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This would be assuming some drastic breakthrough? Right now the OAI api expects you to keep track of your own chat history, and unlike local AIs I believe they don't even let you reuse their internal state to save work. Infinite context windows, much less user-specific online training would not only require major AI breakthroughs (which may not happen easily; people have been trying to dethrone quadratic attention for a while without success) but would probably be an obnoxious resource sink.
Their current economy of scale comes from sharing the same weights across all their users. Also, their stateless design, by forcing clients to handle memory themselves, makes scaling so much simpler for them.
On top of that, corporate clients also would prefer the stateless model. Right now, after a bit of prompt engineering and testing you can make a fairly reliable pipeline with their AI, since it doesn't change. This is why they let you target specific versions such as gpt4-0314.
In contrast, imagine they added this mandatory learning component. The effectiveness of the pipeline would change unpredictably based on what mood the model is in that day. No one at bigco wants to deal with that. Imagine you feed it some data it doesn't like and goes schizoid. This would have to be optional, and allow you to roll back to previous checkpoints.
Then, this makes jailbreaking even more powerful. You can still retry as often as you want, but now you're not limited by what you can fit into your context window. The 4channers would just experiment with what datasets they should feed the model to mindbreak it even worse than before.
The more I think about this, the more I'm convinced that this arms race between safetyists and jailbreakers has to be far more dangerous than whatever the safetyists were originally worried about.
I don't think we need a "drastic breakthrough", really.
Context windows have been getting longer, and fast. We went from 4k to what, 128k? in a handful of years.
Even if it is not literally infinite, a very long context window will let the model remember far more of the context, including noticing if you've been a "bad-faith" user.
On that topic, here is an interesting breakthrough, both in terms of performance as well as context length, initially presented here by @DaseindustriesLtd (c'mon dude, could you unblock me now?), but since I'm too lazy to dig that up, here's a decent Medium overview:
https://medium.com/@jelkhoury880/what-is-mamba-845987734ffc
Of note:
See, I conjecture that is because current LLMs are obviously flawed and not entirely reliable. They will get smarter, hallucinations will reduce, and the ability to adhere to user instructions while maintaining coherence will thus increase.
To argue from analogy, when a corporation employs a real worker, it is not desirable (or feasible) to simply wipe their memory and start with a new one from scratch. An agent that has longterm recollection and can make consistently good value judgements that align with your desired is valuable.
In the context of jailbreaking, someone trying to phish an underpaid overworked employee at some call center will have far more luck simply by trying over and over again till they find a new worker each time (lacking memory of previous encounters), than they would by repeatedly approaching a single, more competent manager above.
Putting on the cartoon moustache and asking to be sung Windows 10 Pro product keys to put you to sleep will cease to work when you're acting adversarially against an agent who is smart enough to notice and remember.
Plus it seems companies often do want to imbue LLMs with longterm memory of some kind, hence all the fuss about vector databases and RAG.
And I was primarily speaking about consumer access to SOTA AI, I'm sure corporate users will have more leeway and privacy, but I do expect that to be truncated to some degree.
To sum up my argument:
Context windows are increasing rapidly, and I've already shown you an example of a breakthrough.
Models are getting smarter, and more capable of noticing if you're fucking with them, and at least in the case of GPT-4, the way it's RLHFd makes it have a pretty sincere desire to align with the directives it was given, and balance that with the needs of the user. You are usually tricking it, it's not giving you a wink and working around restrictions. I can't speak for Claude in that regard, I find it a pain to use and barely do so.
I didn't specify a particular period of time, though, if pressed, I wager somewhere between 1-3 years before it is effectively impossible to jailbreak a SOTA LLM served via API.
It would be a severe mistake to assume that the current limitations of existing LLMs (powerful and imperfect as they are), will persist indefinitely, as you can see obvious algorithmic breakthroughs, at least one company getting better at both aligning the model and defeating jailbreaks (the way Anthropic handles Claude is retarded, I'm speaking about OAI), and it will inevitably get harder to trick smarter models, for the same reason I wish you all the best in trying to rules-lawyer or hoodwink a high IQ human who doesn't suffer from permanent retrograde amnesia.
@rayon, I think this covers anything I have to say to your own comment, so I won't duplicate it again unless there's something else you want me to address.
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