What is GenAI? How does it differ from traditional AI? Why does it matter?

Artificial Intelligence (AI)

Let’s start with the bigger picture - AI is an umbrella term which captures everything about machines/computers that mimics real human intelligence. But what do we mean by “intelligence”? In this context, it’s the ability to make decisions and solve problems.

In earlier iterations of AI - what I call traditional AI - the focus was on building systems that could analyze existing data and provide predictable, rule-based responses. Think of it like a well-oiled machine following a pre-programmed set of instructions. The parameters were fixed, and the outcomes were largely predefined.

The Essence of Generative AI

GenAI, meanwhile, is a more advanced and creative development in AI. Unlike traditional AI systems that are designed to analyse data and provide responses based on pre-existing information, GenAI goes a step further. It can create new content - be it text, images, music, or even code - that never existed before. 

The standard bearer of GenAI is without question the Large Language Model (LLM). These are fed with billions of data sources from websites, books etc. This voluminous training enables them to generate coherent, contextually-relevant text across a wide range of topics and styles. These models are continuously updated, not only to expand their knowledge base but also to refine their understanding of nuanced human communication. If you’re interested in the basics of how these work technically, check out this article.

Let’s take an analogy to illustrate what separates GenAI from previous iterations of AI. You can think of traditional AI as doing a paint-by-numbers set. It takes the pre-sorted colours and applies them to the relevant areas of the page. So far so pedestrian.

Generative AI, on the other hand, is more akin to an artist with a blank canvas - GenAI has almost infinite possibilities of what it might create, including things that have never been done before. That’s not a cheap shot at painting by numbers, by the way: it’s just not really fair to compare it with more original art!

 "Unlike traditional AI systems that are designed to analyse data and provide responses based on pre-existing information, GenAI goes a step further."

The Key Differences Between GenAI and Traditional AI

1. Creation vs. Consumption: Traditional AI systems excel at consuming existing data to perform tasks like classification. GenAI, on the other hand, uses data to generate new outputs that can be entirely novel and unique.
2. Interactivity and accessibility: GenAI can interact with far more users than traditional AI as it deals in natural language rather than programmatic ones so almost anyone can access it. This also allows it to feel more personal as it responds to your own voice and tone. 
3. Dynamic Learning: While traditional AI might require more explicit instructions or programming to handle new types of tasks, GenAI can adapt more dynamically. Using techniques like deep learning, it can improve its output by learning from the feedback it receives on its creations.

Real-World Applications of GenAI

• Written Content: GenAI can provide human-like text output that responds directly to the input to the user. This can help creators help scale their content production by providing an AI with exemplars and then asking for new ideas. It does this in seconds.
• Image and Video Creation: Similarly to written content, GenAI  can also create novel images and videos. 
• Mundane, repetitive tasks: GenAI is particularly well suited to carrying out predictable tasks at a scale and speed previously unimaginable. An example of this would be filling out a RFP form. GenAI can use previous entries to provide answers to the next one. Even if questions happen to be worded slightly differently than before, GenAI can adapt. 
• Expert Assistants: GenAI is also being adopted within businesses for more complex use cases. GenAI systems and products are able to ingest huge amounts of information and provide answers or guidance to new situations based off that information. With this, experts can create an assistant to accompany them through their daily lives. You can discover more about how in-house teams are already leveraging this here.

Challenges and Considerations

Despite the dizzying scope of what can be achieved with it, GenAI is not without its challenges. In order to deliver on the real world applications mentioned, GenAI must be carefully steered and governed by its user. Of particular importance are issues such as managing the quality of generated content, ensuring the accuracy of information.

Enter the dreaded “hallucination”… Far from a psychedelic episode, when used in the context of GenAI this means an output that is incorrect. These undoubtedly pose a risk - a huge one if left ungoverned. This is why for business use cases in particular, users have to be vigilant for hallucinations and take steps to prevent them. For more information on how to get great answers in complex business use cases, see here.

Why does it matter?

The remarkable potential of GenAI is already being realised. It’s not something that can be ignored any longer. But, it’s never too late to start informing yourself about GenAI or even better: getting hands on with it to see if it can solve your problems (or even just impress you!).

GenAI is a truly transformative and disruptive technology that will not only change and streamline existing patterns of work and interaction, but will create new ones.