in real world it would fail. System
has crammed the input data at such level that it is giving the 100% result.
·
Rule
of thumb is that use 70% of the data to train the modal and remaining 30%
system should remain unknown to the modal and would be used for testing.
If results of training data and test data are close
to each other, it means we have done the good job. For example both data are
giving 99% .
I If results of training data and test data are
not close to each other, it means there
are doubts in predicted data. For example training data is producing 99% and
test data is producing 46%. This creates doubts that needs to be addressed.
Example from real life was given that when a
incompetent student gives 99% results, we compare his result with the education
he learnt during the session. He did not attend the lectures, missed the
important session so his results of 99% are doubtful and are not acceptable.
Another example, from a desk of bank counter was
given. He is working so efficiently on key board that we think that he is expert
in computer field. But when we change the software , the same person
performance tells the other story.
t is pertinent to mention that 100% accuracy in out
put in some cases could not be achieved. For example, in case of stock of
summer and winter clothes.
It was asked to set the realistic attainable targets
about the completion of the course.
A modal operates in two modes:
·
In
first mode which is called Training Mode. In this mode we train modal on the
basis of previous data. In preview of the Supervised Learning, input is given
to the modal, modal predicts the output and this predicted output is compared
with the original out put and feedback is given to the Modal about accuracy of
the predicted output. On the basis of this feedback modal will update itself.
·
Prediction
/ Entrance Mode. On the basis of training in above mode, when unknown data is
given to the modal, the modal converts this input to output. If the modal is
well trained there are more chances that its output would be better.
UNSUPERVISED LEARNING
Topic was started with the proverb “A man is known by a company he
keeps”.
A question arises here that why there is need of Unsupervised
learning in the presence of Supervised Learning?
As we discussed in Supervised Learning, there is need of pair of
Input and Out Put Data. In case we have 50,000/- images and in case of
following of Supervised learning who will annotate these 50,000 images? who
will label them, resulting lot of data would remain unlabeled, resulting non
availability of out put.
In order to solve this problem we approach to the concept of
unsupervised learning.
Concept of Unsupervised learning is that, take the large number of
data and make group of similar items. This qualifies the proverb “A person is
known by the company he keeps”.
Suppose we have 1000 images and when we give this data to the
unsupervised learning modal. He identifies the similar groups and tell the
number of similar groups. This concept of grouping the similar items is called
clustering and these groups are called clusters.
Similarly, if we have lot of pdf data, unsupervised learning
algorithm would make different clusters based on different topics like
Marketing, Accounting etc.
For example we have to categorize the fruit and vegetable in one
case and in other case we have to sort the tomatoes of different quality out of
basket of tomatoes. In this case number of categories would be established.
Anomaly Detection
Anomaly is another technique of Unsupervised learning. In anomaly
we tell modal that all inputs should be normally be treated. When something
different comes in front of modal, it says it is not normal.
Clustering + Anomaly
In anomaly detection the process works between detection normal and
abnormal.
Example of Bike Engine Manufacturing was given. The modal will
check the engine on normal parameters and where it is not normal it would
detect it and this abnormality detection is called anomaly detection.
In Internet of Things, the consumer electronics will generate data.
The devices will also communicate each other. IOT and AI are related with each
other.
In classification, Modal is learning function or approximation of
function. For example, sequel function, modal is function that is converting
input to output. For example, detecting the duplicate images in phone gallery
is an example of clustering.
Remember there are multiple ways to solve the problem. If a problem
can be solved with the help of supervised learning, it does not mean that it
can not be solved with unsupervised learning. It is the nature of problem that
will determine which technique have to be opted.
Every modal works on the input to out put function. Better the
function better out put would be produced and vice versa.
There is need to analyze the training data accuracy and test data
accuracy. If result of both is close to each other than it means modal is working
properly.
If the training data accuracy is more than enough and test data
accuracy is less than enough, it is called overfitted modal.
If the training data accuracy is less than 60% it is called
underfitted modal. In this case modal needs to be made more complex using
different algorithm.
DIMENSIONALITY REDUCTION
The number of input features, variables,
or columns present in a given dataset is known as dimensionality, and the
process to reduce these features is called dimensionality reduction.
A dataset contains a huge number of input
features in various cases, which makes the predictive modelling task more
complicated. Because it is very difficult to visualize or make predictions for
the training dataset with a high number of features, for such cases,
dimensionality reduction techniques are required to use.
Dimensionality reduction technique can be
defined as, "It is a way of converting the higher
dimensions dataset into lesser dimensions dataset ensuring that it provides
similar information." These techniques are widely
used in machine learning for obtaining a better fit
predictive model while solving the classification and regression problems.
It is commonly used in the fields that
deal with high-dimensional data, such as speech recognition, signal processing,
bioinformatics, etc. It can also be used for data visualization, noise
reduction, cluster analysis, etc.
REINFORCEMENT LEARNING
In childhood while learning the bicycle, you started to learn the
riding the bicycle with the help of your friend. At first you could not ride it
properly and fall and get wounded. After healing the wounds, and taking the
break of few days you try again and again your muscles that are necessary for
driving a cycle automatically start working . During this stage of learning, if
you apply the correct techniques like keeping the handle straight and paddling
you are rewarded and enjoy the long ride of the bicycle. But if you move the
handle in wrong direction and don’t push the paddle with right power, you will
fall and will be punished by getting hurt again.
Similarly reinforcement learning is art of practicing.
Reinforcement Learning is the branch of Machine Learning that deals with
practicing.
Reinforcement
learning is a machine
learning training method based on rewarding desired behaviors and/or punishing
undesired ones. In general, a reinforcement learning agent is able to perceive and
interpret its environment, take actions and learn through trial and error.
Agent: Who
want to learn skills
Reward:
Enjoyment during the learning. Agent makes effort to maximize the award.
Penalty:
Penalty is the negative reward.
Agent has
finite set of actions that result in reward or penalty. In case of penalty the
agent will not repeat that set of actions.
DIFFERENCE
BETWEEN MACHINE LEARNING – AI – DEEP LEARNING
Artificial
Intelligence is the concept of creating smart intelligent machines.
Machine
Learning is a subset of artificial intelligence that helps you build AI-driven
applications.
Deep Learning
is a subset of machine learning that uses vast volumes of data and complex
algorithms to train a model.
DEEP LEARNING
Human brain
works with the help of Neurons. What if lot neurons artificially produced to
perform various functions? Although we have not yet known how the neurons
works. We use estimation technique to produce the neurons. Artificial neurons
are manufactured to perform various functions.
It is
interesting that it is not difficult to recognize the function of neurons.
RELU is the
Max Function Neuron
ARTIFICIAL NEURAL
NETWORK
When number of
artificial neuron are combined together in such a way that collaborate to take
input , perform some function and convert it to the output.
In a neural network, the activation
function is responsible for transforming the summed weighted input from the
node into the activation of the node or output for that input.
The rectified linear activation function or ReLU for
short is a piecewise linear function that will output the input directly if it
is positive, otherwise, it will output zero. It has become the default
activation function for many types of neural networks because a model that uses
it is easier to train and often achieves better performance.
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