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Today technology is growing very fast and we are in touch with different technologies day by day. Artificial intelligence is one of the fascinating and universal field of computer science which has greatest scope in the future. Artificial intelligence AI basically helps us to it enables the computer or machine. It enables the machine to think, it enables the machine to think that basically means without any human intervention the machine will be able to take its own decision. According to the father of the artificial intelligence John McCkarthy it is “the science and engineering of making intelligent machines especially intelligent computer programs”. The thinking and learning ability of every computer is called AI. Now to simplify this we know that the artificial means non-neutral and intelligence means to think, understand and learn. So we can say that AI is non-natural ability to think understand and learn. The ideal intelligent machines are those which know there environment well and take actions to maximize the goal success chances. Now a day we are using artificial intelligence in our mobile phones in form of Google maps and Google assistance. In the whole universe only human has ability to think and learn. It has the tendency to cause to work as human which is main concept that machine can work like a human. So many gadgets we are using are based on the artificial intelligence. Like from general to specific if we take self-driving car. In AI we do need to preprogram the machine to do some work. You have to create a machine with program algorithm which can work with its own intelligence during a particular task.
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Artificial intelligence for most of us but at present we are at no risk of being destroyed by machines. It is debatable that whether artificial intelligence is a threat or not. In 1956 John McCarty at the Dartmouth conference used the term artificial intelligence he defined artificial intelligence as the engineering and science of making intelligent machines in a sense. AI is a technique to make the machines to behave and work like humans. In the recent past AI has been able to accomplish this by making robots and machines that are being used in a wide range of fields including robotics, healthcare, marketing business analytics and many more. However many AI applications are not perceived as AI because we often mix-up that the artificial intelligence is only robots doing which perform various tasks. It has become so general that we do not realize we use it all the time for instance have you ever wondered how Google is able to give you such accurate search results or how your Facebook feed always gives you content based on your interest the answer to these questions is artificial intelligence.
Artificial intelligence is of three different types. First we have the artificial narrow intelligence artificial general intelligence and finally artificial super intelligence artificial narrow intelligence which is also known as weak AI involves applying artificial intelligence only to specific tasks. Now many currently existing systems that claim to use artificial intelligence are operating as a weak AI focused on a narrowly defined specific problem now Alexa is a very good example of narrow intelligence it operates within a limited predefined range of functions there is no genuine intelligence or no self-awareness despite being a sophisticated example of weak AI other examples of weak AI include the face verification that you see in your iPhone the autopilot feature at Tesla the social humanoid Sophia which is built at Hanson robotics. All of these applications are based on artificial narrow intelligence.
Now let’s take a look at artificial general intelligence artificial general intelligence is also known as strong AI. It involves machines that possess the ability to perform any intellectual task. We have a strong processing unit that can perform high-level computations but they are not yet capable of thinking and reasoning like a human. AI would take off on its own and redesign itself at an ever-increasing rate humans who are limited by slow biological evolution couldn’t compete and would be superseded coming to artificial super intelligence. The artificial intelligence has the capabilities of computers that will surpass human beings.
Why Artificial intelligence?
With the help of artificial intelligence we can built machines or robots that can work in an environment where survival of human cannot possible. We can create such devices which can solve real world problems very easily and with accuracy such as health issues, marketing and traffic issues. With the help of AI you can create your virtual assistance such as google assistance or siri.
Goals of Artificial intelligence:
- Replicate human intelligence
- Solve knowledge intensive task
- An intelligent connection of perception and action
- Building a machine which can perform tasks that requires human intelligence such as : playing chess, plane some surgical operations, driving a car in a traffic.
Applications of the Artificial intelligence:
Artificial super intelligence is presently seen as a hypothetical situation as depicted in movies and science-fiction books where machines will take over the world. However tech masterminds like Elon Musk believe that artificial super intelligence will take over the world by the year 2040.
Artificial intelligence is used in image recognition and machine learning software to analyze legal documents and extract important data points and clauses in a matter of seconds. IBM is one of the pioneers AI software who developed it for the medicine. Watson technology is used by more than 230 healthcare organizations. In 2016 IBM Watson AI technology correctly diagnose leukemia patient and was able to cross-reference 20 million oncology records.
Google AI eye doctor is another initiative taken by google where they are working with an Indian chain to develop an AI system which can examine retina scans and identify a condition called diabetic retinopathy which causes blindness.
Facebook Artificial intelligence is used for face verification to detect facial features and tag to your friends by using deep learning concepts and machine learning. Such example is Twitter’s AI which is being used to identify hate speech and terroristic languages in tweets it makes use of machine learning deep learning and natural language processing to filter out offensive content. The company discovered and banned three hundred thousand terrorist linked accounts ninety-five percent of which were found by non-human artificially intelligent machines.
Machine learning is a subset of AI okay so machine learning is a subset of AI and what does machine learning help us to do? It provides us statistical tools statistical tools to explore the data to explore the data. It provides us some statistical tools to explore and understand about that particular data. In machine learning you have three different approaches one is supervised machine learning, the second technique is something called as unsupervised machine learning and the third technique is something called as reinforcement learning or this is also called as semi-supervised machine learning.
In Machine learning we provide the data to computer and letting them learn without being explicitly programmed. Now this sounds awfully a lot like a human child so let’s consider a small scenario to understand machine learning. Now as a child if you had to distinguish between fruits such as cherries apples and oranges you wouldn’t even know where to start because you’re not familiar with how the fruits look. Now as we grow up we start developing and collect more information then we have the capability to differentiate between various fruits. The only reason why we are able to make this distinction is because we absorb our surroundings we gathered more data and we learn from our past experiences it’s because our brain is capable enough to think and make decisions. Since we have been feeding it a lot of data and this is exactly how machine learning works it involves continuously feeding data to a machine so that it can interpret this data understand the useful insides detect patterns and identify key features to solve problems this is very similar to how our brain works.
Supervised machine learning:
So in case of supervised we will be having some label data. You know some passed data and with the help of this kind of data. We will be actually able to do the prediction for the future. Let me just take a very good example let me just consider that I have height and weight as my two features and I want to classify whether that person will is belonging to an obese category or whether it belongs to the fit category right. So this kind of data initially whenever I am making my model at that time I will have this day time and previously only and what I will do I will create a model train on that data and with the help of those kind of data. I will be actually creating a supervised machine learning model that basically means in case of supervised we have passed data passed labeled data.
Unsupervised machine learning:
In the second category when I talk about unsupervised machine learning here I will not be having any labeled data that basically means in my data set. I will not know what is the output? So in unsupervised machine learning we usually solve clustering planet of problems clustering. You know there are different clustering techniques like k-means clustering, hydrocal min clusterings. So in unsupervised machine learning we will actually be solving clustering techniques that based on the similarity of that data it will try to group that data together and there is some mathematical concepts like Euclidean distance actually used inside that width apart weights or some other techniques also. So most probably here are two different algorithms or three different decorative algorithms one is k-means clustering higher it will mean clustering, DB scan clustering. These are the three popular clustering algorithms that we basically used in unsupervised machine learning.
Reinforcement machine learning:
Now in case of reinforcement learning what will happen is that some part of your data will be labeled and later on some part of the data will not be labeled. So the computer or the machine learning model learn slowly by seeing the past data and it will be learning as soon as the new data will be coming up. So I hope you understood this that is what we are actually doing over here the most important part is that we need to have data. it also provides some statistical tools to analyze explore and analyze the data.
Deep learning good got created you know so what scientists thought is that can we make the Machine learn like how we with the help of human brain actually try to learn things. You know that was a main idea behind deep learning so over here in deep learning you create architecture which is called as multi neural network architecture. We are basically using multi neural network architecture and we are actually creating some deep learning neural networks the main idea behind deep learning is to mimic human brain. You know how human actually learns those concepts similarly. We are creating models over here which is learning those things and the most important thing is multi neural network architecture right and in deep learning also you have various techniques that are:
- Artificial neural network
- CNN that is convolution neural network
- RNN which is called as a recurrent neural network
Most of the data which is actually present in the form of numbers will be solved with the help of a CNN. You know artificial neural network suppose our input is in the form of images. We will basically use CNN that is convolution neural network and suppose if our input is in the form of tiny series kind of data at that time we will be using recurrent neural network. Apart from this there are also techniques like transfer learning. There are some advanced neural networks extensions of the CNN so suppose if I take an example of mass 2 r CNN. So these are some advanced neural network architecture which where the base is actually a CNN architecture. So you should try to understand this first of all I am actually using this concepts of machine learning and deep learning and the main goal is to derive an AI application you know by using this particular techniques I want to create a model which will be I want to create a self-driving car. So that is self-driving track maybe in our AI application.