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Applications of AI
James Cannella

An Overview of Artificial Intelligence in Marketing

What Exactly is Artificial Intelligence?

Put simply by Demis Hassabis, founder and CEO of Google-owned AI company DeepMind, artificial intelligence is the “science of making machines smart” (Ahmed, 2015). As broad of a definition as this may be, it is well fitting because AI is an umbrella term for a wide variety of manifestations.

Within the umbrella of AI includes subcategories, such as machine learning and deep learning, that produce real-world applications of AI, such as voice recognition, image recognition, virtual assistants, search suggestions. These are all forms of weak AI. While equally fascinating, general artificial intelligence, or even super-artificial intelligence (SAI), are far from being practical to implement in business today. For this reason, people typically mean "Weak AI" when they discuss AI, rather than General or Strong AI.

Key Terminology

Below are some of the need-to-know definitions related to AI. You can find a more detailed list of AI terminology here.

Artificial Intelligence

Algorithms that analyze massive amounts of data to identify patterns and trends then take action on those insights automatically, improving itself in the process.

Machine Learning

A subset of AI that uses programs to learn and improve upon itself and process large amounts of data. Machine learning is the aspect of AI that allows for it to learn without being explicitly coded to do so.

Supervised Learning

1)        A type of machine learning in which human input and supervision are an integral part of the machine learning process on an ongoing basis. In supervised learning, there is a clear outcome to the machine’s data mining and its target function is to achieve this outcome, nothing more.

2)        A class of machine learning algorithms that learn patterns from outcome data. Supervised learning algorithms make predictions based on a set of examples.

Unsupervised Learning

A category of algorithms that are trained using a dataset that has not been labeled. Unsupervised learning algorithms look for patterns, underlying structures, and hidden relationships within a training dataset. The algorithm then creates a function to model these relationships between inputs and outputs in an effort to achieve accurate predictions on a previously unseen input.

Deep Learning

The general term for to machine learning using layered (or deep) algorithms to learn patterns in data. It is most often used for supervised learning problems.

Natural Language Processing (NLP)

A branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. This field of study focuses  on helping machines to better understand human language in order to improve human-computer interfaces with use cases like moderation, information extraction, summarization, and more.

Computer Vision

The ability for computers to “see” imagery through mathematical representations of three-dimensional shape and appearance. Computer vision lets a computer comprehend the meaning and context of an image in a similar way as human vision, allowing for sentiment analysis, facial recognition, and much more.

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