7 Types of AI and What Marketers Need Most

Artificial intelligence has quickly gained momentum, with headlines scattered across numerous tech publications However, it has quickly become a catch-all term that is severely misunderstood as not everyone has opened the AI black box to understand what it actually is and what it looks like.

Artificial intelligence (AI) is so much more than ChatGPT. It can range from Narrow AI that performs very simple tasks like Apple’s Siri, to technology that can theoretically surpass human intelligence through Super AI. However you’ve experienced AI, it is only becoming more efficient and powerful, and will definitely be sticking around. Importantly, it will change the way we work and think of mundane, repetitive tasks.

Below, we break down what artificial intelligence looks like, how marketing teams can leverage it, and what lies on the horizon for the technology.

What is AI?

AI refers to the meticulous process of constructing intelligence systems by harnessing vast amounts of data. AI does this by acquiring knowledge from past experience and learning that eventually trains the system to perform human-like tasks. Through complex algorithms and methodologies, AI engineers can create machines that are capable of independent decision-making.

AI can help enhance the speed, accuracy, and efficiency of tasks, which is a boon for workers as this will allow you to focus on other objectives that require deeper thinking and creativity.

Artificial intelligence continues to evolve thanks to Machine Learning and Deep Learning, allowing machines to adapt based on incoming information, mirroring the process of human learning. This is why ChatGPT and Bard have become a sensation as they’re responsive rather than regurgitative.

AI can be divided into two main categories: based on capabilities and based on functionalities. These categories reflect the range and applications of AI given how complex the technology can become.

AI based on capabilities

Artificial intelligence has three capabilities: narrow, general, and super AI. They range from simple and relatively unresponsive to nearly replicating human intelligence.

Narrow AI

Also known as Weak AI, Narrow AI is designed to excel at a specific, narrowly defined task, but it lacks the ability to extend its capabilities beyond those limitations. For instance, Apple’s Siri functions within a predefined range of tasks, but struggles when faced with more complex requests. Other examples of Narrow AI include Google Translate, image recognition software, recommendation systems, and spam filtering. Even though the functions of Narrow AI are limited, it’s still a very practical tool for various specialised applications, enhancing efficiency and accuracy in mundane and repetitive tasks.

General AI

Also known as strong AI, this technology can understand and learn any intellectual task that a human can. Basically, it can apply knowledge and skills in different contexts, which allow it to be more responsive than narrow AI. As of now, no true General AI system exists as engineers are yet to find a method to make machines conscious.

So far, Fujitsu has built the K computer, which is one of the fastest supercomputers in the world. In its attempt to achieve Strong AI, it took the computer nearly 40 minutes to simulate a single second of neural activity. There’s still a long trek to achieving Strong AI, but considering the recent $1 billion investment from Microsoft through OpenAI, it’s not necessarily light years away.

Super AI

Given our current capabilities, Super AI is still the work of science fiction. It would be able to surpass human intelligence as well as perform any task better than a human. It’s envisioned that Super AI will experience human emotions to the point it doesn’t just understand them, but can evoke them. Super AI would also have its own needs, beliefs, and desires – basically, a human made from microchips and metal.

AI based on functionalities

The other side of AI is based on its functionalities. This category is broken down into Reactive Machines,Theory of Mind AI, Limited Memory AI, and Self-aware AI.

Reactive Machines

This is the most primitive form of artificial intelligence as it does not store or use past experience to determine future actions. Instead, it produces outcomes based on present data. It reacts and responds to different prompts, but it does so without the use of memory of a broader understanding of the context. For instance, in marketing, chatbots are an example of reactive machines. It gives answers based on the prompts customers give it, but it can’t give them a full, cohesive answer based on the context as it just responds with present, and not past, data.

Although it sounds basic, reactive machines can be really powerful. For instance, IBM’s Deep Blue defeated chess master Garry Kasparov. All it did was see the chessboard pieces and react accordingly, even if it didn’t have previous experiences to improve its practice. Self-driving cars are another example of reactive machines.

Theory of Mind AI

Theory of Mind AI is another example of artificial intelligence that only exists as a concept. Essentially, it’s AI that can understand people’s emotions, sentiments, and thoughts. The closest we’ve come to this so far is Kismet, a robot made in the late 90s by MIT researchers. It is able to mimic and recognise human emotions. Although this area of AI still needs development, it can have valuable applications in marketing as it could recognise angry customers and try to diffuse the situation.

Limited Memory AI

Limited Memory AI uses recent past data to train its decisions. It can only use the data it is fed for a specific amount of time, which means it cannot add it to a library of experiences. For example, ChatGPT has a limit of 4,000 tokens, which means it can’t recall anything past that number. Other examples are virtual assistants and fraud detection systems.

These systems are great if you’re trying to foster trust among your users because you can guarantee that their data won’t be stored by the system past a certain amount of time or pieces of data.

Self-aware AI

Self-aware AI and Super AI are used interchangeably. Self-aware AI is hypothetical and the aim is to have it become smarter than the human mind. It would be able to understand and evoke emotions in those it interacts with, as well as have its own set of emotions, needs, and beliefs. Although it’s yet to be developed, sceptics warn of the ethical implications of having technologies that surpass human intelligence because it’s unclear how much control we could have over it.

How to use AI in marketing

1. Check the AI software/platform you are using meets data privacy standards – This is vitally important because failure to adhere to data privacy laws can result in hefty fines. If you’re using a cloud-based AI platform like ChatGPT, go to its privacy tab and double-check the description that aligns with your data privacy obligations. Keep in mind that the UK has updated its data privacy laws post-Brexit, so businesses now need to adhere to the new Data Protection Bill.
2. Complete a data audit – Your data needs to be in tip-top shape before you allow any AI technology to data mine, or produce algorithms and workflows. You’ll also need plenty of data to reap the benefits, so if you don’t have much at your disposal, you might want to hold off on this.
3. Set goals and KPIs – This is integral for any form of marketing strategy. With the inclusion of AI technology, your ambitions and aims will surely change because you’ll be able to reach larger audiences and interpret data more effectively. Therefore, you’ll want to take a closer look at your sales forecast to understand what new targets you can realistically reach and reallocate your resources accordingly.
4. If you’re new to AI, pick your top project – Diving straight into AI without prior knowledge can be a bit overwhelming. Rather than overhauling your whole marketing operation, identify the area you want to streamline with AI. For instance, you might want to improve your website landing page, so you might turn to AI website builders. Alternatively, you could enhance your SEO with a website builder AI plugin, like SEOPress. This will make it easier for customers to find you.. We recommend that you first become accustomed to automating bits of your marketing strategy before you make it a normalised feature of your operation.
5. Hire the right people to support – One in 10 global workers have in-demand AI skills, so you’ll want to find the right talent to ensure that AI has a smooth integration into your marketing strategy. Should you struggle to find talent, you can also consider upskilling your staff, ensuring your marketers are ready for the new technological era. The key to staying ahead of the competition is to ensure your staff know how to prompt generative AI platforms, or can easily identify where AI has automated a process incorrectly.

Verdict

AI is a multi-faceted technology that has numerous, ever-expanding uses, making it highly versatile across many industries. This is particularly true for marketing, where teams can automate processes and focus on the creative side of the job. Its promise to boost productivity means teams should seriously consider adopting AI into their day-to-day tasks, particularly because the technology will continue to develop and offer more functionalities.