Difference Between AI, Machine Learning, and Deep Learning

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This particular wing of AI aims to equip machines with independent learning techniques so that they don’t have to be programmed. Artificial Intelligence also has the ability to impact the ability of the individual human, creating a superhuman. Some people think the introduction of AI is anti-human, while some openly welcome the chance to blend human intelligence with artificial intelligence and argue that, as a species, we already are cyborgs. Startup operations include processes such as inventory control, data analysis and interpretation, customer service, and scheduling.

To better understand the relationship between the different technologies, here is a primer on artificial intelligence vs. machine learning vs. deep learning. Machine Learning is prevalent anywhere AI exists, but it has some specific use cases with which we may already be familiar. Companies like Microsoft leverage predictive machine learning models to enhance financial forecasting. Artificial Intelligence is not limited to machine learning or deep learning.

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An example of this is an application built to assess documents for images with sensitive content. Instead of building a model from scratch to identify images in a document, pre-built AI services such as Google’s Document AI or Vision AI could be used to identify where images are in documents and to extract them. What we can do falls into the concept of “Narrow AI.” Technologies that are able to perform specific tasks as well as, or better than, we humans can. Examples of narrow AI are things such as image classification on a service like Pinterest and face recognition on Facebook. Back in that summer of ’56 conference the dream of those AI pioneers was to construct complex machines — enabled by emerging computers — that possessed the same characteristics of human intelligence.