ChatGPT: holy grail or poisoned chalice?
Any new technology attracts attention, and ChatGPT is at the forefront of a significant leap in human-machine interaction. ChatGPT – the first, deeply intelligent chatbot powered by ‘Generative Pre-trained Transformer’ technology – has the potential to boost productivity, automate administrative tasks, and facilitate collaboration.
ChatGPT isn’t your average chatbot. The tool has soared in popularity since its launch in November 2022, delighting over 100 million users with near-human responses to all manner of questions. ChatGPT – the GPT stands for Generative Pre-trained Transformer – was created by OpenAI and built on top of the company’s existing Large Language Models (LLMs). Unlike previous language models, ChatGPT uses a massive body of fine-tuned data to achieve a level of coherence and consistency that was previously impossible, with much higher natural language processing (NLP) capabilities. In other words, it’s smart. Really smart.
What is ChatGPT?
The first chatbots were developed in the 1960s and used simple machine learning (ML) technologies to respond to user inputs. Despite improvements over time, chatbots have remained relatively limited in their capabilities, frequently offering pre-programmed answers or directing users to other sources of assistance. Chatbots powered by GPT have changed all of this, responding with more precision and personalisation. The reason for ChatGPT’s conversational charm is its training. The bot draws on a large dataset of human language to comprehend and replicate the patterns and structures of communication. And, every time it interacts with someone, it learns, and uses this learning to have more nuanced, complex conversations.
The benefits of ChatGPT
Alongside its advanced question-answering capabilities, ChatGPT has obvious benefits for enterprise, enhancing interactions and user experience while freeing employees to concentrate on more valuable tasks:
It’s clear that ChatGPT is a powerful instrument, but language models should not be relied on for decision-making or problem-solving. Language models are only as good as the data on which they are trained. If you ask ChatGPT about current events or news, it can only provide information about events up to the model's knowledge cut-off (in this case, 2021). Or, if you ask a question about a topic that isn’t included in the training data, the model may fail to give a suitable response. Models may also struggle to fully grasp the meaning or context of specific phrases. Like any digital tool, its outputs should be reviewed and corrected by human experts.
To reap the benefits of ChatGPT – and indeed any Large Language Models or advanced chatbots – organisations need to carefully introduce, impart, integrate, and iterate:
The first step is to discover what problems the system can solve and how it can aid teams. This might be answering queries, handling requests for documentation, or making specific suggestions to users. Teams should be familiar with the system and appropriate usage parameters.
Informed decisions and responses rely on accurate, up-to-date training data. By using a large dataset with relevant information, models can be trained on specific tasks and terminology. For instance, fine-tuning ChatGPT on a dataset of legal papers will enhance its comprehension of legal concepts. A diverse and representative training dataset, including data from underrepresented groups, can mitigate model bias. Data governance, privacy regulations, and strong security measures are necessary to protect sensitive information, especially in highly regulated industries like healthcare and banking.
Integrating ChatGPT into existing systems and processes requires meticulous planning and consideration of technical requirements, such as Application Programme Interfaces (APIs). Large Language Models and chatbots can be integrated via messaging platforms like Slack or Microsoft Teams, enabling employees to access ChatGPT using the same channels for day-to-day interaction with colleagues.
Through regular monitoring and evaluation, organisations can ensure the system is performing effectively. This might mean conducting surveys, gathering user feedback, and analysing metric data (like response time). Ongoing testing can help to identify and resolve potential problems.
Chatbots have the power to significantly improve day-to-day operations across all industries and sectors by facilitating effective communication between humans and machines, and automating necessary but mind-numbing tasks. As AI advances, intelligent systems will become even more significant. But while ChatGPT might sound like the holy grail of organisational efficiency, it isn’t a miracle elixir. It’s a tool – and should be used as part of a diverse toolkit.