AI vs Machine Learning: Understanding the Key Differences and Applications

From self-driving cars to human-sounding chatbots, we’re surrounded by artificial intelligence (AI)—or is it machine learning (ML)?

People often use AI and ML interchangeably, especially when talking about modern applications such as predictive analytics or generative AI. But AI and ML aren’t the same thing. Each serves a unique purpose and use case.

What is artificial intelligence?

John McCarthy, professor emeritus at Stanford University, coined the term “artificial intelligence” in 1955. He defined it as “the science and engineering of making intelligent machines.” 

Today, AI refers to any process done by machines that traditionally requires human intelligence. 

AI is a family of technologies and scientific subjects with various subfields that allow people to develop solutions that mimic human behavior without human intervention. The subfields include computer vision, machine learning, robotics, and speech recognition. 

What is machine learning?

Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. 

In traditional software, programmers define the rules of a system, and the system adheres rigidly to those rules. In machine learning, the machine figures out the rules as it obtains more data. 

Various types of machine learning models exist, and different methods exist to train those models depending on the model’s purpose and use. 

Machine learning also has subfields, including natural language processing (NLP), artificial neural networks, and deep learning. 

“For a while, I strictly adhered to the idea that AI was defined as a machine that can think and reason for itself, which has now been classified as Artificial General Intelligence (AGI),” says Dr. Hagen Fritz, Research and Development Engineer at Rogers-O’Brien Construction. “What people call AI today is complex ML systems and rules that work together to create a fully automated system.”

Differences between AI and ML

Not only are people conflating the terms “AI” and “ML” more and more, but the terms themselves are changing in meaning. 

“ML is the ability of systems to learn patterns from data and make decisions, whereas AI is a system that can perform more complex tasks,” says Dr. Fritz. “ML is the backbone, providing the math, the rules, and the iterative process that allows AI services to churn out pre-formatted essays on any topic and exquisite photos of anything you can dream up that used to require human-like intelligence.”

Scope and purpose

ML models must be trained with large amounts of data. The purpose of machine learning is to enable pattern recognition from underlying data. 

Modern generative AI solutions are heavily dependent on machine learning algorithms. However, AI as a subject doesn’t necessarily require machine learning. 

AI is the search for tools that can match or exceed human intelligence. Such a tool could merely be a traditionally programmed decision-making system that follows a complex pattern of “if this is true, then do that” statements. Many AI systems still follow this type of pattern.

More recent AI tools now use machine learning, deep learning, and big data to improve their ability to carry out complex tasks. 

Learning and programming approaches

Machine learning involves creating systems that can learn independently when given big data to analyze. Various learning approaches exist. 

In its strictest sense, AI doesn’t need machine learning to work. This type of AI is called “GOFAI”—Good Old-Fashioned AI—or “Symbolic AI” because it uses symbols (human-readable representations of data) instead of numbers like machine-learning does. 

An example of symbolic AI code in Python might look like this:

Python

Although the above example is oversimplified, it shows the “symbols” explicitly defined in the code. The result of such a computer system is deterministic — the same inputs will always produce the same output. 

ML algorithms work differently. ML converts all data to numerical representations and then draws statistical correlations between those representations to determine patterns.  

The purpose of machine learning code is to allow systems to adapt their outputs as they receive new data. The code in a machine learning system is far less valuable than its training data.  

Modern AI solutions typically include machine learning coupled with direct programming or rules-based AI systems to solve specific tasks. 

Data dependencies

Both ML and modern AI rely heavily on data and data analysis. 

Even in symbolic AI systems, data is key. For example, to create the example diagnosis system we described above, a domain expert would need to know the various medical symptoms so they could be explicitly coded into the system. The domain expert would also need to verify the data’s accuracy so the predictive model could do its job correctly. 

When you’re not using ML, you have to know all the domain data before developing the system because that data will literally be coded into the system. When building an ML solution, data scientists can release the model with only the essential data and then add to that data without having to change the underlying code. 

Problem-solving methods

Machine learning doesn’t solve problems. It learns from data so that other tools can implement solutions.

For example, you might have a machine learning system for forecasting the effect of retail pricing. The ML system’s neural network analyzes the last 18 months of price points plus additional, enriched data to give it as much information from which to draw patterns. 

As a pure ML solution, that forecasting tool only displays the data. To add “human-like” agency to this tool, we’d need to write code that specifically acts on those forecasts. The AI tool might do one or more of the following: 

• Send alerts based on discoveries

• Automatically trigger budget changes in real-time

• Optimize employee schedules based on predictions

Real-world applications

Few AI applications exist that are purely AI or ML. Some examples of AI applications that rely heavily on ML include:

• Generative AI models

• Fraud detection systems that monitor transaction data

• Cybersecurity applications that must correlate data from multiple sources and determine patterns between them

• Data analytics solutions

ML is being used more and more in AI applications, such as the new version of the Siri voice assistant on iOS devices or in Amazon’s new shopping assistant, Rufus. 

Autonomy levels

Autonomy is limited in AI systems that depend on hardcoded decision trees instead of the insights delivered by machine learning. Older systems are built on predefined code structures that don’t change, even when presented with new data.

Agentic AI—one of the latest iterations in the AI revolution—focuses heavily on autonomy. Agentic AI tools learn from their environment and then take actions based on those learnings. Agentic AI is a combination of machine learning and AI. 

If you’d like to build an agentic AI solution, you can get help from Fiverr agentic AI programmers.

ML is not autonomous. You must program a separate system to perform specific tasks related to ML’s deductions, such as in agentic AI solutions. 

ML systems learn patterns from data but require human expertise for design, training, and deployment.

Machine-learning methods

The two broad categories of machine learning training methods are supervised and unsupervised learning. Each has a distinct purpose, and choosing one over the other depends on the model’s use case. 

Supervised learning is more common. Data scientists use labeled data so the model knows which answer is correct. The model can then iteratively adjust its internal weights until it reaches the correct answer. When given new data that wasn’t part of its training set, the model uses the same weights it was trained on to predict a result. 

Some examples of supervised learning use cases are:

• Stock predictions

• Detecting spam

• Sales recommendations

In each of the above scenarios, the model learns from example data and then uses its learnings to predict how to achieve the same “correct answer” for new data.

Supervised learning further subdivides into:

• Classification: Trains models to put data into categories

• Regression: Trains models to predict numerical values

For example, a data classification model might be used to categorize email as ”spam” or ”not spam,” or in AI image detection to determine the category of objects inside an image. A regression model could predict temperatures or stock prices. 

The other broad category is unsupervised learning.

In data science, we use unsupervised learning to train models that discover patterns in unstructured data and transform them into structured data. Unlike supervised learning, there is no “correct” answer in unsupervised learning, and the model must find connections between data on its own. 

Some examples of unsupervised learning contexts include:

• Customer segmentation based on buying behavior

• Grouping news articles by type or subject

• Identifying groups of genes

• Detecting anomalies

Another popular machine learning method is reinforcement learning, where the model receives rewards or penalties based on its actions. This is often used in robotics. 

Through some super-complicated math, a robot’s internal learning model can be programmed to aim for higher scores. When the model walks into walls or makes other errors, its internal score reduces. When it does something correctly, its score increases. This rewards and penalties system ensures the robot follows patterns reinforcing actions that result in a higher score. 

How businesses use AI and ML

Businesses across all sectors are increasingly using both technologies. 

“Reflecting on the recent strides in ML/AI research, I concede that these new tools coming out today are not strictly ‘AI,’ but they cannot be neatly classified as ML either,” says Dr. Fritz. “Thus, we need to give these tools a new name. They perform complex tasks with relative ease that, if a human were to do them, we would call it ‘intelligence.’”

Here are just a handful of examples of how AI and ML are being used across sectors. 

Healthcare and life sciences

The health sector has long used “expert systems,” which are AI systems using if-else statements like we described earlier to answer questions. These systems don’t require machine learning yet provide a rapid way for medical professionals to determine conditions based on symptoms and other data. 

More recently, the healthcare sector has been investigating the use of unsupervised learning models to categorize high-risk patients for certain diseases and to help make breakthroughs in drug research. 

Like all sectors, the industry also uses generative AI chatbots to assist patients in filling in forms more rapidly or obtaining fast answers to common questions. 

Manufacturing

Predictive maintenance is an exciting use of AI and ML in manufacturing to forecast potential failures before they occur. It works by using machine learning on sensor data and other data to determine potential weak spots. 

Manufacturing was also using AI computer vision systems to improve efficiency long before the current AI boom. 

Ecommerce and retail

One of the most popular uses of AI in ecommerce is AI chatbots. You can train an AI chatbot on your business data so that it prioritizes answers from your knowledge base. This allows ecommerce store owners to create sophisticated customer chatbots that can significantly reduce the workload for your customer service team. 

Many Amazon merchants also use AI to automatically modify their pricing based on competitor pricing and other factors. 

If you need help building an AI chatbot for your ecommerce site, check out Fiverr’s AI chatbot development services. 

Financial services 

The banking sector has been using AI and ML for decades, especially in credit scoring and fraud detection. 

Traders use various ML and AI tools to try to predict trends. 

Whether those tools result in greater profits is another matter altogether. 

However, the lack of profits in trading when using AI tools might have less to do with AI and more to do with basic economic theory. The Efficient Market Hypothesis essentially states that it’s impossible to “beat the markets” regularly and consistently unless you take extreme risks. 

Telecommunications

One key ML use case in telecommunications is to use it to enable more efficient networks, which results in better connectivity for devices. 

Benefits of using AI and ML together

It’s hard to imagine a time when AI and ML worked purely independently. Modern solutions leverage both these technologies fully. 

Some of the benefits of using AI and ML together are: 

1. Better decision-making: AI’s ability to decide and act improves based on new ML findings

2. Less maintenance: In a rules-based system, you’d need to reprogram the tool to add changes, but ML allows the statement to adapt based on new data

3. Improved scalability: Scaling becomes purely a hardware matter, and you don’t have to worry about scaling the software itself

Get AI help from Fiverr experts

AI has been with us for a long time, but we’re only seeing its full benefits now that it’s been coupled with ML and new learning algorithms.

Chatbots now routinely run on AI and ML to provide better customer service by directly answering users’ questions.

The industry is moving so fast that you might sometimes feel like you’ve missed the bandwagon. Well, let us assure you that you haven’t. Even small businesses can leverage AI in their day-to-day operations.

You don’t need to be an AI or AML expert to implement a chatbot on your website or an internal AI solution at your business. Fiverr has all the AI experts you need to do that.

To get started on your AI journey, sign up for a free Fiverr account and start searching for the right AI freelancer for your project.

AI vs ML FAQs

What is the difference between AI and ML?

AI is any technology that attempts to mimic human intelligence and perform tasks traditionally only humans could do. Machine learning (ML) is a subset of AI that attempts to draw conclusions from massive datasets. AI and ML often work together to solve specific problems. 

Is ChatGPT AI or ML?

ChatGPT is an AI tool built on top of a deep learning model. The machine learning model was trained on massive amounts of data, and the ChatGPT interface lets you interact with that data. 

Can AI work without ML?

Yes, AI can and does work without ML, although the two are increasingly working together as computing power becomes more accessible. AI without ML is sometimes referred to as GOFAI—Good Old-Fashioned AI. 

Is ML easier than AI?

Machine learning is an advanced computer science topic that requires an in-depth understanding of sophisticated mathematical concepts. AI is a broader topic that includes programmatic tools and technologies that leverage ML to solve real-world problems. Both are topics that require advanced study to understand fully.