Artificial intelligence (AI) has been around for years - decades, even - but the term crashed into our collective consciousness with a bang with the public launch of ChatGPT in 2022. Since then, millions of Americans have tasked this almost-magical online guru with everything from editing resumes and creating recipes to rewriting Shakespeare as a Quentin Tarantino movie.
Artificial intelligence (AI) has been around for years - decades, even - but the term crashed into our collective consciousness with a bang with the public launch of ChatGPT in 2022. Since then, millions of Americans have tasked this almost-magical online guru with everything from editing resumes and creating recipes to rewriting Shakespeare as a Quentin Tarantino movie.
Marketing is just one of the many fields that are currently being impacted by A.I. While the technology still has a long way to go before The Matrix-style shenanigans, it’s hard to understate just how useful A.I. can be for marketing today.
Since the topic is so timely - and, honestly, complex - we’re launching a series to answer some of the most pertinent questions about marketing in A.I. In this article, we’re kicking things off by covering the history of A.I., key terms to know, and what you can expect moving forward. Here is a recent example of where we incorporated AI with a video series about GA4.
Artificial intelligence wasn’t created with the advent of ChatGPT. It’s a technology almost 75 years in the making. We put together a timeline to chart the key milestones that have led to our current crossroads.
The history of artificial intelligence began with just a simple test. It goes like this: Two humans, an evaluator, and a participant engage in a conversation with a machine designed to generate human-like responses through a text-only channel. If the evaluator can’t tell the human from the machine, the machine would pass the test. This pivotal tool, called ‘the Turing Test’ was developed by computer scientist and WWII hero Alan Turing in 1950.
Just six years later, artificial intelligence was introduced as a field of study at the Dartmouth Conference by 11 experts in fields such as computer science, physics, and mathematics.This conference is popularly considered to be the founding event of artificial intelligence.
Soon after, another pioneer in artificial intelligence, Arthur Samuel, would coin the term, “machine learning,” as he developed a program that mastered the game of checkers and defeated checkers master Robert Nealy.
On April 30, 1993, the World Wide Web - developed by British computer scientist Tim Berners-Lee, was made available to the public. Up until this moment, when the Internet became accessible to everyone with a connection, the impact of artificial intelligence had been minimal due to the lack of ability to collect the data required for its use. The Internet made the data required for everything from machine learning to large language models much more readily available, though, leading to the emergence of ‘big data’ in the early 2000’s.
In 1998, Swedish computational linguist Jussi Karlgen introduced the concept of consumer behavior clustering by introducing digital bookshelves at Columbia University. In the same year, Amazon would develop “collaborative filtering,” which was the first use of A.I. to provide recommendations to users based on their online behavior.
This technology is still used to this day; you might recognize it in action when Spotify recommends songs you may like, or Netflix gives you a list of movies you might want to binge next.
The 2010’s saw the popularization of artificial intelligence in marketing. Companies such as Google and Facebook began implementing artificial intelligence and machine learning in their programmatic advertising. Artificial intelligence was instrumental in everything from personalizing customer experiences to providing predictive analytics to increase sales. Chatbots powered by A.I. also became popular customer service tools for websites worldwide.
On November 30, 2022, ChatGPT was released to the public, exploding onto the marketing scene and rapidly becoming a buzzword (as well as triggering countless references to terminators hunting Sarah Connor). Within months, marketers began using ChatGPT and other generative A.I. to help ideate, create content, and streamline marketing processes.
To this day, marketers are discovering new frontiers in artificial intelligence, making content creation, web development, and data analysis faster and more efficient.
To understand a concept, you first have to have the right words for it. We outlined a few key terms and how they help explain this brave new world.
According to Oxford Languages, artificial intelligence is “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”
In more simple terms, A.I. is the science behind creating computer systems that solve problems typically associated with human thought and cognitive function. These include everything from winning a game of chess (or a game of Jeopardy!) to writing website code and creating content.
Generative A.I. is a subset of artificial intelligence that produces various outputs of content such as text, images, audio, and video based on the data it is trained upon. Examples of generative A.I. include ChatGPT, DALL-E, Midjourney and Google Bard.
Machine learning is a subset of artificial intelligence that uses algorithms and statistical models to imitate human learning without explicit human instruction.
IBM’s Arthur L. Samuel is credited with coining the term in his research around the game of checkers. Now machine learning is used in marketing for personalizing marketing campaigns, predicting customer behavior, identifying patterns in large data sets, and so much more.
A large language model is a type of artificial intelligence that takes massive datasets and a type of machine learning called deep learning to summarize, predict, and generate text content.
Large language models usually begin their training with petabytes (that’s 1,000,000 gigabytes!) of usually unstructured data to determine the relationship between different words and concepts.
Big Data refers to the extremely large datasets that generative A.I. use for training. These datasets are used to reveal patterns and trends, along with relationships between certain larger types of data.
The data processed involves too much volume, velocity, and variety (the three V’s of big data) for traditional data-processing to handle. In order to maintain big data, many large corporations invest great amounts of IT resources and manpower. However, big data allows these corporations to address business problems that would otherwise be much more difficult, or impossible to solve.
ChatGPT is a specific large language model trained to answer questions and other prompts in a human-like manner. Its design allows it to not only answer initial prompts, but also answer follow-up questions, reject inappropriate requests, and even admit when it’s wrong.
ChatGPT was developed by OpenAI as a sibling model of another L.L.M. called InstructGPT, and was released to the public on November 30, 2022, popularizing the L.L.M. and changing the marketing landscape.
To understand AI in marketing, a good place to start is with the role of machine learning. By processing large volumes of data and pulling valuable insights from it, we're able to make more informed decisions and construct effective campaigns that resonate with our audience.
AI's impact goes beyond strategy development – it's changing fundamental practices like customer segmentation and targeting. By deciphering complex patterns in consumer behavior, AI allows businesses, even small ones, to group their customers more accurately and tailor their messaging for each group.
AI doesn't just help us understand our current customers – it gives us a glimpse into future trends as well. By using past data, AI can forecast future consumer behaviors. This capability lets businesses plan their marketing efforts ahead of time.
While it’s amazing to consider how AI can help shape our current and future marketing strategies more efficiently and effectively, there are some pitfalls.
For starters, we must consider the ethical implications. Ensuring privacy when using personal data for targeted advertising is vital, especially as consumers continue to become more aware of just how much of their data is out there in the ether.
Equally important is recognizing potential biases in algorithmic decision-making. With great power comes great responsibility. The ability of AI to influence behavior improves with each generation. You don’t have to imagine rogue AI empires subjugating humans to understand how biases can negatively influence society.
Learning to use AI tools with practices that are ethically sound, transparent and fair is something we’ll talk more about in our series.
Artificial intelligence in marketing is a fascinating and ever-growing field that we would probably need several series to completely cover.
No worries; we’re going to cover as much as we can with this series. Stay tuned for these upcoming articles:
AI in marketing moves quickly. Follow our series to keep up - and get ahead of your competition.
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