chatgpt: Breaking down the hype

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ChatGPT: breaking down the hype 

As many of us emerged from our holiday bubbles, and re-entered reality, checked news feeds on our phones, or flipped through the headlines over morning coffees, you may have noticed what seems like the sudden influx of headlines and articles around ChatGPT. It’s making jobs easier by automating report writing, freeing up time for other tasks, next it’s making roles redundant, then it’s passing law and medicine exams, passing bot checks and universities are up in arms about how to adapt.  

What is it? What does it all mean? What is truth and what is fiction? Let us help break it down. 



Taking ChatGPT a step further, with our AI presenter “Monica” reading from our article.

What exactly is ChatGPT? 

ChatGPT is an Artificial Intelligence (AI) assistant, and part of a bigger trend around Generative AI. Generative AI are large language models that can generate text, images, and code. It is not without controversy, such as last year when an AI generated artwork won an art prize. The GPT in ‘ChatGPT’ stands for Generative Pre-Trained Transformer, which is a language model that harnesses learning to produce human-like text.

ChatGPT was released in November 2022 and is the brainchild of OpenAI, an AI research and deployment company based in San Francisco. Currently it is free to use if you sign up for an account, however the surge in demand, and the resulting need to scale their systems means there may be times there are issues. 


When you login you can ask a question, or make a request and it will generate a response in real-time including explanations and elaborating on ideas in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests”. 

Let’s give ChatGPT a chance to explain itself: 

ChatGPT is a language generation model developed by OpenAI. It is trained on a large dataset of human-generated text and uses machine learning techniques to generate human-like responses to a given prompt. It can be used for a variety of natural language processing tasks such as language translation, text summarization, and question answering. The model is based on the transformer architecture, which has been shown to be effective for language understanding and generation tasks.” 

Why the sudden surge in popularity?

Well, it’s new, and following the first phase of the classic Gartner Hype Cycle where early stories are emerging and it is attracting a lot of media attention. See below for their Hype Cycle tailored for Emerging Tech: 


It has raised a lot of questions as people grapple with the impacts, work through the noise, use cases, value, limitations and figure out what it means so it may be while before things to settle down and we start to get a clearer view of what it is truly capable of and how we can best work with it. 


What are the benefits?

So far, there are a few: 

  • Variety: it can perform several tasks such as answering questions, summarising text, translation, and image creation. 
  • Fast: responses are generated in real time 
  • Free: currently, it remains free to use though this may change to a freemium model or similar 
  • Human-like, conversational responses: it’s been trained and is continued to be trained on a huge dataset of human-generated text 



How could my business use this? 

One of the most high profile use cases is Microsoft who invested $1 billion into OpenAI and looking to integrate into its search engine Bing to create a more conversational search engine as part of its efforts to gain market share from industry leader Google. 

There are plenty of companies using it, looking at it, and commentary around potential use cases, such as: 

  • Drafting copy including: 

Simplify and summarise complex content (e.g., legal matters) 

Product guides 

Keyword research for search engine marketing 

  • It can help draft responses and make existing chatbot experiences more conversational, which could provide value in situations where human-like conversation with customers is preferred. For example, think about airlines using chatbots to triage queries, with the bot handling simpler questions and escalating as necessary to people either in the same channel or other. It can also provide further opportunities to automate parts of the customer and employee experience 
  • Writing and reviewing code in IT 

More broadly, generative AI can be even do things such as create slide presentations, creating diagrams, images and more. All these use cases have the potential to drive significant efficiencies, saving time and money. 

If you are considering it for your business, you may want and see how the offering evolves and improves beyond the initial hype. Do your homework, have a look at research, read the news and while it’s free you can jump in and have a play to see what it is all about. 


What are things I need to be aware of? 

While is plenty of excitement around ChatGPT and Generative AI more broadly, it is still a nascent technology, and even OpenAI’s CEO Sam Altman tweeted caution, stating: 

“ChatGPT is incredibly limited, but good enough at some things to create a misleading impression of greatness. it’s a mistake to be relying on it for anything important right now. it’s a preview of progress; we have lots of work to do on robustness and truthfulness” 

Some of the current considerations and limitations are: 

  • Recency: ChatGPT’s was created with data up to 2021, so is unable to provide context on recent events 
  • Context matters: where prompts are too vague, or something niche, it may struggle to provide accurate information. Essentially, the quality of output depends on the quality of input. 
  • Ethical concerns: It does raise some ethical concerns. A digital mental health company faced heat for using GPT as an experiment for support messages without informing users. 
  • Bias: it is trained on human-generated text, which may contain bias in language and information 
  • Meaning: it is trained to provide words based on input, but is limited in interpreting meaning behind words 
  • Limited understanding the complexities and nuances in human language and conversation such as idioms and sarcasm so may misinterpret inputs with results impacted accordingly 


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