Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL): What’s the Difference?

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are often used interchangeably. However, they are not exactly the same. Artificial intelligence is the broadest concept that gives machines the ability to imitate human behavior. Machine learning is the application of artificial intelligence to systems or machines to help them learn and continuously improve themselves. Finally, deep learning uses complex algorithms and deep neural networks to repeatedly train specific models or patterns.

Let’s look at the evolution and journey of each term to better understand what artificial intelligence, machine learning, and deep learning actually refer to.

Artificial Intelligence

Artificial intelligence has come a long way since the past 70-plus years. Whether we know it or not, whether we like it or not, it has penetrated into every aspect of our lives. Over the past decade, advances in machine learning and deep learning have created an AI boom across industries and organizations of all sizes. Cloud service providers are further driving this momentum by developing free open source services and offering new scenarios.

Figure 1: Overview of AI, ML and DL

Artificial intelligence is probably the most talked about concept since 1956 . By 2015, widespread use of GPUs made parallel processing faster, more powerful, and cheaper. And increasingly cheap storage can store big data (from plain text to images, mappings, etc.) at scale. This created a need for data analysis, which is more commonly known as data science, leading to the development of machine learning as a method to achieve artificial intelligence.

Machine Learning

Machine learning is the use of algorithms to process, learn and understand or predict patterns in available data. Recently, the low-code and no-code concepts of software development are used as a self-learning process in machine learning, which gives specific instructions to complete a specific task. Machines are “trained” using data and algorithms so that they can learn how to perform tasks and, more importantly, apply that learning to an evolving process.

Figure 2: Evolution of AI, ML and DL

Machine learning developed when the developer community focused on AI , and then developed algorithmic decision tree learning, logic programming, clustering, parallel processing, and reinforcement learning. These are good steps in the right direction, but not enough to address scenarios of interest to the world.

Deep Learning

Deep learning is the evolution of neural networks and machine learning, and is the brainchild of the artificial intelligence community. It learns how the human mind works in a specific scenario and then does it better than humans at that job! For example, IBM's Watson played chess against itself and made great progress in the game, eventually beating the world champion. Google's AlphaGo also learned how to play the game Go, playing it over and over to improve itself and become a champion.

Artificial intelligence, machine learning and deep learning are constantly evolving. Everyone involved in data science hopes to advance these concepts to improve our daily lives. And the open source community, private industry, scientists, and government agencies are all working together on this.

Figure 3: Types of AI, ML and DL

In summary, while AI helps in creating intelligent machines, machines Learning helps build AI-driven applications. Deep learning is a subset of machine learning. It trains specific models by processing large amounts of data using complex algorithms. Since narrow AI is extremely difficult to develop, machine learning is addressing opportunities in this area through rigid computation. At least for achieving general AI, deep learning helps bring AI and machine learning together.

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