Fundamentals of Artificial Intelligence - Course on Deep Learning

 


Definition of Artificial Intelligence

The term “Artificial Intelligence” was coined by McCarthy in 1956. He defined it as the science and engineering of making intelligent machines. Artificial Intelligence consists of two words which are mentioned below:
  1. Artificial: It means the machine is acting with a virtual brain reducing the task of human thought process.
  2. Intelligence: Just like human beings, the machine not only perform an operation on its own but also with success rate equal to or more than human mind.
So, AI is a property of machines which gives it capacity to mirror the human manner of thinking. The intelligent machines are created dependent on the insight of a subject, creator, individual, and a person. There are different perspectives of AI discussed by different authors and/or researchers as follows:
  1. First perspective is that AI is tied in with planning frameworks that are close to human intelligence. This view includes attempting to comprehend human idea and a work to fabricate machines that imitate the human perspective. This view is the intellectual science way to deal with AI.
  2. Second perspective is the idea of the Turing Test. Turing was a scientist who said that in future computers can be modified to gain abilities which will rival human intelligence. To support his words, he proposed the idea of 'imitation game', in which a person and a computer would be examined under same conditions, such that an investigator will ask same question to both via text message. Turing added that if the investigator couldn't recognize them by addressing, it would be appropriate to call the machine intelligent. Turing's 'imitation game' is currently known as 'the Turing test' for intelligence.
  3. Next perspective is that AI deals with rational thought process and inference. In other words, the machine has a thought process which explains how it finds the logic behind the operation performed, whether it is the best approach or not.
  4. Last but not the least, AI focus on performance of system, and not much on the procedure. It gets adapt to the atmosphere and find the best possible approach to perform the task.

Basic Terminologies

Now let us learn some basic terminologies related to Artificial Intelligence.
  1. Soft Computing: Soft Computing is a field of science comprising soft computing techniques. It gives fast spreading of significant outcomes in delicate computing advancements, a combination of evolutionary algorithms and genetic programming, neural science and neural networks, fuzzy systems, and genetic algorithms to solve a particular problem rivalling human intelligence.
  2. Fog Computing: The term fog computing, coined by Cisco, is an alternative to cloud computing. It is also known as fog networking or fogging. It depicts a decentralized computing structure located between the cloud and gadgets that produce information. This adaptable construction empowers clients to put assets, including applications and the information they produce, in logical areas to improve execution. By performing continuous investigation into a cloud computing mist found nearer to gadgets, it is simpler to get benefit from the existing computing power present in those gadgets. This further develops users’ experience and decreases loads on the cloud in general. Fog computing is particularly imperative to gadgets associated with the internet of things (IoT).
  3. Machine Learning: An approach to AI in which an algorithm learns to make predictions from data that is fed into the system.
  4. Neural Network: A machine learning approach in which algorithms process signals via interconnected nodes called artificial neurons. In AI, neural network is usually referred as Artificial Neural Network (ANN). Human intelligence transfers data using neurons, fundamental element of human brain. Similarly, machine intelligence is measured using number of artificial neurons.
  5. Deep Learning: A deep study of neural networks with many layers of computation, enabling the algorithm to powerfully analyze the input data.
Fig. 1 Venn diagram representing relationship between artificial intelligence, machine learning, neural network, and deep learning


Need of Artificial Intelligence

Artificial Intelligence (AI) is needed in light of the fact that the work we need to do is increasing every day. So, it's a smart thought to mechanize the normal work. This saves the labour of the association and furthermore increases the efficiency. Through this AI, the organization can likewise get the skilled labour for their development. Besides the organizations today feel that they need to mechanize all the normal and routine work. Furthermore, they want to computerize those customary works through the basic program Because, with the development of data science, automation becomes more common. For an instance, most of the websites have a chatbot which shows a message like “Welcome to this website!” or “How can I help you?” when we visit it.

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37 comments:

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  2. Very informative. Waiting for the next article.

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  3. waiting for nxt article..nice one😊

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  14. Nice Informative Article.Well Done👍👍

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