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Artificial Intelligence, Machine Learning and Deep Learning explained

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Artificial Intelligence

Technology is progressing at a lightning speed, and we keep interacting with AI powered applications every day. Voice assistants such as Siri, Cortana, Google, and many more such applications that address our daily life pain points are AI powered. However, the confusion amongst the terms Artificial Intelligence (AI), Machine Learning (ML), and deep learning still persists. More often than not, people use these popular tech words interchangeably.

Let’s clear things up:

  • Artificial Intelligence(AI) is a science like mathematics or biology. It studies ways to build intelligent programs that can sense, reason, act and adapt with human-like intelligence.
  • Machine Learning (ML) is a subset of AI that provides software the ability to learn and improve from the data that is being fed into it.
  • Deep learning is a subset of machine learning. It focuses on building neural networks, similar to a human brain, in machines so that they can perform cognitive functions

What is AI and why is it important?

AI encompasses many different technologies working together to enable machines to learn, sense, comprehend, act, and perform human-like cognitive functions. Technologies such as machine learning, deep learning, and natural language processing are all part of the AI landscape. Each of these technologies are evolving individually, and when applied collectively with data analytics enable machines to perform complex functions by applying human-like intelligence.

Types of artificial intelligence—weak AI vs. strong AI

Weak AI – It is also known as Narrow AI or Artificial Narrow Intelligence (ANI). This is the type of AI that is used in applications today. Currently, our exposure to AI is limited to narrow AI, wherein the machine is trained to perform a specific task.

General or strong AI is more like what we see in sci-fi movies today, where machines are equivalent to humans. They have cognitive abilities and can think strategically to perform a range of complex tasks. While machines can perform some tasks better than humans, this fully realized vision of general or strong AI does not yet exist outside the silver screen.

Deep learning vs. machine learning

Although deep learning is a subset of ML, it is worth noting the nuances between the two. While ML focuses on powering machines to learn from data by drawing out patterns to perform tasks without being explicitly programmed. However, machines still act like machines, they can’t perform cognitive functions. For that, they need to have a neural network, similar to a human brain. This is exactly what deep learning focuses on accomplishing. The “Deep” in deep learning refers to a neural network comprising of more than three layers— i.e. an input layer, an output layer and multiple hidden layers. A deep learning system is self-teaching, learning as it goes by filtering information through multiple hidden layers, in a similar way to humans.

The Artificial Neural Networks (ANNs) have unique capabilities that enable deep learning models to solve tasks that machine learning models could never solve. All recent advancements in intelligence are due to deep learning.

What does the future look like for AI?

AI may have advanced by leaps and bounds in the last few years. However, we are still far away from designing truly intelligent machines – machines that can reason and make decisions like humans. ANNs may prove to be the solution to this as they have the capability to enable machines to interact with unrestricted and unfamiliar environment and learn from it. ANNs seek to emulate the connected network of neurons that the human brain is made up of in machines. This will allow machines to act like interconnected brain cells. With progress in this field, in the near future we can witness a revolution in AI, where machines can feel, sense, think and act like humans.

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