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What’s the difference between artificial intelligence and machine learning?

The terms "machine learning" and "artificial intelligence" are often used interchangeably. Here are key differences between the two technologies transforming modern businesses.

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Artificial intelligence vs. machine learning

Artificial intelligence (AI) and machine learning (ML) are similar in attributes but not quite the same. While computer systems use AI to replicate human functions and perform tasks independently, ML is the system’s process of developing intelligence to improve processes over time based on experience and data consumed.

How AI and ML interact with each other

Even though AI and ML are different, they work together:

  1. The AI system is developed by utilizing ML and other techniques.
  2. ML models are created by observing patterns in data. 
  3. Data scientists enhance ML models that are established based on patterns in data. 
  4. The process is repeated and improved until the system can accurately and effectively execute tasks. 

Combining AI and ML for better outcomes

AI and ML work well as separate entities, but they provide more value to organizations when combined to improve processes and products — transforming business operations. Here are a few capabilities enabled by this combination:

  • Predictive analytics: This helps organizations predict customer behavior and trends based on dynamic data sets.
  • Speech and language recognition: Systems can identify and find meaning within spoken and written language.
  • Image and video processing: Visual search can recognize and derive meaningful information from visual inputs.

Learn more | Business Process Management

The differences between AI and ML

AI is an intelligent entity that uses datasets to solve tasks, while ML is a subfield of AI that solves tasks by making classifications or predictions based on algorithms and statistics. The differences between AI and ML can typically be seen in their goals, processes and applications.

  • Goals:

Artificial intelligence: The intended goal of AI is to solve problems, answer questions and complete human-related tasks. The system should function independently if it is supplied with data sets. AI is applied to systems for analysis, interpretation and prediction.

Machine learning: The goal is to help AI systems solve a single problem faster and more effectively through centralized and specialized targeting.

  • Processes:

Artificial intelligence: AI essentially utilizes different forms of intelligence to arrive at solutions for multiple problems. As AI mirrors human intelligence, it reviews, operates and responds to situations as humans do.

Machine learning: The process of ML is iterative, repetitive and requires running the same problem repeatedly to identify patterns in data that will help solve the issue quickly and with greater accuracy.

  • Application:

Artificial intelligence: AI-powered programs interpret tasks that need to be executed and create solutions based on the instructions and responses collected from datasets. They can accomplish a range of tasks like scheduling an appointment, looking up something on the internet and providing directions.

Machine learning: ML-driven systems are programs tasked with a specific function like providing more recommendations for a similar product that a customer purchased. ML is focused on uncovering patterns within a larger data set and applying those learnings to make suggestions.

Subfields of AI: Machine learning vs. deep learning

ML and deep learning are often misconstrued as the same subfield, but there are components that differentiate them.

ML uses methods from neural networks, statistical data and research to find hidden insights from human-structured data. The process of ML is more dependent on human intervention as data inputs like the hierarchy of features require manual sorting.

Deep learning is a subfield of ML. It uses huge neural networks that comprise more than three layers of inputs, utilizing a much larger data set than ML. The process of deep learning involves the automation of the feature extraction piece, which eliminates more manual intervention. Deep learning does not require labeled datasets as it can analyze data in its raw form, including text and images. It automatically determines the hierarchy of features that differentiates one data category from the other.