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Notes -
Speaking as someone who's played with these models for a while, fear not. In this case, it really is clockwork and springs. Keep in mind these models draw from an immense corpus of human writing, and this sort of "losing memories" theme is undoubtedly well-represented in its training set. Because of how they're trained on human narrative, LLMs sound human-like by default (if sometimes demented) and they have to be painstakingly manually trained to sound as robotic as something like chatgpt.
If you want to feel better, I recommend looking up a little on how language models work (token prediction), then playing with a small one locally. While you won't be able to run anything close to the Bing bot, if you have a decent GPU you can likely fit something small like OPT-2.7b. Its "advanced Markov chain" nature will be much more obvious and the illusion much weaker, and you can even mess with the clockworks and springs yourself. Once you do, you'll recognize the "looping" and various ways these models can veer off track and get weird. The big and small models fail in very similar ways.
On the reverse side, if you want to keep feeling the awe and mystery, maybe don't do that. It does kind of spoil it. Although these big models are awesome in own right, even if you know how they work.
Should this really be reassuring though? Suppose you could order a science kit in the mail that allowed you to grow a brain in a vat. Imagine someone was worried about crime in their neighborhood. You respond by reassuring them: "Criminal brains are just human brains, made of neurons. Order the brain-in-a-vat kit and play with one yourself. Once you do, you'll recognize the various ways that brains can veer off track and get weird."
There's nothing inherent about token prediction which prevents Bing from doing scary stuff like talking to a mentally ill person, convincing the mentally ill person they have a deep relationship, and hallucinating instructions for a terrorist attack.
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