What is the difference between machine learning and AI?

Two buzzwords that continuously keep popping up in the field of marketing are artificial intelligence (AI) and machine learning (ML). These words also seem to be used interchangeably as many people believe them to be the same. Although they are similar, they are not quite the same, but many people are perceived into thinking they are, which hence leads to some confusion. This article will clear all confusion and explain the difference between machine learning and AI, as well as their applications and benefits for marketing.  

What is AI?  

The word artificial intelligence comprises of two words “artificial” and “intelligence”. Artificial refers to something which is made by human and intelligence means the ability to understand or think. AI can be defined by “the study of how to train computers so that they can do things which at present humans can do better.” Therefore, it is an intelligence where we want to add all the capabilities that humans contain and train a machine to do them better.  

AI is a broad term that encompasses many subfields and applications, such as natural language processing, computer vision, speech recognition, robotics, and more. AI can be classified into two types: weak AI and strong AI. Weak AI, also known as narrow AI, is the type of AI that is designed to perform a specific task or function, such as playing chess, driving a car, or recognizing faces. Strong AI, also known as general AI, is the type of AI that can perform any intellectual task that a human can do, such as reasoning, learning, and creativity. However, strong AI is still a hypothetical concept that has not been achieved yet. 

The aim of AI is to increase the chance of success and not accuracy. AI works as a computer program that does smart work, such as solving problems, making decisions, and mimicking human behaviour. AI leads to developing a system to mimic humans to respond and behave in similar circumstances. The goal of AI is to simulate natural intelligence to solve complex problems.  

What is Machine Learning?  

Machine learning is the learning in which machines can learn by their own without being explicitly programmed. MI is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.  

Machine learning can be divided into three types: supervised learning, unsupervised learning, and reinforcement learning.  

• Supervised learning is the type of machine learning where the machine learns from labelled data, such as images, texts, or numbers, and produces an output based on the input. For example, a machine learning model can learn to classify images of cats and dogs based on the labels provided.  
• Unsupervised learning is the type of machine learning where the machine learns from unlabelled data, such as images, texts, or numbers, and finds patterns or structures in the data. For example, a machine learning model can learn to cluster images of cats and dogs based on their features.  
• Reinforcement learning is the type of machine learning where the machine learns from its own actions and feedback, such as rewards or penalties, and optimizes its behaviours based on the goal. For example, a MI model can learn to play a video game by trial and error and improve its score over time.

The aim of machine learning is to increase accuracy, but it doesn’t care about success. MI is a simple concept machine that takes data and learns from it. Machine learning involves creating self-learning algorithms that can adapt and improve over time. The goal of machine learning is to learn from data on certain tasks to maximize the performance of that machine.  

How are MI and AI Related?  

Machine learning and AI are related but not the same. Machine learning is a subset of AI, which means that all MI is AI, but not all AI is machine learning. Machine learning is one of the ways to achieve AI, but not the only way. There are other methods and techniques that can be used to create AI systems, such as expert systems, fuzzy logic, neural networks, and more.  

Machine learning and AI have different purposes and goals. MI is focused on learning from data and improving accuracy, while AI is focused on mimicking human intelligence and increasing success. MI is based on data and algorithms, while AI is based on logic and rules. Machine learning is more flexible and adaptable, while AI is more rigid and predefined.  

Why are Machine Learning and AI Important for Marketing?  

Both MI and AI can have valuable business applications, especially for marketing. These two terms have something in common: they can help marketers create and execute effective and efficient marketing strategies that can boost their sales, revenue, and customer loyalty. According to a survey, 61% of marketers say artificial intelligence is the most critical aspect of their data strategy. . 

Some of the benefits of MI and AI for marketing are: 

• They can help marketers analyse large and complex data sets and extract meaningful insights that can help them understand their customers, competitors, and markets better. 
• They can help marketers segment their customers based on their behaviour, preferences, and needs, and create personalized and relevant marketing campaigns that can increase their engagement and conversion rates. 
• They can help marketers create and optimize content, such as ads, emails, blogs, videos, and more, that can attract and retain their customers and increase their brand awareness and reputation. 
• They can help marketers automate and streamline various marketing tasks and processes, such as lead generation, lead scoring, lead nurturing, customer service, and more, that can save their time and resources and improve their productivity and efficiency. 
• They can help marketers measure and evaluate their marketing performance and outcomes, such as ROI, KPIs, and metrics, and provide actionable feedback and recommendations that can help them improve their marketing strategies and results.

MI and AI are two terms that are often confused and used interchangeably, but they are not the same. MI is a subset of AI that focuses on learning from data and improving accuracy, while AI is a broader term that encompasses many subfields and applications that focus on mimicking human intelligence and increasing success. Both MI and AI are important and beneficial for marketing, as they can help marketers create and execute effective and efficient marketing strategies that can boost their sales, revenue, and customer loyalty. Determining which one is best for your company depends on what your needs are and what you want to achieve.