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Artificial Intelligence vs. Machine Learning vs. Deep Learning

The world as we know it is moving towards machines — big time. But we cannot fully utilize the working of any machine without a lot of human interaction. In order to do that, we need some kind of intelligence for the machines. This is where artificial intelligence comes in. It is the concept of machines being smart enough to carry out numerous tasks without any human intervention.
The terms artificial intelligence and machine learning often lead to confusion and many of us don't exactly know the difference between them. Hence, we end up using these terms interchangeably. Machine learning is basically the learning concepts of machines through which we can achieve artificial intelligence. Deep learning is the latest thing in the artificial intelligence field. It is one of the ways to implement machine learning to achieve AI.
Most of us have seen AI-based movies with machines having their own intelligence like the Terminator series or I, Robot. But in real life, the concept of AI isn't optimized enough to handle these real-life situations and act accordingly. Mostly, AI implementations are only situation-based codings. Machine learning was introduced to handle large amounts data and to make machines learn using inputs/examples to process further problems.

Artificial Intelligence

Artificial intelligence, as the name suggests, involves creating intelligence artificially. It's the word that we've been repeating for more than half a century. It was introduced in the 60s and caught everyone's eye very quickly. The goal of AI is to reduce human interaction for a machine to do its work properly.
AI has been implemented in several ways. It doesn't always have to be smart implementation. Many implementations are just hardcoded functionalities used to run according to a given choice or situation. But in real-time scenarios, we have a lot of variables, and according to them, some action must be chosen to be executed. In those scenarios, hardcoding does not give us good results. This is when machine learning comes into the picture.

Machine Learning

Machine learning is an approach to implementing artificial intelligence. It is basically the study of the algorithms that make use of vast datasets to be parsed and ingested as examples, and based on these examples, further problems are solved.
Machine learning means making the machine learn to solve problems by providing enough examples/inputs, just like a human learns something, with examples on how to use this intelligence to solve further problems.
There are several algorithms that are used for machine learning. For example:
  • Find-S
  • Decision trees
  • Random forests
  • Artificial neural networks

Deep Learning

Deep learning is the newest term in the era of machine learning. Deep learning is a way to implement machine learning. It basically uses the artificial neural networks algorithm. Neural networks are inspired by our understanding of the biology of our brains and all the interconnections between neurons. But unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation. We'll learn more about deep learning in further blogs.

Conclusion

Artificial intelligence is a broad concept that is implemented through machine learning (which involves many efficient algorithms for real data). Deep learning involves neural network-based algorithms based on machine learning.
Deep learning has given a new level of possibilities to the AI world. Currently, deep learning is being used in the research community and in the industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Still, what we have today is the concept of narrow AI. Narrow AI (or weak AI) symbolizes that the AI we are working on is related to specific tasks. For example, vehicle automation (such as the Google self-driving car) or image classification/face recognition (such as Facebook's deep learning algorithms) are some specific tasks that have been made possible with deep learning.
The aim of AI from the start was to create a general AI (strong AI) to achieve the functionality of human brain that was not related to a specific task but to perform all general tasks and to respond to situations well, i.e. mimicking a human brain processing. So, we still have a long way to go.

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