Artificial Intelligence, Explained
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From personal assistants like Siri, to movie suggestions on Netflix, artificial intelligence (NYSE:AI) is rapidly becoming ubiquitous in everyday life. As this technology continues to advance in capability and prevalence, we sought to explore AI and several closely related subtopics: machine leaning, deep learning, and neural networks.
What are the Differences between Artificial Intelligence, Machine Learning, and Deep Learning?
While artificial intelligence (AI), machine learning (ML), and Deep Learning (NYSE:DL) are often used interchangeably, there are several key differences. One way to visualize the relationship is through a series of concentric circles. AI is the macro topic which encompasses the entire field of study, while ML is a subtopic within AI. DL is a further refinement of ML and represents the most cutting edge of AI applications that are being used today.1
At a basic level, artificial intelligence is the concept of machines accomplishing tasks which have historically required human intelligence.1 AI can be broken down into two distinct fields:
Applied AI: Machines designed to complete very specifics tasks like navigating a vehicle, trading stocks, or playing chess – as IBM’s Deep Blue demonstrated in 1996 when it defeated chess grand master Gerry Kasparov.
General AI: Machines designed to complete any task which would normally require human intervention. The broad nature of General AI requires machines to “learn” as they encounter new tasks or situations. This need for a learned approach is what gave rise to modern Machine Learning.2
Today, many firms at the cutting edge of AI are focusing on machine learning (ML). In simple terms, ML is the process of building machines which can access data, apply algorithms to this data, and then train themselves to deduce valuable insights based on these underlying datasets.
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