Published: Apr 25, 2022

machine learning vs artificial intelligence

Technology is becoming more embedded in almost all walks of life. As the list of new tech advancements grows every day, the way people learn and operate things continuously changes. Artificial intelligence (AI) and machine learning are two technological innovations on top of the hype cycle. 

You probably hear these trending tech terms every day in businesses, jobs, healthcare, and education. But what do these terms mean? How are they transforming the world today and the next decade? 

The biggest misconception about AI and machine learning is that the terms can be used interchangeably. While they both play an essential role in making lives easier, faster, and better, they have different functions in computer science. 

This post will help you better understand AI and machine learning, their primary connection, differences, as well as potential benefits. 

Artificial intelligence, commonly referred to as AI, is the ability of a computer system to perform complex tasks that typically require human intelligence. It typically involves ingesting large amounts of data and human intelligence into AI models. 

The primary objectives of artificial intelligence are to teach machines to become self-reliant and simulate human behaviour. But above all, this technology aims to develop the following cognitive skills: 

• Learning: Learning is one of the essential building blocks of AI programming. This process involves acquiring data and creating algorithms to turn the data into actionable insights. Such algorithms are step-by-step instructions for the computer system to learn how to independently operate or accomplish a task. 

• Reasoning: Reasoning is vital in AI programming for the machine to reason like the human brain. This process involves selecting a suitable algorithm and extracting critical information from large data sets. It uses clustering analysis and statistical inference to make conclusions and predictions about a particular problem or event. 

• Self-correction: Through learning and reasoning processes, machines can decide how to respond to a particular action without any human intervention. Unlike humans, the AI system is not prone to errors and mistakes. It has self-correction and self-enhancement capabilities designed to modify algorithms, continuously ensuring the most accurate outcomes possible.

Four Types of Artificial Intelligence

There are various ways to categorise AI systems, depending on their capacity to mimic human characteristics. The current system of classification identifies artificial intelligence into the following types: 

Reactive Machines

This is the most basic type of artificial intelligence system. They cannot form memories or use prior experiences and historical data to make or influence current decisions. As the name suggests, all they’re capable of is reacting to what they see in existing situations. Spam filters and the Netflix recommendation engine are examples of reactive machines. 

Limited Memory

In addition to having the capabilities of a reactive machine, this system extracts knowledge from past events and historical data. However, they can only retain and use such information briefly. Almost all applications we have today can come under this type of artificial intelligence, including chatbots, virtual assistants, and autonomous vehicles. 

Theory of Mind

This is the next level of AI systems. It will have the same decision-making ability as a human mind, meaning it better understands the world it’s interacting with. Researchers are looking to build artificial intelligence to discern emotions, needs, and thought processes. 

Self-Awareness 

This is the final stage of the artificial intelligence system. This makes machines aware of themselves, predicting their own needs and demands. Like the theory of mind AI, no hardware or algorithms supports this system. 

Meanwhile, machine learning is an exciting subfield of artificial intelligence. This machine learning enables computers to learn and improve on their own, based on experience. The primary goal is to find hidden insights in data without explicitly programming. Instead, they use machine learning algorithms to grow, learn and develop automatically. 

As users feed new data to these algorithms, computers, and machines learn and optimise their operations. If any corrections are identified, the algorithms can integrate the data to enhance their future decision-making, developing intelligence over time. 

In particular, the learning system of these machine learning algorithms can be broken into these three components:  

• The decision process: This is where the algorithm generates an estimate about the kind of pattern in the data it’s looking to find. High-quality existing data is necessary to train the computer to understand and accurately predict.

• The error function: The role of this function is to measure how accurate the prediction of the machine learning model is by comparing it to known examples. Here, the algorithm will assess whether the decision process is proper. 

• The optimisation process: The algorithm will look at the errors. If the machine learning model can better suit the data points in the training set, the algorithm will automatically adjust the weights to minimise the disparities. It will keep assessing and optimising until the algorithm reaches an accuracy threshold.

Three Types of Machine Learning Model

Machine learning comes in several varieties, depending on the presence or absence of human influence on raw data. But primarily, they fall into three types: 

Supervised Learning

This kind of machine learning is often used to classify and predict real-world problems. It uses pre-labeled training data sets to accurately analyse classified data or predict outcomes. One example of supervised learning is training the algorithm to predict house prices. Users need to gather data about houses and their corresponding features and prices. Based on the labeled training data set, the machine will predict a new home’s prices. 

Unsupervised Learning

This is beneficial in online recommendation systems and advertising. It uses unlabeled and unstructured data sets to identify patterns and relationships within the existing data without users' help. One example is finding customer segments in marketing data. It uses neural network clustering to find natural groups in the data and create meaning. 

Reinforcement Learning

This type of machine learning is mainly used in game-playing AI or navigational robots. Instead of a training dataset, it uses a reward system to train the machine in performing a specific task. The reinforcement learning algorithm will learn through trial and error over time. One example is training self-driving cars. Users reward the machine every time it makes the right decisions, enabling the machine learning algorithms to learn what specific tasks to take.

Applications of machine learning and artificial intelligence are helpful. However, it’s essential to set realistic expectations for what each term can and can’t accomplish. We’ve highlighted the key differences between AI and machine learning below to ensure practical applications

    Artificial Intelligence
🎯
Goals    Creates intelligent machines or smart computer system that can think and do problem-solving like people
⚙️
Processes    Uses various thinking methods and forms of intelligence to solve complex problems
🔭
Scope    Has a broad scope, looking to build a multitude of complex problems
💻
Applications    AI-driven applications include Siri, Expert System, online game playing, customer support using catboats
    
    Machine Learning
🎯
Goals    Helps AI systems solve one particular problem by empowering the machine to give accurate conclusions more efficiently
⚙️
Processes    Performs only those specific tasks repeatedly on a single problem to look for patterns in the data
🔭
Scope    Specialises in a particular task, process, or program
💻
Applications    Machine learning-driven applications include Google search algorithms, online recommender system, Facebook auto friend tagging suggestions

As a subfield of artificial intelligence in computer science, machine learning is connected to most of its applications in several aspects. But essentially, artificial intelligence involves machine learning algorithms and other techniques to solve actual problems.

The all-encompassing vision is to create intuitive, intelligent systems that can perform various complex tasks independently. Meanwhile, machine learning is among the processes and tools used to achieve that goal. It operates in the background, teaching the computer system to develop and mimic human intelligence through algorithms and deep learning neural networks. 

It’s worth noting, though, that not every AI application requires machine learning. Machine learning was not part of the system when researchers first created AI. Chess playing AI using local min-max search of the gamespace technique is one of the example.

Artificial intelligence and machine learning have optimised complex processes in different fields, such as healthcare, marketing, financial services, etc. So, while one of them can exist without the other, they’re better off working together on most operations. These intelligent machines can make things seamless for the organisation utilising them. 

Plus, human-AI interaction gadgets also simplified the application of machine learning algorithms, deep learning concepts, and explicitly programmed platforms. 

Here are the benefits of AI and machine learning across various industries. 

Better and Quick Decision Making

In a fast-paced world, making quick decisions is essential for organisations today. Slow decision-making can stagnate growth, costing both time and money. While your company is still waiting for approval, competitors have already overtaken. 

This is one of the scenarios in many businesses today. The slow pace of decision-making cycles hampers their profitability and productivity. One reason for this is the number of decision-makers in a single approval step. 

Fortunately, delegating the tasks through AI and machine learning can speed up the process and help develop better decisions. They can automate repetitive undertakings, analyse trends, and provide forecasts with reduced human errors. You’d be surprised to find out that progress which often takes months or years can be achieved in weeks. 

More Data Input

Artificial intelligence and machine learning can perform tasks that humans simply cannot, such as calculating large sums or delving into enormous amounts of data. This big data is itself an asset, giving you a competitive advantage. Why is that? Every information your business collects will serve as a signal to alert you to what needs to be done in your operations. 

But while knowledge about what’s going on within your business is essential, you need a centralised place for storing and data analysis. Another problem is that processes and information can become more complex as your business grows. They can be too difficult to administer with a primary database system. This is where the intelligent system comes into play. 

An AI database can simultaneously ingest, explore, and analyse fast-moving and complex within milliseconds. Because of this, businesses can integrate more machine learning models to make more efficient and data-driven decisions. This can, in turn, result in lower costs and more revenue. 

Operational Efficiency

Having efficient operations is crucial for every business. Without them, companies may end up wasting money and effort. However, many organisations turned out to have inefficient processes. Employees often spend time on repetitive manual tasks. Technical issues like network downtime and equipment failure significantly disrupt daily operations. 

But machine learning artificial intelligence provides new ways to transform how the business operates. Their ability to automate tasks helps companies reduce their time in operational efficiencies. Companies can free up more time on growth opportunities and high-value jobs with intelligent software. 

AI and machine learning both have the power to transform your organisation and even your personal life. But it’s notable to point out that not all solutions powered by these technologies will always be effective. It greatly depends on your unique needs and how you utilise these tech innovations. Before implementing any of their applications, make sure you also have a strategic plan in place.