You have likely heard how artificial intelligence can revolutionize the way in which marketers work. In reality, you might be using AI-powered tools without delay.
But if you happen to’re like me, you have not “pulled back the curtain” to see how this technology works — until now.
Here, we’ll cover the 4 essential forms of artificial intelligence — response machines, limited memory, theory of mind, and self awareness — and the way each type can power your marketing.
What number of forms of AI are there?
There are 4 essential forms of AI: reactive machines, limited memory, theory-of-mind, and self-aware.
Nevertheless, since AI may be categorized by function (the categories listed above) and capabilities, you add three more to the combination: narrow intelligence (ANI), general intelligence (AGI), and superintelligence (SGI).
Below we’ll explain each type.
4 Kinds of Artificial Intelligence
Reactive Machines
Because the name suggests, reactive machines react and reply to different prompts. It does this without the usage of memory or a broader understanding of the context.
For instance, this sort of AI is often utilized in game design to create opponents. The opponent will reply to your actions, movements, or attacks in real time but is unaware of the sport’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 lots of marketing tools. A notable example is chatbots. These programs use reactive AI to answer messages (or inputs) with the fitting information.
Chatbots are a well-liked tool in customer support, but they may boost the productivity of marketers. As an illustration, HubSpot’s ChatSpot is a handy AI-powered assistant that may 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
Limited memory AI is capable of learn from a limited amount of information or feedback. Nevertheless, it doesn’t “bank” any memories for prolonged periods of time.
A terrific example of the ‘limited’ aspect of this AI is ChatGPT. It has a limit of 4000 tokens (types of text like words) and may’t recall anything from a current conversation after that limit. So, if a conversation is 4097 tokens, ChatGPT responds based on the most recent 97 tokens.
This technology may be present in self-driving cars. It could actually detect lanes and map out the road ahead. It could actually also adjust the automobile’s speed and break in real time based on traffic patterns and road conditions.
In marketing, limited memory AI may be used to research large amounts of information, helping marketers make smarter decisions about their strategies and tactics. It could actually also make predictions and suggestions based on this data.
While limited memory algorithms are effective, they are not foolproof. They will make mistakes or provide inaccurate predictions, especially when working with outdated data. In other words, the output is barely nearly as good as your input. So, it is vital to coach these algorithms with accurate, relevant, and up-to-date information.
Reactive machines and limited memory AI are essentially the most common types today. They’re each a type of narrow intelligence (which we’ll discuss further below) because it may possibly’t perform beyond programmed capabilities.
Theory of Mind
Theory of mind exists only as an idea. It represents a complicated class of technology that may understand the mental states of humans.
As an illustration, if you happen to yell at Google Maps since you missed a turn, it doesn’t get offended or offer emotional support. As a substitute, it responds by finding one other route.
The concept behind theory of mind is to create machines that may 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 instance, it may possibly respond more tactfully.
In the long run, theory of mind AI could have significant implications for marketing. Nevertheless, it’s still in its early stages, making it difficult to predict when it can grow to be a reality.
Self Aware
Self-aware AI is seen as the following phase within the evolution of theory of mind, where machines are capable of understand human emotions and have their very own emotions, needs, and beliefs. Currently, this sort of AI only exists hypothetically.
M3gan, the robot from the movie of the identical name, is an example of self-aware AI. She’s sentient and knows who she is and experiences emotions, and may understand the emotions of those round 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 duplicate human capabilities:
- Narrow Intelligence (ANI): Narrow AI represents most AI systems that exist today. At this stage, AI is designed to perform a particular task or set of tasks. It doesn’t have the flexibility to learn or adapt beyond their programming. Examples include chatbots and virtual assistants (like Siri), and advice algorithms.
- General Intelligence (AGI): That is the following evolution of AI. These systems are designed to have human-like intelligence, allowing them to learn and adapt to recent situations, think abstractly, reason, and solve problems. At this moment, AGI continues to be largely theoretical.
- Superintelligence (ASI): ASI is a complicated type of AI that surpasses human intelligence, enabling it to unravel complex problems, create recent technology, and make decisions beyond the scope of human understanding. ASI is a hot topic of debate, and its potential advantages and risks are highly speculative.
While these stages are widely accepted, there’s ongoing debate about what defines each stage and once we might achieve them — or if we must always evolve AI in any respect.
Top Kinds of AI in Marketing
As mentioned, reactive and limited memory AI (each are narrow AI) are all that exist today. This implies the AI tools marketers use are strictly reactive, or reactive + limited memory.
We surveyed 1350+ marketers within the U.S. to learn more about their use of AI and automation and the tools they use of their roles. Listed below are some key takeaways.
First, when asked in regards to the generative AI tools utilized in their marketing roles, most marketers use AI use chatbots (66%).
Chatbots may be each reactive and limited memory AI. For instance, a rule-based chatbot following an if/then model and is programmed with canned responses might be called reactive AI since it follows a set structure and may’t deviate from the structure.
Machine learning chatbots, like conversational chatbots, are limited memory AI because they leverage data and past conversations to answer customers. They grow to be more practical over time, but their memory is proscribed.
Marketers also said they commonly use visual AI tools (57%) and text generation tools (56%). Whatever the tool they use, all generative AI is proscribed memory AI since the tools can create recent content based on the information it’s trained on.
All AI/automation users that responded to our survey say that AI and automation tools save a mean of two hours and 24 minutes per day.
Back to You
From reactive machines to limited memory AI, theory of mind, and self-awareness, each form of AI has its strengths and limitations. Knowing these differences is essential to picking the fitting tools, leveraging them effectively, and staying ahead of the curve.
