But imagine a chatbot, enhanced by artificial intelligence (AI), that can not only expertly answer your questions, but also write stories, give life advice, even compose poems and code computer programs.
It seems ChatGPT, a chatbot released last week by OpenAI, is delivering on these outcomes. It has generated much excitement, and some have gone as far as to suggest it could signal a future in which AI has dominion over human content producers.
What has ChatGPT done to herald such claims? And how might it (and its future iterations) become indispensable in our daily lives?
ChatGPT builds on OpenAI’s previous text generator, GPT-3. OpenAI builds its text-generating models by using machine-learning algorithms to process vast amounts of text data, including books, news articles, Wikipedia pages and millions of websites.
By ingesting such large volumes of data, the models learn the complex patterns and structure of language and acquire the ability to interpret the desired outcome of a user’s request.
ChatGPT can build a sophisticated and abstract representation of the knowledge in the training data, which it draws on to produce outputs. This is why it writes relevant content, and doesn’t just spout grammatically correct nonsense.
While GPT-3 was designed to continue a text prompt, ChatGPT is optimised to conversationally engage, answer questions and be helpful. Here’s an example:

ChatGPT immediately grabbed my attention by correctly answering exam questions I’ve asked my undergraduate and postgraduate students, including questions requiring coding skills. Other academics have had similar results.
In general, it can provide genuinely informative and helpful explanations on a broad range of topics.

ChatGPT is also potentially useful as a writing assistant. It does a decent job drafting text and coming up with seemingly “original” ideas.

Why does ChatGPT seem so much more capable than some of its past counterparts? A lot of this probably comes down to how it was trained.
During its development ChatGPT was shown conversations between human AI trainers to demonstrate desired behaviour. Although there’s a similar model trained in this way, called InstructGPT, ChatGPT is the first popular model to use this method.
And it seems to have given it a huge leg-up. Incorporating human feedback has helped steer ChatGPT in the direction of producing more helpful responses and rejecting inappropriate requests.

Refusing to entertain inappropriate inputs is a particularly big step towards improving the safety of AI text generators, which can otherwise produce harmful content, including bias and stereotypes, as well as fake news, spam, propaganda and false reviews.
Past text-generating models have been criticised for regurgitating gender, racial and cultural biases contained in training data. In some cases, ChatGPT successfully avoids reinforcing such stereotypes.


