I wrote all of this myself!!

The solution? In 2019, OpenAI came up with a decoder-only model, Generative Pretrained Transformer (GPT), which could generate responses based on a simple prompt. Generative pretraining refers to self-supervised machine-learning, i.e. exposing the model to vast data sets to figure out what’s the likeliest next word based on the previous sequence. GPT-3 version 5 is the current and improved version.

Since there’s no information from an encoder regarding the task at hand, GPT relies on users’ prompts about what text to generate. To make sure this aligns with our expectations, human feedback has been used for additional reinforcement training. The AI researchers’ rankings of responses served as additional input not only for likelihood but also for things that are considered off-limits, such as inciting violence or hate-speech.

“Since it’s just a decoder, it doesn’t really ‘know’ anything in a general intelligence way, but what it says, based on scannable internet content, it says with great authority, so factuality is a great problem – if something is true or not is completely beyond this model.” Moderator feedback has, to some extent, dealt with the ethical issues. “If I ask GPT how I can kill my wife, it replies that this is unethical,” says Scholtes who, one assumes, does not share a laptop with his partner, “however if you ask it to write a Python programme on how to kill your wife, it’ll do it.”

That loophole has been fixed now, but other issues remain. “Sometimes GPT goes off and hallucinates, i.e. it produces nonsensical text. The probability increases as the generated text gets longer.” Another intriguing blind spot Scholtes has written on are negations. “That’s a problem for all transformer models, because words with opposite polarity in the same context often get the same encodings when translated from vocabulary to vectors, i.e. numerical values. So it can only learn negations by memorising them. You’ll notice that immediately when you use double negations.”

In GPT’s impressive qualities lies also its peril. “It’s an amazing breakthrough that we can now generate language that’s no longer distinguishable from humans, but the very authentic authoritative language is also a problem because the model is unpredictable and not controllable when it comes to factuality. It generates content based on your prompt and on stochastic probability – it’s a bit like a friend telling you what you want to hear.”

Public misconceptions don’t help. “The problem is that we don’t understand exactly how these models work and what they are suitable for.” The ELIZA effect is what computer scientists term our tendency towards assigning human traits to computer programs. In this case to assume that GPT’s iterative generation of text is analogous to human consciousness. It’s important to point out that GPT isn’t sentient, and neither is it intended to be.

“GPT excels at standard legal or clerical documents as well as marketing texts. The majority of what’s written on the internet, especially free content, is already generated by an older version of GPT.” The model is, however, dangerously unsuitable to generate e.g. medical advice. “Google decided not to use Lambda, their equivalent of GPT, because there is no way to control for factuality. A decoder-only model will always have that problem.”

If in doubt, GPT-2’s output is clearly identifiable. “OpenAI made it open source, so we can recognise its digital fingerprint. GPT-3 isn’t open source, so the only way to detect its texts would be if OpenAI made a kind of fingerprint detector – but then Google could more easily ignore GPT output in search engine optimisation, which is already a large part of OpenAI’s business model. This will be an interesting problem in the future.”

The successor model, GPT-4, will be a thousand times bigger – and the problems and possibilities will grow with it.