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What Is the Difference Between AI and Deep Learning?

As the buzz around AI and deep learning grows, so do the questions. What are they, and what’s the difference between them? To help clear things up, we’ve put together a brief summary of what these technologies mean and how they help businesses.

What Is Artificial Intelligence?

Artificial Intelligence (AI) refers to the intelligence exhibited by machines and computer systems. It’s a branch of computer science that aims to replicate human cognitive functions, such as learning, problem-solving, and decision-making.

More profoundly, AI focuses on solving problems associated with human reasoning, learning, planning, and natural language understanding. Over the years, research on artificial intelligence has led to the development of notable technologies like virtual assistants, speech recognition systems, self-driving cars, etc.

PwC study found that about 52% of organizations “accelerated” AI adoption in 2021.

What Is Deep Learning?

The term deep learning refers to a type of machine learning. It is not just a single method but an umbrella term for algorithms and techniques used to train non-linear, multi-layer artificial neural networks (deep neural networks) to enable computers to learn and make predictions based on data.

Deep learning draws inspiration from the structure and function of the human brain to learn multiple layers of abstraction. No doubt, deep learning is an exceptionally promising technology projected to grow at a CAGR of 39.2% until 2027.

AI – A Diverse Collection of Technologies That Includes Deep Learning

Artificial intelligence is a broad set of algorithms and technologies that perform tasks and make decisions in a highly human-like manner. Practically, AI is divided into subfields of machine learning, natural language processing, computer vision, robotics, etc.

Machine learning helps the system automatically adapt without being explicitly (manually) programmed. Deep learning is a part of the broader machine learning landscape and subsumes many of the learning algorithms, including convolutional neural networks, self-organizing maps, recurrent neural networks, long short term memory networks, etc. It’s focused on mimicking the learning patterns of the human brain to make complex correlations.

Deep Learning Mimics Human Brain, While AI Empowers Machine to Think Like Human

As mentioned above, deep learning mimics the human brain to solve complex problems. When we look at a cat photo, our brain carries out different tasks to determine what we’re looking at. These tasks include recognizing the shape of the cat, identifying its color, and realizing that it’s a living thing. Deep learning emulates this pattern—it breaks down complex problems into smaller pieces that can be solved individually by artificial neural networks (ANNs). ANNs are modeled after biological neurons and can learn and recognize patterns.

Artificial intelligence (AI), on the other hand, empowers machines to think like humans. For instance, AI is mostly used in digital assistants like Siri, Alexa, and Cortana. These programs mimic human speech patterns, language comprehension, and responses.

As mentioned, deep learning is one of the important subfields of AI. Its algorithms can “learn” from large datasets and make predictions based on patterns they find in those datasets.

Deep Learning Creates Accurate Models, While AI Can Perform Repetitive Tasks

Artificial neural networks (ANN) underlie deep learning, similar to human neurons and synapses. ANNs can process data with multiple layers, where each layer contains thousands or millions of neurons arranged in a specific pattern. Using inputs from many other neurons, neurons calculate output values.

In deep learning, the first layer performs feature extraction by unearthing raw features from input data, while the last layer is responsible for generating predictions by applying learned functions to these extracted features. In between these two layers are hidden layers, which perform complex computations based on the raw features extracted from previous layers. These hidden layers contain more neurons than input or output layers and help improve accuracy by increasing model capacity and reducing overfitting risk.

AI can perform repetitive tasks efficiently. This can include recognizing patterns from a large amount of data acting on them. For example, robots within a manufacturing facility are an example of AI. Likewise, virtual assistants responding as per the consumers’ preferences, face detection programs working to unlock devices, self-driving cars making sense of sensor data, etc., are all examples of AI.

In a Nutshell

Profound knowledge of both AI and Deep Learning is essential to drive innovation, propel business growth, and establish a competitive advantage. Both AI and Deep Learning markets are expected to proliferate rapidly in the coming years.

Devising the right strategy to embrace AI and Deep Learning into your business process is immensely important. Reach out to us to learn more about how we can help your organization stay ahead of the curve.

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