Artificial Intelligence New Future
I'll take you through a very interesting topic that is none other than deep learning unlike most of you even are deeply fascinated with artificial intelligence, think about this instead of you doing all your work you have a machine to finish it .
For you or it can do something which you thought it was not possible for instance predicting the future like predicting earthquakes, tsunamis, so that preventive measures can be taken to save lives.
Chat bots virtual personal assistants like Siri in iPhones ,Google Assistant and believe me it is getting smarter day by day with deep learning self-driving cars, it will be a blessing for elderly people and disabled people who find it difficult to drive on their own.
And on top of that it can also avoid a lot of accidents that happen due to human error .This is a recent initiative by Google where Google is working with an Indian I care chain to develop an AI software which can examine retina scans to identify a condition called diabetic nephropathy which can cause blindness.
As I'm a composer who thought that we can have an AI music composer using deep learning and maybe in the coming years even machines will start winning Grammy and one of my favorites a dream reading machine with so many unrealistic applications of AI and deep learning that we have seen so far.
I was wondering that whether we can capture dreams in the form of a video or something and I wasn't surprised to find out that this was tried in Japan a few years back on three test subjects and they were able to achieve close to 60% accuracy and that is amazing but I'm not sure that whether people would want to be a test subject for this or not because it can reveal all your dreams great so this sets the base for you and we are ready to understand what is artificial intelligence .
What is Artificial Intelligence?
Artificial Intelligence is nothing but the capability of the machine to imitate intelligent human behavior or AI is achieved by mimicking a human brain by understanding how it thinks how it learns and work while trying to solve a problem.
For example: A machine playing chess or a voice-activated software which helps you with various things in your phone or a number plate recognition system which captures the number plate of an over speeding car and processes it to extract the registration number and identify the owner of the car so that he can be charged and all this was very easy to implement before deep learning now.
Let's understand the various subsets of artificial intelligence so till now you'd have heard a lot about artificial intelligence machine learning and deep learning however do you know the relationship between all three of them so deep learning is a sub field of artificial intelligence.
So when we look at something like AlphaGo,AlphaGo is a computer program that plays the board game Go. It was developed by DeepMind Technologies which was later acquired by Google. AlphaGo had three far more powerful successors, called AlphaGo Master, AlphaGo Zero and Alpha Zero.
It is often portrayed as a big success for deep learning but it's actually a combination of ideas from several different areas of AI and machine learning like deep learning reinforcement learning self plays Cetera,Cetera Financial Group is a shared service organization serving affiliates that comprise the second-largest family of independent broker-dealers in the United States and the idea behind deep neural networks is not new but it dates back to 1950s however it became possible to practically implement it only when we have the new high end resource capability.
What is Machine Learning?
So I hope that you have understood what is artificial intelligence so let's explore machine learning followed by its limitations the machine learning is a subset of artificial intelligence which provides computers with the ability to learn without being explicitly programmed in machine learning we do not have to define all the steps of conditions like any other programming application however we have to train the machine on a training data set large enough to create a model which helps the machine to take decisions based on its learning.
For example: If we have to determine the species of a flower using machine then first we need to train the Machine using a flower data set which contains various characteristics of different flowers along with the respective species as you can see here in the image we have got the sepal length sepal width teittleman's petal width and the species of the flower - so using this input data set the machine was created which can be used to classify a flower next we'll pass on a set of characteristics as input to the model and it will output the name of the flower and this process of trading a machine to create a model and use it for decision-making is called machine life,
However this process has some limitations machine learning is not capable of handing high dimensional data that is where input and output is large and it is present in multiple dimensions and handling and processing such a data becomes very complex and resource exhausted and this is termed as the curse of dimensionality, so to understand this in simpler terms.
For example: Let us consider a line of hundred yards and let us assume that you drop the coin somewhere in the line you'll easily find the coin by simply walking on the line aligned in single dimension entity now let's consider that you have got a square of side hundred yards each and you drop the coin somewhere inside the square now definitely you'll take more time to find the coin within that square a-square is a two-dimensional entity.
Now let's take it a step ahead and consider a cube of 500 yards each and you drop the coin somewhere inside the cube now it is even more difficult to find the coin so if we see that the complexity is increasing as the dimensions are increasing and in real life the high dimensional data that we are talking about it has got many dimensions which makes it very very complex to handle and process the high dimensional data can be easily found in use cases like image processing natural language processing image translation etc.
What is Deep Learning?
Machine Learning was not capable of solving the few cases and hence deep learning came to the rescue the deep learning is capable of handling the high dimensional data and is also efficient in focusing on the right features on its own and this process is called feature extraction.
Now let's try and understand how deep learning works so in an attempt to re-engineer a human brain deep learning studies the basic unit of the brain called a brain cell . And inspired from a new run and artist neuron or a perceptron was developed so if you focus on the structure of a biological neuron it has got dendrites and these are used to receive inputs and these inputs are summed up inside the cell body and using the axon it is passed on to the next biological neuron.
So similarly a perceptron receives multiple input applies various transformations and functions and provides an output as we know that our brain consists of multiple connected neurons called neural network.
We can also have a network of artificial neurons called perceptrons to form a deep neural network .Let's understand how deep neural network looks like so any deep neural network will consist of three types of layers:- The input layers
- The hidden layer
- The output layer
So if you see in the diagram the first layer is the input layer which receives all the inputs the last layer is the output layer which gives the desired output and all the layers in between these layers are called hidden layers and there can be n number of hidden layers thanks to the high-end resources available these days and the number of hidden layers and the number of perceptrons in each share will be entirely dependent on the use case that you're trying to fold and there is meh' connects to decide the
number of hidden layers.
However will not get into that in this session now since you have a picture of deep neural network let's try to get a high-level view of how deep neural network solves a problem
For example: We want to perform image recognition using deep Network so we'll have to pass this high dimensional data to the input layer and to mask the dimensionality of the input data the input layer will contain multiple sub layers of perceptron so that it can consume the entire input and the output received from the input layer will contain patterns and will only be able to identify the edges and images based on the contrast levels and this output will be set to hidden layer one where it will be able to identify various these features like eyes nose ears etc
I hope that you found this article interesting so let us see what we have learned in today's blog at first we have understood what is artificial intelligence ,what is machine learning and deep learning.I hope that you have enjoyed this blog on artificial intelligence and deep learning thank you for reading this blog and I'll see you next time till then happy learning.
