The Terms Behind AI & Machine Learning
All machine learning is artificial intelligence (AI), but not all AI is machine learning… confused? You’re not the only one. AI and machine learning are two hot terms in the healthcare industry right now, not to mention even more confusing terms that are associated with the hot button topics. So what do all of these terms mean?
Cognitive Computing
Cognitive computing stems from a mix of cognitive science, which is the study of the human brain and how it functions, and computer science, the result of this mixing will have far-reaching impacts on our private lives, healthcare, business, and more.
The goal of cognitive computing is to simulate the way a human would think in a computerized form. It uses self-learning algorithms that utilize data mining, looks at patterns, and adds natural language processing (NLP), which in then turn, the computer can mimic the way the human brain works.
So what can cognitive computing do?
In healthcare, it would potentially help gather information and knowledge around any given item. Items could include: patient history, lab reports, etc. and then could provide a recommendation. This would allow doctors to make better treatment decisions.
Machine Learning: Which consists of Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning
Machine learning is a type of AI that will automate analytical models to continuously improve upon themselves.
There are four approaches to machine learning which are broken into categories based on how the machine learns:
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Supervised Learning – teaches model by example, with human input informing model changes
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Unsupervised Learning – finding identifiable patterns in data and results to spot similarities and outliers to allow a model to self correct
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Semi-Supervised Learning – a small amount of human supervision to label small, specific pieces of data to empower a model to produce better results
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Reinforcement Learning – a trial and error process in which the proper actions are emphasized
Other Important Terms within machine learning:
Model- is the system making a prediction or identifications
Parameters- are the criteria that is used by the model to formulate the decisions
Learner- the systems that will adjust the model by looking at what the differences are in the actual outcome vs. what the prediction was.
Extract recognizes the vastness and complexity of both AI and machine learning. If you are interested in learning more, check out our machine learning page, here, or by contacting us today.
