The different terminologies can be confusing. Though used interchangeably, they are not same. Recent years see a lot of change and new technology. Artificial Intelligence, Machine Learning, Deep Learning and Data Science are paving the path for the future. The following years will see a lot of growth in the AI industry. Consequently, in this blog, we will discuss the difference between the terms, AI VS ML VS DL.

Machine learning and Deep learning are part of Artificial Intelligence
Machine learning and Deep learning are part of Artificial Intelligence

Artificial Intelligence

AI is a wide field that allows the computer to impersonate human intelligence. In addition, AI can help a computer make decisions, process texts and perceive visuals. Machine Learning, robotics, etc,. are the sub-fields of AI.

AI have two categories.

  • Weak AI: The machines give a response on the basis of the set of rules provided. The rules confine the machines to act accordingly. Therefore, the ability to make decisions and changes are non-existent.
  • Strong AI: This category has Machine Learning and Deep Learning. It involves the machines learning by themselves using the data as well as providing accurate output.
Artificial Intelligence

Machine Learning

ML allows machines to improve at a task with practice. In other words, it means improving the capabilities of a machine with learning. The presented data enables the machine to learn by itself and make predictions.

Mostly, the data provided trains the machine while it develops an algorithm. With this algorithm and data, it can predict any values accurately. Similarly, ML has three categories.

  • Supervised Learning: Here, the machine is provided with a labeled data set, input and output parameters. When a new data set is given to the machine, it will use its supervised learning algorithm to examine the data. Therefore, it will produce a correct output based on the labeled datasets.
  • Unsupervised Learning: Here, there are no labeled datasets. The algorithm is made up in such a way that the machine learns by itself. This involves the clustering of data.
  • Reinforcement Learning: Here, the machine forms an algorithm based on finding an optimal solution. In this learning, the machine makes a lot of mistakes, performs the task again and again until it succeeds.
Machine Learning

Deep Learning

It is a specialized field of ML that depends on the training of Deep Artificial Neural Networks (ANNs) using images or texts. ANNs are processing units similar to the human brain. We know that billions of neurons make up the human brain and help in communication. Thus, enabling humans to make decisions while observing the environment. Similarly, ANNs work in such a manner, connecting “artificial” neurons. With more layers, the network can go deeper.

Deep Learning

What next?

The advances in AI are making machines capable of solving harder problems. AI is changing fast and predictions can be made about the future with robots and intelligence. Many are planning to develop the capabilities of intelligent software to make life easier in the future. As we enter the new era of machines, it is important to know the differences in AI VS ML VS DL.

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