Artificial intelligence (AI) vs. machine learning (ML)
Understand the difference between AI and machine learning with this overview.
The difference between AI and machine learning
Artificial intelligence and machine learning are very closely related and connected. Because of this relationship, when you look into AI vs. machine learning, you’re really looking into their interconnection.
What is artificial intelligence (AI)?
Artificial intelligence is the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate the reasoning that people use to learn from new information and make decisions.
Are AI and machine learning the same?
While AI and machine learning are very closely connected, they’re not the same. Machine learning is considered a subset of AI.
What is machine learning?
Machine learning is an application of AI. It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience.
One way to train a computer to mimic human reasoning is to use a neural network, which is a series of algorithms that are modeled after the human brain. The neural network helps the computer system achieve AI through deep learning. This close connection is why the idea of AI vs. machine learning is really about the ways that AI and machine learning work together.
How are AI and machine learning connected?
An “intelligent” computer uses AI to think like a human and perform tasks on its own. Machine learning is how a computer system develops its intelligence.
How AI and machine learning work together
When you’re looking into the difference between artificial intelligence and machine learning, it’s helpful to see how they interact through their close connection. This is how AI and machine learning work together:
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Step 1
An AI system is built using machine learning and other techniques.
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Step 2
Machine learning models are created by studying patterns in the data.
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Step 3
Data scientists optimize the machine learning models based on patterns in the data.
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Step 4
The process repeats and is refined until the models’ accuracy is high enough for the tasks that need to be done.
Capabilities of AI and machine learning
Companies in almost every industry are discovering new opportunities through the connection between AI and machine learning. These are just a few capabilities that have become valuable in helping companies transform their processes and products:
Predictive analytics
This capability helps companies predict trends and behavioral patterns by discovering cause-and-effect relationships in data.
Recommendation engines
With recommendation engines, companies use data analysis to recommend products that someone might be interested in.
Speech recognition and natural language understanding
Speech recognition enables a computer system to identify words in spoken language, and natural language understanding recognizes meaning in written or spoken language.
Image and video processing
These capabilities make it possible to recognize faces, objects, and actions in images and videos, and implement functionalities such as visual search.
Sentiment analysis
A computer system uses sentiment analysis to identify and categorize positive, neutral, and negative attitudes that are expressed in text.
Benefits of AI and machine learning
The connection between artificial intelligence and machine learning offers powerful benefits for companies in almost every industry—with new possibilities emerging constantly. These are just a few of the top benefits that companies have already seen:
More sources of data input
AI and machine learning enable companies to discover valuable insights in a wider range of structured and unstructured data sources.
Better, faster decision-making
Companies use machine learning to improve data integrity and use AI to reduce human error—a combination that leads to better decisions based on better data.
Increased operational efficiency
With AI and machine learning, companies become more efficient through process automation, which reduces costs and frees up time and resources for other priorities.
Applications of AI and machine learning
Companies in several industries are building applications that take advantage of the connection between artificial intelligence and machine learning. These are just a few ways that AI and machine learning are helping companies transform their processes and products:
Retail
Retailers use AI and machine learning to optimize their inventories, build recommendation engines, and enhance the customer experience with visual search.
Healthcare
Health organizations put AI and machine learning to use in applications such as image processing for improved cancer detection and predictive analytics for genomics research.
Banking and finance
In financial contexts, AI and machine learning are valuable tools for purposes such as detecting fraud, predicting risk, and providing more proactive financial advice.
Sales and marketing
Sales and marketing teams use AI and machine learning for personalized offers, campaign optimization, sales forecasting, sentiment analysis, and prediction of customer churn.
Cybersecurity
AI and machine learning are powerful weapons for cybersecurity, helping organizations protect themselves and their customers by detecting anomalies.
Customer service
Companies in a wide range of industries use chatbots and cognitive search to answer questions, gauge customer intent, and provide virtual assistance.
Transportation
AI and machine learning are valuable in transportation applications, where they help companies improve the efficiency of their routes and use predictive analytics for purposes such as traffic forecasting.
Manufacturing
Manufacturing companies use AI and machine learning for predictive maintenance and to make their operations more efficient than ever.
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