What’s the difference between AI and Machine Learning?

Artificial intelligence (AI) is an umbrella term for different strategies and techniques you can use to make machines more humanlike. AI includes everything from smart assistants like Alexa to robotic vacuum cleaners and self-driving cars. Machine learning (ML) is one among many other branches of AI. ML is the science of developing algorithms and statistical models that computer systems use to perform complex tasks without explicit instructions. The systems rely on patterns and inference instead. Computer systems use ML algorithms to process large quantities of historical data and identify data patterns. While machine learning is AI, not all AI activities are machine learning.

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What are the similarities between AI and machine learning?

Machine learning (ML) is a narrowly focused branch of artificial intelligence (AI). But both of these fields go beyond basic automation and programming to generate outputs based on complex data analysis.

Humanlike problem-solving

Artificial intelligence and machine learning (AI/ML) solutions are suited for complex tasks that generally involve precise outcomes based on learned knowledge.

For instance, a self-driving AI car uses computer vision to recognize objects in its field of view and knowledge of traffic regulations to navigate a vehicle.

A property pricing ML algorithm, for example, applies knowledge of previous sales prices, market conditions, floor plans, and location to predict the price of a house.

Computer science fields

Artificial intelligence and machine learning are fields of computer science that focus on creating software that analyzes, interprets, and comprehends data in complex ways. Scientists within these fields attempt to program a computer system to perform complex tasks that involve self-learning. A well-designed software will complete tasks either as fast as or faster than a person.

Cross-industry applications

There are applications of AI across all industries. You can use AI to optimize supply chains, predict sports outcomes, improve agricultural outcomes, and personalize skincare recommendations.

ML applications are also broad. They can include predictive machinery maintenance scheduling, dynamic travel pricing, insurance fraud detection, and retail demand forecasting. 

Key differences: AI vs. machine learning

Machine learning (ML) is a specific branch of artificial intelligence (AI). ML has a limited scope and focus compared to AI. AI includes several strategies and technologies that are outside the scope of machine learning.

Here are some key differences between the two.


The goal of any AI system is to have a machine complete a complex human task efficiently. Such tasks may involve learning, problem-solving, and pattern recognition.

On the other hand, the goal of ML is to have a machine analyze large volumes of data. The machine will use statistical models to identify patterns in the data and produce a result. The result has an associated probability of correctness or degree of confidence.


The field of AI encompasses a variety of methods used to solve diverse problems. These methods include genetic algorithms, neural networks, deep learning, search algorithms, rule-based systems, and machine learning itself.

Within ML, methods are divided into two broad categories: supervised and unsupervised learning. Supervised ML algorithms learn to solve problems using data values labeled input and output. Unsupervised learning is more exploratory and attempts to discover hidden patterns in unlabeled data. 


The process of building an ML solution typically involves two tasks:

  1. Select and prepare a training dataset
  2. Choose a preexisting ML strategy or model, such as linear regression or a decision tree

Data scientists select important data features and feed them into the model for training. They continuously refine the dataset with updated data and error checking. Data quality and variety improve the accuracy of the ML model. 

Building an AI product is typically a more complex process, so many people choose prebuilt AI solutions to achieve their goals. These AI solutions have generally been developed after years of research, and developers make them available for integration with products and services through APIs.


ML solutions require a dataset of several hundred data points for training, plus sufficient computational power to run. Depending on your application and use case, a single server instance or a small server cluster may be sufficient.

Other intelligent systems may have varying infrastructure requirements, which depend on the task you want to accomplish and the computational analysis methodology you use. High-computing use cases require several thousand machines working together to achieve complex goals.

However, it’s important to note that both prebuilt AI and ML functions are available. You can integrate them into your application through APIs without the need for additional resources.

What would an organization need to get started with AI and machine learning?

If you want to use artificial intelligence (AI) or machine learning (ML), start by defining the problems you want to solve or research questions you want to explore. Once you identify the problem space, you can determine the appropriate AI or ML technology to solve it. It’s important to consider the type and size of training data available and preprocess the data before you start. 

With on-demand cloud services, you can create, run, and manage AI. And learning functions can be created, run, and managed from the Amazon Web Services (AWS) Cloud.

How can organizations use AI and ML?

Some machine learning (ML) solutions apply to most organizations:

And here are artificial intelligence (AI) solutions that apply to most organizations:

Summary of differences: AI vs. machine learning



Artificial Intelligence

Machine Learning

What is it?

AI is broad term for machine-based applications that mimic human intelligence. Not all AI solutions are ML.

ML is an artificial intelligence methodology. All ML solutions are AI solutions.

Best suited for

AI is best for completing a complex human task with efficiency.

ML is best for identifying patterns in large sets of data to solve specific problems.


AI may use a wide range of methods, like rule-based, neural networks, computer vision, and so on. 

For ML, people manually select and extract features from raw data and assign weights to train the model.


AI implementation depends on the task. AI is often prebuilt and accessed via APIs.

You train new or existing ML models for your specific use case. Prebuilt ML APIs are available.


How can AWS support your AI and machine learning requirements?

AWS offers a wide range of services to help you build, run, and integrate artificial intelligence and machine learning (AI/ML) solutions of any size, complexity, or use case.

Amazon SageMaker is a complete platform to build your ML solutions from the ground up. SageMaker has a full suite of prebuilt machine learning models, storage and compute capabilities, and a fully managed environment.

For AI, you can use AWS services to build your own AI solutions from scratch or integrate prebuilt artificial intelligence (AI) services into your solution. 

Next Steps with AWS

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Start building with Machine Learning