Differences Between AI, ML, and DL

Artificial Intelligence is made up of internal cogs like Machine Learning and Deep Learning. AI has become increasingly popular and has several real-world applications like Google’s AI-Powered Predictions, Ridesharing apps like Uber and Lyft, Commercial Flights Use an AI Autopilot, etc. For ML, there are a growing number of real-world applications that include Virtual Personal Assistants: Siri, Alexa, Google, etc., Email Spam, and Malware Filtering. Examples of DL applications include sentiment-based news aggregation, image analysis, and caption generation. Let's dig in.
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Ananya Avasthi
March 24, 2022
March 24, 2022
October 29, 2021
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“Can machines think?” Alan Turing pondered on this question, and in the 1950s dramatically changed the way we look at machines. In 1956 John McCarthy coined the term artificial intelligence (AI) which described machines that perform tasks that usually require human intelligence. In the past few years, AI has become increasingly popular and often vendors promote how their products and services access AI.

Artificial Intelligence

What exactly is artificial intelligence? AI is the ability to incorporate human intelligence into machines through a set of rules (algorithm). AI is made up of two words: “artificial” meaning something created by humans and “intelligence” meaning the ability to understand or think according to the situation or problem and to come up with a solution. One can consider AI to be the study of training computers to mimic a human brain and its thinking capabilities. AI real-world uses cases are changing and evolving every day, and in a wide range of areas, ranging from robotics to healthcare, and from banking to universities. AI focuses on three skills: learning, reasoning, and self-correction to obtain maximum efficiency.

Machine Learning

Machine learning (ML) is an off-shoot of artificial intelligence itself. ML is the application that provides the computer to learn automatically through experiences it has had and improve according to the situation being explicitly programmed, i.e. being flexible. ML is mainly used for developing programs so that it can reach the dataset to use it for itself. The entire process is a self-evaluation as it makes observations on data to spot possible patterns that are being formed and create better future decisions according to the dataset being provided to them. The quality of the data matters immensely, without a proper data bank, the machine cannot learn accurate solutions. The major aim of ML is to allow the systems to learn on their own via their experience without any kind of human intervention.

Deep Learning

Deep learning DL) is also a subset of AI and machine learning. Deep learning makes use of Neural Networks—a neural network or simulated neural network (SNN)—which is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation) to mimic human brain-like behavior. DL algorithms create an information-processing pattern mechanism to discover patterns. It is similar to what our human brain does as it ranks the information accordingly. DL works on larger sets of data than ML, and the prediction mechanism is an unsupervised process as in DL the computer self-administrates.

Differences Between AI, ML, and DL


AI is a computer algorithm that exhibits intelligence via decision-making. ML is an algorithm of AI that assists systems to learn from different types of datasets. DL is an algorithm of ML that uses several layers of neural networks to analyze data and provide output accordingly.


AI uses complex math. In ML, one can visualize complex functionalities like K-Mean, Support Vector Machines—different kinds of algorithms—etc. In DL, if you know the math involved but don’t have a clue about the features, you can break the complex functionalities into linear/lower dimension features by putting in more layers.


AI does not focus as much on accuracy but focuses heavily on success and output. In ML, the aim is to increase accuracy but there is not much focus on the success rate. Deep Learning mainly focuses on accuracy, and out of the three delivers the best results. DL needs to be trained with a large amount of data.


There are three types of AI: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI). Three types of ML are: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. DL can be visualized as neural networks with a large number of layers lying in one of the four fundamental network architectures: Unsupervised Pre-trained Networks, Convolutional Neural Networks, Recurrent Neural Networks, and Recursive Neural Networks.

When we're looking at AI, ML and DL, the three go hand in hand: ML—just like DL—is an off shoot of AI Different sectors use different kinds of algorithms to fulfill their need, and the use cases of each are growing and evolving by the day. We can see many examples of AI making sectors more effective and cost-friendly. If you’d like to talk more about AI, Ml, and DL, feel free to reach out to info@datasaur.ai. We'd love to chat!

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