Most people associate generative AI (GenAI) with large language models (LLMs). While LLMs focus specifically on generating text, GenAI encompasses a wider range of content generation tasks beyond just language, including images, music and more.
Broadly speaking, GenAI uses machine learning algorithms to analyze and learn from existing data sets and then generate new, original content that resembles the learned material.
One of the most interesting things about GenAI is that it transforms human and computer intelligence interaction. You don’t necessarily need to be able to code or have any computing skills. Instead, it is easy to interact with GenAI using a typed question or even a voice prompt. This democratizes access to AI and extends it to a non-technical audience. With this in mind, here are three things that everyone needs to know and understand about GenAI.
1. Not all GenAI systems are LLMs
LLMs are a subset of GenAI focusing on generating and understanding human language. They use deep learning techniques such as neural networks to respond to and generate human-like text.
Many people are becoming more aware of ChatGPT and similar LLMs. However, there is far more to GenAI than that.
While these are interesting and valuable, other applications of GenAI are even more transformational. Here are two to be aware of:
- Synthetic data generation: Synthetic data generation comprises data with similar characteristics to real-world data. Synthetic data is valuable when you don’t have enough or biased real-world data. Synthetic data is already being used or considered in many industries, from health care to agriculture, manufacturing, automotive and disaster prediction.
- Digital twins: Digital twins are digital representations of real-world entities or systems. Digital twins are most often used to test scenarios and predict how real-world systems will react. Many organizations across industries are already taking advantage of its capabilities.
2. The importance of GenAI will lie in whether it delivers business value
Ultimately, what will matter about GenAI is its ability to provide tangible business value, addressing challenges effectively and at scale. Organizations evaluate GenAI's worth in four key domains: software development, content generation, intelligent AI assistants, and customer engagement. Depending on the level of investment and effort, businesses may opt for various approaches, from leveraging third-party GenAI solutions to training their own GenAI models.
GenAI's success rests on its capacity to deliver sustainable solutions and scalability, driving innovation and competitive advantage in diverse sectors.is whether it delivers business value and helps businesses solve problems sustainably and is scalable.
At the moment, companies are moving with considerable caution. People want to avoid jumping first or getting locked into a particular technology. Equally, everyone wants to be part of any first-move advantage likely to accrue.
3. Human oversight and governance will remain important for many years to come
With GenAI, human oversight and governance play pivotal roles. Organizations need to consider potential issues and problems. This is an extremely powerful technology, but it also has risks ranging from security and data privacy to bias and fairness resource intensity and robustness. For example, training large generative models requires significant computational resources and energy consumption, raising environmental concerns. Developing more efficient models and training methods is vital. It also needs to be determined what regulatory approach will be taken to AI and how this will vary worldwide.
Furthermore, these algorithms heavily rely on the data they are trained on, inheriting any biases present within that data, reflecting the biases of their creators.
They also make harmful mistakes. The Artificial Intelligence Incident Database collects details of incidents where AI caused harm. According to the database, around 30% of incidents are related to race, around 20% to gender/sex and 10% to religion.
Vigilance is the only way to avoid these incidents. We cannot avoid or control GenAI. Instead, we need to understand it. Only then will we be able to use it for what we want.
Wrapping it all up
GenAI extends far beyond LLMs, encompassing diverse applications such as synthetic data generation and digital twins. Its value hinges on delivering tangible business outcomes at scale, but adoption requires careful consideration of risks such as bias and privacy concerns. Human oversight remains important due to AI’s inherent limitations. Only through understanding and vigilance can we responsibly use its power for desired outcomes.
