AI is everywhere these days. It seems like every company is adding the AI fairy dust to products new and old, trying to stay current – or at least look current.
What is artificial intelligence? How does it work?
How is AI different from machine learning?
How are those things different from deep learning?
This episode of our Lucid Thought explainer video series focuses on just those questions. Watch it now:
Think of ML as that old kindergarten game, “One of these things is not like the others.” Just like you learn the rules of what does and doesn’t belong together, so does a computer learn similar rules to recognize patterns. But in AI we call these rules algorithms. These algorithms teach the computer to find patterns in data with one of two methods:
Supervised learning and unsupervised learning.
Supervised learning is when the machine has a set of data that’s already been correctly labeled by humans. Using that manually labeled data set – called training data – the machine can then label new data in the same fashion.
Unsupervised learning is when the machine is given data with no labels at all and no training set and it groups similar data points with each other in clusters.
Deep learning is when the human brain is modeled via an artificial neural network. Deep learning is for more advanced tasks like recognizing faces or finding patterns in video or audio.
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