AI vs ML Explained: Differences & How They Work

AI vs ML: Differences and how they work

When you hear about artificial intelligence (AI), you often come across various new acronyms. One common term you frequently hear related to AI is machine learning (ML). Although ML and AI are sometimes used interchangeably, they have distinct roles and definitions. 

Machine learning is actually a subset of AI. So, while AI can refer to a wide range of different tools and methods, ML is a specific approach that uses computer algorithms to analyze and manage your data. Let’s look at what these terms mean and how they’re used today. 

What is artificial intelligence?

Artificial intelligence involves using computers with various tools and techniques to simulate human decision-making. While we often refer to AI as a technology, it’s really a broad field made up of many different technologies and techniques that aim to achieve human-like problem-solving capabilities and cognitive functions. 

While AI is being broadly applied across industries, products, and sectors, its most basic function is to analyze data, identify patterns, and use that data ways that mimic human decision-making. It goes far beyond basic computer programming based on the complexity of the tasks and problems it’s being used to solve. 

What is Machine Learning?

Machine learning is one branch among many within the field of artificial intelligence. As its name suggests,  ML involved developing computer algorithms and tools to perform a specific task and then improve future outputs by analyzing additional data. ML can be used on its own or combined with other AI tools to achieve specific objectives. 

The goal of ML is to train computer models with large amounts of data. These ML models look at the data to identify patterns and use that information to perform complex tasks without specific instructions. For example, ML models can be trained on historical data and then used to predict trends or forecast future outcomes. 

ML can be divided into two main subsets: supervised and unsupervised learning:

• Supervised learning

In supervised ML, the algorithms are trained on the input and output data. This means the algorithm is trained on what the data looks like coming in and what the expected result should be. These supervised models are then applied to new datasets to determine the correct output. These types of models are commonly used to classify data, such as  detecting and blocking spam in your email. 

• Unsupervised learning

Unlike supervised learning, unsupervised learning is trained on data that is not labeled. These ML models analyze and group data based on patterns they find. This can be particularly useful when analyzing massive datasets to discover patterns that have not yet been recognized. For example, these algorithms can be used to analyze customer data and segment customers based on patterns. Or they could be used to comb through financial transactions to identify and flag fraud patterns. 

How AI and ML are connected

As mentioned earlier, though AI and ML are often used interchangeably, they do have different meanings and functions. AI is a broad concept and field, while ML is just one of the many ways to apply AI to your data. 

As ML algorithms become more refined, intricate, and complex, interacting with other ML algorithms or AI techniques, the connection between AI and ML grows stronger. ML algorithms designed for one purpose harness relationships with other ML algorithms to solve more complex problems and emulate cognitive decision-making. 

Differences of AI vs. ML

The following table outlines some key differences between AI and ML. 

Benefits of using AI and ML together

While any use of ML is a form of AI, the real benefits come from using both AI and ML together. By combining tools, techniques, and actions, you can achieve your desired output much more effectively and efficiently. Here are some big benefits of using AI and ML together: 

Massive data set analysis

No human can process data the way machines can. By harnessing the power of AI and ML combined, you can process, classify, or group massive data sets. This enables you to discover valuable insights and help make informed decisions based on that data.

Problem-solving

One big problem with some types of data is the delay between analysis and action. By using AI and ML together, you not only get to analyze the data but can take specific steps based on the insights you’ve gained.  This ensures that your organization can respond and adapt quickly to nuances and fluctuations in the data. 

Enhanced job performance

When people discuss AI or ML, they often talk about AI taking over people’s jobs. The reality is that, given the current state of  AI, it’s much more likely to enhance people’s jobs rather than take them over. By utilizing ML to perform routine and repetitive tasks, employees can focus on more strategic or complex problems to support a business. 

Application across industries

One key benefit of AI and ML is that they aren’t confined to a single industry but have a variety of use cases across different fields that will continue to allow for wide adoption and use. 

Use cases and examples of AI and ML

One way to better understand AI and ML is to see how they’re being used today across different businesses and applications. 

Writing

ChatGPT’s release helped usher in a new wave of awareness and interest in AI. ChatGPT is often associated with the term generative AI, which at its core relies on ML models to function. These models are trained to recognize patterns and structures in the data they are provided. They are then capable of generating new information for the user in a similar format that aligns with the recognized input. 

Manufacturing

One benefit of utilizing AI and ML is increased efficiency. Because these tools can analyze more data than humans can, they can identify unique patterns and areas to improve processes. This is especially useful in complex processes like manufacturing. By integrating AI and ML into manufacturing, companies can better identify and predict bottlenecks, prepare for supply chain disruptions, and manage machine maintenance. 

Finance

Many financial organizations are excited about the application of AI and ML to help them identify and prevent fraud in the financial services industry. However, there are wider applications for AI in this field. For example, some financial services companies use ML models to analyze stock market data so they can  automate investment and trading decisions. Others use AI tools to more easily and securely authenticate users to accounts or enhanced services. 

Healthcare

Much of the attention surrounding the use of AI in healthcare is on improving patient outcomes and diagnosis. While AI is contributing to these areas and will continue to do so, some of the biggest healthcare advances using AI and ML are in pharmaceutical development. 

For example, some companies employ ML to analyze vast quantities of data related to drug development and clinical trials. This analysis allows companies to better understand new and beneficial applications for drugs currently on the market or in development. 

E-commerce

One field that greatly benefits from the near real-time power of AI is e-commerce. By applying ML and AI models to real-time customer data, organizations can better segment customers based on subtle patterns within the data. This enables them to provide personalized recommendations and efficiently manage supply chains to meet both current and future demands. 

Marketing

Using AI and ML to support marketing departments allows teams to better understand customer needs and forecast trends. It also allows teams to automate and streamline simple, everyday tasks like reporting, testing, and analyzing so marketers can focus on more strategic initiatives. 

Customer service

Many call centers try to find ways to replicate the success of their best agents across the organization. Some tools use AI and ML to train and guide employees through dynamic prompts and support scripts that adapt based on the unique needs of each caller. These tools rely on multiple layers of ML algorithms that are integrated to form a neural network. This allows customer service reps to have a valuable tool to help them problem-solve and support unique needs of individual customer. 

Understanding the distinctions between artificial intelligence and machine learning is crucial for leveraging their combined potential. As AI continues to evolve, the integration of ML will drive innovation across diverse fields, from healthcare to finance and beyond. By harnessing the strengths of both, organizations can unlock deeper insights, enhance decision-making, and foster efficiencies that were previously unimaginable. Embracing this synergy not only prepares businesses for the future but also empowers them to navigate the complexities of an increasingly data-driven world.

Find out how Domo can help your business embrace AI and ML tools.