AI vs Machine Learning vs Deep Learning: What’s the Difference

AI vs Machine Learning vs Deep Learning: What’s the Difference

Is machine learning better than AI? Why is deep learning better than machine learning? How does deep learning differ from artificial intelligence or AI?

As we move towards a Web 3.0 that is heavily reliant on data, it is essential to understand the difference between AI, machine learning, and deep learning and their significance to an increasingly digital future.

In this article, we’ll help you understand the difference between AI, machine learning, and deep learning. But first, let’s define them.

What Is AI?

Artificial Intelligence or AI is not a new field of study. The objective of this study was to get computers to do things that require human intellect.

Since then, Artificial intelligence has become a catch-all phrase for applications in which a machine simulates " cognitive " functions that humans identify with other human brains, such as "learning" and "problem-solving."

At its most basic level, AI can be as simple as a set of if-else statements that program a machine to do specific tasks in specific scenarios. However, as time moved on, the need for more sophisticated techniques arose.

Amongst those techniques the most popular are Machine Learning and Deep Learning, both subfields of AI.

AI is the process of getting machines to learn, reason, and self-correct.

The Difference Between AI vs ML vs DL - Source: Mentalstack

What Is Machine Learning?

In short, machine learning (ML) is training a machine to deliver better results by getting smarter. 

Machine learning may be used to automate a wide range of jobs. It has an impact on almost every business, from providing better healthcare to helping identify fraudulent transactions.

For tasks like image recognition or extracting meaning from text, hard-coded algorithms or rigid, rule-based systems just didn't cut it. The answer turns out to be not merely imitating human behaviour (AI), but also imitating how people learn.

We only mastered reading after we read simple books first, and got better at reading as we moved on to more advanced reading material. The more data our brain received the better we got at reading.

That is exactly how machine learning works. 

Feed a large amount of data on financial transactions to an algorithm, tell it which ones are fraudulent, and let it figure out what signals fraud so it can anticipate fraud in the future. Alternatively, you may feed it data about your consumer base and let it figure out how to segment them most effectively.

Machine learning models are, in a nutshell, optimization algorithms. They decrease their inaccuracy by guessing, guessing, and guessing again if you tune them properly.

As these algorithms progressed, they could be used to solve a wide range of issues. However, several tasks that humans considered simple (such as voice or handwriting detection) were difficult for robots. Machines could never compete with the human brain.

ML is all about letting systems learn by themselves without human interference.

What is Deep Learning?

Deep learning (DL) is a subset of machine learning. Deep Learning uses a multi-layered structure of algorithms called the neural network:

Neural Network. Source: www.deeplearning-academy.com/ai-wiki

These neural networks aim to imitate the activity of the human brain by enabling it to "learn" from enormous quantities of data. While a single-layer neural network may produce approximate predictions, more hidden layers can assist to optimize and improve for accuracy.

Many artificial intelligence (AI) apps and services rely on deep learning to increase automation by executing analytical and physical activities without the need for human participation. 

Digital assistants and self-driving cars are just examples of everyday goods and services that rely on deep learning.

Before a car can determine its next action, it needs to know what’s around it. It must be able to recognize people, bikes, other vehicles, road signs, and more. And do so in challenging visual circumstances. Standard machine learning techniques can’t do that.

What are the main differences between AI, machine learning, and deep learning?

DL is a subset of ML and focuses on information processing patterns.

AI vs Machine Learning vs Deep Learning

We broke down the main differences between AI, machine learning, and deep learning into the following to make it easier to get a clearer understanding of what they are and how they are related to each other.

Definition:

  • AI: Artificial Intelligence (AI) is the study/process that enables machines to replicate human behaviour via the use of a specific algorithm
  • ML: Machine Learning, or (ML), is a study that use statistical approaches to allow machines to develop over time.
  • DL: Deep Learning (DL) is the study of using Neural Networks (sim ilar to neurons found in the human brain) to mimic the functionality of a human brain.

How They Relate to Each Other:

  • AI: AI is a broad field which consists of ML and DL. AI is a decision-making algorithm that demonstrates intelligence. Effectiveness determined by the efficiency of ML and DL applied.
  • ML: ML is a subset of AI. ML is an algorithm that allows machines to learn from data feeds. Cannot work with larger amounts of data like DL.
  • DL: DL is a ML technique that analyses data and generates output using deep neural networks. Highly efficient because it can work with large amounts of data.

Objectives:

  • AI: Aims to enhance the success of machine fulfilling tasks.
  • ML: Aims to enhance accuracy of those tasks.
  • DL: Aims to reach the highest level of accuracy through larger volumes of data.

Types:

  • AI: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI)
  • ML: Supervised Learning, Unsupervised Learning and Reinforcement Learning
  • DL: DLs are neural networks with a high number of parameter layers that are arranged in one of the four basic network architectures - Unsupervised Pre-trained Networks, Convolutional Neural Networks, Recurrent Neural Networks and Recursive Neural Networks

Examples of Applications:

  • AI: Google’s AI-Powered Predictions, Ridesharing Apps Like Uber and Lyft and etc.
  • ML: Virtual Personal Assistants: Siri, Alexa, Google, etc., Email Spam and Malware Filtering.
  • DL: Sentiment based news aggregation, Image analysis and caption generation, etc.

Using AI, ML, and DL in Process Automation

Sitech is helping enterprises and business stay ahead of the curve through AI powered digital transformation solutions. From optimizing processes to getting a deeper insight into their manufacturing, supply chain, inventory, and even their customers, our clients have gained a strategic competitive advantage by adopting a technology-driven business model.

Helping our clients transform their current services and products through Managed Software Teams enabled them to gain access to a fully dedicated team that includes data scientists that create algorithms that make their systems smarter, faster, and deliver strategic insights accurately.

If you are looking to replicate this success or learn more about how we can help your business evolve, feel free to contact us today for a free consultation.