HOW EXACTLY DO NEURAL NETWORKS MIMIC HUMAN LANGUAGE?
About five years ago, researchers at companies like Google and OpenAI, a San Francisco startup that recently released the popular ChatGPT chatbot, began building neural networks that learned from enormous amounts of digital text, including books, Wikipedia articles, chat logs and all sorts of other stuff posted to the internet.
These neural networks are known as large language models. They are able to use those mounds of data to build what you might call a mathematical map of human language. Using this map, the neural networks can perform many tasks, like writing their own tweets, composing speeches, generating computer programmes and, yes, having a conversation.
These large language models have proved useful. Microsoft offers a tool, Copilot, which is built on a large language model and can suggest the next line of code as computer programmers build software apps, in much the way that autocomplete tools suggest the next word as you type texts or emails.
Other companies offer similar technology that can generate marketing materials, emails and other text. This kind of technology is also known as generative AI.
NOW COMPANIES ARE ROLLING OUT VERSIONS OF THIS THAT YOU CAN CHAT WITH?
Exactly. In November, OpenAI released ChatGPT, the first time that the general public got a taste of this. People were amazed – and rightly so.
These chatbots do not chat exactly like a human, but they often seem to. They can also write term papers and poetry and riff on almost any subject thrown their way.
WHY DO THEY GET STUFF WRONG?
Because they learn from the internet. Think about how much misinformation and other garbage is on the web.
These systems also don’t repeat what is on the internet word for word. Drawing on what they have learned, they produce new text on their own, in what AI researchers call a “hallucination.”
This is why the chatbots may give you different answers if you ask the same question twice. They will say anything, whether it is based on reality or not.
