Machine Learning vs Artificial Intelligence

Artificial intelligence is a broad field of research which seeks to create systems that mimic or even surpass human intelligence. Machine learning is an approach to system or model development which sees the machine learn from the data itself without direct human control. It means an algorithm can reach a level of complexity and flexibility possible from a system designed by a human programmer. Machine learning can be seen as a subset of artificial intelligence, but the two are intrinsically linked. Because of the incredible complexity of a system with high levels of artificial intelligence, machine learning will be a key part of any artificial intelligence evolution.  

Research into artificial intelligence is in its (relative) infancy. Any artificial intelligent system in existence today is defined as a ‘weak’ artificial intelligence, in that it is deemed less intelligent than a human. Such a system may be powerful when performing specific tasks, but will lack the ability to perform a wide range of tasks like a human. Elements like self-awareness and rationality might be a prerequisite to achieving true artificial intelligence. This shows how artificial intelligence has a much wider range of considerations than machine learning, which is mainly focused on solving a specific optimisation problem. 

Machine learning has enjoyed a boom in popularity and application in recent years, driven by the sheer amount of data in the modern world and an increase in computing power. These advances have also greatly benefited the field of artificial intelligence. Although artificial intelligence is a much broader field, the two are intrinsically linked and benefit from the same technological advances in the modern world. These advances mean machine learning and artificial intelligence have evolved considerably in the last few years. 

This guide explores the main differences between machine learning and artificial intelligence, as well as providing an explanation for both fields of research. The similarities and interdependencies of the two areas are also explored. 

What is machine learning?

Machine learning is the development of algorithms through a process in which the model learns from the data itself with no direct  human coding. This approach replaces the need for a human programmer to code every line of a system. Instead, the algorithm learns the most efficient way of performing a task by processing large arrays of training data. These models need vast amounts of training data to achieve accuracy, but can perform more complex tasks than a human-written algorithm. It is this efficiency in developing algorithms that makes machine learning a key component in the development of artificial intelligence.  

Machine learning is already used in a variety of settings and environments. Models excelsin performing tasks in data-rich environments, so already power many customer-serving chatbots, natural language processors, and product recommendation systems. Algorithms are already in use in a range of settings, and the popularity of this approach will only increase. Generally, the strength of machine learning lies in classification tasks, predicting and understanding trends in data, and in the automation of menial tasks.  

The main different types of machine learning are: 

  • Supervised machine learning 
  • Unsupervised machine learning 
  • Reinforcement machine learning 

Each type has a different approach to training the algorithm, and as a result a different need for data and a different final application. Supervised machine learning uses labelled training data to understand the relationship between data points. The input and output of the training data must be labelled, which is usually an intensive process led by human data scientists. The trends and patterns learned by the model on training data can then be applied to new and unseen data. Supervised machine learning can be used to classify objects or predict continuous outcomes such as market forecasts. 

Unsupervised machine learning on the other hand is used to discover trends and segments in  unlabelled or raw data. Unlike supervised machine learning, the unsupervised approach learns from unlabelled data. It’s used to understand relationships within the dataset, and is often deployed to segment audience data or discover underlying market trends. The main applications of unsupervised machine learning models is to cluster data into different groups and to discover how data features relate to other data features. It is also used early in the machine learning model lifecycle as part of the exploratory data analysis stage. 

Finally, reinforcement machine learning takes a trial and error approach to learn how to perform tasks in a specific scenario. The model will learn and improve based on success of past actions, with successful tasks releasing reward signals. The algorithm learns the optimal way to perform a task or make a decision. Reinforcement machine learning models are often used to power self-driving car systems or chessbots. The approach reflects human intelligence in our ability to learn and improve from trial and error, and the result is a system that can perform complex actions in different environments.   

What is artificial intelligence?

Artificial intelligence is a field that focuses on developing ‘intelligent’ machines used to solve problems or make decisions. Simple artificial intelligence systems may perform basic tasks. But the final aim for true artificial intelligence is to achieve intelligence in machines that mirrors or surpasses human intelligence. This could be a system or machine that learns from experience, generalises to perform tasks in a new environment, or rationalises its decision-making process. Research into artificial intelligence focuses on different elements of what can be defined as intelligence. This definition is influenced by what is understood to make human intelligence unique.  

The pursuit of true artificial intelligence would mean a system must:  

  • Learn from past experiences, and apply this knowledge to new tasks and environments.  
  • Use reasoning and rational thought processes to reach conclusions which are relevant to different scenarios. 
  • Be able to solve wide ranging problems instead of just specific tasks. 
  • Understand complex language and be able to interact with others. 

One of the key elements of current artificial intelligence research is the ability for systems to generalise. Today, systems and algorithms are already used to perform complex tasks to very high efficiency. In many specific applications, an algorithm can even surpass a human’s performance and accuracy. This is usually in data-rich environments where algorithms have been trained to categorise images or process documents. However, it is the issue of generalisation which remains a hurdle to artificial intelligence. This is the ability for a system to perform a task over different domains or environments, or to apply past experience to new or related tasks. 

There are three main types of artificial intelligence, each describing the relevant strength or power of artificial intelligence. Currently, only the weakest type of artificial intelligence exists. The two higher forms of artificial intelligence are for when (or if) systems mirror then surpass human intelligence.  

The three types of artificial intelligence are: 

  • Artificial Narrow Intelligence (ANI) 
  • Artificial General Intelligence (AGI) 
  • Artificial Super Intelligence (ASI) 

What is Artificial Narrow Intelligence (ANI)?

Artificial Narrow Intelligence is defined as weak artificial intelligence, the type that exists in the world today. Weak artificial intelligence systems may perform one specific task very well, such as categorising images, identifying objects, or playing a video game with defined rules. The virtual assistants found in Google Home, Alexa or Siri will be powered by ‘weak’ artificial intelligence. This is because the intelligence of these systems are ‘weak’ in comparison to the scale of human intelligence. 

Although these systems may be incredibly efficient at performing a narrow task, they lack the ability to generalise like human intelligence. The algorithm and task may be complex, but the range of ability will be narrow. In many cases, these systems will perform specific tasks better than a human could. Applications like data processing, clustering or object identification can be performed on a larger scale and more efficiently than if a human were to perform the task. However, the level of intelligence is still relatively low. These systems aren’t thinking for themselves, but instead just performing a predetermined function.  

What is Artificial General Intelligence (AGI)?

Artificial General Intelligence is the next evolution in artificial intelligence, and is described as ‘strong’ artificial intelligence. It will occur when a system is on the same level of ability when compared to human intelligence. General artificial intelligence would mean a machine can perform tasks to the same degree as a human. This includes the ability to learn from past experiences and generalise to complete new tasks in different environments. 

Instead of only performing specific tasks, a machine with this level of artificial intelligence would be able to complete any task a human can perform. Instead of narrow intelligence, this will mean rationality, creativity, generalisation, and using previous experience in decision making. An important element of this will likely be self-awareness, at which point a machine is conscious of itself. We have not yet reached this level of artificial intelligence.  

What is Artificial Super Intelligence (ASI)?

Artificial Super Intelligence is the final step in the evolution of artificial intelligence. It will occur if a system surpasses the intelligence of a human, with a range of ability that goes beyond a human’s. This would mean a machine could plan and solve problems beyond the ability of any human. This stage of artificial intelligence would be incredibly powerful and would likely change the course of human progress.  

Is machine learning the same as artificial intelligence?

Machine learning can be seen as a component of artificial intelligence, as it is a subsection of the wider field of research. One of the main aims of artificial intelligence is to achieve a rational system that can perform complex tasks using previous knowledge. An important element of this is how the system learns to perform tasks. Human programmers can write code to complete specific tasks, but it would be impossible to manually code an algorithm with the complexity required to achieve artificial intelligence. Machine learning is a system or model learning how to perform a task from the data itself, so it is an integral part of constructing artificial intelligence algorithms. This process of learning from past experience is integral to achieving artificial intelligence.  

As machine learning and artificial intelligence are intrinsically linked, they’ve faced similar evolutions. Recent improvements in computing power and the increase in availability of big data is a boost to both artificial intelligence and machine learning development. Both have been concepts for many years, but the exponential growth in computing power and data has supercharged research. Our modern world is processing and collecting more data than ever. The accuracy of complex systems using machine learning is generally scalable alongside the amount of training data and computing power that is available. In particular, improvements in GPU processing have helped to power neural network architecture found in deep learning 

Deep learning and neural networks are subsets of machine learning. The approach aims to mirror the function of the human brain through ‘deep’ digital neural networks, as the structure for complex machine learning algorithms. The structure is deceived as ‘deep’ because it has many different hierarchical processing layers. Each layer can recognise object features of varying abstraction levels, and is trained from the data without direct human control. Deep learning allows systems to understand and identify a hierarchy of features, building an understanding of complex concepts. Deep neural networks are leading to breakthroughs in speech recognition and image recognition, and play an important role in artificial intelligence research. 

Difference between artificial intelligence and machine learning

The main difference between artificial intelligence and machine learning is the scope of each field of research. Artificial intelligence research has the aim of creating a machine that can mimic or even surpass human intelligence. This means it’s a wide ranging field, as the scope of the task is incredibly complex. There are a variety of elements which make up human intelligence, many of which are hard to define. Self-awareness, generalisation, and reasoning are some of the considerations within artificial intelligence. As machine learning is a subset of artificial intelligence, the scope is naturally more focused. It is the process whereby a machine learns and evolves from data instead of being developed by a human programmer. Machine learning is therefore a key part of training machines within the field of artificial intelligence, as self-sufficient learning is a key part of developing intelligence. But it means that each field has distinct goals and considerations.  

The major differences between artificial intelligence and machine learning include:  

  • Different overall goals and aims. Artificial intelligence looks to simulate human intelligence, whereas machine learning focuses on machines automatically learning from data. 
  • Different applications and areas of deployment. Artificial intelligence is a test of a system’s ability to generalise, applying historic experience to a broad array of new tasks. Machine learning on the other hand is generally applied to specific tasks and problems.
  • Different ways of categorising each field. The three types of artificial intelligence are based on the capability of the system relative to a human mind. As explained earlier in this guide, the three main types are categorised from weak to strong in relation to human intelligence. On the other hand, machine learning is categorised depending on the approach to training the model, for example supervised vs unsupervised machine learning approaches. 

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