Blog Post

Understanding the Differences and Benefits between Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) may be the among the most over-hyped buzzwords of the last decade. Still, behind the hype, these technologies have tangible and meaningful applications that can help companies gain insights into large volumes of unstructured data, support predictive decision-making, improve customer experience and detect vulnerabilities, among other things. Given the accelerating pace of change within organizations and across technology advancements, it can be difficult for business leaders to stay abreast of the benefits and risks associated with AI and ML as well as the key differences between these evolving technologies.

AI vs ML – What’s the difference?

So, what are the key differences? Simply put, AI is a field of data science in which intelligent capabilities, such as perception, reasoning and decision-making, are programmed (via algorithm) into computers. ML is rather a technological capability that refers to the autonomous recognition of patterns and regularities.

There are four types of AI:

  1. Reactive: reactive machines cannot learn, store input or evolve. They are programmed for a purpose and can perform the associated tasks. An example is the IBM chess computer DeepBlue, which calculates the fastest path to checkmate based on given situations.
  2. Limited memory: this type of AI can store input and operates with limited memory. This is the most common form of AI, while Type 3 and 4 are experimental. Examples include Google search, personal assistants in smartphones and self-driving cars.
  3. Theory of mind: theory of mind is intended to replicate the processes of the human brain. Understanding emotions is an essential factor. Type 3 AIs learn and can improve automatically based on experiences and learnings.
  4. Self-awareness: in this experimental and theoretical form, machines know that they exist and have the ability to think and possess emotions.

ML on the other hand, is a subset of AI, in which the machine recognises patterns and regularities. There are three types of ML:

  1. Supervised learning: when an AI applies this form of ML, it is actively supervised while learning. It receives data (inputs) from which to learn and example results (outputs). Later, it can predict outcomes from new data. For example, supervised learning uses image and spam detection programs and media recommendation systems. Examples include Spotify and Netflix. Another example is a web application firewall (WAF) that protects a web application from attacks. The WAF is told what “normal” input is. Anything more, the machine classifies as attacks and sounds an alarm.
  2. Unsupervised learning: unsupervised learning is not about the machine recognising expected predictions. Instead, it learns without a target variable and independently detects patterns in the data. For example, a machine independently learns about a complex enterprise network to alert on future anomalies (possible attacks). Another example is machine learning of customer data to identify patterns in behaviour and use the insights to inform marketing strategies or business decisions.
  3. Reinforcement learning: in this form, the machine interacts in an environment and performs actions. It receives positive and negative rewards for the efforts. Through the reward system, it learns to improve performed steps to maximise positive rewards. One example is Google's DeepMind AI, which learns to run with reinforcement learning. Other examples include machines for automated stock trading or self-learning industrial robots.

An important subset of machine learning is deep learning, in which the machine emulates human neural networks. The learning process takes place without any previously processed data and is done by independently processing large amounts of unstructured data. While many machine learning algorithms have only two layers (input and output), deep learning is based on multiple hidden layers. For example, via deep learning, a machine can learn natural language processing.

Machine code travelling

Applying AI and ML

AI and ML have numerous use cases in a wide range of industries. They are often used to increase efficiency or improve IT and information security processes.

For example, conversational AI ensures that self-service processes are optimized to improve customer satisfaction. The AI learns which outputs should be made depending on customer input via ML. One example is customer service via a chatbot. Customers quickly get an answer to their questions without the need for customer service staff to take every call.

AI can also help extract and analyse information from important documents. This feature can be useful in analysing medical records, grouping similar issues from a large volume of complaint letters, finding key evidence in an investigation and more. Another possible application is in learning and monitoring manufacturing processes. AI can predict bottlenecks, improve manufacturing quality, and evaluate the safety of operations.

In an information security scenario, AI and ML can help detect, prevent and respond to incidents and vulnerabilities. The technology can perform a risk analysis, recommend actions or take action independently, which allows the IT and information security team to improve processes, efficiency and accuracy, even when dealing with a large volume of inputs.

However, the use of AI carries risks as well and these technologies can be exploited. The BSI (German Federal Office for Information Security) has published standards to assess the security of AI-based systems operating in the cloud. These standards provide an important framework for evaluating solutions and ensuring they are adequately protected.


Ultimately, AI and ML are more than hype. They can improve processes, unlock new data and make IT more effective. The key to unlocking the benefits is to understand the capabilities, set clear objectives for how they will be utilised and address potential risks at the outset of implementation.

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The views expressed herein are those of the author(s) and not necessarily the views of FTI Consulting, its management, its subsidiaries, its affiliates, or its other professionals.