Artificial Intelligence- Machine Learning and Deep Learning

AI is nothing but the capability of machine to imitate intelligent human behavior. AI is achieved by mimicking a human brain by understanding how it thinks, how it learns and works while solving any problem. AI includes various areas of specification:
                           -Game playing
                           -Expert System e,g number recognition from the number plate
                            of speedy car
                           -Natural Language Processing
                           -Neural Networks
                           -Robotics etc
Artificial Intelligence is huge term, it includes Machine Learning and Deep Learning. Basically it is the super-set of ML and DL.
             
Machine learning is a type of AI which provides computers with the ability to learn without being explicitly programmed. Here we do not need to define all the steps of conditions like any other programming application. however we have to train the machine on a data set(training data), viz large enough to create a model which helps to take decisions based on its learning capability.
For Example: We've to train a machine to determine the species of flower; for that we give it flower data set which contains various characteristics along with respective species like; flower name, sepal and petal length, species name, color,number of sepals and petals, life span etc. using this data set machine will create a model which can be used to identify the flower. Next time we pass some characteristics of flower, machine will display the name of the flower by analyzing these characters.
Although it sounds good and easy to implement but it has a limitation. ML is not capable of handling high dimension data where it has to deal with bulk of input and output data, processing such data becomes complex and resources get exhausted. This is termed as Curse Of Dimensionality. So the crucial problems with huge data cannot be solved with ML.
Another big challenge for traditional ML is Feature Extraction. For Example object recognition, handwriting recognition. These tasks cannot be accomplished by ML, clearly we needed a better approach and here came the concept of Deep Learning.

Deep learning is capable of handling high dimensional data. It is also efficient in focusing on the right features on its own that is feature extraction. Deep learning is implemented through Neural Network which is an Artificial Neuron also called as Perceptron and motivation behind the neural network is Biological Neuron. Deep learning attempts to re-engineer a human brain and studies basic units of brain called Brain cells/Neuron.
DL is very vast and cannot be narrated in one paragraph so I am going to write on Deep learning with more exhaustive details in my coming blogs

Robotics, Automation Vs Artificial Intelligence

Previously we peruse introduction to Artificial Intelligence, crux was that AI is the capability of machine to imitate the intelligent human behavior and it is achieved by mimicking the human brain by understanding how it learns and works while solving the problems.
Since there are various emerging crafts of technology that people may sometimes get disoriented about various terminology like AI, Robotics, Automation and use these terms interchangeably like a triune.
Here I will make it clear, differences and similarities about these three terms in order to get rid of this mystification.
Automation is described simply by two words- Manual Input and Rule-Based output.
It means you set a formula or an Algorithm which applies some mathematical substitutions and transformations on the given data and gives us output. Every time we give an input, it applies same rules and conditions and begets the output. For example if there is a company, customers can mail them any time( that is the manual input), if the employee is free only then he interacts otherwise an automatic reply is send "we will assist you later" or something like that, and that is the rule based output which depends on the condition whether the employee is free or not. This is how it works, no change in rules no improvement in functionality or efficiency.



In case of Artificial Intelligence, the more data we give to our machine, the more experienced and intelligent it gets and every time it produces an output with more efficiency then the previous output. AI improvises exactly the way humans does. Automation applies rules to data and produces output, but AI not only applies rules but learns from the data, memorizes it and apply the rules more accurately next time.

Robotics includes AI, robotics involves many different things with lot of mechanics like sensors, activators, maths and their is a part of AI in it which makes it intelligent. Robotics also includes the moment(which actually makes it entirely different from other fields). Moment is not always the displacement of machine, in many industries there are robot arms that works and carries things from one place to another. This motion of machine at correct positions/locations involves mathematics and AI and lot of other things. Robotics is not same as AI but in robotics part of AI is applied in it.


So we can't use the three terms interchangeably all three are different from each other from their logic and specification point of view.