Artificial Intelligence 101

Artificial Intelligence 101: There is a lots of hype of Artificial Intelligence nowadays. People have massive myths and confusion related to Artificial Intelligence and the way it will affect our future. So, today we will take a deep look on Artificial Intelligence 101 and burst the most popular myths on Artificial Intelligence.

Most of the applications, we use in our day to day life is powered by Artificial Intelligence, so as to make our experience much better. The applications like Amazon and Flipkart is using AI for showing us the most relevant products. Applications like YouTube, Instagram and Facebook make use of AI under the hood, for showing us the most relevant content we need to see.

Artificial Intelligence 101

To understand the working of Artificial Intelligence, we have to firstly understand the meaning of Intelligence. Intelligence is the ability to take decisions, reasoning and learning from the past experiences. Now, implementing this on to a machine is known as Artificial Intelligence.

The basic need of AI is data. More the data will be, higher will be precision and efficiency of an AI model. This is because AI needs a data for training the models, lots and lots of data. Algorithms are also equally important, which is needed for the implementation. Without algorithms, data if of no use.

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Types of Artificial Intelligence

  1. Artificial Narrow Intelligence – Narrow AI is also known as Weak AI. It is called Weak AI because of it’s limitations. Here, the level of Intelligence is literally diminished. This is because it is the initial level of Intelligence powered to the Machine.

    Currently, the whole world is using Narrow AI. Whether it’s your mobile phone or the most powerful supercomputer in the world, all are making use of Narrow AI. So, we are still at the initial stage of the development of Artificial Intelligence.

  2. Artificial General Intelligence – General AI is basically a practice of making a mimic of human intelligence. We are still not able to achieve this level of Intelligence on machines. But theoretically if we will be able to achieve this level of AI, then machines will be able to think like humans as well.

    Many researchers and scientists are still working for reaching this level of intelligence. But it will take decades to reach here and make machines able to think like human, for sure.

  3. Artificial Super Intelligence – Super AI is a completely hypothetical term or idea. It is a successive growth of Intelligence after General AI. It refers to the Intelligence where machines will surpass or exceed the Intelligence of humans. This means machines will be more intelligent than humans. Again, it’s a hypothetical situation and no one have any idea about the after effects of Super AI on our world.

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Parts of Artificial Intelligence

Artificial Intelligence is a huge field in itself. But there are some major parts or field of AI that we should know.

  1. Machine Learning – Machine Learning is the study or practice of making machine able to think and react like humans. Nowadays, Machine Learning is a hot topic among developers and programmers. This is because of the drastic scope of Machine Learning in the future.

    In Machine Learning, we make the machines able to learn from it’s past experiences or data. Afterward, various data analysis techniques and algorithms are used to perform tasks like – Predictions.

    In Machine Learning, we train the data based on the different algorithms for making accurate models. Generally, the data we use is classified or labeled and we make use of supervised learning algorithms for training our data. For complete information visit here.

  2. Deep Learning – Deep Learning is a subset of Machine Learning. In Deep Learning, we make the machine able to learn as our brain learn from the past experiences. Instead, we can also say that deep learning is a concept or practice of creating a mimic of human brain.

    In Machine Learning, we make use of labeled or classifies data to train our machine, which is not really a practical way. But in Deep Learning, we make use of unclassified or unlabeled data to train our machine and use unsupervised learning algorithms. This is a more practical way of learning as we human also learn the same way.

  3. Artificial Neural Network – Artificial Neural Network is a application of Deep Learning. In Deep Learning, we make use of artificial neurons and neural network to train the unlabeled and unclassified data.

    Artificial Neural Network is a replica of neural network in human brain. It process the data in the same way as a human brain does. For this there is a lots of mathematics and algorithms work behind it. But with the help of specified libraries, we can implement neural network very easily and efficiently. For complete information visit here.

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