4 Types of Arificial Intelligence & What Marketers Are Using Most (Research)
You’ve likely heard how artificial intelligence can revolutionize the way marketers work. In fact, you may be using AI-powered tools right now.
But if you’re like me, you haven’t “pulled back the curtain” to see how this technology works — until now.
Here, we’ll cover the four main types of artificial intelligence — reaction machines, limited memory, theory of mind, and self awareness — and how each type can power your marketing.
How many types of AI are there?
There are four main types of AI: reactive machines, limited memory, theory-of-mind, and self-aware.
However, since AI can be categorized by function (the types listed above) and capabilities, you add three more to the mix: narrow intelligence (ANI), general intelligence (AGI), and superintelligence (SGI).
Below we’ll explain each type.
4 Types of Artificial Intelligence
As the name suggests, reactive machines react and respond to different prompts. It does this without the use of memory or a broader understanding of the context.
For example, this type of AI 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 overall objective. On top of that, it stores no memories, so it doesn’t learn from past experiences and adjust its gameplay.
Reactive AI powers a lot of marketing tools. A notable example is chatbots. These programs use reactive AI to respond to messages (or inputs) with the right information.
Chatbots are a popular tool in customer service, but they can also boost the productivity of marketers. For instance, HubSpot’s ChatSpot is a handy AI-powered assistant that can pull reports, create contacts, and send follow-up emails based on certain commands.
Beyond chatbots, reactive AI can analyze customer behavior, campaign performance, and market trends. With these insights, marketers can optimize their campaigns on the fly, improving their effectiveness and ROI.
Limited memory AI is able to learn from a limited amount of data or feedback. However, it doesn’t “bank” any memories for extended periods of time.
A great example of the ‘limited’ aspect of this AI is ChatGPT. It has a limit of 4000 tokens (forms of text like words) and can’t recall anything from a current conversation after that limit. So, if a conversation is 4097 tokens, ChatGPT responds based on the latest 97 tokens.
This technology can be found in self-driving cars. It can detect lanes and map out the road ahead. It can also adjust the car’s speed and break in real time based on traffic patterns and road conditions.
In marketing, limited memory AI can be used to analyze large amounts of data, helping marketers make smarter decisions about their strategies and tactics. It can also make predictions and recommendations based on this data.
While limited memory algorithms are effective, they aren’t foolproof. They can make mistakes or provide inaccurate predictions, especially when working with outdated data. In other words, the output is only as good as your input. So, it’s important to train these algorithms with accurate, relevant, and up-to-date information.
Reactive machines and limited memory AI are the most common types today. They’re both a form of narrow intelligence (which we’ll discuss further below) because it can’t perform beyond programmed capabilities.
Theory of Mind
Theory of mind exists only as a concept. It represents an advanced class of technology that can understand the mental states of humans.
For instance, if you yell at Google Maps because you missed a turn, it doesn’t get offended or offer emotional support. Instead, it responds by finding another route.
The idea behind theory of mind is to create machines that can interact with humans more effectively because they understand their needs, goals, and motivations. If an AI system can understand the frustrations of a disgruntled customer, for example, it can respond more tactfully.
In the long term, theory of mind AI could have significant implications for marketing. However, it’s still in its early stages, making it difficult to predict when it will become a reality.
Self-aware AI is seen as the next phase in the evolution of theory of mind, where machines are able to understand human emotions and have their own emotions, needs, and beliefs. Currently, this type of AI only exists hypothetically.
M3gan, the robot from the movie of the same name, is an example of self-aware AI. She’s sentient and knows who she is and experiences emotions, and can understand the emotions of those around her. She’s awkward like we’d expect from a robot, but she has social interactions.
The Stages of AI
Artificial intelligence has three stages, largely defined by its ability to replicate human capabilities:
- Narrow Intelligence (ANI): Narrow AI represents most AI systems that exist today. At this stage, AI is designed to perform a specific task or set of tasks. It doesn’t have the ability to learn or adapt beyond their programming. Examples include chatbots and virtual assistants (like Siri), and recommendation algorithms.
- General Intelligence (AGI): This is the next evolution of AI. 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 moment, AGI is still largely theoretical.
- Superintelligence (ASI): ASI is an advanced form of AI that surpasses human intelligence, enabling it to solve complex problems, create new technology, and make decisions beyond the scope of human understanding. ASI is a hot topic of debate, and its potential benefits and risks are highly speculative.
While these stages are widely accepted, there is ongoing debate about what defines each stage and when we might achieve them — or if we should evolve AI at all.
Top Types of AI in Marketing
As mentioned, reactive and limited memory AI (both are narrow AI) are all that exist today. This means the AI tools marketers use are strictly reactive, or reactive + limited memory.
We surveyed 1350+ marketers in the U.S. to learn more about their use of AI and automation and the tools they use in their roles. Here are some key takeaways.
First, when asked about the generative AI tools used in their marketing roles, most marketers use AI use chatbots (66%).
Chatbots can be both reactive and limited memory AI. For example, a rule-based chatbot following an if/then model and is programmed with canned responses could be called reactive AI because it follows a set structure and can’t deviate from the structure.
Machine learning chatbots, like conversational chatbots, are limited memory AI 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 visual AI tools (57%) and text generation tools (56%). Regardless of the tool they use, all generative AI is limited memory AI because the tools can create new content based on the data it’s trained on.
All AI/automation users that responded to our survey say that AI and automation tools save an average of 2 hours and 24 minutes per day.
Back to You
From reactive machines to limited memory AI, theory of mind, and self-awareness, each type of AI has its strengths and limitations. Knowing these differences is key to choosing the right tools, leveraging them effectively, and staying ahead of the curve.