The term AI was first invented in the year 1956 by John McCarthy. It was the birth of AI in 1956. John McCarthy explained Artificial Intelligence as the science and engineering of making intelligent machines. In other words, artificial intelligence is the theory and development of Computer Systems able to perform tasks that normally require human Intelligence such as visual perception, speech recognition, decision-making and translation between languages. So guys in a sense AI is a technique of getting machines to work and behave like humans in the recent past artificial intelligence has been able to accomplish this by creating machines and robots that have been playing a vital role in todays techonology including robotics and many more.
Brief about Artificial Intelligence
The easiest way to think about Artificial Intelligence is in the environment of a Human. After all humans are the most intelligent creatures we know of. Artificial Intelligence plays a vital role and it is a branch of computer science. The main motto of Artificial Intelligence is to create systems that can function intelligently, wisely and independently. Humans can talk and hear to communicate through their own language. This is a field of speech recognition. Much of speech recognition is totally of statistically based, hence. It called statistical learning.
Humans can read and write text in their language. This is a field of NLP (Natural Language Processing). Humans can notice with their eyes and operate or process what they see. This is a field of computer vision. Computer vision falls under the illustrative way for computers to operate or process information. We humans recognize the scene around them through their eyes, which create images of that world this field of image processing which even though it's not directly related to AI is required for computer vision.
We can understand their environment and move around smoothly. This is a field of robotics. Humans have the ability to see patterns such as grouping of objects. This is a field of pattern recognition machines are even better at pattern recognition because they can use more data and dimensions of data. This is the field of ML (Machine Learning).
Now, let's talk about the human brain. The human brain is a network of neurons and the use these to learn things. If we can duplicate the structure and the function of the human brain, we might be able to get internal capabilities in machines. This is a field of neural networks. These networks are more complex and deeper and we use those to learn complex things. That is from the field of deep learning.
There are different types of deep learning and machines which are essentially different techniques to replicate what the human brain does. If we get the network to scan images from right to left, up to down. It's a convolution neural network.
CNN (Convolution Neural Network) is used to recognize objects in a scene. This is our computer vision fix in an object recognition is accomplished through AI.
We can remember the past like what you had for breakfast this morning. Well at least most of you. We can get a neural network to remember a finite past. This is a recurrent neural network as you see there are two ways AI works one is symbolic based and another is data-based. For the database side called machine learning. We need to sustain the machine lots of data before it can learn.
For example, if you had lots of data for pressure versus volume you can plot that data to see some kind of a figure. If the machine can learn this figure, then it can make predictions based on what it has learned while one, two, even three dimensions is easy for humans to recognize and learn machines can learn in many more Dimensions like even thousands or hundreds.
That is why machines can look at lots of high-dimensional data and determine figures, patterns, designs. Once it learns these patterns you can make predictions that humans can't even come close to.
We can use all these machine learning techniques to do one or two things classification or prediction. As an example when we use some information about workers to assign new workers to a group like young age workers. Then you are classifying that worker, if you use data to predict if they are likely to defect to a participator, then you are making a prediction. There is another way to think about learning algorithms used for Artificial Intelligence. If you instruct a logic or an algorithm with data that also contains the answer, then it's called supervised learning.
For example, when you train a machine to recognize your friends by name, you'll need to identify them for the computer. If you train an algorithm with data where you want the machine to figure out the patterns, then it is unsupervised learning.
For example, you might want to feed the data about celestial objects in the universe and expect the machine to come up with patterns in that data by itself. If you give any algorithm a goal and expect the machine through trial and error to achieve that goal, then it's called reinforcement learning.