We’re back with another new technology that we believe will transform healthcare. Machine learning… Here’s what you need to know in a nutshell!
Hosts: Em Tombash, MD, Todd Ponsky, MD, Ramy Shaaban MD, PhD
Machine learning is a subfield of AI which allows a machine to automatically learn from past data without programming.
The difference between machine learning and AI is frequently misunderstood. Machine learning learns and predicts based on passive observations, whereas AI implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals.
#Machinelearning algorithms allow us to use the data we gather to make predictions about the future, taking humans out of the equation as much as possible. Present day machine learning has two purposes, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes.
A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify cancerous moles and a machine learning algorithm for stock trading may predict the future potential trends.
Machine learning has proven valuable because it can solve problems at a speed and scale that can't be duplicated by the human mind alone.
With massive computational ability for a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.
But it is important to understand what machine learning can and cannot do. As useful as it is in automating the transfer of human intelligence to machines, it is far from a perfect solution to your data-related issues.
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