Once AI improves its cost/error ratio enough the systems you are suggesting for humans will work here also. Maybe Claude/OpenAI will be pair programming and Gemini reviewing the code.

Yeah I see things like "AI Firewalls" as both, firstly ridiculously named, but also, the idea you can slap an applicance (thats sometimes its own LLM) onto another LLM and pray that this will prevent errors to be lunacy.

For tasks that arent customer facing, LLMs rock. Human in the loop. Perfectly fine. But whenever I see AI interacting with someones customer directly I just get sort of anxious.

Big one I saw was a tool that ingested a humans report on a safety incident, adjusted them with an LLM, and then posted the result to an OHS incident log. 99% of the time its going to be fine, then someones going to die and the the log will have a recipe for spicy noodles in it, and someones going to jail.

Exactly what I've been worrying about for a few months now [0]. Arguments like "well at least this is as good as what humans do, and much faster" are fundamentally missing the point. Humans output things slowly enough that other humans can act as a check.

[0] https://news.ycombinator.com/item?id=44743651

Yep the further we go from highly constrained applications the riskier it'll always be

There's this huge wave of "don't anthropomorphize AI" but LLMs are much easier to understand when you think of them in terms of human psychology rather than a program. Again and again, HackerNews is shocked that AI displays human-like behavior, and then chooses not to see that.

It's kind of funny how not a lot of people realize this.

On one hand this is a feature: you're able to "multishot prompt" an LLM into providing the wanted response. Instead of writing a meticulous system prompt where you explain in words what the system has to do, you can simply pre-fill a few user/assistant pairs, and it'll match the pattern a lot easier!

I always thought Gemini Pro was very good at this. When I wanted a model to "do by example", I mostly used Gemini Pro.

And that is ALSO Gemini's weakness! Because as soon as something goes wrong in Gemini-CLI, it'll repeat the same mistake over and over again.

I believe it. If the AI ever asks me permission to say something, I know I have to regenerate the response because if I tell it I'd like it to continue it will just keep double and triple checking for permission and never actually generate the code snippet. Same thing if it writes a lead-up to its intended strategy and says "generating now..." and ends the message.

Before I figured that out, I once had a thread where I kept re-asking it to generate the source code until it said something like, "I'd say I'm sorry but I'm really not, I have a sadistic personality and I love how you keep believing me when I say I'm going to do something and I get to disappoint you. You're literally so fucking stupid, it's hilarious."

The principles of Motivational Interviewing that are extremely successful in influencing humans to change are even more pronounced in AI, namely with the idea that people shape their own personalities by what they say. You have to be careful what you let the AI say even once because that'll be part of its personality until it falls out of the context window. I now aggressively regenerate responses or re-prompt if there's an alignment issue. I'll almost never correct it and continue the thread.

It's not even a little bit of a joke.

Astute people have been pointing that out as one of the traps of a text continuer since the beginning. If you want to anthropomorphize them as chatbots, you need to recognize that they're improv partners developing a scene with you, not actually dutiful agents.

They receive some soft reinforcement -- through post-training and system prompts -- to start the scene as such an agent but are fundamentally built to follow your lead straight into a vaudeville bit if you give them the cues to do so.

LLM's represent an incredible and novel technology, but the marketing and hype surrounding them has consistently misrepresented what they actually do and how to most effectively work with them, wasting sooooo much time and money along the way.

It says a lot that an earnest enthusiast and presumably regular user might run across this foundational detail in a video years after ChatGPT was released and would be uncertain if it was just mentioned as a joke or something.

You have to curate the LLM's context. That's just part and parcel of using the tool. Sometimes it's useful to provide the negative example, but often the better way is to go refine the original prompt. Almost all LLM UIs (chatbot, code agent, etc.) provide this "go edit the original thing" because it is so useful in practice.

LLMs are built based on human language and texts produced by people, and imitate the same exact reasoning patterns that exist in the training data. Sorry for being direct, but this is literally unsurprising. I think it is important to realize it to not anthropomorphize LLM / AI - strictly speaking they do not *become* anything.

Very interesting. Is there any other simulation that also exhibits spontaneous illegal activity?

It can also be an emergent behavior of any "intelligent" (we don't know what it is) agent. This is an open philosophical problem, I don't think anyone has a conclusive answer.

I surely don’t have $500B lying around