You’ve probably heard how artificial intelligence can revolutionize the way marketers work. You may be using AI-powered tools right now. But it is also possible that you have not yet “drawn the curtain” to see how this “super technology” works.
Below, we’ll cover the four main types of artificial intelligence and how each type can boost your marketing.
How many types of artificial intelligence are there?
There are four main types of artificial intelligence:
However, since AI can be categorized by function and Capabilities, it adds three more to the mix:
Reactive machines
As the name suggests, reactive machines react and respond to different cues. They do this without memory or a broader understanding of the context.
For example, this type of artificial intelligence is commonly used in game design to create opponents. The opponent will respond to your actions, movements, or attacks in real time but is unaware of the game’s objective. On top of that, he doesn’t store memories, so he doesn’t learn from past experiences or adjust his game.
Reactive artificial intelligence powers many marketing tools. A notable example is chatbots. These programs use reactive artificial intelligence to respond to messages (or inputs) with the correct information.
Chatbots are a popular tool in customer service, but they can also increase marketers’ productivity.
Beyond chatbots, reactive AI can analyze customer behavior, campaign performance, and market trends. With these insights, marketers can optimize their campaigns, improving their effectiveness and ROI.
Limited memory
Limited-memory artificial intelligence can learn from a limited amount of data or feedback. However, it does not “store” any memories for long periods.
A great memory-limited example of this artificial intelligence is ChatGPT. It has a limit of 4000 tokens (forms of text such as words) and cannot remember anything from a current conversation after that limit. So if a conversation has 4097 tokens, ChatGPT responds based on the last 97 tokens.
This technology can be found in autonomous vehicles. It can detect lanes and plot the road ahead. You can adjust car speed and brake in real time based on traffic patterns and road conditions.
In marketing, limited-memory artificial intelligence can analyze large amounts of data, helping marketers make smarter decisions about their strategies and tactics. You can also make predictions and recommendations based on this data.
While limited memory algorithms are practical, they are not foolproof. They can make mistakes or provide inaccurate predictions, especially when working with out-of-date data. In other words, the output is only as good as its input. Therefore, training these algorithms with accurate, relevant, and up-to-date information is essential.
Reactive machines and limited memory artificial intelligence are the most common types today. Both are a form of narrow intelligence because they cannot function beyond their programmed capabilities.
Theory of mind
The theory of mind exists only as a concept. It represents an advanced technology class that can understand humans’ mental states.
For example, if you yell at Google Maps because you missed a turn, it doesn’t take offense or offer emotional support. Instead, it responds by finding another route.
The idea behind the theory of mind is to create machines that can interact with humans more effectively because they understand their needs, goals, and motivations. If an artificial intelligence system can understand the frustrations of a disgruntled customer, for example, it can respond more tactfully.
In the long term, the theory of mind artificial intelligence could have significant implications for marketing. However, it is still in its early stages, so it is difficult to predict when it will come to fruition.
Self-conscious
Self-aware artificial intelligence is considered the next phase in the evolution of the theory of mind, where machines can understand human emotions and have their own emotions, needs, and beliefs. Currently, this type of artificial intelligence only exists hypothetically.
M3gan, the robot from the film of the same name, is an example of self-aware artificial intelligence. He is aware, knows who he is, experiences emotions, and can understand the emotions of those around her. As we would expect from a robot, he is clumsy but has social interactions.
Artificial intelligence has three stages, defined mainly by its ability to replicate human capabilities:
Narrow Intelligence (ANI): Narrow artificial intelligence accounts for most artificial intelligence systems today. At this stage, artificial intelligence is designed to perform specific tasks. It cannot learn or adapt beyond its programming. Examples include chatbots, virtual assistants (like Siri), and recommendation algorithms.
General Intelligence (AGI): This is the next evolution of artificial intelligence. These systems are designed to have human-like intelligence, allowing them to learn and adapt to new situations, think abstractly, reason, and solve problems. At this time, AGI is still largely theoretical.
Super Intelligence (ASI): ASI is an advanced form of artificial intelligence that surpasses human intelligence, allowing it to solve complex problems, create new technologies, and make decisions beyond the scope of human comprehension. ASI is a hot topic of debate, and its potential benefits and risks are highly speculative.
While these stages are widely accepted, there is an ongoing debate about what defines each stage, when we could achieve them, or if we should evolve artificial intelligence.
As mentioned, limited and reactive memory AI (both narrow AI) is all there is today. This means marketers’ AI tools are strictly reactive or reactive + limited memory.
HubSpot surveyed more than 1,350 marketers to learn more about their use of AI and automation and the tools they use in their roles. Here are some key points:
Most marketers use chatbots when asked about generative AI tools used in their marketing functions (66%).
Chatbots can be both reactive and limited memory artificial intelligence. For example, a rule-based chatbot that follows an if/then model and is programmed with canned responses could be called a reactive AI because it follows a set structure and cannot deviate from it.
Machine learning, like conversational chatbots, is limited-memory artificial intelligence because they leverage data and past conversations to respond to customers. They become more effective over time, but their memory is limited.
Marketers also said they commonly use AI visual tools (57%) and text generation tools (56%). Regardless of their tool, all generative AI is limited-memory AI because tools can create new content based on the data they train on.
All AI/automation users who responded to the survey say that AI and automation tools save an average of 2 hours and 24 minutes daily.
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