Let’s call time on the ChatGPT hype cycle
Public Address CEO and CommTech expert Shane Allison argues that OpenAi’s ChatGPT is just another tool in the communicator’s arsenal, not the revolution many promise it to be.
It might be too early to call it, but I hope that after 41 days of public release, we’ve reached peak hype-cycle on ChatGPT with Ryan Reynold’s Mint Mobile ad.
The only element of the Australian hype-cycle left is for a creative PR agency (I’m looking at you Thinkerbell) to use it in a domestic campaign.
Since ChatGPT was opened to a public beta, there’s been no shortage of breathless commentary about the ascendency of Ai and how we’re all out of a job – especially those employed in white-collar jobs – like PR professionals – who turn out a variety of copy for a living.
In fact, I was chatting with a friend over the summer break who believes that ChatGPT is going to cut swathes through the ranks of customer service agents, knock off a few communicators, and polish off a few associated fields on the way through.
Fortunately (or perhaps unfortunately when it comes to some occupations), this is not going to be the case. This might sound a little bit odd coming from the CEO of a technology company – but let me explain.
To understand why I’m not as excited as my friend was about ChatGPT, we should first simplify some of the hype around the GPT-3 model that it is built on.
The way that I find it useful to think about ChatGPT as a program that has two very different capabilities:
- ChatGPT can understand a user’s question phrased in natural language
- ChatGPT can program this question to generate or summarise text
Because of the large data sets it is trained on, the generated text can also be perceived as having an ability to conduct research, but ChatGPT doesn’t consider the accuracy of the facts it produces in its research, so it is up to the user to verify the accuracy (or not) of this ‘research.’
In fact, the generative underpinnings mean it’s often just flat out lying.
These two capabilities, particularly the ability to summarise or generate text based on a provided prompt, is not particularly new.
The difference with ChatGPT is that all of these are bundled in an incredibly user-friendly interface thanks to its ability to understand a natural language question, allowing millions of people to easily use a summariser or generator for the first time. You don’t need to boot up python (the most common code for machine learning), find a model, train a model on a specific dataset and then write a program to ask it to generate text.
But, for all of that, the text that ChatGPT is generating is just that – generative. It can’t come up with original thought, it will use the same structure for almost every media release that you ask it to write, and try to write a five paragraph essay when you ask it to write an article.
In short, ChatGPT is a relatively well-trained intern, capable of generating text, but still sharpening its critical thinking and ability to understand the user’s context.
So if your current writing level is that of a PR intern – yes – your role might be under threat.
Fortunately, this means that communicators are safe from ChatGPT. A colleague or client may be able to ask ChatGPT to write a media release, but the media release will be bland, boring and predictable.
All of this is not to say that programs like ChatGPT and future GPT iterations aren’t going to become useful tools in a communicator’s arsenal.
There’s no denying that the ability to ask a computer to easily write a first draft of something is useful. This might help increase the average writing ability of communicators, as we spend more time editing, refining and contextualising – and less time building the basic structures of content. ChatGPT puts permanently to bed the blank page problem in seconds.
At Public Address we’re already experimenting with how the underlying text generation models can be used to help further personalise pitches sent to the media through our platform. This is because language generation models are already very good – and reliable – at iterating on a given paragraph or two, and ChatGPT hasn’t changed that, but has improved the accessibility of this technology.
If you want to see these models in action, you can look at how the social content and SEO industries have been using OpenAi’s work in this space for a couple of years. In fact, you can pull up Gmail and look at the prompts that the application provides as you’re typing.
But these models aren’t going to replace us in the next decade.
All of these tools need a human being to drive them, make sense of the output, and contextualise it. To borrow from Reagan’s Russian proverb – trust, but verify.
Natural language generation will eventually (and perhaps with Microsoft’s proposed acquisition of a good chunk of OpenAi, sooner) give you back more time in your day, delivering on the fundamental promise of technology.
But it is a long way off from replacing communicator’s creativity, curiosity and intellect.
Shane Allison is Australia’s leading CommTech expert, the CEO of Public Address and the President of the Public Relations Institute of Australia
You should ask ChatGPT to write a response article to this
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Write a response to Mumbrella’s recent article ‘Let’s call time on the ChatGPT hype cycle’
‘I am an AI trained by OpenAI, I don’t have access to the recent news and articles but I can tell you that GPT-3 is a state-of-the-art language generation model that has been trained on a massive dataset and can generate human-like text. However, like any technology, it has its limitations and may not be suitable for every use case. Additionally, it is important to remember that GPT-3 is simply a tool and its effectiveness ultimately depends on how it is used and integrated into a given application. It’s important to set realistic expectations and evaluate whether GPT-3 is the best solution for a particular problem before deciding to use it’.
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Sorry, too busy playing Pokémon go to read this article.
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Appears the technology is useless then!
I understand that the recent hype around GPT-3 and other language models has led to some skepticism about their capabilities and potential applications, but I believe that these models are still in their infancy and have the potential to revolutionize the way we work and interact with technology. As with any new technology, it is important to understand the limitations and use cases that are best suited for these models, but we should not write off their potential just yet. As the technology improves and we continue to explore new ways to utilize it, I believe we will see even more amazing advancements in the field of natural language processing.
Oh, and this comment was written by ChatGPT 😉
Was this article was written by ChatGPT?
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The Thinkerbell material is always original!
The blacksmith thought that cars were just hype also. Unfortunately your job is cooked mate.
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Interesting comments and, to some extent, I agree with you.
The key is that the current crop of natural language AI engines are pretty much representative of the early stages of the tech.
And they’re just going to get better.
At a recent Greylock seminar*, Sam Altman, the founder & CEO of OpenAI, opined that the “marginal costs of intelligence and energy will decline close to zero over the next decade”. If he is correct, and there is a lot of data that suggests he might be, it will have enormous impacts on business and society both.
So yes, at the moment, ChatGPT probably isn’t going to radically alter professionals’ work/life structures much.
But in five years?
Imagine seeing the first version of Facebook and saying it would have little impact on Marketing and Comms?
Anyway, prep now to take advantage of AI as it’s not gonna go away 😁
*https://greylock.com/greymatter/sam-altman-ai-for-the-next-era/
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You appear to be besotted by the tech capabilities. You may need to understand the way it is wielded by financial bean counters. I’ve gone through this experience before in my industry: all the pro-tech people talked up the capability of one software I was proficient in. They never saw the job cuts that followed. Nor the fact that tech is in a constant state of evolution.
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As communicators, developments in artificial intelligence/machine learning will increasingly be at the cutting edge of our discipline.
I believe we have much to learn from marketers, as their ways of working have over the past two decades been transformed into being much more technologically-driven. (Some very innovative work is being done on this in the U.K., for instance – it’s worth keeping an eye on people like Stephen Waddington. He also has some useful additional insights on the AI/ChatGPT discussion in a recent blog.) Books like Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data are a great introduction to communicators of this way of thinking and operating.
Communicators will need to continue to identify and implement ways to demonstrate ongoing commercial value. Large organisations and agencies in particular are likely to increasingly want to reduce costs by integrating technology into workflows and automating lower value activities.
The advent of a new generation of AI-assisted tools and capabilities may also enable us to work more effectively towards development of a sound, widely accepted framework for measuring/validating the role played by earned media coverage in assessment of an audience/customer journey and their perspectives/opinions about an organisation or issue. This “fractional” measurement would attribute appropriate value to the multiple touchpoints in this process (which may include, but is not usually limited to, media coverage achieved either by an agency, or in-house (or both), rather than potentially misleading “last touch” attribution.
This would strengthen the ability of organisations to more effectively assign value/contribution to communications/PR/media relations alongside advertising, customer acquisition/retention/re-activation marketing, community outreach, and other related activities – essentially, to ‘unbundle’ and assign value to multiple components of perception/reputation.
Developments in AI/machine learning could potentially be a catalyst for enabling communicators to conclusively demonstrate our commercial value.