The possibilities and potential of AI have exploded into mainstream public consciousness over the last few years. Primarily, this interest is driven by apps like ChatGPT. However, while neural language processing (NLP) tools are remarkable, they’re only one type of artificial intelligence.
Let’s explore the different types of AI, how they work, and what you can use them to do.
What is AI?
Before we delve into different types of AI, it’s worth defining what AI is. There is a lot of disagreement in technology circles about how we should define AI. Partly, this is because we can’t always agree on a perfect description of intelligence.
However, we don’t need a strict philosophical definition to understand what intelligence is. Intuitively, we all get that intelligence centers around efficient and accurate information processing that helps solve problems and answer questions.
Considering the above, we can define AI as a collection of different technologies that allow machines to mimic some of the cognitive functions of the human mind. These characteristics include learning, reasoning, categorizing, and synthesizing information. The advantage of AI is that it can perform many of these cognitive tasks quicker or more accurately than humans by using datasets that are so big they would be impossible for us to handle.
It’s also worth pointing out that AI is a collection of different technologies rather than one particular thing. We can think of it as an umbrella term for a variety of technologies and techniques that help us process information. Some of these include techniques like machine learning, deep learning, computer vision technologies, and so on.
Different types of AI
As outlined above, AI is a catch-all term for different technologies and techniques. However, we can also categorize AI by what it does and what outputs it produces. Broadly, we can fit AI into the following categories:
- Reactive AI: This kind of AI reacts to inputs and data and creates outputs
- Limited Memory AI: Learns from previous data and creates outputs
- Theory of mind AI: Can understand human emotions and intentions
- Self-aware AI: An AI that can reflect on its own existence
The last two categories (theory of mind and self-aware AI) do not exist yet. While better computational power and further innovation could see them become a reality, many experts are skeptical that these kinds of AI are even possible. Of course, some researchers insist AI has already achieved the theory of mind.
However, we don’t need general intelligence machines to revolutionize the world of work. For now, traditional and generative AI definitely exist, and both can help us achieve incredible things.
What is traditional AI?
For the sake of clarity, we’ll define non-generative AI as traditional AI. However, typically it’s just referred to as AI. This type of AI uses programmed rules and algorithms to achieve
defined tasks. These technologies work best in predictable or rule-based environments.
How does traditional AI work?
AI algorithms are trained on huge data sets to recognize underlying patterns in information. These patterns can help people access insights into historical data or make predictions about the future. These tools produce outputs that are trained for accuracy until they find patterns in data and can produce answers to specific questions.
What is traditional AI used for?
AI is used across a wide range of industries for a variety of purposes. Some use cases include:
- Driverless cars
- Predictive analysis
- Fraud detection and KYC
- Marketing automation
- AI assistants
- Facial recognition
- Chess robots
What is generative AI?
Generative AI also uses massive data sets. However, it looks for patterns within these data sets to create content, such as text, images, video, music, and even programming code.
A big part of why generative AI has captured public attention is that the interface is so user-friendly. Users just need to punch in a prompt, and NLP tools spit back content.
How does generative AI work?
Let’s take ChatGPT as an example. OpenAI trained this tool on huge volumes of data from the internet. The technology uses probability models to construct text based on particular prompts. For example, it analyzes the probability of one word following the next when a prompt is “Write me an article about the best holiday spots in Europe.” In many ways, it’s like super-powered predictive text.
What is generative AI used for?
Generative AI can be used for various purposes, including:
- Text generation (blogs, technical documents, articles, news articles, etc.)
- Images
- Videos
- Music
- Software coding
- Game design
- Product design
What are the main differences between Traditional and Generative AI?
While it’s tempting to think about generative AI as a next-gen improvement on traditional AI, the reality is a bit more complex. Both types of AI have utility depending on your objectives.
Traditional AI: Analyzes data to perform tasks and generate insights, and analyzes historical data to make inferences about the future.
Generative AI: Analyzes data to make new content.
Some businesses will need insight, predictions, and content; others might only need one of these benefits. So while each type of AI is useful, it’s hard to suggest one is better than another outside of what goals or objectives you need them to accomplish.
It’s worth noting that traditional AI is used in serious, highly critical work across industries like finance, healthcare, and manufacturing. While quality varies depending on the developers, this kind of AI is highly trusted by governments, businesses, and institutions.
Generative AI, on the other hand, is still in its infancy. While it produces impressive work, it can produce factually inaccurate or inappropriate content. There are also legal, regulatory, and even ethical concerns about content created from these data sets. While these issues may be ironed out in time, they’ve made some businesses cautious about adopting generative AI for now.
Final thoughts
AI is reshaping the business world by opening up new possibilities and allowing us to work more quickly and accurately. While Generative AI is the talk of the town, it’s just one example of how AI can help us boost productivity, automate business processes, and unearth patterns in data that would be impossible to discover without this technology.
