What is the difference between Machine Learning and Artificial Intelligence?

Machine Learning and Artificial Intelligence is all around us. Machine learning and AI are embedded into many of the gadgets we use on a daily basis. Many people use these terms interchangeably and I wanted to point out the specific characteristics between the two.

Artificial Intelligence

Artificial Intelligence utilizes computers and machines to imitate the problem solving and decision making ability of the human mind with the goal being systems that can think and act rationally.

In its simplest form, artificial intelligence is a field that combines computer science and large datasets to enable problem solving of all types.

We see artificial intelligence (AI) all around us every day. Below are a few examples:

  • Customer Service: When your internet is out and you have to wrestle with an automated system or virtual agent to get to a human representative.
  • Computer Vision: Enables systems to derive meaningful information from digital images, videos, and other visual mediums. You can see this If you are on social media and get prompted to tag yourself in a picture. AI recognized your face in the image and made the suggestion. Another example is self driving cars. Input is taken from cameras and decisions are made by a computer very quickly.
  • Recommendation Engines: AI algorithms determine trends in data that can be used to develop cross-selling strategies. We see this all the time when recommendations are made during the check out process for online retailers.

Machine Learning

Machine Learning is a branch or Artificial Intelligence that focuses on the use of data and algorithms to “learn” improving its accuracy over time. Machine Learning can recognize patterns and models from very large datasets and provide insights, classifications, predictions, and more.

When I was at Microsoft I saw several very interesting use cases that utilized machine learning. One healthcare organization had thousands of images they applied machine learning algorithms to. They were comparing diagnosis with images to train the models, then using the models to predict certain illnesses like eye disease and cancer. The goal was not to replace the physician, but to augment their abilities and to help them make better predictions.

Other use cases for Machine Learning:

  • Automated Stock Trading: Trades are made every day without human intervention.
  • Hospital Readmissions: One organization I worked with implemented machine learning to predict hospital readmissions due to sepsis.
  • Cyber Security and Ransomeware: Many security companies are utilizing machine learning to combat viruses, hackers, and ransomeware.
  • Fraud Detection: Banks and credit card companies utilize machine learning to detect fraudulent charges.
  • Maintenance: Machine Learning to learn from historical data and use live data to analyze failure patterns. Rolls Royce uses this with their jet engines.
  • Weather Prediction: In the past few years, we have seen the use of machine learning used in weather and climate forecasting. Called “Climate Informatics”, this field has already proved to be a very fruitful one, enabling greater collaboration between data scientists and climate scientists, bridging the gaps in our understanding.

The use cases are unlimited. AI and Machine Learning continue to change the world around us and bring value to organizations and our every day lives.

We are currently looking into how we can augment Clinical Decision Support in Telehealth. I’ll come back with an update when we implement something awesome.

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