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Exploring the Basics of Artificial Intelligence

Everyone is talking about artificial intelligence. That’s understandable — suddenly, free (or cheap) tools are available to create a variety of AI-generated content, including text and images, in an unlimited range of styles, seemingly in seconds. But do you really understand what AI is, how long it's been around, the difference between AI and machine learning, and what deep learning is? If you hesitated over some of these questions, read on.

Let’s start by filling in some background. AI conceptually dates back to 1950, although it began to flourish in the 1960s and 1970s as computers became more widely available. AI has long had many applications in marketing, including content recommendations, product recommendations, and predictive analytics. Well-known vendors, including Adobe Sensei and Salesforce Einstein, have been baking AI into their solutions for almost a decade. Generative AI and AI writing tools, all of which use “prompts”, have also been around for years, though they are now more widely available.

So what is AI? An algorithm is a set of rules to be followed in calculations or other problem-solving operations, but using algorithms for a calculation or a task is not the same as using AI. AI is when algorithms can teach themselves — with or without some human supervision — and machine learning is AI in its original sense. Alan Turing published a landmark paper in 1950 on the imitation game, which is now known as the Turing test: if a human interlocuter cannot tell the difference between responses to her questions from a machine and responses from another human being, we can ascribe intelligence to the machine. Deep learning is a big step beyond machine learning; it is based on a neural network, a layer of artificial neurons which are activated by an input, communicate with each other about it, then produce an output.

Today, AI is used to create commerce recommendations, write email subject lines, recommend next-best-actions, power chatbots, write advertising copy, generate content, and more. Many martech vendors use AI, but very few are selling AI as an independent product. AI is like salt; it's added to technology to make it taste better, but nobody ever says, “I’ll have AI for dinner”. Of course, ethical questions need to be addressed, such as when machine learning models are trained on biased data sets, or when generative AI plagiarises human content.

This article is just intended to scratch the surface of an enormously complex and rich topic. Hopefully it's enough to chew on for now.

Originally reported by Martech:
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