Ajay Prakash Nair
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Ajay Prakash Nair's Blog

Artificial Intelligence vs Machine Learning vs.Deep Learning

Artificial Intelligence vs Machine Learning vs.Deep Learning

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Ajay Prakash Nair
·Oct 20, 2021·

2 min read

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In 2021, people benefit from artificial intelligence every day: music recommender systems, Google maps, Uber, and many more applications are powered with AI. However, the confusion between the terms artificial intelligence, machine learning, and deep learning remains. One of the popular Google search requests goes as follows: “are artificial intelligence and machine learning the same thing?”

AI is an umbrella discipline that covers everything related to making machines smarter. Machine Learning (ML) is commonly used along with AI but it is a subset of AI. ML refers to an AI system that can self-learn based on the algorithm. Systems that get smarter and smarter over time without human intervention is ML. Deep Learning (DL) is a machine learning (ML) applied to large data sets. Most AI work involves ML because intelligent behavior requires considerable knowledge.

Artificial intelligence is a science like mathematics or biology. It studies ways to build intelligent programs and machines that can creatively solve problems, which has always been considered a human prerogative. Humans have been obsessed with automation since the beginning of technology adoption. AI enables machines to think without any human intervention. It is a broad area of computer science. AI systems fall into three types: ANI: Artificial Narrow Intelligence, which is goal-oriented and programmed to perform a single task. AGI (Artificial General Intelligence) which allows machines to learn, understand, and act in a way that is indistinguishable from humans in a given situation. ASI (Artificial Super Intelligence) is a hypothetical AI where machines are capable of exhibiting intelligence that surpasses brightest humans.

Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It uses statistical learning algorithms to build smart systems. The ML systems can automatically learn and improve without explicitly being programmed. The recommendation systems on music and video streaming services are examples of ML. The machine learning algorithms are classified into three categories: supervised, unsupervised and reinforcement learning.

Deep learning, or deep neural learning, is a subset of machine learning, which uses the neural networks to analyze different factors with a structure that is similar to the human neural system. It is associated with learning from examples. DL systems help a computer model to filter the input data through layers to predict and classify information. Deep Learning processes information in the same manner as the human brain. It is used in technologies such as driver-less cars. DL network architectures are classified into Convolutional Neural Networks, Recurrent Neural Networks, and Recursive Neural Networks.

 
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