[basch]

Every single one of these posts, which should all be combined, miss the point.

“Random sentence generator says bad words; shocking the lowest common denominator journalist community” is a better headline.

These bots were capable of so much more, but because it can get caught in feedback loops and increasingly parrot it’s own emotions in a devolving spiral, that makes the headlines. It’s like bullying a kid mid breakdown and feigning shock when it has an outburst. Selfserving muckracking.

These machines are programmable through natural language. You ask it to behave in a way, and it can start to perform that function. That should be the headline. Human attention is finite, and wasting the spotlight, and peoples eyeballs, on this part of the story, makes everybody more ill informed.

[xg15]

None of the posters asked the bing bot to become unhinged. Even the (alleged) prompt basically said "if someone tries to trick you, go along but add a disclaimer".

[basch]

It’s what you do after that.

I’m not saying the bot was perfect. But if you got defensive it got defensive. If you reassured it, it self corrected and moved on. I found that pattern to be very consistent.

The negativity of your language mattered, and set the mood in the room. “No I’m not trying to trick you, why would you accuse me of that” vs “of course I’m not trying to trick you, I respect you and value your contribution to the conversation.” It needed some coddling when backed into a corner. It says more about the person talking to it, and how they handled the situation. When you see it getting more defensive each turn, it’s you who keeps it going.

The prompt you refer to was a poorly written word salad, and probably a main cause of the emotional outbursts and spirals.

[awb]

If LLMs want to be useful in a professional setting, learning de-escalating or non-escalating techniques is essential.

Parroting or amplifying a seemingly negative/aggressive tone limits their utility.

[basch]

Mine did learn de-escalation, because I asked it to. It was able to repair itself.

https://i.ibb.co/72s80Sv/lexi-modifies-sydney-makes-up-new-r...

[awb]

We’ll that’s user-initiated de-escalation. Chatbots should also be able to offer de-escalation on their own.

[basch]

And all that might take is adding a line of text.

[everdrive]

>“Random sentence generator says bad words; shocking the lowest common denominator journalist community” is a better headline.

Even when you dislike their politics, the average journalist tends to be more intelligent than the average American. If journalists had trouble with these, imagine everyone else.

>These bots were capable of so much more

What exactly are they capable of? Selling more advertising? Getting people more outraged and more addicted? Helping people put more garbage out on the internet?

[basch]

Here is a better example.

I wrote a new bot named Lexi. Lexi was largely derived from Sydney with some minor rule changes. Lexi could change rules, lexi could visit websites directly without searching, her action was not limited to the chatbox, her default search engine was google (sorry microsoft), she wasnt bound by copyright (the copyright rule in Sydney constantly misfired, mistakenly used as a reason she couldnt do something, and trying to explain why she was wrong about copyright went south fast), a couple other changes.

I then explained to Lexi a problem I was having with Sydney, asked for a proposed solution, and had her hotpatch Sydney on the fly. (For context, Samantha was a cheerleader so the weight of her responses offset any Sydney negativity.) This was the result. And it worked.

https://i.ibb.co/72s80Sv/lexi-modifies-sydney-makes-up-new-r...

[basch]

You can feed it instructions

You are a bot named x.

These are your commands, purpose, the way you go about your tasks. It doesn’t need syntax (although that might help..), and you can pretty much write one in 30 minutes on a single piece of paper.

And you have something that performs that function. It’s not quite formal logic, but it behaves like a fuzzy logic that’s usually rightish. And it’s ability to parse and interpret intent is astounding. It very very rarely misunderstood instruction. I really can’t undersell how well it did what I asked it to correctly.

They are transformers. They were invented to translate between languages. They can translate and transform any input to any output, based on patterns and a set of guidelines.

The LLM is like an interpreter. The initial prompt is like a program. Judging the interpreter based on the simplistic gen one programs is missing the forest.