Source: Alex Knight/ Pexels

Artificial Intelligence or AI, in short, is a term that is used for computers that can do things that involve human intelligence and behavior.

If we talk about it, then there are various versions of its understanding. But the actual meaning of this topic was defined by John McCarthy in 2004 in his paper. In that paper he explained that-- It is the science and engineering of human intelligence of making intelligent machines, especially intelligent computer programs. It is a similar task for computer programs to understand human intelligence, but AI does not have to confirm itself to biologically observable methods.

But before this, there were many attempts to define such a phenomenon in which machines behaved and acted like humans. The first credit for such a try goes to Alan Turing. In 1905, he raised a question in his paper which started the debate that whether machines can think like humans or not.

Later on, a book named Artificial Intelligence Modern approach was published under the names of Stuart Russel and Peter Norvig. It explored the four important aspects of AI-based on two approaches:

Idealistic Approach:

  1. Systems that think rationally
  2. Systems that act rationally

Humane Approach:

  1. Systems that think like humans
  2. Systems that act like humans

So, AI can replace humans in the future. It can do so by the utilization of deep learning and machine learning. These techniques will help in making decisions easier along with some future predictions based on some previously collected data.

In the future, techniques like AI may have a developmental and evolutionary process. During this process, a particular technique will result only after a process of creation. This process will also result in clearing our doubts about the technology.

Now let’s just see the types of AI. So, there are two kinds of AI, weak AI, and strong AI. Weak AI is also called Narrow AI or Artificial Narrow Intelligence. It is a type of AI which is trained to focus on a particular task. The applications of AI which we see today are examples of this kind of AI.

The second type of AI is strong AI. It is made up of two parts Artificial General Intelligence (or AGI) and Artificial Super Intelligence (or ASI). In this case, the AGI is the one that is equally capable as a human. On the other hand, ASI is the one that is better than a human being.

Two important parts of AI

1.Deep learning

Deep learning is a subpart of machine learning. it helps in dealing with a huge amount of data without human intervention. It involves two important things – statistics and predictive modeling.

Deep learning consists of neural networks. These neural networks have various layers of interconnected nodes. These layers help in optimizing and refining the prediction. This step is collectively called forward propagation. Another process is called backward propagation. In this second process, algorithms are used to remove the errors in the results.

Now, if we take neural networks into the consideration, then it is of different types: -

  1. CNN (Convolutional Neural Networks)
  2. RNN (Recurrent Neural Networks)

The applications of deep learning are in various fields:

(a) Maintaining law and order: Deep learning is used in identifying the culprits with the help of processing data.

(b) Chatbots: Deep learning helps companies to apply an extra layer of chatbot in their system. This helps the customers to get their queries solved at the earliest.

(c) Health and safety measures: In big industries, it is deep learning which helps in preventing mishaps from happening. It consistently monitors whether workers are coming too close to dangerous machinery. At that moment they provide warnings of caution.

In the medical field, research is being supported by deep learning. The data from previous research on the same matter helps in currently running projects in one way or the other.

2.Machine learning

It is an integral part of AI in which a machine or computer learns how to behave like a human being. This process slowly allows for improving the learning capability of a machine or computer.

Machine learning has three important steps for it

  1. Decisive step: In this step, a prediction is made based on some input data. The data can be labeled or unlabelled.
  2. Error detection: In this step, the entire process is evaluated for any errors. Any previous similar work helps in the evaluation process.
  3. Optimizing step: The entire process is now optimized. So that any remaining shortcomings can be removed.

There are mainly three categories of Machine Learning

  1. Supervised machine learning - In this case, a labeled dataset is used.
  2. Unsupervised machine learning - In this case, an unlabelled dataset is used.
  3. Semi-supervised machine learning - It uses both labeled and unlabelled datasets.

Machine learning is used in various fields. Some of the examples are:

  1. Recommendations: Machine learning is used in tandem with the past data of customer buying patterns. This helps in providing recommendations in the future.
  2. Stock Market: Machine learning is being used in this field as an evaluator. It helps in identifying past data patterns and thus results in helping in making millions worth of transactions every day.

So, Artificial Intelligence has been able to transform our lives to a great extent. We all have a lot more improvements to make in the process of utilizing this technology in the future. There are various things around which can be made more perfect in terms of functionality. But all of these things will need some more research and work on AI. There are also speculations that technology like AI can be a job destroyer in the future. But this is not true as it is there to improve our lives, especially our work environment. There will also be some new job creation in some new fields. There will also be some new answers to some old questions in the technological field. Ultimately, what lies ahead in our future remains a mystery as we still are progressing toward a better tomorrow.

.    .    .