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The hype surrounding ChatGPT has been inescapable, with countless commentators speculating on its promise and potential. However, there has been a precious little meaningful exploration into how generative pre-trained transformer (GPT) capabilities might drive industry transformation. Artificial intelligence (AI) in the traditional sense tends to rely on pre-gathered datasets in order to draw predictive results. Generative AI, on the other hand, is capable of producing unique results in an unsupervised manner.
GPT uses generative AI, which effectively means that the language model is trained to predict the next word or action in a sequence of words or actions. It is also pre-trained, meaning that the language model is trained with data with each new iteration. The use of large language models (LLMs) in this fashion is potentially game-changing, but GPT’s potential for revolutionizing industry and driving innovation has not yet been fully explored. So, let’s set aside the hype, and take a look at how GPT technology might shape what many are now referring to as a new frontier for various industries.
Beyond Automation
One of the much-talked-about aspects of AI is its ability to automate trivial or time-consuming tasks. Generative AI takes this automation one step further, automating tasks that until very recently would require a healthy dose of human intuition and input. Take translating books and documents into other languages, for instance. Or writing a long-form article from scratch. If that’s not impressive enough, how about generating images or videos with a target audience in mind, or analyzing huge datasets to quickly and accurately identify patterns and trends? These use cases are already being deployed by businesses to create new products and services or enhance existing ones.
What’s different about the use of LLMs, which operate under the umbrella of GPT, is that they can be used to not only discover insights hidden in plain sight, but generate novel ideas based on those insights. Where humans would once have needed to pick up the baton and run with it, they can now wait at the finish line to check the results.
The benefits and limitations of generative AI
The application of generative pre-trained transformer capabilities rests on the use of LLMs and natural language processing (NLP). LLM technology brings game-changing automation capabilities and sets the table for generative AI to get to work, but it is still a nascent technology. Generative AI requires incredibly large volumes of data to work effectively, which can be difficult or virtually impossible to acquire for the average business. This is what makes GPT headline news – its ability to offer up generative AI capabilities “out of the box” in a pre-trained manner.
However, the results are far from perfect. Human-like behavior and output can’t be guaranteed in every scenario, and underlying biases that skew results can still persist. These biases are incredibly difficult to uncover, because the complex algorithms and mathematical equations on which language models are based can be incredibly hard to unpack and define. In other words, the “working out” happens behind the scenes, leaving businesses with no recourse to explain why a model has arrived at a particular decision.
Generative AI’s impact on industry
While general tools like Open AI’s ChatGPT are trained on generic data, thus democratizing the use case for all, there will soon be generative AI systems that are designed for specific verticals such as marketing, medical research, or manufacturing. These applications will be resource-intensive, but the results could far exceed anything a team of humans is currently capable of – at least in terms of resourcing and speed of output. Looking beyond task automation, LLP and GPT will be able to identify patterns and make proactive suggestions about how things like product pipelines and customer experiences could be improved and create the content to help make it happen. That content extends beyond words, and could include video, images, websites, advanced code, and more.
While there has been a lot of hype surrounding GPT and its potential to transform industries, the true impact of generative pre-trained transformer technology is yet to be fully explored. There are no doubt countless benefits in terms of automation and the generation of novel ideas. However, there are also drawbacks that must be considered, such as the need for large datasets, the potential for underlying biases, and risks around information security and idea plagiarism. Nevertheless, GPT and LLM technology will no doubt provide the basis for a new state of industry, where automation becomes a starting point rather than an end goal, and products and services become more finely tuned to users’ needs and expectations.