Artificial intelligence (AI) and machine learning (ML) are two of the most talked about topics in tech in recent years. Although sometimes spoken about as if they are one and the same, artificial intelligence and machine learning have their own unique characteristics, applications, and benefits.

For business, these benefits are real and tangible. In fact, Harvard Business Review says machine learning is now fundamental to improving customer experience, enabling customers to receive better recommendations and higher quality search results. Meanwhile, McKinsey Digital predicts that 75% of the value created by generative AI will be in customer operations, marketing and sales, software engineering, and R&D.

Before exploring in more detail how companies are using AI and machine learning to their advantage, first it is necessary to understand the differences between AI and machine learning and how they work together to drive business transformation and standout customer experiences.

What is artificial intelligence (AI)?

According to Gartner, “Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.”

AI applications

Artificial intelligence is a broad field of computer science that aims to create systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, problem-solving, natural language processing (NLP), recognizing patterns, and decision making. AI systems are designed to enable automation by behaving in the same way as the human brain — adapting to different scenarios and learning from experience to improve their performance over time.

AI can be categorized into two main types: Narrow AI (or Weak AI) and General AI (or Strong AI). Narrow AI refers to AI systems that are specialized in performing specific tasks. Examples of narrow AI include cloud-based virtual assistants like Apple’s Siri and Amazon’s Alexa. On the other hand, General AI refers to a theoretical form of AI that possesses human-like intelligence and can perform any intellectual task that a human can.

What is machine learning (ML)?

Defined in the 1950s by AI pioneer Arthur Samuel, machine learning is the “field of study that gives computers the ability to learn without explicitly being programmed.”

Today we encounter machine learning in our everyday lives when we engage with chatbots, choose predictive text on email and messaging apps, use language translation apps, and on streaming platforms such as Netflix and Spotify when we are served recommendations based on our previous content choices.

How does machine learning work?

Machine learning is a subset of AI that focuses on developing algorithms and techniques that enable computers to learn from data. Rather than being programmed to perform a task, ML systems learn from examples. Using machine learning models created from machine learning algorithms, these ML models are trained with a learning algorithm that is provided with training data, so that performance can be improved through experience.

Machine learning enables computer systems to learn to make predictions from observations and data and can use the information provided by the study of big data to generate valuable business insights. For this reason, one of the most important machine learning applications is within the field of data science. Data scientists use machine learning to speed up the process of trend analysis, predictions, and forecasting.

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

In supervised learning, algorithms are trained on labelled data, where the input-output pairs are provided. Unsupervised learning involves training algorithms on unlabelled data to discover patterns or structures within the data. Reinforcement learning is centered around training agents to take actions in an environment to maximize a reward, learning through trial and error.

What is the difference between AI and ML?

Although correlated, artificial intelligence and machine learning offer very different capabilities. While the goal of AI is to create intelligent machines that can simulate human capabilities and behavior, machine learning is an application or subset of AI that enables machines to learn from data without being programmed. Here are the main capabilities of AI and machine learning at a glance:

Artificial intelligence capabilities

  • AI enables a machine to simulate human intelligence to solve problems
  • AI’s goal is to create an intelligent system that can perform complex tasks
  • AI builds systems that can solve complex tasks like a human
  • AI has a broad range of applications
  • AI employs technologies in a system to mimic human decision-making
  • AI works with different types of data including structured, semi-structured, and unstructured
  • AI systems use logic and decision trees to learn, reason, and correct themselves

Machine learning capabilities

  • ML allows a machine to learn autonomously from past data
  • ML’s goal is to build machines that learn from data to increase the accuracy of output
  • Machines are trained with data to perform specific tasks and deliver accurate results
  • Applications for machine learning are relatively limited
  • Machine learning employs self-learning algorithms to produce predictive models
  • ML is only capable of using structured and semi-structured data
  • ML systems require statistical models to learn and self-correct when they are provided with new data

How machine learning works with AI

As already illustrated, machine learning and artificial intelligence are different concepts, but closely intertwined. Machine learning techniques provide the foundation upon which many AI applications are built. AI systems use ML algorithms to learn and adapt, enabling them to perform tasks that would otherwise require explicit programming.

Take for example, a virtual assistant like Siri. Siri's ability to understand and respond to natural language queries is powered by both AI and ML. The AI component allows Siri to comprehend the nuances of human language, while the ML aspect enables it to improve its speech recognition over time by learning from the interactions it has with users.

Simply put, AI provides the overarching framework and goal, while machine learning provides the tools and methodologies to achieve that goal by enabling systems to learn from data.

Large language models used to train AI

A large language model (LLM) is an artificial intelligence (AI) algorithm that employs deep learning techniques and large data sets to understand, summarize, generate, and predict new content.

In recent years, large language models have emerged as a significant advancement in the field of generative AI. These models, such as OpenAI's ChatGPT are capable of understanding and generating large amounts of text. They have been trained on extensive text data and can perform tasks like language translation, content generation, answering questions, and even writing code snippets.

Large language models work by using a neural network architecture that consists of multiple layers of interconnected nodes, or "neurons." Each layer processes the input data and passes it to the next layer, with each layer extracting higher-level features from the data. During the training process, the model adjusts its internal parameters to minimize the difference between its predictions and the actual target values in the training data.

How companies are using AI and ML

Real-world use cases for AI and ML are increasing by the day. Across every industry, start-ups and established companies are leveraging improvements in AI and ML to fulfil complex tasks, transform workflows, and deliver value. Here are some of the most common applications of AI and ML:

  1. Customer experience enhancement: Companies are using AI-powered chatbots and virtual assistants to provide instant customer support, enhancing user experiences through personalized interactions and efficient issue resolution.
  2. Data analysis and insights: ML algorithms are employed to analyze large datasets and extract meaningful insights. This helps companies make informed decisions, predict trends, and identify opportunities for growth.
  3. Healthcare diagnostics: AI is being utilized in medical imaging to assist doctors in diagnosing diseases like cancer. Machine learning algorithms can analyze medical images to identify patterns that may be difficult for human eyes to discern.
  4. Financial services: AI algorithms are used for fraud detection and do so in real-time by analyzing transaction patterns and identifying anomalies.
  5. Autonomous vehicles: ML is crucial in the development of advanced robotics and self-driving cars. Autonomous vehicles use computer vision (a type of AI), machine learning, and real-time data from sensors to navigate and make split-second decisions to ensure passenger safety.
  6. Manufacturing and supply chain optimization: AI-driven predictive maintenance helps manufacturers anticipate equipment failures and optimize maintenance schedules, reducing downtime and increasing efficiency.
  7. Content recommendation: Online content providers, streaming services, and e-commerce brands use ML to analyze user preferences and behavior to provide personalized content recommendations, increasing user engagement. Using unstructured data, marketers can also gain an understanding of which content and keywords to continue using.
  8. Language translation: AI-powered translation tools enable companies to communicate with global audiences effectively, breaking down language barriers.
  9. Cybersecurity: AI and ML algorithms are used for analyzing network traffic, URLS, and DNS requests to recognize and block malicious websites, phishing attempts, malware downloads and other cyber threats.
  10. DevOps: AI and ML can help to streamline DevOps processes in a number of ways by enabling continuous testing, automation, and performance monitoring.
AI and ML: Reshaping the future

With the ability to mimic human intelligence, artificial intelligence and machine learning are already working together to power various applications that have transformed everyday experiences.

As AI and ML continue to advance, so too does their potential to reshape industries across the board, from marketing and manufacturing, to software development, retail, finance, agriculture, transportation, and healthcare.