Everyone is using ChatGPT: What does my organisation need to watch out for?
In December 2022, OpenAI released ChatGPT, a powerful AI-powered chatbot that could handle users’ questions and requests for information or content in a convincing and confident manner. The number of users signing up to use the tool increased very rapidly, with users using the tool to write letters, edit text, generate lists, prepare presentations and generate code, among a myriad of other things.
But whilst the use of ChatGPT could mean efficiency gains and cost savings for businesses, its use by organisations and their staff does give rise to a number of different issues, which organisations must consider and manage.
In this note, we identify the types of issues organisations need to watch out for in relation to the use of ChatGPT and what they should do to manage those issues.
First, what makes large language or image models like GPT or DALL-E different to other forms of AI or search?
In many ways this type of AI is very similar to prior AI in terms of development and structure. The large language model(s) underneath GPT consist of highly complex artificial neural networks (ANN). There have been many other ANNs prior to OpenAI’s models. The key difference between OpenAI’s models and other ANNs is the vast size of GPT-3 and GPT-4’s training corpus and the special mathematical features of the individual ANN nodes called “transformers” that make them particularly adept at analyzing and generating language. “Transformers” were actually invented by the “Google Brain” division around 2017, but then open-sourced. See https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html?m=1 . At bottom, the real difference between the OpenAI GPT models and prior ANNs is the sheer efficacy of GPT in analyzing, generating, and predicting language. In terms of usage and adoption, OpenAI GPT models have likely already outstripped all other models to date, by multiple orders of magnitude. Although the potential issues associated with training data, bias, overfitting, etc. are generally similar to ANNs that came before, those issues will now be relevant to organizations and in contexts where such considerations to date have been irrelevant or unknown.
As noted, a key feature is the accessibility of the recently released large language models. OpenAI’s ChatGPT and DALL-E have consumer interfaces that allow anyone to create an account and use the technology to generate answers. OpenAI also have an enterprise API version of GPT-4 where more sophisticated integrations can be made, including using the technology to select answers from a more limited specialist training or reference data set. Due to the computing power required to train this type of AI, much use of these tools is on a subscription basis (e.g. GPT-4). However, there are some models, which are generally smaller and narrower in their application, which are open source and can be downloaded and hosted locally.
In this post we are discussing OpenAI’s ChatGPT and GPT-4 offerings. There are other large language models (LLMs) which will operate differently and have different legal terms and so not all the points below will be universally applicable to all LLMs.